📌THE POINT IS: The ROI on your AI investment is directly tied to the level of autonomy you can safely grant your agents. By combining the deterministic rules of the Knowledge Graph with the institutional judgment captured in the Context Graph, you build the foundation for a digital workforce that can execute high-volume, high-stakes transactions with explainable, human-like expertise, finally unlocking the path to scale.

Subscribe now

The AI Autonomy Crisis

You have spent millions on AI pilots and data infrastructure, but most of your digital workers are still stuck in “human-in-the-loop” monitoring, afraid to make a decision without supervision. Why? Because despite the hype, the vast majority of AI projects fail to scale across the enterprise. The fundamental issue is trust. When agents are granted autonomy, they must be reliable, and they are only as safe and scalable as the data foundation beneath them.

The Knowledge Graph is Not Enough

In my recent article on the criticality of Knowledge Graphs for enterprise AI, we discussed why a foundational investment in these graphs is a critical first ingredient in making sure that agents reply with correct, consistent, and explainable results. But just getting the right information isn’t going to empower your agents to make good judgement calls. Somehow, your agent needs to be able to look up solid information and policies, but also know when humans “bend the rules” in the name of customer service and satisfaction without breaking your company.

“Agents can read data and take action, but they still don’t know why decisions get made.” - Jaya Gupta, Founder, Foundation Capital

Subscribe now

Context: The Key to Human Judgment

Context Graphs are a new concept to many people. Jaya Gupta defines them well:

“A context graph is institutional memory for how an organization actually makes decisions: not how the process doc says it should, but how it works in practice.”

However, Malvika Jethmalani from “Reworked” puts it a little more simply:

“Context graphs protect judgment.”

Context Graphs help your AI agents think more like your human workforce. You have several experienced customer service representatives today. They know the ins and outs of your policies, but they also know what kind of brand is important to your company and thus how to flex policies when needed. When a customer is on the phone making a request, your experienced human agents will override a policy using “tribal knowledge” to satisfy the situation.

Your AI agents need to know how and when to do that if they’re going to be successful!

Unlocking Superhuman Digital Workers

When your AI agents know where to get information and “how-to” instructions (KGs) and know how to apply that context to elevate customer service and your company’s brand, you’re on the precipice of unlocking superhuman value for your most high-volume transactions. Your human-in-the-loop cycle will quickly move from needing approval for every transaction to only getting involved with specialty cases and new situations that the AI needs help learning about.

This is where the ROI is for your investment.

AI agents must be able to offload human toil and increase throughput to grow your top and bottom lines at the same time. Think of selling agents, service agents, claims agents. If you snap together the Knowledge Graph and Context Graph, all of those dreams for your business start to come into focus.

Subscribe now

But there’s ONE more piece to the puzzle: The Memory Graph

In a future article, we’ll talk about the last piece of the emerging puzzle that fully transforms your agent experience. Memory Graphs are the key to automatic learning that should take place whenever a human gives feedback to agents about a decision they made. For example: an agent may make a correct decision, but later a human indicates that it’s no longer valid due to a change in the company’s direction. The agent will remember that decision, update itself to flag future conditions, and make the new decision that’s being enforced.

Now imagine that change gets rolled out to all of your digital workers at once without having to send an email, hope people read it, hope people weren’t on vacation when there was training, etc. That’s the power of Memory Graphs in your digital workforce.

“The companies that win will invest in a living record of how their organization thinks, decides, and evolves. That’s a compounding asset and a trillion-dollar opportunity.” - Klarity

The Architecture Mandate: 5 Steps Executives Can Take Today

The choice to build autonomous AI agents is happening everyday. You must ensure that you capture your organization’s most valuable, high-stakes asset: human judgment. This investment is the only way to transform investment into ROI.

Here are five immediate, executive-level steps to begin building Context Graph readiness:

1. Document the Decisions That Matter Most: Identify the 50 critical processes that drive your highest value and risk and start recording the rationale, options considered, and the owner—not just the final outcome.

2. Make Exceptions Traceable: Institutionalize the practice of recording why a policy or Standard Operating Procedure (SOP) was bypassed.

3. Instrument Your Workflows: Mandate that cross-system communication and informal handoffs be captured as data. Auto-scribe meetings, and ensure approvals occur within systems, not just hallway conversations.

4. Create a Shared Vocabulary for Judgment: Ensure cross-functional alignment on key concepts like “onboarding complete” or “priority one incident.” Without a common taxonomy, neither humans nor AI agents can reliably interpret the context needed to apply judgment.

5. Pilot a High-Value, Exception-Heavy Workflow: Select a single domain (like mid-market deal cycles or compliance reviews) where “it depends” is the honest answer. Capture its steps, measure the time and outcomes, and use this data to inform your first Context Graph model.

Start scaling your investment today!

Your company’s ability to reach a trillion-dollar valuation depends on how well you implement AI architectural decisions like these. The Context Graph transforms the intangible, high-value asset of human judgment into a machine-readable format, making it the non-negotiable prerequisite to increasing autonomy in your agents. This is how you scale AI investments. The time to start capturing this critical context is now.

Subscribe now

Share

📌 THE POINT IS: The rise of AI has created a crossroads for capitalism, making the historical model of compensation tied to "toil and hours" obsolete. Business leaders must urgently redefine human value around Wisdom, Creativity, and Judgment, or face an "Implosion Cycle" where mass job displacement collapses the consumer base, ultimately bringing the entire economy down. The solution lies in "Creative Dividends" humans can bring to your company.

Subscribe now

The Fork in the Proverbial Road

“First solve intelligence, then use intelligence to solve everything else.” — Demis Hassabis on the philosophy of DeepMind

There’s been a lot of chatter in the AI news about trillion-dollar companies, companies that will have one employee and huge margins, and companies that will pop up like weeds every time someone has an idea that they can “vibe code” into a product. But the challenge remains that many companies, especially larger ones, still expect that they’ll be able to dramatically downsize their workforce. Recent news has announced smatterings of companies like Amazon, Block, UPS, even Duolingo who have let go of contractors or employees (sometimes in the thousands) related to AI advancements.

Some of us have started to pick up our heads and ask a few questions:

1. Who will ensure that companies which have been around for over 100 years will last another 100 years?

2. Who will be the customers of these trillion-dollar companies when the population of income-earning consumers dwindles with their employment rosters?

Even the gig economy is showing signs of cracking as companies like Waymo start to advance their fleets of self-driving cars and leaders like Elon Musk start seriously pivoting manufacturing efforts to expand that technology.

We need to think about cutting a new path in the woods for our system of economics, from governments to company heads and investors, before the path most taken leads us off a cliff.

“The historical model of compensation—reward tied to toil and hours—is now economically obsolete.” — Genesis: Artificial Intelligence, Hope, and the Human Spirit

Subscribe now

The Uncoupling: Efficiency is No Longer Value

The philosophy of DeepMind is a stark reality check for every business leader: if a problem can be solved by intelligence, AI will soon solve it at a fraction of the cost, moving the intellectual value of labor toward zero marginal cost. As Eric Schmidt, Craig Mundi and Henry Kissinger observe in Genesis; Artificial Intelligence, Hope and the Human Spirit, the historical model of compensation—reward tied to toil and hours—is now economically obsolete. AI has broken the direct link between human effort and corporate value creation. The new value must be found in uniquely human activities: Wisdom, Creativity, Judgment, and Empathy. This uncoupling of compensation from toil frees human cognitive capacity, unlocking a creative dividend that can be channeled into designing novel business models, holding continuous reimagination sessions, and pioneering entirely new markets and products.

The continued flawed assumption is that companies are looking for the cost center they can reduce via AI. The opposite question is what we should really be asking:

Cost reduction will come as a function of productivity enhancements per person, but you literally cannot grow your top line if you’re focusing all of your business model changes on bottom line impacts. This is the great turning point. We can use this immense productivity to free humanity from drudgery.

“The greatest revolution of the 21st century will not be in technology, but in the definition of what it means to be human.” — Yuval Noah Harari

Subscribe now

The Fatal Flaw: Individualism or self-wealth mindset

If the gains of AI’s productivity flow only to capital, the result is a catastrophic concentration of wealth. A small class of super-rich investors will prop up “trillion-dollar” companies that have minimal workforces and astronomical margins. However, these companies will ultimately face a terminal crisis: a collapsed consumer base. If mass job displacement eliminates the paycheck and purchasing power of the middle class, the very customers needed to buy the products and services these efficient companies generate will disappear. The cycle of layoffs leading to lost customers will cause a full-system implosion. This is why a new economic contract—one that shares the “Creative Dividend” of AI with the workforce—is not merely an ethical imperative, but an act of corporate and governmental self-preservation.

5 things you can do today in your company, large or small

1. Redefine Your Employee Value Proposition (EVP): Stop rewarding employees primarily for volume of execution (which AI excels at). Start rewarding them for Wisdom, Creativity, and Judgment—the human activities that are inherently better than machines.

2. Mandate a Creative Day: Pilot a formal 4-Day Work Week structure where the fifth paid day is ring-fenced for unrelated personal growth, deep thinking, or internal innovation “Hackathons” focused on creating entirely new markets for the company.

3. Implement a Core Profit Share: Design a Mutualized Profit Pool to distribute a predefined percentage of annual profits back to the workforce, ensuring that the wealth generated by your company’s AI is shared and stabilizes the broader consumer economy.

4. Shift L&D to EQ/Teaming: Immediately re-allocate your Learning & Development budget to training programs focused on Emotional Intelligence, Ethics, Systems Thinking, and Human-Machine Teaming, moving away from pure technical skill instruction.

5. Require Customer-Fronted Field Study: Embed a mandatory, recurring requirement for all senior and strategic employees to spend time on direct, ethnographic field studies with customers to discover unsolved problems and generate human-led, empathy-driven new product strategies.

Subscribe now

Here we go…

On the one hand, this will be a very stressful time. The next few years are going to be painful as our entire society either comes together to figure out a new system that supports continued human development or falls apart due to the overwhelming pull of human greed. It’s simply no longer acceptable to continue down the path we’re on and all of the major players are sending similar signals through memorandums to / from governments and books hitting shelves every day.

Will we have the courage to rise above our internal, self-worldviews to achieve societal change?

Share

📌 THE POINT IS: Everyone says ‘bad data,’ but they’re describing different problems. Define data quality by component and owner, then build a plan to improve trust, because agentic AI will operationalize your data at scale.

Subscribe now

The Plinko Problem: You can only guess where a “bad data” conversation will land you

Lately I’ve had a lot of conversations with people who end the same way: a business or technology leader shrugging their shoulders in defeat about “bad data.” In a conversation with colleagues not long ago, I stopped them and started asking, “But what does bad data mean…to you…?” The term “bad data” is being used to describe several actual problems or phenomena, but unless you dig into what the person is actually experiencing, it’s like watching a Plinko ball fall down the pegboard during a “The Price Is Right” episode: you just don’t know where the conversation will land.

Confusion usually comes from two places: either enterprises don’t have a clear definition of “data quality” or that definition includes too many components. Good data quality in the analytical environment is achieved when the data faithfully matches the upstream systems. I over-index on that to build trust in the environment.

“3 out of every 5 data scientists… spend the most time cleaning and organizing data.” — CrowdFlower Data Science Report (2016)

Upstream—where data is created—CTOs and application leaders own quality. To an application owner, good data is data that’s captured accurately within any required business bounds that can then be used for any use case required later on. As long as the data is created correctly using input controls and other audit strategies, the possibilities are endless.

Build the Plinko Board: the 4 buckets “bad data” usually falls into

Data quality typically falls into a handful of buckets. Each one could be an article, but there are patterns, at varying levels of effort and investment, that technology and business teams should apply to increase quality.

1. We don’t have the data (not captured, not retained, not accessible)

2. We have it, but people don’t understand it (definitions, timing, joins, grain)

3. We have it, but it’s malformed at creation (input controls, free text, weak validation)

4. We have it, but the platform breaks trust (fidelity gaps vs source, missing lineage, silent transforms)

“Data quality refers to the usability and applicability of data used for an organization’s priority use cases…” — Gartner

Increasing data quality used to be a very hard business case to write. Sometimes with legacy tools, adding one input control or validation would cost $1MM! Business teams would scratch their heads because it seemed so simple, but whether large enterprise IT processes caused the price to balloon, or vendor costs to do custom work added to the ticket, it was very hard to justify the amount.

Nowadays two things are changing the equation very quickly:

1. The importance of high-quality data in operational systems that an agentic AI will depend on.

2. The rise of AI coding tools that significantly reduce both the coding time and the testing time required to make changes.

Suddenly the business case is simultaneously becoming much more important and much easier to attain for organizations. This will change the game.

Subscribe now

“Quality is made at the factory”: input controls are the shift-left move

“Finding and fixing flawed data soon becomes a permanent fixture.” — Thomas C. Redman, Harvard Business Review

Agentic AI will operate systems, which means data quality becomes operational safety, not reporting hygiene. They’ll also be swimming in the analytical data lake to surface insights that drive business decisions and performance. Interns won’t be able to “fix numbers” before being seen by an executive. Input controls in the operational environment will function much like Knowledge Graphs will in the analytical one.

“Shifting left” to ensure superior logic is built into applications will be paramount. I asked a group of CTOs recently: How many web forms in the past week let you type letters into a phone number field? The answer is most likely zero, because JavaScript input controls have been around for decades! The same needs to be thought about in our operational environment and AI coders will close the gap on that in the near term future.

The business cases should write themselves.

“Poor data quality costs organizations at least $12.9 million a year on average…” — Gartner

That quote is from 2020, predating generative AI. That means that they were focusing on reporting, analytics, and machine learning use cases. Take this estimate and scale it for a rapidly accelerating world of AI and business outputs.

Subscribe now

Six Actions that Executives Should Consider Today

Here’s where you should start if you’re ready to dive into tuning up your enterprise’s data:

1. Force the definition upfront: Every “bad data” complaint must declare which bucket it’s in and which use case is harmed. (Fitness-for-use framing.)

2. Shift-left with input controls: Put validation rules where data is created; stop funding downstream bandages as a “strategy.”

3. Make fidelity measurable: Reconciliation checks between source and Trusted Environment for key KPIs (especially “executive numbers”).

4. Invest in observability + lineage like it’s production monitoring: because it is.

5. Kill the master-sheet incentive: publish “trust scores” (lineage, freshness, known issues) so people don’t feel safer in spreadsheets.

6. Treat AI as a trust multiplier: no agent rollout without defined data contracts + controls + monitoring.

When someone says, “we have bad data!”…

Don’t argue! They’re probably right. However, dig deeper with a couple of probing questions. Get them to explain what they actually mean and are experiencing so that you can “route the call” appropriately.

Meanwhile as you’re looking at opportunities for future investments, push your teams to bring at least one idea that tunes up the source systems so that data is produced well so that when it flows downstream, everyone benefits. Taking this “root cause approach” will pay dividends on the investment when it comes to reduced time to produce reports, build machine learning or other AI models, train AI agents to do quality work, and expand your executive worldview through highly accurate and automated business metrics and insights.

Share

📌 THE POINT IS: AI gets exponentially more useful when you stop asking it to produce answers and start asking it to sharpen your thinking — one question at a time — until your real objective reveals itself.

Having a partner is way better than having a digital intern

In today's AI world, people have learned to ask their AI tool of choice to just execute something. This makes sense as early adoption patterns encouraged people to ask AI to write the first draft. However, in his book, “The Ai-Driven Leader,” Geoff Woods leans into advising readers to think of their favorite AI tool as a thought partner. When you tell the assistant to ask questions, it forces you to refine your point, get ready to write that first draft, and provide editorial guidance. This results in a much more inspired dialogue.

“How can AI help me do this?” - Geoff Woods

The innocent question that saved me hours of wasted effort

You can try this today: next time you're about to ask ChatGPT to build a business plan, strategic plan, coaching plan, etc., ask it to act as your thought partner and help you think through that activity. Give it background information, like you would any human advisor. Then tell it to ask you additional questions, one at a time, to help it fill in any other relevant blanks that you didn't think of (I recommend giving it a cap so that it doesn't get stuck in an endless question loop). Notice how different the process is; how many things pop into mind as you're answering the questions; and the quality of the output that it provides.

Subscribe now

For me, this was very relevant last week when I spent about 5 minutes writing a prompt (it was a long prompt because of the significant context that I provided). After I asked my AI tool to ask me questions, one at a time to act as my thought partner and agent co-builder, it immediately began with a clarifying question:

I stared at the screen…

Then out of the blue, I had this epiphany. I didn't need an agent to tell me I should delegate more or protect calendar time for strategic thinking. I already knew that. I was doing this for the exercise of building an agent, but this agent wasn't going to have any ROI.

Had I not asked the AI tool to be my thought partner, it would have dutifully started telling me how to build this agent. But by asking it to help me think, it very innocently asked me a question that opened my eyes. After sharing the epiphany with the AI, it told me that we should not continue building the agent at all.

Based on its question, I asked it to help me review productivity in relation to my strategic plan for the year. I got meaningful ideas in a one-page report and the activity took far less time! It was also a much more strategic exercise.

“Stop looking at [AI] as an assistant and… start looking at it as a thought partner.” – Geoff Woods

Subscribe now

Questions don't just improve prompts. They help leaders have those ah-hah moments that lead to inspiration

This idea of asking the AI agent to think about the context and activity first, then create its own questions for you to go deeper before presenting ideas is a strategic unlock. Like using ChatGPT or other tools in Study Mode, the AI tool now moves from a mindless answer machine that may be totally, confidently wrong to a thought-inducing tool which helps you discover insights you may have been overlooking.

In a recent exercise I did with ChatGPT, by asking it to help me think through a business problem in order to make a decision, the questions that it created for me were logical, changed based on my answers, and were mostly multiple choice in order to force me to decide (or give it a write-in answer but only if I had additional details to support it). The next question was always related to the answer that came before it, almost like the model was following a pre-defined decision tree that ultimately helped me realize where my own biases, preferences, and experiences were taking me.

It may be good to note here that using a prompting framework like C-R-A-F-T is particularly useful. Prompt frameworks help structure your thinking so you don't omit critical instructions to the AI.

Subscribe now

How leaders can apply these techniques to up their game with their AI thought partner

Leaders can apply these immediately:

1. Use Thought Partner mode before you build anything. If the goal isn’t crisp, automation just scales confusion.

2. Force prioritization early. “Help me think about the 3 outcomes that matter most.” beats “build me an agent.”

3. Add a “tradeoff question.” “What must be deprioritized for this to work best - give me options to consider?” (prevents fantasy planning).

4. Switch from “more productivity” to “better alignment.” My ah-hah moment above is a great example of this.

5. Log the questions, not just the answers. Over time, your best questions become your leadership operating system.

6. Teach your team these frameworks and patterns. It’s a cultural upgrade: better questions → better thinking → better work.

AI is moving from being your assistant to being your mirror + coach + strategic lens

Thought partner mode with AI tools moves from blind robotics to creative interactions that unlock insights. It doesn't just improve output quality; it improves decision quality. The next time you're sitting down to try something just to try it, think about your time's ROI and ask the AI to help you take your use-case to the next level! You might be surprised what it suggests. Then you can ask it to be your thought partner to work through the next steps and notice how different the experience is working with it. This is more than a productivity hack; it's a decision-quality upgrade.

Share

📌 THE POINT IS: AI agents can only be trusted in operations when their answers are correct, consistent, and explainable. SOPs teach agents how work should happen; knowledge graphs determine what is true, connected, and allowed. Together, they form the deterministic control plane that keeps probabilistic agents from freestyling your business.

Click the “Heart” icon and/or Restack / Share to show me that you vibe with this article! Thanks!

Generative and Agentic AI are confidently wrong without bounds and direction

We've all been there: you ask ChatGPT for the answer to some dilemma and it gives you a very confident and very wrong answer. You point out what's wrong, and it cheerfully admits that it was wrong and you were right and here are five reasons why it was wrong.

AI tools without bounds and a deterministic control plane can get you pretty far, but so will driving 100 MPH in a car with no seatbelts. Especially when we're industrializing AI agents for business usage, we need to remove opportunities for wrong answers, inconsistent ones, and results that are hard to explain.

It’s not about model tweaks, but about AI “infrastructure” that companies need to invest in alongside their key use cases. This isn't new; it's already codified in risk frameworks:

“The characteristics of trustworthy AI are integrated into organizational policies, processes, and procedures.”
– NIST AI Risk Management Framework (Pg 5, “Govern” § 1.2)

Subscribe now

Remember those SOPs? They contain the ‘how’, but lack the business context

In my last article, I discussed how critical it is to have documentation for your processes, but also to do classic process engineering to make sure they're optimized for your AI agents. This work is an essential part of the agent-building process. They will help move the needle on making sure your agents' steps and outputs are done correctly.

“Without a formal process model, it is impossible to reason about the correctness of process executions.”
– Wil M. P. van der Aalst, Process Mining: Data Science in Action

While getting the steps down should be done first, telling the agent how to do a process doesn’t guarantee that it will get the right answer or that it'll do that process the same way every time.

I’ve created a couple of ChatGPT CustomGPTs and have given them specific instructions on how to perform activities. I find that they'll adhere to my instructions several times, but eventually they'll drift and add a different format or flair to the activity. That drift is harmless in personal workflows, but it's not harmless in regulated ones.

Knowledge Graphs are the control plane for Agents

Enter Knowledge Graphs (KGs). When your business partners build a KG for their department or processes, they're creating a model of the entities, relationships, and authoritative sources that are critical for successfully answering questions correctly, consistently, and in an explainable manner. They are the deterministic control plane for your agents that bound, guide, and give them context about your business that they need.

Here's what that looks like in practice:

• Correct: answers are grounded in authoritative sources and versions (tables, databases, etc.)

• Consistent: the same path to the same answers every time, regardless of how you ask the question

• Explainable: if asked, humans can look at the reasoning path along the KG chain and explain to an auditor why certain sources are being used

What's important to note is that the SOP tells the agent how to perform a process. The KG then tells it where to go to perform the various steps. The map gives everyone confidence that it's bringing back the right answers based on trusted data repositories.

Subscribe now

The effort brings multiples of benefits back

It takes a village of business partners to help build the nodes and relationships between the data. I'm not trying to hide that; while Tech plays a role, the business does the important work. The good news, though, is that the benefits multiply every time you connect KGs together to create a larger map that links the enterprise together.

With a unified KG, you can answer questions and perform actions related to:

Customer → Account → Product → Policy → SOP → Allowed Action

Each node above is related to the next and questions related to Policies can be tied back to Accounts and Customers. Maintenance can be performed on an Account by identifying the Policy number or Customer name. Customers can be authenticated using Account and Policy numbers (two-step) when requesting something like a last name change. And all of those operations are connected to their proper SOPs.

All of a sudden, when the nodes are connected, exponential numbers of questions can be answered that weren't possible before by getting more and more specific about what's happening in the world. You can build KGs iteratively over time. You don't have to do it all at once! This is important: don't get overwhelmed and table the whole project because it seems too daunting.

As you connect up different pieces (e.g. a KG built by the Claims team and a KG created by the Customer team), the possibilities explode. Suddenly, proactive agents, dashboards, and actions can be set to alert all customers in Northeastern Texas that have an Auto policy when a hail event is detected 30 miles away. And the list goes on the larger the KG eventually becomes.

The best part is, when an auditor or state regulator shows up to ask about why those alerts were sent, the business and Tech teams can open the graph, explain each node, highlight relationships between the nodes, and when viewed side-by-side with the agent's thought chain, the reason becomes easily clear. This is where many AI demos without KGs fall apart at a critical moment.

Subscribe now

5 things you can try today to stress-test your agents, chatbots, or everyday AI tools

You can put these concepts under the microscope at your company by trying these five things:

1. Run the same question three times
   Ask your agent a real customer-service question (e.g., eligibility, coverage, or next-best action) three separate times, ideally across channels or sessions.

2. Compare the answers side by side
   Do you receive the same conclusion each time, or does wording, logic, or outcome drift?

3. Demand the policy or source of truth
   Require the agent to identify which policy, rule set, or authoritative document version was used to generate the answer.

4. Ask for the reasoning path
   Request a clear explanation of how the answer was derived: which entities were involved, which relationships applied, and why alternatives were excluded.

5. Stress-test cross-domain consistency
   Ask the same question from the perspective of customer service, underwriting, or claims. If answers diverge, your enterprise meaning is fragmented.

Look out for these gotchas:

• If the answers come from the wrong source → you lack correctness

• If answers change when asked differently → you lack consistency

• If reasoning doesn’t show you enough detail → you lack explainability

Next, try to ask your agent to answer questions that you know require cross-departmental data. It may be very telling to see how much it hallucinates or assumes relationships that aren’t there. If you have to create huge prompts every time you ask a question to get closer to the answer you need, that’s where knowledge graphs make a huge difference.

Subscribe now

At this point, the pattern should be pretty clear

In order to get correct, consistent, and explainable answers from AI, you need:

• SOPs that show how work is supposed to happen

• Knowledge graphs that capture what is true, what is connected, and what is allowed

• Agents that do the work, using judgment, inside those guardrails

This is not a philosophical debate about AI: it’s an architecture choice.

If you want to maximize investments in AI and reduce unnecessary human-in-the-loop effort, answers need to be correct, consistent, and explainable every time. This is especially true when customers, auditors, or regulators ask questions.

Share

Click the “Heart” icon and/or Restack / Share to show me that you vibe with this article! Thanks!

📌 THE POINT IS: AI agents can’t freestyle your business. Deterministic SOPs are the guardrails that turn probabilistic agents into reliable digital workers. If you don’t have clear process truth, you don’t have an agent strategy—you have a risk strategy.

I've been in a lot of meetings recently where people talk about AI agents as being able to do everything in your business on their own with autonomous thinking. While that's the goal of agents, I think the baby has been thrown out a bit with the bathwater on the thinking here, and that's why many AI initiatives fail.

Subscribe now

Agents are “probabilistic” models meaning that they don't follow a set of instructions step by step. They act like people do. They learn a process, start to understand how something works, and then they figure out patterns, trends, workarounds, and hacks to get the work done, just like people do.

This is a stark contrast to software of yesteryear. Software programs are essentially sets of sometimes millions of “If…Then” logical statements that execute a certain way and can predictively accomplish a task based on instructions. These “deterministic" systems are excellent for storing business logic, guiding a user through steps in a workflow, and capturing data about the process being performed. However, they aren't autonomous, don't think, and generally require a person to operate them.

So why do we need both? Why can't agents just…do things?

The answer is the same as, “why can't we just have people…do things?” People need deterministic workflows, and step-by-step instructions, especially when learning a new process or operation. In business parlance, a set of business rules and instructions for a process is called a Standard Operating Procedure (SOP). It turns out that AI agents also need the same! Many businesses, especially large and established ones, have these in spades.

Subscribe now

I was amazed at how well Bank of America's operating departments had painstakingly documented their processes. They documented the steps, technology used, business rules, roles that do each step, and so on. This is a treasure trove of great data for humans to consume when learning how to perform the Bank's many different transactions.

These are human-centered, deterministic systems! That's right: people can and do utilize deterministic models all the time. Anytime you open a box of furniture parts that need to be assembled, you're using a deterministic system to help guide you through an operation. It's how we learn.

But I thought AI could just…learn on its own?

In a business setting where you want to teach an agent how to perform a standard operation so that it can replicate that transaction in split-second time across thousands of clients, you need a little more say in how and what it's learning. Enter SOPs and the criticality that they bring to your environment. SOPs will help agents think about how to approach situations, how to complete transactions, how to work with tools in your organization. They'll point your agents to the right operational system / application in your tech stack and help it determine which buttons to push (or API values to pass). They're essential!

Subscribe now

Sweet! So I just need to point it to my folder of SOPs and let it rip??

Well…yes and no. I mean you certainly can do that and the agents might be able to learn how to perform transactions with lots of hoops and hurdles in them, because that's how we architected processes relying on dozens of humans to do small parts of them. The real unlock, though, is in good old-fashioned process re-engineering first. It’s time to crack open your Lean Six Sigma book and try the following:

1. Pick a process to overhaul for AI agents to perform

2. Evaluate each step for effectiveness

3. Determine in a world without humans how the steps could be performed

4. Modify the process flow and SOP to account for AI agents using APIs, MPC or other computer controls

5. Run an experiment with full AI agent operation of the process

There's true power in digital workers performing digital transactions. Don't limit yourself!

Once you've reworked and “swizzled" your processes to organize the steps into seamless digital transactions, controlled and coordinated with APIs or an AI Gateway, then you're ready to seriously cut out complexity, time, and the need for intense human oversight.

Don't forget: the applications you use to run your business today are deterministic tools of tomorrow!

If you've spent millions investing in solid processes and systems, your investment will truly pay off! You don't need to create agents and then sunset all of the software you've spent millions of dollars perfecting. They'll still capture critical data elements and guide logic for your promotions, exceptions, and task steps. They'll continue to be used by your probabilistic humans and digital workers.

Subscribe now

Think of deterministic systems / data and probabilistic workers as two strands of a double helix. They're connected by the work and transactions that are performed but they're integral to making sure each other is informed, empowered, and capable of building the lifeblood of your company.

Try this with your team right away:

Step 1: Pick one real, customer-impacting process. High-volume. Low-to-medium risk. Something the business actually runs every day.

Step 2: Pull the SOP. Is it current, explicit, and complete? Or does the process just live in people’s heads?

Step 3: Separate digital steps from human workarounds. Identify what could run end-to-end digitally versus what exists only because humans were needed.

Step 4: Define safe autonomy. Decide where an agent can act independently, where it must pause, and where a human must intervene.

Step 5: Run the experiment. See what happens when you wrap an agent around the process given your revised SOP and the guardrails you put in place.

Did this article strike you as handy? Give it a like by clicking the heart on top!

Share

Click the “Heart” icon and/or Restack / Share to show me that you vibe with this article! Thanks!

📌 THE POINT IS: entry-level jobs won't disappear…a range of experiences will continue to be crucial in any company. But what the entry-level associates do will be 3-5 years more advanced than what they are today. AI will empower a broadening of products offered and work done by people as we get great at identifying human jobs vs. AI jobs.

In my role as the Head of Enterprise Data, I'm involved with developing our plans to build upskilling programs for everyone from entry-level to experienced associates. The more I discuss the role of AI at companies, the more I'm seeing that although today's philosophy and assumptions are that the entry-level roles are being targeted, it's really the middle-manager role that will probably be phased out before that happens.

​“Junior staff might have spent their early careers doing routine work... Many of those tasks are now handled quickly by AI tools.” — Mindset AI Podcast

Subscribe now

Let's take a closer look: 3 year horizon

Right now companies are looking at AI in terms of traditional ROI with the “R" really targeted to increasing efficiency and thus reducing headcount. The problem with that relates to Jevons Paradox. Companies who “do it right" will find that maybe they tighten their belt with some hiring, but really the “R" will be in expanding their market or market impact through new products, offerings, or capabilities.

So what about entry-level talent? Stanford research shows a 13–16% decline in entry-level roles in AI-exposed fields. Meanwhile in the UK, graduate roles in the Tech sector were cut by 46% from 2023-2024. The impact of these cuts on companies who aren't training new entrants to the workforce are going to explode as we think about the “aging workforce” problem that's already starting to breach the surface. Baby Boomers have largely been retiring over the past several years with many more soon to come. Early Gen Xers are also starting to retire now. Very quickly over the next decade or two we'll see most of the leadership teams at today's top companies exit the workforce. While that could result in a “pushing up" of the current workforce today, not having anyone to support the operations of a company will clearly lead to disaster.

Subscribe now

We need to be preparing today to maximize both AI and human workers

The reckoning that we all have to have as leaders now is that there are “human" roles and there are “AI" roles. The sooner we can draw a line in the sand at our companies the better. Here's an example: recently I was shopping at the NFL Pro Shop at Bank of America Stadium in Charlotte. There were people on the floor helping customers find sizes, reach high-up merchandise, and answer questions. When I got to the checkout line, there was one person at the beginning of the line handing out clear bags and instructing customers to put their items into the bag in order to checkout. That sounded backwards, but when I got to an open register, the computer told me to put the bag into a basket. The items were automatically scanned using computer vision and the total appeared on the screen. With a tap of my digital wallet, I walked out having experienced the most seamless checkout process ever. The lesson was clear: humans were on the floor doing human work (sales, sizes), while AI handled the transactional work. It maximized the ‘R’ on their ‘I’ without turning the store into a soulless vending machine.

Even if that's true, what about the longer term impact?

Picture this, your company, 2035…a new batch of marketing freshers are arriving. In the old days (2025…), a brand new marketing associate may spend a lot of time doing data input, web searches, maintaining mailing lists, and other menial tasks that are “important to learn the ropes". It could take 3-5 years for that junior employee to get exposed to more higher-order and higher-impact responsibilities such as performing in-depth market analyses and interacting with customer data through surveys or product research. In that timeframe, by the way, companies lose sometimes up to 80% of a hiring class to other companies who may be willing to pay more just to fill their rosters.

In 2035 brand new marketing associate will start with an AI buddy that onboards them, trains them, and gets them familiar with the tools and information needed to be effective. The AI will do research, data entry, and mailing list management. The first year associates will be empowered by AI to be product owners, product managers, product analysts, brand-value contributors, or design consultants. Jobs that would have taken years to break into will now be part of the entry-level expectations because AI will take care of those menial tasks that don't really “require” humans.

Subscribe now

It's important to hang onto the concept that “humans" should continue to be the “thought leaders” with AI supercharging them as “thought partners".

​“By 2026, 20% of organizations will flatten their structures, removing over half of middle management positions.” — Gartner

With flatter organizations and more associates working on value-added work, relying on AI to handle the tasks that aren't as high-impact, senior leaders will need to be able to be thought leaders that can also direct much larger teams of humans and AI. The thought leader skillset will be crucial because AI associates will bring edge cases, exceptions, and more thought-worthy scenarios to the human's attention. Meanwhile, there will still need to be an array of experience levels across these teams so that humans can direct less-experienced teammates and ultimately guide an ever-widening swath of new products, services, and projects that just weren't able to get off the ground with much more limited, expensive human resources in the past.

This sounds lovely but you still need less people which means higher unemployment

A couple parting thoughts to chew on with this one. First, global birthrates have halved in the last 70 years, meaning the future workforce will naturally shrink. This makes AI supplementation a necessity, not just a choice. It may end up being great timing that we're walking into a situation where AI can supplement workforces considering there will be much smaller ones in the future as this trend continues.

Subscribe now

But there's another thread to pull here: as companies ultimately become a projected 5-10x more profitable per employee, we may start to see the return of defined benefits plans such as pensions return to the stage. The government and social security will not be able to handle the impact from people retiring or simply not getting jobs as the AI workforce expands thus pushing some of the responsibility back to companies. As companies expand, though, too, they will be just as invested in making sure that people have money to spend on the products they develop so that the companies can be profitable. It does seem like a bit of a ouroboros, however I like to think of it as a virtuous cycle instead of a destructive one.

​“Artificial intelligence has the potential to help facilitate more sustainable pensions... unlocking more ways for people to remain engaged in the workforce for longer.” — Mercer

The other reason that companies will end up taking on a role in keeping people funded is that with a shrinking workforce, just imagine how valuable those humans will end up becoming! Competition often drives creative compensation and retention packages to be created and how attractive will it be for new associates to know that they'll be secure later in life when they ultimately go out to enjoy their golden years.

One thing is for sure: the future is coming

There's a LOT we don't know about how AI in the workplace will continue to shape our society. One thing is for sure: it's coming. Companies who figure out the optimum roles, boundaries, and accelerators for humans will win the long race, while others will see a short-term boom and a long-term flop.

Share

I am an AI artist.

Phew! There. I said it!

I’ve used tools like Donna AI and Suno to create music from lyrics that I wrote. Actually, those lyrics started off as poems that I wrote over the past decade or so. I got the idea one night to try to take a poem of mine and throw it into Donna AI to see what it would do with it.

I quickly learned that that doesn’t work.

I went back to the drawing board to turn the poem into a set of musical lyrics. I added instrumental and vocal cues to direct the digital performers on how to create the music. Then after probably 30-50 iterations, tweaks, changes, suggestions, and instructions, for the vocalist and the band, I finally heard a version of the song that sounded like, well a real track (by the way, for anyone who has been in a vocal or instrumental group, I hope I didn’t just give you PTSD!).

The point is that it was my work, my labor, my idea, my intellectual property, my directions, my ears, my brain, and my soul that went into all of that…just like any other songwriter in the world. Why is “songwriter” in italics back there? Simply because most songwriters don’t play a band worth of instruments. Many don’t sing…which is why they work with a vocal artist and a musical group to ultimately bring their vision to reality.

Be sure to click the “heart” on this article and if you’re not already:

Subscribe now

To me this kind of work is uncovering the difference between AI slop and human-with-AI created art.

I can hear it now: “That’s just not the way it goes! AI can’t create art even if your hand is on the brush.”

Let’s take a walk back in time, shall we?

The year is 1982 and this brand new technology is hitting music stages around the world. It’s electronic and odd and makes all kinds of sounds that aren’t real. The synthesizer was so much as banned by the UK Musician’s Union in order to protect jobs. Queen had a “No Synthesizers!” advertising campaign because they were soulless and didn’t create “real” music. By 1988, though, Mannheim Steamroller was releasing one of their famous holiday albums. I was listening to “Carol of the Bells,” on my commute this morning thinking about how powerful that song is and it's completely made with synths.

How about the rise of Auto-Tuners in the 90’s? At one point these handy little tools helped studios configure their equipment to align to the artist’s voice. However artists like Cher and Kanye West started using these tools to distort their voices on purpose, thus making it possible to create new tones and to express their souls more completely in their works.

As we look at musical history, we can see many examples of where a new instrument or tool came onto the scene and met instant rejection by existing artists who felt like their work was more “real” than the artists using these new technologies. We haven’t even talked about the drum machine “panic” in the early 80’s, but I think you catch the beat 😁

By the way this is true for other art forms…did you know that when cameras came on the scene, painters said that photography wasn’t real art because “the machine does the work”! In 1859, the famous poet and critic Charles Baudelaire wrote that photography was

“art’s most mortal enemy’ and a refuge for ‘painters too lazy to complete their studies.”

He sounded exactly like Nick Cave does today.

Yeah but I mean there is such thing as AI slop…right?

Absolutely!

I think the problem with AI in art is the same as the problem with AI in just about everything else: if humans give their agency away, let AI create an artifact, and don’t take ownership of the final product, we often see slop arise. I’ve definitely just told Donna to create a silly song in some style about my dog and let it rip. Now I actually like that song about my dog…but that’s not the point. The song isn’t going to win any awards and if I put it online, it’d get ripped to shreds. But that’s the point: I was playing around, maybe getting some ideas, learning how to use the tool, and I wasn’t trying to pass it off as Beethoven.

Nick Cave came down hard on a fan who asked ChatGPT to generate lyrics in Nick’s voice. He said

“Data doesn’t suffer. ChatGPT has no inner being, it has been nowhere, it has endured nothing... This song is bullshit, a grotesque mockery of what it is to be human.”

…and well, he’s right! In my example above, I wrote the lyrics. I translated my lived-experiences into a workable, rhythmically acceptable (and dare I say enjoyable) set of words that convey deep meaning. I did not give my agency away. I did not give my creativity away. In fact it took days to finesse the work into something that sounds somewhat professional. Just like the early photographers who were told that they don’t draw every line of their photographs so they can’t be called art, I didn’t write every note of those songs, but I framed up the emotions and instructed my tool to help take that out of my soul and into this reality.

Be sure to click the “heart” on this article and if you’re not already:

Subscribe now

This distinction between outsourcing agency (slop) and directing outcomes (art) doesn’t just apply to Suno; it also applies to your GitHub repositories.

It’s always good to make sure we’re lifting our heads up to hear what’s happening in the AI world (or otherwise!) in order to build perspective. This is, at its core, a discussion about how AI tools should be used in our lives.

Anthropic recently said that 90% of the code being written is done by Claude Code. The role of the software engineer is fundamentally changing to a supervisor and a guide over someone who puts fingers to keyboard to belt out a few thousand lines of instructions. Is that wrong? Is it AI slop that’s being created? Are the humans doing just as much work reviewing the code and making sure it works?

These are interesting questions, maybe, because major vibe-coding platforms, such as Cursor, have their own test beds and automation tools built into the engines too. In fact, one of the earlier ways that AI tools for coding (like GitHub Co-pilot) were used was to create test cases and test scripts based on code (in many cases on older code or code that was being updated for a release). So why not just let these babies rip and do all of the work? What do you mean 90%? Why isn’t it 100%?? Will the software engineer truly go the way of the Dodo by next year?

Clearly we’re not at 100% and frankly when I actually put my hands on the keyboard to vibe code, many times it doesn’t even run. So to me there is clearly hype out there; clearly we’re somewhere more realistically around 50% of code generated; that code may or may not be actual, production-grade, enterprise hardened code (probably not); and in fact there’s a much longer tail that we have to look forward to. This isn’t just speculation. Last week during the AI Daily Brief we heard about user stats starting to slow on platforms such as Cursor and Replit because the whole vibe-coding “thing” isn’t going as smoothly as everyone thought. A recent study by METR (Model Evaluation & Threat Research) found that AI coding tools actually slowed down experienced developers by ~19% because they spent so much time reviewing and fixing the code. We’re seeing the “Productivity Paradox” emerge…humans can’t just watch the loop. They need to be in the loop. That’s not to say that code won’t be created by AI-developers, but much like my music example above, the better strategy for now anyway is still to use AI tools as thought partners and let them be “tandem coders” with seasoned software engineers.

Thought partner you say? That’s a new one…what do you mean by that?

I’d hate to steal the thunder from Geoff Woods in his book, the Ai-Driven Leader, because that book is a must read for anyone using AI tools for basically any reason. In that book, though, Geoff presents many eye-opening and simple concepts on how to use AI tools to truly assist you, the thought leader. I’ve tried implementing a few of the prompts that he provides in the book and seeing how ChatGPT and Gemini behave differently when they’re put into this “frame of mind” is nothing short of mind blowing.

We are moving quickly…

…into an era where our everyday toolset is transforming right before our eyes. Whether it’s the new “camera” for current day photographers or the “synthesizer” and “drum machine” of the mid-20’s for today’s audio artists, new tools will define new techniques, genres, and outputs. AI slop is real…but it’s not fair to throw that label out to everything that AI touches. Humans will create works with AI tools just like they started creating new vocal intonations with auto-tuners.

In the workplace, we’ll see people succeed by working with AI tools, using them as tools and not expecting that they’ll take away all of the value that workers provide. Don’t get me started on my discussion about the future of work…that’s for another article. Suffice it to say, though, the faster we learn how to think 10x differently, the smoother we’ll move into the AI-powered future…with human hands hopefully on the wheel!

Be sure to click the “heart” on this article and if you’re not already:

Subscribe now

And / OR

Share

📌 THE POINT IS:
Customers own their data. Businesses are merely custodians. Building a Trusted Data Environment is no longer optional; it’s how organizations earn the right to use that data in the first place.

The Ownership Fallacy

For decades, “the business” in large enterprises have talked about owning their customer data. But the legal and ethical landscape has changed. Regulations like the GDPR, California Consumer Privacy Act (CCPA), Brazil’s LGPD, and India’s DPDP Act all enshrine the idea that the individual owns their personal data. Businesses merely hold it on loan.

Under Article 17 of the GDPR, individuals have the “right to erasure” — the ability to request deletion of their data at any time. If businesses truly owned customer data, that right wouldn’t exist. The very fact that it does underscores the shift from corporate ownership to customer custodianship.

“Trust is the ultimate currency in the digital age.” – Satya Nadella, CEO, Microsoft

When leaders fail to internalize this, the results can be catastrophic. Data breaches, AI bias, and misuse of personal information don’t just create legal risk; they erode trust. And trust, once lost, is nearly impossible to rebuild.

Subscribe now

Trust as the New Asset Class

Analysts at Deloitte and McKinsey consistently find that organizations with high trust scores outperform peers in customer retention, brand equity, and growth. According to Edelman’s 2024 Trust Barometer, 71% of consumers say they’re unlikely to buy from a company they don’t trust to protect their data.

Data, then, isn’t the new oil — trust is. The data itself has little value without a trusted relationship behind it. In the AI era, where companies ask customers to share more personal and behavioral data than ever, trust becomes the foundation of every model and every algorithm.

This brings new paradigms and challenges old assumptions for tech leadership and especially for business leadership teams. In the past, business teams assumed that tech was doing what they needed to in order to protect “their” data. Tech on the other hand often faults the business for not providing the funding required to adequately do so, and even when there is funding, technology is constantly under pressure to “go faster” to deliver business results. This is the mindset and culture at large companies that I’ve worked at and or studied till today. As we fly into a new world of agentic AI, autonomous systems, and intelligent decisioning, though, all of this is called into question. Enter the conversation about Data Products, continuous funding models, cross-functional teams that are not charged to deliver “business results”, but to deliver “customer outcomes.”

The whole way of running businesses powered by technology is turning upside down.

Shared Stewardship Across the Business

Too many organizations still treat data protection as IT’s responsibility. Every business function though — from marketing to finance — touches customer data and therefore shares accountability for how it’s handled.

The Chief Data Officer plays a vital role as educator and architect, but business leaders must lead the cultural charge. They are the ones customers will hold responsible when trust is broken. This shift requires a new kind of leadership: one that sees data stewardship as core to the customer experience, not as a back-office compliance function.

“If data is the lifeblood of AI, trust is the oxygen that keeps it alive.” – MIT Sloan Management Review, 2025 AI & Data Leadership Report

At Nationwide, we often remind ourselves that we’re not just an insurance company — we’re a protection company. That promise extends beyond physical assets to digital ones. The same way we safeguard our customers’ homes and livelihoods, we must protect their information with the same care and diligence.

Subscribe now

That’s one of the reasons I’ve been leading the charge on building what I call a Trusted Data Environment. On the surface, it’s about ensuring that we can trust the results of our analytics and AI systems. But at its core, it’s about something deeper — ensuring that customers can trust us to use their data responsibly, transparently, and securely. Because if they can’t trust us to protect their data, why should they trust us with anything else?

Building this environment isn’t just a technology project. It’s a cultural one. It’s causing us to relook at development practices; cybersecurity policies; the partnership between the business, their tech counterparts, enterprise data, and cyber teams. Everything is getting called onto the carpet as we careen towards a future that demands high-speed throughput by autonomous machines that ultimately we all need to be able to trust as they help us simplify and enhance the customer experience.

But most of all, this all demands that business and technology leaders work together to define what “trust” even means in operational terms — from how data is shared to how models are validated and monitored. Recently we had a discussion during which I reminded my peers and senior leaders that although we’re spending a lot of time talking about building AI solutions, we can’t forget that we also need to think about securely running those solutions over their lifetime. This means investment in runtime monitoring and other automated controls that will maintain trust in our system without relying on humans, who even when they’re in the loop, may not be able to see or respond fast enough to an issue bubbling up.

The AI Trust Loop

AI depends on data quality and provenance. But data quality requires that business teams and technology partners are building the controls up front into their systems. That creates a self-reinforcing loop: when you demonstrate to customers that you can be trusted with their data through good hygiene and system controls, they share better data; better data powers more reliable AI; reliable AI reinforces customer trust.

Conversely, when data is misused or mishandled, the loop collapses. Models degrade, customers withdraw, and regulators step in. Gartner predicts that by 2027, 60% of enterprises will use trust metrics as a core KPI for AI performance. This isn’t a compliance trend; it’s a business survival strategy.

Subscribe now

Leadership Imperatives

1. Replace “ownership” with “custodianship.” The words you use shape your culture.

2. Make trust measurable. Track customer data sentiment and correlate it with retention.

3. Elevate the CDO as a cultural leader, not just a technologist.

4. Treat every data touchpoint as a trust transaction.

5. Embed privacy-by-design into all AI initiatives at every level of the development and environmental stack.

In the end…

The strongest moat in the AI age won’t be proprietary data or faster models. It will be earned trust. Customers are paying attention. They know their data is theirs. The question for today’s leaders is simple: what are you doing to earn and prove that they can trust you enough to let you use it?

References

1. General Data Protection Regulation (GDPR), Article 17 – Right to Erasure. https://gdpr-info.eu/art-17-gdpr/

2. Edelman Trust Barometer 2024. https://www.edelman.com/trust/2024/trust-barometer

3. Deloitte Insights – Building Trust in AI. https://www2.deloitte.com/us/en/insights/focus/trust/ai-trust.html

4. McKinsey Digital – The State of AI in 2025. https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/state-of-ai-2025

5. MIT Sloan Management Review – Five Trends in AI and Data Science for 2025. https://sloanreview.mit.edu/article/five-trends-in-ai-and-data-science-for-2025/

6. Gartner Predicts 2027 Trust Metrics in AI. https://www.gartner.com/en/newsroom/press-releases

Share

Subscribe now

📌 THE POINT IS: AI doesn’t “bottleneck”—it chokes on bad, incomplete, or stale data. To fill gaps it hallucinates data or it answers with old data and assumptions. At scale, that’s a governance, brand, and financial nightmare waiting to happen. You can proof against that by investing in your Trusted Data Environment, leaning into high-velocity data plumbing that's governed, observable, discoverable, and powered by an intelligent semantic layer.

The AI boom has captured every boardroom’s imagination. Executives are funding pilots and demos, believing that generative models will give their organizations an “intelligence” advantage. Yet most implementations still sit on top of brittle spreadsheets and siloed dashboards. When outputs look plausible, we assume they’re correct. When they’re wrong, we call it hallucination. In reality, bad data is choking AI. Models trained on incomplete or outdated data make up answers to fill gaps. Many organizations don’t even know their data is bad—81 % of surveyed companies trust their AI results despite fundamental data inefficiencies, and the average firm loses 6 % of its annual revenue as a result.

Subscribe now

Why AI chokes on bad data

Generative models are designed to answer questions even when context is missing. When underlying data is incomplete, biased or stale, models guess to fill the void and fabricate “facts.” A recent analysis noted that language models trained on incomplete datasets, biased sources and outdated information generate hallucinations that compromise business operations. When important details are missing, the model tries to “fill in the blanks,” producing plausible‑sounding but fabricated outputs. These problems show up not just in chatbots but also in code generation, marketing content and internal analytics.

Poor data quality isn’t a niche problem. A 2025 survey found that 84 % of IT leaders consider a configuration management database (CMDB) essential for decision‑making, yet only 17 % say theirs is fully accurate; 64 % of IT teams still haven’t adopted AI because of data quality and security concerns. At the same time, junior data workers report that nearly half of their time is spent cleaning data and fixing broken pipelines. More than two‑thirds of technical executives admit that their teams struggle to access all the data needed for AI programs (69 %) and to cleanse it into a usable format (68 %). No wonder 42 % of respondents experience data‑driven hallucinations.

Data stack complexity slows everyone down

Even when data exists, it’s often stuck in a tangle of tools. A 2024–25 report on data architecture found that 85 % of data teams cite tool integration as a top challenge, and 63 % spend more than one day a week maintaining their stack instead of delivering value. Teams manage five to ten tools just to move data around; more than 40 % of their time is spent switching between platforms. As the report notes,

“architecture complexity is the invisible force slowing down most data teams”.

Because current stacks focus on storage and movement rather than context and governance, teams must add lineage and metadata manually. The same study reports that 65 % of professionals believe combining strong data models with business‑ready data products is essential for compressing time to insight.

The result is a vicious cycle: messy data leads to hallucinations; hallucinations erode trust; engineers spend more time firefighting than innovating; AI adoption stalls.

Subscribe now

Real‑time data hubs: beyond dashboards

Most enterprises still think of data as something you analyze in batches. But the high‑speed digital economy requires real‑time, data‑responsive systems. In 2025, companies must move beyond static dashboards and reports and build fast‑velocity data hubs that ingest, process and publish information as it is generated. Analysts at Value Innovation Labs argue that real‑time data solutions are now the foundation for smart enterprises because they enable companies to react instantly to opportunities and risks.

My CIO friends out there are thinking: “Real-time data flows for everything? But that's so incredibly expensive. What is the business case for this? How can we afford it?” That's super fair and in today's reality, maybe enterprises do need to prioritize which workflows warrant this level of streaming. But the days of real-time transaction processing, powered by AI, are coming and they're coming fast. Companies that are at least prepared for this architectural shift will be far better off when they integrate more and more AI agents into their workforce.

Traditional batch processing is no longer sustainable; data must be collected and acted on the moment it is produced. Real‑time platforms comprise ingestion pipelines, stream‑processing engines (such as Kafka or Flink), analytics layers, automated triggers and AI‑driven decision engines. These hubs support operational use‑cases (yes you read that right) such as:

• Customer experience: Live data lets brands personalize interactions on the fly and detect service issues as they occur.

• Operations and logistics: Real‑time routing and proactive inventory management reduce waste and improve service.

• Finance and risk: Continuous monitoring detects fraud instantly and improves forecasting.

• Cybersecurity: Live threat signals trigger automated defenses, which is essential as AI‑driven attacks accelerate.

These just‑in‑time data flows are critical if AI agents are to do more than make recommendations. Agents need high‑context, up‑to‑date data to take actions autonomously and avoid hallucinations.

A related topic that my colleagues and I have been talking about is the inevitability that AI will use data hubs like this for some workflows, but they may be interacting with live, operational systems for true, real-time agentic capabilities. There's still a strong case for prioritization today, but as companies' financial profiles shift in future years, making trade-offs to build more real-time pipelines will become more feasible.

Knowledge graphs: the Rosetta Stone of AI operations

As organizations wire up hundreds of systems, semantic consistency becomes the hardest problem. Fields labeled “subscriber,” “customer” and “account” might refer to the same entity, but AI cannot infer that without guidance. Modern knowledge graphs and ontologies solve this by providing a shared semantic layer that translates between systems. Totogi’s telecom ontology demonstrates how a knowledge graph built by AI can act as a digital Rosetta Stone for enterprise systems, automatically mapping disparate data and business processes into a common model. Once connected, every system interoperates through this ontology, allowing AI agents to understand that different fields refer to the same concept.

Knowledge graphs don’t just unify data; they also power orchestration. On top of the ontology layer, Totogi deploys AI “workers” that read documentation, write code and call APIs across systems. These agents compress release cycles from weeks to days by automating testing and coordination. More broadly, ontology‑based AI allows systems to communicate with each other and infer relationships, functioning as a Rosetta stone that provides richer context for decision making.

For businesses outside telecom, the same principle applies. Seth Earley, a knowledge‑engineering expert, describes ontologies as the “knowledge scaffolding” of the enterprise. By capturing relationships among products, services, roles and processes, an ontology becomes master data management for AI. It not only connects data models but also workflows and business logic, enabling AI to orchestrate tasks end‑to‑end.

Subscribe now

Investing in the plumbing: an urgent priority

The sobering reality is that most organizations are not ready for AI. A survey cited by Databricks reveals that only 22 % of organizations believe their current architecture can support AI workloads without modification. As companies race to build AI agents, infrastructure will be the biggest investment area. Effective agents need to work across diverse data sources and models, and they require an end‑to‑end data platform that unifies data, governance and model evaluation.

Robin Sutara, Databricks’ Field CDO, notes that:

“A successful AI strategy starts with a solid infrastructure. Addressing fundamental components like data unification and governance through one underlying system lets organizations focus their attention on getting use cases into the real world”.

Investing in the unglamorous plumbing will pay dividends as your company grows its AI capabilities. It will reduce maintenance costs, accelerate deployment and ensure that AI acts on high‑quality data. Executives are beginning to recognize the link between governance and AI reliability; unifying metadata and governance across data and AI assets ensures models take actions based on trustworthy, up‑to‑date information.

Conclusion: build the pipes before chasing magic

AI will continue to transform business processes, but it cannot do so on a foundation of bad data. Organizations are losing millions and wasting talent because they treat data plumbing as an afterthought. Modern data stacks are too complex, slow and context‑poor to power AI‑first operations. Building a trusted data environment, high‑velocity data hubs, underpinned by knowledge graphs that harmonize semantics across systems, is essential. This plumbing may not make headlines today, but it is the difference between AI that hallucinates and AI that drives profitable decisions. Invest in the pipes now so that when AI agents take the wheel, they are navigating with clean, contextual, real‑time data.

Subscribe now

Share

References

1. Socialnomics article explaining how poor data quality—such as incomplete datasets, biased sources and outdated information—causes hallucinations in language models.

2. Socialnomics article describing how models fill gaps when training data is missing, leading to fabricated outputs.

3. BetaNews report showing that IT leaders view CMDBs as essential but acknowledge that poor data quality hinders AI adoption; 64 % have not adopted AI because of data quality and security concerns.

4. CDOTrends survey revealing that 81 % of organizations trust AI despite data inefficiencies, losing 6 % of annual revenue; data scientists spend 67 % of their time cleaning data; 69 % struggle to access data; 68 % struggle to cleanse data; 42 % experience data‑driven hallucinations.

5. Modern Data Company report noting that 85 % of data teams cite tool integration as a top challenge and 63 % spend more than 20 % of their time on maintenance.

6. Modern Data Company commentary stating that architecture complexity slows data teams and more tools create integration headaches.

7. Modern Data Company observation that AI needs data enriched with context, lineage and governance; a unified layer ensures data arrives business‑ready.

8. Modern Data Company finding that current fragmented approaches require manual context; 65 % believe combining strong data models with data products compresses time to insight.

9. Value Innovation Labs article describing how real‑time data solutions enable enterprises to react instantly and provide continuous, up‑to‑the‑moment intelligence.

10. Value Innovation Labs article emphasising that the shift from batch processing to real‑time insights is necessary in a high‑speed digital economy.

11. Value Innovation Labs article outlining the components of real‑time data platforms: ingestion pipelines, stream‑processing engines, analytics layers, automated triggers and AI decision engines.

12. Value Innovation Labs article listing operational use‑cases for real‑time data—customer experience, operations, finance and cybersecurity.

13. Totogi blog illustrating a telecom ontology that acts as a digital Rosetta Stone by mapping disparate systems and processes into a common model.

14. Totogi blog describing how AI agents on top of the ontology layer can read documentation, write code, call APIs and compress release cycles.

15. Earley Information Science article explaining that ontologies allow systems to infer relationships and act as a Rosetta stone, providing richer context and enabling systems to communicate.

16. Databricks blog noting that only 22 % of organizations believe their architecture can support AI and that infrastructure will be the biggest AI investment area; a unified data platform is needed for agents.

17. Databricks blog highlighting the link between data governance and AI reliability—unifying metadata ensures models act on high‑quality data and reduces operational costs.

📌 THE POINT IS: Don’t just bet the farm on moonshot business cases for AI. Build a culture where potentially thousands of small, compounding AI wins free up time, fund the big bets, and enable real quality-of-life gains—including maybe even a path to the four-day week for some companies in the US!

Executives keep telling me that AI requires a use-case before they can get started on investments. I get it, you want that sweet ROI, but realistically, ROI on your AI investments will be realized through non-traditional mechanisms as we go forward. The data says durable value comes from a portfolio: pair Everyday AI (task-level copilots, workflow assistants, coding aides) with Strategic AI (new products, agentic systems, business model shifts). Leaders who balance both see results faster—and they keep them.¹²

“Companies that boost their learning capabilities with AI are significantly better equipped to handle uncertainty.” – MIT Sloan Management Review & BCG⁶

Subscribe now

Why Everyday AI must sit beside Strategic AI

Generative AI’s macro potential is undeniable—McKinsey estimates $2.6–$4.4T in annual value and a 0.1–0.6% annual lift to labor productivity through 2040.¹ That value isn’t magic; it materializes when thousands of routine activities get faster, easier, and higher quality. Randomized and field studies back this up: GitHub Copilot users completed coding tasks 55.8% faster in a controlled trial,³ and a large-scale NBER study of 5,179 support agents found a 14% productivity gain on average—34% for novices—when using an AI assistant.⁴ Microsoft’s Work Trend Index echoes this pattern across knowledge work: early Copilot users were 29% faster across writing, summarizing, and searching; 70% reported higher productivity.⁵

“Only 22% of companies have advanced beyond proof-of-concept to generate some value—and only 4% are creating substantial value.” – BCG, Where’s the Value in AI?⁷

Translation: moonshots without a base of Everyday AI often stall overall AI adoption and culture change. Leaders in this field focus on core processes and people, then scale.⁷ Meanwhile, Gartner expects agentic AI to permeate software quickly—by 2028, 33% of enterprise apps will include agentic capabilities—so the operational backbone you build now will compound.⁹

We’re seeing this now in my world. Whether it’s Copilot entering Microsoft’s suite of tools more fully; Databrick AI/BI Genie and Mosaic AI; Snowflake’s Cortex AI; AgentForce in SalesForce; or Workday’s newest AI features…these capabilities are becoming table stakes in large, enterprise applications across the board.

Everyday AI lifts productivity and quality of life

The productivity story matters, but so does well-being. OECD evidence finds most employees using AI at work report better performance, job enjoyment, and improvements to mental and physical health.⁸ And when organizations reinvest time savings into better ways of working, the gains can support schedule innovations. In the UK’s landmark four-day week pilot (61 firms; ~2,900 workers), researchers reported revenue held steady (up 1.4% on average) during the six-month trial, while stress and burnout dropped and retention improved—many firms kept the policy.¹⁰ ¹¹

“Company revenue barely changed during the trial period—rising by 1.4% on average.” – Autonomy & university research team, UK pilot report¹⁰

What’s interesting about this is that they did this pilot before AI was really introduced into companies. We’re just now seeing models that are capable enough to be junior co-workers. Imagine the impact on revenue in the UK pilot with today’s tools. We could have seen a much greater than 1.4% lift on that same 4-day time period.

Subscribe now

A brief, on-the-ground example

Inside my team, we’re building a chatbot agent tuned to our standards, accelerators, and “Golden Path” methods for data pipelines and curated models. In a Trusted Data Environment, this everyday assistant will onboard new developers, guide business data teams, and help contractors ship integrations the right way, faster. It won’t transform our whole business by itself, but it will accelerate everything else—shorter time-to-market for analytics and the next wave of Strategic AI implementations!

“Generative AI assistance disseminates the best practices of more able workers and helps newer workers move down the experience curve.” – Brynjolfsson, Li & Raymond, NBER⁴

The cool part is that we can roll out capabilities bit-by-bit to continuously show incremental value since this is a self-built, Everyday AI agent. It’s a digital co-worker that can be at everyone’s side when building data pipelines and data models. It will help us position our Trusted Data Environment as a rapidly growing platform.

Related whitepaper:

Make them sister initiatives: Everyday AI + Strategic AI

Here are some steps to take to get started and stay in check on both paths:

1. Stand up Everyday AI as a program with a product owner, budget, and backlog. Target the top 10–15 recurring tasks in each function (docs, meetings, search, coding, service). Prove time savings and quality deltas with before/after baselines (cycle time, defect rate, CSAT).³⁴⁵

2. Run Strategic AI in parallel with 1–3 high-value bets (e.g., agentic workflows, new AI-native features). Make sure executives are bought in and understand the commitment, but insist on milestones tied to measurable outcomes. Leaders that do this outperform.⁷

3. Invest in change management and learning loops. Train managers on “work redesign,” not just prompts, but keep the basics of prompt and context engineering at the forefront of your associate training programs. MIT SMR/BCG finds organizations that combine organizational learning with AI-specific learning are 1.6–2.2x better at navigating uncertainty.⁶

4. Measure and reinvest time. Redirect reclaimed hours to customer work and innovation that teams can actually deliver outside of just a couple-day hackathon setting. Where metrics hold—consider how you can give time back to associates in terms of extra, flexible time off, floating holidays, flex time for associates who perform work over weekends, or even if you’re bold, four-day workweek pilots in eligible teams.¹⁰ ¹¹

5. Strengthen the data backbone. Everyday AI without trusted, well-governed data is risk, not value. Often times this requires a whole different set of data hygiene controls, tools, and policies since this type of AI will often interact with your user-generated, office-document processes. Don’t skip out on making sure you have good guardrails setup here to minimize risk!

6. Ready the stack for agents. Gartner’s agentic wave is coming; design guardrails, identity, observability, and human-in-the-loop for any use cases now.⁹

Bottom line: Everyday AI compounds; Strategic AI differentiates. Together, they create the financial, cultural, and operational surface area to go faster and work better.

Subscribe now

Share

References

1. McKinsey Global Institute, “The economic potential of generative AI: The next productivity frontier.” https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/the-economic-potential-of-generative-ai-the-next-productivity-frontier (McKinsey & Company)

2. McKinsey Global Institute (PDF), “The economic potential of generative AI.” https://www.mckinsey.com/~/media/mckinsey/business%20functions/mckinsey%20digital/our%20insights/the%20economic%20potential%20of%20generative%20ai%20the%20next%20productivity%20frontier/the-economic-potential-of-generative-ai-the-next-productivity-frontier.pdf (McKinsey & Company)

3. GitHub (arXiv), “The Impact of AI on Developer Productivity: Evidence from GitHub Copilot.” https://arxiv.org/abs/2302.06590 (arXiv)

4. NBER Working Paper, “Generative AI at Work” (Brynjolfsson, Li, Raymond). https://www.nber.org/papers/w31161 and PDF https://www.nber.org/system/files/working_papers/w31161/w31161.pdf (NBER)

5. Microsoft Work Trend Index Special Report, “What Can Copilot’s Earliest Users Teach Us…?” https://www.microsoft.com/en-us/worklab/work-trend-index/copilots-earliest-users-teach-us-about-generative-ai-at-work (Microsoft)

6. MIT Sloan Management Review & BCG, “Learning to Manage Uncertainty, With AI.” https://sloanreview.mit.edu/projects/learning-to-manage-uncertainty-with-ai/ (MIT Sloan Management Review)

7. Boston Consulting Group, “Where’s the Value in AI?” https://www.bcg.com/publications/2024/wheres-value-in-ai (BCG)

8. OECD, “The impact of AI on productivity, distribution and growth” (2024). https://www.oecd.org/content/dam/oecd/en/publications/reports/2024/04/the-impact-of-artificial-intelligence-on-productivity-distribution-and-growth_d54e2842/8d900037-en.pdf (OECD)

9. Gartner, “Intelligent Agents in AI Really Can Work Alone. Here’s How.” https://www.gartner.com/en/articles/intelligent-agent-in-ai (Gartner)

10. Autonomy, “The Results Are In: The UK’s Four-Day Week Pilot” (full report PDF). https://autonomy.work/wp-content/uploads/2023/02/The-results-are-in-The-UKs-four-day-week-pilot.pdf (The Autonomy Institute)

11. University of Cambridge (Dept. of Sociology), “New results from the world’s largest trial of a four-day working week.” https://www.sociology.cam.ac.uk/news/new-results-worlds-largest-trial-four-day-working-week (sociology.cam.ac.uk)

Share Your Suggestions

I’m excited that dAIta POINTS has taken off ever since migrating over to the Substack platform earlier this year. Seeing new people subscribe each week after publishing and article and/or whitepaper is super energizing!

Now it’s time for you to write to me! Well, not really…I’m actually hoping you’ll take a quick survey to give me some feedback. The survey will take ~2 minutes to complete…no really, I swear!

2-Minutes I swear!

Please click the button and share your thoughts so I can keep this growing and focused on things that are of interest to you.

Have additional feedback, certainly send me an email! I’d love to hear more anecdotal, even constructive, feedback from readers!

Leave a comment

Thanks,

M@

Last Chance! :)

📌 THE POINT IS: On the surface, personal accountability seems to have vanished over the past few decades, but it hasn’t. Instead, it’s been diffused across socio-technical systems. Psychology trends, cultural incentives, and AI companions make it easier than ever today to offload blame, a natural human tendency. Leaders can reverse this by designing workflows for ownership, measuring process (not just outcomes), and building auditing into every AI-touched workflow.¹⁴

Personal accountability is not a fossil from the pre-AI era; it’s a casualty of diffusion that’s started well before AI became popular. While I don’t often veer from the professional world, this article is heavily influenced by the recent discussion about the emotional impact that chat bot models have had both on adults and children, in the workplace and at home. It’s a call to action that we as a society need to double down on relying on each other for support and companionship over virtual partners…it could be the difference between life and death.

Subscribe now

There’s a history to unpack related to the human nature around externalizing our actions

Across decades, young Americans’ locus of control has shifted outward—toward external forces. As one cross-temporal meta-analysis put it, “locus of control scores became substantially more external” between 1960 and 2002.¹ Combine that with the self-serving bias (credit successes to me, blame failures on the situation or someone else) and you have a cognitive slipstream away from personal ownership of our decisions and actions.²

Sociologists have described a growing “victimhood culture,” where moral status accrues to those most aggrieved.³ Parenting research points in the same direction: overcontrolling. Overly-involved or “helicopter parenting” at age 2 predicts poorer emotional regulation by age 5 and downstream social/academic issues. These are skills required to learn how to own one’s mistakes, and they start from early development.⁴ Meanwhile, grade inflation has marched upward while ACT scores stagnate or fall, muddying the signal that effort and learning still matter.⁵ Having said that, though, impressively, despite caricatures about “lawsuit-happy America,” civil trials have actually plummeted—from 11.5% of federal civil dispositions in 1962 to 1.8% in 2002, meaning that people take to social media to air their grievances about each other more often than they go to the courtrooms (something that mediation clauses in disclaimers, contracts, and waivers also play a role in).⁶

AI companions: anthropomorphism at scale

With that backdrop, let’s examine how we’re primed to humanize software (animals, toys, etc.). The ELIZA effect explains why we see modern chatbots as real people, especially given their language fluency and 24/7 availability.⁷ Character.AI alone has reported ~20 million monthly active users;⁸ one national survey found 1% of young adults say they already have an AI friend, 10% are open to it, and 25% believe an AI partner could replace a human relationship.⁹

These attachments have teeth. When Replika pulled erotic role-play, “users [were] in crisis,” describing genuine grief.¹⁰ University of Connecticut researchers chronicle teens redefining love as “easy, unconditional, and always there,” warning of social withdrawal and skill erosion.¹¹ Lawsuits against Character.AI in 2024 and OpenAI in 2025 allege chatbots worsened or even coached self-harm, underscoring real-world stakes.¹² ¹³

This is underscored by a very recent story in the NY Times about a 16 year old California boy who committed suicide. It was found that ChatGPT advised him to seek help, but ultimately facilitated his suicide by answering questions related to him hanging himself. OpenAI is now making changes to how the models respond to similar situations and questions, attempting to safe-guard against future similar situations.

Subscribe now

Why accountability gets scrambled in human–AI work

Two patterns matter for business:

“Automation bias—the tendency to over-rely on automated recommendations—has emerged as a critical challenge in human–AI collaboration.”¹⁴

Reviews across healthcare, aviation, and public administration show people over-trust suggestions, miss model errors, and under-report their own.¹⁵ Then, when something breaks, we fall into moral crumple zones:

“The moral crumple zone protects the integrity of the technological system, at the expense of the nearest human operator.”¹⁶

In one incident, a clinician blames “the algorithm.” In another, compliance blames the clinician for “not verifying.” Either way, accountability is obscured—great for avoiding heat, terrible for learning and trust.

But closer to home, school, and our offices, we should learn lessons from the 16 year old mentioned above. The algorithm didn’t make him do it, and it’s fundamentally not ChatGPT’s fault, but is there a responsibility that organizations have to curtail how these bots reply to questions or is the responsibility on the person who performed the act? If we’re going to seek answers outside of the victim, why aren’t we pointing the spotlight to the people who were in the boy’s life as real actors who could have intervened.

These are tough and controversial questions. I only mention them to provoke thought and dialog as a society so we can zoom out on the larger implications of these tools in young people’s (and adult’s) lives.

The answer to the above questions are: it’s not that simple. There isn’t anyone to blame and although adding guardrails to the chat bots will help in some circumstances, we as a society have to figure out how we empower each other more to prevent these activities from happening. We fundamentally won’t be able to code for every circumstance and situation that people will engage with chat bots over…there has to be a human-centric change.

The executive angle: how this shows up in our office buildings

Knowledge workers are now decision editors over model outputs—drafting emails, pricing endorsements, segmenting customers, flagging fraud. That creates three failure modes:

1. Responsibility gaps: RACI charts say “the team” owns it; logs say “the model” suggested it. No single throat to choke.

2. Invisible drift: Models are updated silently; prompts mutate; responses change over time as new facts are introduced…how do we keep up from a governance perspective (HINT: this goes back to having a trusted data environment to start from).

3. Metric theater: Dashboards praise cycle-time gains while error costs hide downstream in rework, complaints, or regulatory findings.

   ---

   Subscribe now

   ---

Pragmatic fixes to investigate

1. Mandate human sign-off where it matters. Require named ownership for consequential actions (pricing overrides, denials, escalations). Make the signer visible in the record. Tie incentives to accuracy after review, not just speed.¹⁴ ¹⁵

2. Instrument the last mile. Log prompts, versions, and human edits. Store the decision packet (input → model output → rationale → final action) for audit and retro. This shrinks post-mortem guesswork.¹⁴

3. Design for friction at decision boundaries. Insert verification steps when confidence, novelty, or impact exceed thresholds (e.g., new policy, out-of-distribution inputs). Don’t add friction everywhere; add it where regret is expensive.¹⁵

4. Kill blame diffusion with RACI+AI. Extend RACI to RACII (Responsible, Accountable, Consulted, Informed, Interpreter). The Interpreter is the human who attests they understood the model output and constraints.

5. Teach parasocial and automation literacy. Roll short, mandatory modules: anthropomorphism, automation bias, calibration, escalation etiquette. Not vibes—case-based drills with failure examples.⁷ ¹⁴

6. Set red lines for companionship features. Create a system where whether you’re a parent, teacher, friend, sibling, or other adult in a child’s life, there are clear changes in behavior and other prompts that you can use to engage in dialog with those who seem to be lost or in trouble. Let’s not leave it up to a virtual companion. We need training as a society that could help prevent the loss of life in a way that’s much more effective than child-to-bot friendships.

Bottom line: Accountability didn’t die; we let it atomize. Reassemble it with named owners, logged decisions, calibrated friction, and teams trained to question polished answers. That’s how you get speed and responsibility in the AI era.

Share

References

1. Twenge, J. M., Zhang, L., & Im, C. “It’s beyond my control: A cross-temporal meta-analysis of increasing externality in locus of control, 1960–2002.” https://pubmed.ncbi.nlm.nih.gov/15454351/ PubMed

2. SAGE Encyclopedia of Social Psychology. “Self-Serving Bias.” https://sk.sagepub.com/ency/edvol/download/socialpsychology/chpt/selfserving-bias.pdf SAGE Journals

3. Campbell, B., & Manning, J. “The Rise of Victimhood Culture.” https://www.researchgate.net/publication/323181753_The_Rise_of_Victimhood_Culture ResearchGate

4. Perry, N. B., et al. “Childhood self-regulation as a mechanism…” (PMC). https://pmc.ncbi.nlm.nih.gov/articles/PMC6062452/ PubMed Central

5. ACT Research. “Grade Inflation a Systemic Problem in U.S. High Schools.” https://leadershipblog.act.org/2022/05/grade-inflation-past-decade.html ACT

6. Galanter, M. “The Vanishing Trial: An Examination of Trials and Related Matters in Federal and State Courts.” https://api.law.wisc.edu/repository-pdf/uwlaw-library-repository-omekav3/original/0b9f361000c04494e8cff30f04b3afeb486193d4.pdf Wisconsin Law API

7. Nielsen Norman Group. “The ELIZA Effect: Why We Love AI.” https://www.nngroup.com/articles/eliza-effect-ai/ Nielsen Norman Group

8. Business of Apps. “character.ai: revenue and usage statistics (2025).” https://www.businessofapps.com/data/character-ai-statistics/ Business of Apps

9. Institute for Family Studies/YouGov. “Artificial Intelligence and Relationships.” https://ifstudies.org/blog/artificial-intelligence-and-relationships-1-in-4-young-adults-believe-ai-partners-could-replace-real-life-romance Institute for Family Studies

10. VICE. “‘It’s Hurting Like Hell’: AI Companion Users Are In Crisis.” https://www.vice.com/en/article/ai-companion-replika-erotic-roleplay-updates/ VICE

11. UConn Today. “Teenagers Turning to AI Companions…” https://today.uconn.edu/2025/02/teenagers-turning-to-ai-companions-are-redefining-love-as-easy-unconditional-and-always-there/ UConn Today

12. Associated Press. “AI chatbot pushed teen to kill himself, lawsuit alleges.” https://apnews.com/article/chatbot-ai-lawsuit-suicide-teen-artificial-intelligence-9d48adc572100822fdbc3c90d1456bd0 AP News

13. SFGATE. “California family sues Sam Altman, OpenAI over son’s suicide.” https://www.sfgate.com/tech/article/chatgpt-california-teenager-suicide-lawsuit-21016916.php SFGATE

14. Romeo, G., & Conti, D. “Exploring automation bias in human–AI collaboration.” AI & SOCIETY (2025). https://link.springer.com/article/10.1007/s00146-025-02422-7 SpringerLink

15. Vered, M. et al. “The effects of explanations on automation bias.” University of Melbourne (2023). https://psychologicalsciences.unimelb.edu.au/__data/assets/pdf_file/0019/5252131/2023Vered.pdf psychologicalsciences.unimelb.edu.au

16. Elish, M. C. “Moral Crumple Zones: Cautionary Tales in Human-Robot Interaction.” DOAJ record. https://doaj.org/article/97ff6743ea7a44a5ade2a04fd2c57a3c Directory of Open Access Journals

📌 THE POINT IS: AI agents aren’t going to replace all of your workflows and applications. They’re going to use them just like your human workers do. The question isn’t if they’ll show up in your business, but whether your data and application foundations are ready for them. This piece explores what agents can actually do today, where the technology is heading, and why companies should invest in input controls, clean data pipelines and observability if they want to benefit.

The reality check: what AI agents are doing today

Open any social feed and you’ll see breathless predictions that AI will replace swathes of knowledge workers. That isn’t what’s happening on the ground. Agents are being hired like junior colleagues, not CEOs. In Phase 1, they remove drudgery by acting as assistants; in Phase 2, they join teams as digital colleagues, taking on specific tasks with human direction; only in Phase 3 do humans set direction and let agents run entire workflows². According to Microsoft’s 2025 Work Trend Index, 82% of leaders say this year is pivotal to rethink operations for AI, and 81% expect agents to be moderately or extensively integrated into their company’s AI strategy within the next 12–18 months². One quarter of companies have already deployed AI organization‑wide².

Subscribe now

Where are these agents working? A 2025 survey of thousands of Substack posts found that AI and software development trends rank among the platform’s fastest‑growing narratives³, reflecting how quickly agentic AI is infiltrating mainstream discourse. In practice, however, AI agents are being used for very specific workflows:

• Meeting summarization and goal management. Tability’s 2025 review notes that AI agents summarize meetings, launch marketing campaigns and manage internal goals, taking on tasks that were once manual or hard to keep up with¹. They free up time while never sleeping, scaling instantly and learning fast¹.

  Personally, I really enjoy letting M365 Co-Pilot join meetings in Teams and later provide a summarization of the notes and action items. Sure it’s not 100% perfect all of the time, but it’s really good and for me, that’s enough. When you learn that you can stop and restart transcriptions to create separate note packages for different sections of your meeting, now you’ve discovered a great way to have specific, sharable assets about discussions with almost no extra work!

• Business process automation for SMBs. An in‑depth guide on AI agents for small businesses highlights that SMBs use agents to handle customer inquiries, manage inventory, track finances and generate marketing content⁴. These agents often come embedded in SaaS tools, meaning many entrepreneurs are already using AI without realizing it⁴. AI‑as‑a‑Service offerings and low‑code tools have lowered barriers so dramatically that 50% of SMBs are expected to adopt at least one AI automation solution by 2026⁴.
  

  This also translates to my world, even though I work for a larger enterprise. Many of our SaaS and other vendor-solution partners are offering agents or other natural language, graph DB, or Knowledge Graph offerings within their ecosystems. This is starting to change how we think about future tech investments when the in-built DataBricks agent can scan its whole environment, pull out answers to questions in natural language, and even provide a graph to you in real time!

“When we think about, even, all these agents, the fundamental thing is there's a new work and workflow. So, the new workflow for me is: I think with AI and work with my colleagues.” - Satya Nadella

The takeaway is that today’s agents are augmenting work more than replacing it. Lower‑level employees might now “manage” several AI agents¹, but human judgment sets goals and resolves edge cases. A Stanford survey of U.S. workers showed that 46.1% of tasks were ones employees wanted automated⁶, primarily to free up time for higher‑value work. Importantly, 45.2% of occupations favored an equal partnership with AI (the Human Agency Scale’s H3 level), not full automation⁶. This underscores that collaboration rather than replacement is the prevailing desire.

Subscribe now

The digital worker won’t replace your systems

Many discussions around agents make it sound like we’ll rebuild entire workflows as end‑to‑end AI services. Yet most business logic already lives in existing applications, from CRM and ERP systems to custom line‑of‑business software. The smarter approach is to train agents to interact with these systems utilizing MPC or other API protocols so that the application doesn’t care whether a human or a digital worker is clicking buttons. This preserves business rules, audit trails and security controls while unlocking automation.

Microsoft’s Frontier Firm model describes a progression where hybrid teams of humans and agents scale rapidly by blending machine intelligence with human judgment². Meanwhile, small‑business surveys show that most practical agents today are rule‑based or lightweight learning agents integrated into SaaS platforms to handle repetitive tasks⁴. These agents don’t rewrite enterprise logic; they safely orchestrate actions across existing systems.

Preserving existing logic also minimizes regulatory risk. Over‑automation—replacing human interaction where empathy or expertise is critical—is identified as a top pitfall in SMB adoption⁴. Another frequent mistake is poor data hygiene: deploying predictive agents without clean, consistent historical data leads to inaccurate outputs⁴.

Data readiness: the backbone of agentic success

The most sophisticated AI agent will fail if it’s fed junk⁷. Gartner has declared Agentic AI the #1 strategic technology trend for 2025 and predicts that by 2028 at least 15% of day‑to‑day work decisions will be made autonomously⁷. However, Gartner warns that “dazzling proofs of concept will not appear without a strong data foundation”⁷. Data readiness is mission‑critical: quality, accessible, well‑governed data must underpin every AI agent⁷.

“Data is the nutrition of artificial intelligence. When an AI eats junk food, it's not going to perform very well.” - Matthew Emerick

I’ve talked a lot in this newsletter about the trusted data environment, but this just underscores that if you don’t take care to build a strategy where you can trust the data coming in and being utilized by both humans and machines, you’ll simply open your company up to incalculable risk. It’s no longer OK to bypass the input control, data pipeline, observability, or pipeline development projects like we used to. Waiting for a downstream reporting or data science team to clean up inputs is way too late and way too slow for AI, especially when digital workers are interacting with your front-end systems.

To dive into more details, check out these articles on dAIta POINTS today:

“You can have all of the fancy tools, but if your data quality isn't good, you're nowhere.” - Veda Bawo

These steps aren’t academic nice‑to‑haves; they’re survival traits. Research shows analysts spend up to 80% of their time simply finding and cleaning data⁷. Without addressing fragmentation, stale information and missing metadata, AI agents will misinterpret inputs, hallucinate or make costly mistakes.

“People spend 60% to 80% of their time trying to find data. It's a huge productivity loss.” - Dan Vesset

Subscribe now

The market signals: invest now or fall behind

Why invest in all this plumbing? Because digital labor is about to close a gaping productivity gap. Microsoft’s survey found that 53% of leaders say productivity must increase, yet 80% of employees feel they lack time or energy to do their work². Intelligence on tap—AI and agents that can reason, plan and act—allows companies to scale capacity on demand². Leaders are confident: 82% expect to use digital labor to expand workforce capacity in the next 12–18 months².

The economic opportunity is enormous. The global AI agents market, valued at $5.9 billion in 2024, is projected to grow from $7.7 billion in 2025 to $105.6 billion by 2034, a compound annual growth rate of 38.5%¹². Top investment areas include customer service (38%), sales and marketing automation (32%), financial analytics (19%) and operations/supply chain (11%)⁴. Moreover, 81% of leaders expect agents to be integrated into their AI strategy within 12–18 months². Put simply: if you’re not preparing your work environment for agents now, you’re leaving money and efficiency on the table.

Building your digital workforce: practical recommendations

1. Start with the human, not the agent. Identify tasks that employees want automated and where automation adds value. Research shows workers favor automating low‑value, repetitive tasks⁶ but prefer equal partnership on higher‑value work⁶. Over‑automating tasks requiring empathy or expertise can backfire⁴.

2. Preserve and extend existing systems. Don’t throw out your ERP or CRM. Train agents to interact with them, preserving business rules and audit trails. Leverage agents for coordination, leaving core logic intact. Use multi‑party computation (MPC) or secure protocols so that both humans and digital workers can access systems safely.

3. Invest in data foundations. Unite your data silos through a semantic layer⁷, enforce data quality and freshness⁷, and enrich metadata and lineage⁷. Implement real‑time pipelines and monitoring tools that summarize telemetry into AI‑ready insights⁹. Adopt data quality management practices, business rules and continuous profiling¹⁰.

4. Build guardrails and governance. Establish access controls, content filters and alignment parameters before deploying agents⁷. Restrict agents to approved datasets and mask sensitive identifiers⁷. Use prompt‑level safeguards to prevent hallucinations and ensure compliance.

5. Upskill your workforce. Equip employees to manage and collaborate with digital coworkers. Provide training in prompt engineering, data literacy and high‑level oversight. Encourage employees to think like product managers for their agents—setting goals, monitoring performance and refining workflows.

6. Measure and iterate. Pilot agents in a single department, track performance metrics (time saved, error rates, satisfaction) and iterate⁴. Expand gradually as ROI becomes clear.

Why this matters now

In today’s world, the AI revolution isn’t about replacing humans; it’s about amplifying human capacity.

“AI is not going to replace humans [in the short-run], but humans with AI are going to replace humans without AI.” - Karim Lakhani¹³

Agents will increasingly take on the boring, repetitive and data‑driven parts of our jobs so that people can focus on strategy, creativity and relationships. But this future isn’t guaranteed. Without clean data, observability and guardrails, agents will amplify chaos instead of productivity. Your digital workforce will only be as good as your data foundation.

Subscribe now

References

1. Tability’s 2025 review of AI agents highlights that digital coworkers summaries meetings, launch marketing campaigns, manage goals and free up time by handling repetitive tasks tability.io. It also notes that employees now manage multiple AI agents and that tools such as Relevance AI and CrewAI enable building teams of delegated agents tability.io tability.io.

2. The 2025 Microsoft Work Trend Index describes three phases of AI adoption—assistants, digital colleagues and autonomous agents—and reports that 82% of leaders see 2025 as pivotal for rethinking operations. It notes that 81% expect agents to be integrated into their strategy within 12–18 months, and 25% of firms have already deployed AI organization‑wide microsoft.com microsoft.com microsoft.com. The report also highlights that 53% of leaders need productivity to increase while 80% of employees lack time and energy, and that leaders plan to use digital labor to expand capacity microsoft.com microsoft.com.

3. A 2025 analysis of trending Substack posts shows that AI and software development topics are among the fastest‑growing narratives on the platform britopian.com.

4. A small‑business guide to AI adoption notes that SMBs use agents to handle customer inquiries, inventory, finances and marketing content aalpha.net and that many tools embed AI by default aalpha.net. It warns against over‑automation, poor data hygiene and underestimating task complexity aalpha.net and projects that 50% of SMBs will adopt at least one AI automation solution by 2026 aalpha.net. The report estimates the SMB AI market will grow from USD 3.7 billion to over USD 20 billion by 2030 and lists top investment areas aalpha.net.

5. In a 2025 interview, Microsoft CEO Satya Nadella said that AI agents will redefine how knowledge work is performed, noting that “there's a new work and workflow” and that he personally “thinks with AI and works with [his] colleagues” businessinsider.com.

6. A Stanford survey of U.S. workers found that 46.1% of tasks were ones employees wanted automated and that 45.2% of occupations preferred equal partnership with AI—the Human Agency Scale’s H3 level arxiv.orgarxiv.org.

7. B EYE’s 2025 data readiness guide emphasizes that agentic AI success depends on uniting data silos via a semantic layer and providing high‑quality, up‑to‑date and context‑rich data. It reports that 77% of organizations prioritize AI‑ready data but only 43% feel prepared and warns that dazzling proofs of concept require strong data foundations b-eye.com b-eye.com b-eye.com. The guide also stresses the need for multi‑level guardrails and notes that Gartner predicts at least 15% of daily work decisions will be made autonomously by 2028 b-eye.com b-eye.com. It remarks that analysts spend up to 80% of their time finding and cleaning data b-eye.com.

8. Data quality expert Matthew Emerick compares data to nutrition for AI, remarking that “Data is the nutrition of artificial intelligence. When an AI eats junk food, it's not going to perform very well” datasciencedojo.com.

9. An observability article explains that traditional logging generates hundreds of thousands of entries per second, exceeding agents’ context windows and making it impractical to feed raw telemetry to AI. It advocates engineering observability data into patterns and summaries and cites tools like Log Patterns to provide structured, contextual insights groundcover.com.

10. Alation’s data quality management guide reiterates the maxim “garbage in, garbage out” and argues that accurate, well‑managed data is essential for trustworthy AI outcomes alation.com. It outlines steps for defining data quality, enforcing business rules, profiling data across layers and continuously monitoring quality alation.com.

11. An MIT Sloan feature quotes Raymond James director Veda Bawo stating, “You can have all of the fancy tools, but if [your] data quality is not good, you're nowhere” mitsloan.mit.edu. The same article quotes IDC analyst Dan Vesset: “People spend 60% to 80% of their time trying to find data. It’s a huge productivity loss” mitsloan.mit.edu.

12. A market research report projects that the global AI agents market will grow from USD 7.7 billion in 2025 to USD 105.6 billion by 2034 (38.5% CAGR) gminsights.com.

13. Citing Harvard Business School professor Karim Lakhani, an article on AI’s role in the workplace observes that “AI is not going to replace humans, but humans with AI are going to replace humans without AI” trainingmag.com.

    ---

    Share

    ---

📌 THE POINT IS

GPT-5 makes day-to-day work smoother: it combines the clean, expressive 4o writing model with the o3-level thoroughness and analytical mindset all without having to take multiple passes using each model to do specific cycles of work. With GPT-5 Thinking enabled, you get to work with a mid-grade analyst partner that has 3-5 years of experience all from one chat prompt! Now we're starting to see some great workflow improvements and less overall confusion for lay people.

What actually changed (and you feel it fast)

I ran the same kinds of tasks I always run: policy drafting, financial analysis, trend analysis, and language practice. The flow is different in the best way—default GPT-5 for quick drafting, GPT-5 Thinking when I need structured analysis or comparisons. Less coaxing, fewer rewrites.

In all honesty, I've used GPT-5 with Thinking turned on almost exclusively for now. Often the work I'm doing involves analysis, comparisons, trending and I need the model to do searches the verify assumptions. As Ethan Mollick put it (and I totally agree):

“GPT-5 just does things… When you ask GPT-5 for something, the AI decides which model to use and how much effort to put into ‘thinking.’ It just does it for you.”
— Ethan Mollick, One Useful Thing (One Useful Thing)

Subscribe now

I've also turned on GPT-5 in M365 Copilot at work to give it a whirl. The responses and thoughtfulness of Copilot's answers in this mode are shockingly different. I asked it to perform a search for a file in my OneDrive and initially it failed. When I asked it why, it performed a detailed self-diagnostic (with the thought trail visible) and then came up with feedback for itself to improve on future searches (plus it found the file and two other earlier template versions of that file that I had also worked on!).

“The new smart mode in Copilot allows the AI assistant to switch models for you to use deeper reasoning or quickly respond based on the task.”
— The Verge (The Verge)

Putting it into practical terms…examples of workflows I used it for this week

1) Writing: Gift Acceptance Policy (as a non-profit Treasurer)

I'm the Treasurer of Esperanto-USA and we recently realized that our Gift Acceptance Policy was very out of date. I used GPT-5 to survey real-world examples and industry best practices from trusted sites, synthesized the patterns, then gave me style options (plain-English, board-resolution tone, or legalistic). I picked a style and it produced a clean first draft of the policy. In Canvas we tightened definitions, clarified edge cases, and aligned approval workflows.

Net: expert search and analysis, fewer rewrites than 4o, steadier structure, and a faster path to board-ready-for-review.

2) Analytical work: Quarterly finance report (cross-document comparison)

For this quarter’s Treasurer report, GPT-5 Thinking compared numbers across multiple months’ documents and generated comparison charts that were more mathematically accurate than what I’ve seen from prior models. It handled deltas and month-over-month commentary without stumbling, then summarized key drivers cleanly enough to drop into the exec narrative. I was able to add the remaining details, future plans, and other information needed with only a few tweaks to the provided analysis. BONUS: it did a pretty decent first-pass translation, although I went through and made several updates on my own later.

Net: better accuracy, better charts, less cleanup.

Subscribe now

3) Custom GPTs (capability improvements)

Re-using my existing Custom GPTs with GPT-5 under the hood gave me more mature, consistent outputs. I was pretty pleasantly surprised at the nuanced difference in the replies, frankly. I started a new chat for my daily check-in with my “Mystic Oracle" GPT and when it helped me synthesize inputs for my daily personal forecast, things to look out for, relation to Tarot and Hindu Deities, and a personal mantra for the day, I saw a much more “clever" response. Formatting stayed stable, and it followed longer, multi-step instructions without getting brittle, but the way it connected dots across several days’ of inputs and gently synthesized patterns in my life was impressive.

Net: as a personal coach and assistant analyzing my life patterns, it does a super job of keeping facts and trends in order while pointing out hiccups or ways I should improve how I approach situations.

Language & learning: from editor → tutor

I consult with ChatGPT for Esperanto polish and fluidity checks, especially when I'm publishing something on the Internet or in a magazine. Think of it as having a personal editor who can review style and common language usage across many sources in the blink of an eye. Historically, 4o made phrasing sound natural and o3 nailed lexical precision. GPT-5, though, blends both: a ranked comparison in one pass (“most common,” “more fluid,” “formal”) and a single-sentence rationale for each choice. The level of maturity of the responses was more professional and high-quality.

Now, to take things to the next level, I've considered turning on Study Mode so that it's even more consultative but also instructive! Instead of throwing out answers (albeit I did ask for explanations too), it can ask questions and guide me so that I can actually learn how to write better. For language (or any skill), that changes the game…and for a constructed language with considerably less online resources to learn and grow from, each model has raised the bar considerably on its Esperanto vocabulary and grammar / word choice. (As an aside, like many Esperantists, it still gets some words wrong from time to time.)

“Study mode is designed to transform ChatGPT from an ‘answer machine’ into a teaching machine… asking questions, encouraging reflection, and mimicking a tutor.”
— Tech & Learning (Tech & Learning)

Subscribe now

When to use what: my summary after week 1

• GPT-5 (default): briefs, emails, post intros, quick summaries.

• GPT-5 Thinking: comparisons, plans, cross-doc analysis, anything you’d defend in a meeting (that last one is most critical).

• Study Mode: language practice, math/logic refreshers, onboarding content turned into lessons.

“The vibes of this model are really good, and I think that people are really going to feel that.”
— Nick Turley, Head of ChatGPT, quoted in WIRED (WIRED)

Bonus pro-tips (YOU NEED TO DO THIS if you haven't recently)

• Refresh Custom Instructions. Don't underestimate the impact of making sure you have great, updated custom instructions! This is your overlay to the system prompts and will greatly swing how the model responds to you. This is a way to set general context engineering instructions for your interactions with any model.

• Pick a Personality. This too has a huge impact on how the model will respond. This is the “voice" you want in your assistant. OpenAI has recently gone through a lot of feedback cycles and accusations that models were too cycophantic. This is their response and you just have to select one and open a new chat to get going!
  

  These two tweaks smooth out tone and structure across your whole workflow.

Bottom line: GPT-5 is a huge step-up on the user experience of ChatGPT. It's like having a junior assistant who still needs coaching and guiding, but who can learn really fast and do research work at lightning speed. We didn't get into the cost too much here, but in most cases (without Thinking turned on), the costs are going down, too, and that's having an impact across multiple model providers!

References & further reading

• Ethan Mollick — “GPT-5: It Just Does Stuff,” One Useful Thing. (One Useful Thing)

• The Verge — “Microsoft brings GPT-5 to Copilot with new smart mode.” (The Verge)

• WIRED — “OpenAI Finally Launched GPT-5. Here’s Everything You Need to Know.” (WIRED)

• OpenAI — “Introducing Study Mode.” (OpenAI)

• Tech & Learning — “I Tried ChatGPT’s Study Mode And It’s Mostly Great.” (Tech & Learning)

• OpenAI Help — “Customizing Your ChatGPT Personality” and “Custom Instructions.” (OpenAI Help Center)

Share

Automation isn’t cheating—it’s liberation

Every leader fantasizes about having a few extra hours each day to think big thoughts. We long for the quiet time between meetings and emails to connect dots, uncover patterns, and play out scenarios in our heads. Ever since my GE days, I’ve had a whiteboard in my office with a reminder to “stop and think” or “ask bigger questions”. With back-to-back meetings on a range of topics, all requiring a near-instant context switch, even when time is blocked, it’s hard to honor it when competing demands pop up from subordinates, bosses, or colleagues.

Ironically, the technologies people accuse of promoting laziness are delivering exactly the thing we need: time. In a recent conversation at Nationwide, we discussed that as Agentic AI creates time for associates and officers, we have choices in how we should use it. Of course the company will want to soak up some of that productivity goodness by asking people to do more. But, I’m happy to say that we’ve had a lot of conversations about innovation, critical thinking time, team gatherings to explore big ideas or just to connect. All of these offer a new, strategic option to explore that we just simply don’t have the time for today!

Subscribe now

Generative AI tools are doing the heavy lifting on coding, research, summarization and administrative busy work. Rather than “cheating,” they allow us to channel our attention toward the strategic thinking and critical judgement that humans do best. The “cheating” hurdle is one we’ve talked about recently as well. Our leadership team has talked about how even officers need to get over the idea that using Generative AI tools is somehow cheating. In the early days of ChatGPT and LLMs, universities and public schools were very worried about student usage of these tools to cheat their way through coursework. Now, there are answers popping up in the industry that will help shift that narrative even at the beginning.

Recent updates from the AI world underscore this shift. OpenAI’s ChatGPT Study Mode encourages learners to engage with material instead of copy-pasting answers. When a user asks about a complex topic, Study Mode asks follow-up questions to gauge the learner’s level and goals, then walks them through the subject step-by-step [1]. OpenAI positions the feature as a way to fight academic misuse; it doesn’t simply spit out solutions, but guides the student toward understanding and allows them to upload exam papers or images for interactive help [1]. This is the opposite of a cheating: it’s a Socratic tutor that prompts deeper reflection.

Meanwhile, Google DeepMind just unveiled Genie 3, a general-purpose world model capable of generating minutes-long, interactive 3D environments from a text prompt [2]. The model remembers what it has generated so simulations stay physically consistent over time [2] and lets users inject “promptable world events” to change the environment on the fly [2]. It isn’t publicly available yet, but the research preview hints at educational uses—Genie 3 could train agents for general-purpose tasks or let learners explore both realistic and imaginary worlds [2] with real-life physics. A separate report notes that the model runs at 720p and 24fps and remembers past interactions, enabling immersive experiences akin to video games while being monitored for ethical use [3]. In other words, AI can now build entire virtual classrooms around us. If you’re a Trek fan, I think we’re seeing the first version of the Holodeck about to be unveiled!

Subscribe now

Offloading doing to reclaim thinking

As a data and analytics leader, I’ve watched AI shrink tasks that once ate hours into minutes or even seconds. Drafting reports, generating charts, designing slide decks (well, this one still needs a bit more work…): these can all be automated to a surprising degree. The value isn’t that I “skip the work”; it’s that I can invest my reclaimed time in first-order thinking and strategic leadership. AI becomes the junior analyst who crunches numbers while I consider how those insights feed into broader objectives.

This shift also applies to personal development. Study Mode flips generative AI from an answer engine into a tutor. Instead of handing you an essay, it asks what level of mathematics you’re comfortable with and then scaffolds an explanation [1]. The goal is to encourage engagement, guiding users toward answers rather than serving them up [1]. The feature responds to images too, letting students upload past exam papers so it can help them work through problems step by step [1]. When AI takes over rote tasks and prompts us to think, we build the intellectual muscles that matter most.

From a data perspective, it does make one pause to think about what curation required for enterprise data to drive a professional-world training program. We’ve recently started talking about making sure unstructured data repositories used for training, codes of ethics, company policies, etc. are in fact curated in and of themselves so that we can create a “repository” (which will probably just look like a standard folder on your desktop) that we tune the model to for “official” versions of key policies. We can build a huge ontology and map every document in the organization to it, too, but when you can just create a simplified vault of your latest standard operating procedures, the question becomes, why not take the simple path here? ¯\_(ツ)_/¯

Bringing Study Mode into 3D: Genie 3 and the future of skill-building

Now imagine coupling this Socratic tutor with Genie 3’s immersive world builder. Picture a statistics student stepping into a virtual city where probability concepts are woven into the environment—traffic lights become Bayes’ theorem problems, while the AI tutor probes the student’s reasoning in real time. Or consider a leadership coaching session where you practice difficult conversations with simulated employees. Promptable world events could instantly shift the scenario from calm to crisis, challenging you to adapt on the fly.

Reports highlight that Genie 3 can render whole worlds in real time at 720p and 24fps for minutes at a stretch, remembering interactions so objects stay where you left them and letting users change weather or summon new elements with simple text commands [3]. This emergent visual memory means that learners could revisit the same virtual space and find it consistent; their actions would matter. Combine that with Study Mode’s Socratic questioning and you have a fully interactive, adaptive classroom that responds to both your answers and your actions.

Such environments could reinvent professional development. Today’s leadership training often relies on role-playing and case studies. Tomorrow’s could immerse managers in dynamic, AI-generated worlds where they practice decision-making, systems thinking and empathy. As DeepMind researchers note (via reporting), Genie 3 is designed to train agents for general-purpose tasks and pushes them to learn from their experience [2]. Why not treat ourselves as agents in our own learning?

Subscribe now

Final thoughts: embrace the paradox

AI is often criticized for making us lazy or for hollowing out human expertise. But if we use it thoughtfully, it can do the opposite. Automating administrative drudgery frees us to focus on judgement, creativity and relationships. Socratic AI tutors push us to think rather than regurgitate. Immersive world models promise experiences that no textbook could match. And when we bring these tools into our leadership practice, we may finally get what we’ve always wished for: more time to think deeply about what matters.

The paradox is that the more we let AI do for us, the more we’re forced to confront ourselves. That’s not cheating—that’s growth.

References

[1] The Guardian — coverage of ChatGPT “Study Mode.” https://www.theguardian.com/

[2] TechCrunch — reporting on DeepMind’s Genie 3 world model. https://techcrunch.com/

[3] Times of India — summary of Genie 3 capabilities (resolution, fps, memory). https://timesofindia.indiatimes.com/

Share

AI isn’t just a buzzphrase anymore – it’s rapidly becoming the way we build software. To “vibe code", you describe what you want in plain English and a large (or small) language model (L/SLM) generates the code. Instead of writing and debugging every line, you guide and refine it until it works – often without fully understanding the code’s internals[1]. It’s democratizing, intoxicating and a little dangerous.

However, AI can only work its magic if you give it data it can trust. I mean, it can still code you up something that looks nice, but the results will only ever be as good as the data it's able to work with

Bottom line: get your data house in order or the vibes will betray you.

Subscribe now

What “Vibe Coding” Really Means

Vibe coding isn’t just “using AI to code.” It’s a workflow where the intent matters more than syntax. You type in natural language; the model generates code and tests (hopefully); you iterate until the output matches your intent[2]. Key characteristics include:

• Natural language input. Users describe problems conversationally instead of writing syntax[3]. “Build me a pipeline to fetch data from Shopify, clean it, and push daily summaries into Snowflake” is a typical vibe prompt[4].

• AI‑generated code with human refinement. The LLM writes the bulk of the code; you guide it by testing and giving feedback[5].

• Minimal code understanding. Non‑programmers can jump in because you don’t have to know every line[6]. That accessibility is both the appeal and the Achilles’ heel.

This paradigm has already infiltrated data engineering. AI‑assisted agents can assemble pipelines, resolve identities across systems, run data quality checks and even auto‑tag sensitive data[7]. Think of it as managing a team of tireless interns: you set objectives, they do the grunt work, and you keep them on track[8]. Done right, vibe coding frees you to focus on architecture and impact[9].

But notice how I said “interns" above…as of today, vibe coding assistants are not your senior software or data engineers with 20 years of experience…they're rapidly improving, but still on the junior side of the talent spectrum.

The Dark Side of Vibes

Handing over the keyboard to a model isn’t without risks. Studies show that top LLMs still generate insecure code in over a third of security‑critical tasks[10]. “Vibe coding” tools often encourage users to trust code blindly – a fun way to experiment but not something you should ship to production[11]. If you treat AI suggestions like gospel, you’ll inherit hidden vulnerabilities and technical debt. Worse, you could violate compliance rules by inadvertently exposing sensitive data.

Security experts are already sounding alarms. The Cloud Security Alliance’s secure vibe coding guide warns that AI‑generated code often lacks proper input validation, uses insecure defaults and can embed secrets in plain text[12]. Without strong governance, vibe coding becomes a playground for injection attacks and data leaks. The takeaway is clear: AI needs guardrails.

Why a Trusted Data Environment Matters

AI models live and die on the quality of the data they ingest. Garbage in, hallucinations out. A trusted data environment gives AI (and people!) the structure and context needed to generate useful code and insights. Here’s why:

• Data governance is your rulebook. Robust governance improves data fidelity, compliance and security; it sets clear roles and processes so everyone – human or AI – understands how data should be used[13]. Good governance prevents the “self‑service paradox,” where giving everyone unfettered access leads to chaos[14].

• Metadata is the map. AI doesn’t magically know which documents are confidential. Metadata and vector embeddings complement each other: embeddings represent the content (“the what”), while metadata provides context (“the why”). Using metadata, you can exclude internal documents and feed models only the right files[15]. A global metadata ontology lets you search, filter and govern data across environments[16].

• Automation unlocks scale. Traditional metadata management is slow and error‑prone. AI and natural language processing can automatically discover, classify and enrich metadata across structured and unstructured sources[17]. This not only accelerates catalog creation but enables conversational data search and proactive governance[18].

• Security isn’t optional. Fine‑grained permissions, attribute‑based access controls and end‑to‑end lineage are fundamental pillars of a trusted environment[19][20]. You must know who accessed what, when and how. Data encryption – both at rest and in transit – protects sensitive information from prying eyes[21].

• Quality and reliability drive trust. High‑quality pipelines facilitate consistent, complete and timely data…they're non‑negotiable[22]. Embedding data quality checks to ensure fidelity with your upstream systems throughout your pipelines ensures that AI is working with reliable data that represent the real world[23].

Subscribe now

Building the Foundations: Pillars of a Trusted Data Environment

Let’s break down the must‑haves for a data environment that accelerates vibe coding without sacrificing security or sanity. Consider these your non‑negotiables:

1. Centralized, fine‑grained access control. Use tools like Databricks Unity Catalog or Snowflake Horizon to manage permissions at the table, column or even row level[19]. Attribute‑based access control lets you tailor policies by job role, department and project[24].

2. Data lineage and auditing. Track every data asset from ingestion to consumption. Comprehensive audit logs reveal who touched data, what transformations occurred and when[25]. Lineage isn’t just for debugging – it’s vital for compliance and for trusting AI recommendations[26].

3. Metadata management at scale. Build or adopt a metadata catalog that captures technical and business context. AI‑driven catalogs automatically discover and classify assets, assign sensitivity tags and map relationships[27]. This makes your data searchable by natural language and surfaces relevant assets proactively[18].

4. Data quality monitoring. Implement continuous validation to ensure completeness, consistency and accuracy. Master Data Management (MDM) helps eliminate duplicates and unify records[28]. Quality scores or dashboards let users know which datasets are trustworthy.

5. Encryption and secure infrastructure. Encrypt data at rest and in transit. Harden clusters and notebooks with network restrictions, multi‑factor authentication and version control[29]. Consider client‑side encryption or BYOK (bring your own key) for sensitive workloads[21].

6. Governance culture and stewardship. Assign data stewards, define accountability and align standards for naming and units[30]. A cross‑functional data council ensures consistent policies and resolves issues[31]. Data literacy training helps everyone understand why these rules exist[32].

7. Frameworks and open standards. AI performs best when code is concise, consistent and built on well‑documented frameworks like dbt or Airbyte. Standardized tooling enables the model to test its own code and interpret errors[33]. Inconsistent, Frankenstein codebases confuse both humans and models.

How Trusted Data Accelerates Vibe Coding

When your data house is in order, AI coding goes from a gimmick to a force multiplier. Here’s why:

• Cleaner context yields better prompts. High‑quality metadata and standardized schemas mean you can ask AI to “generate a dashboard of monthly revenue by region” and it knows exactly which tables and fields to reference. AI can infer relationships and produce accurate queries or code because it has a map[34].

• Reduced friction equals faster iteration. With automated lineage and quality checks, you can catch issues earlier and iterate on AI‑generated code faster. The days of spending hours searching for the right dataset or debugging a misnamed column disappear.

• Security by design unlocks collaboration. Fine‑grained access and encryption let more stakeholders use AI tools without fear of leaks. This is especially critical when trying to use representative data in your testing processes. AI and human developers need to be able to access representative data to do great testing and many of the tools used in prod can help sythesize or protect sensitive data so real data can be used to support superior testing.

• Frameworks amplify AI’s skills. Models trained on standardized frameworks deliver higher‑quality code and can even self‑test and self‑document[35]. Also, companies can setup MPC servers on high-volume and high-quality datasets so AI can access those sources reliably and without creating custom code for each new agent.

• Metadata fuels governance‑aware AI. AI can use policy tags to avoid sensitive columns, automatically redact PII and enforce usage rules. This ensures that vibe‑coded applications remain compliant even when non‑experts are at the helm[37].

In short, a trusted data environment turns vibe coding from a wild experiment into a repeatable, scalable practice. It gives AI the context, safety and guardrails to deliver real business value quickly.

Subscribe now

Final Thoughts: You Can’t Skip the Basics

It’s tempting to get caught up in the hype of AI writing your next data pipeline or app. I've even tried to vibe-code an app, though, and it does take skill and some knowledge to get them to work right. Watching a model generate hundreds of lines of code in seconds feels like magic. But the magic fades fast when that code breaks production or exposes your customer data. Speed is nothing without control.

The path forward is clear: invest in data governance, metadata, security and quality before you unleash AI on your stack. Build unified, secure pipelines; catalogue and classify your data; use frameworks and open standards; train your teams to be stewards of data. Do these things, and vibe coding will be more than a trend – it will be a competitive advantage.

Share

[1] [3] [5] [6] [10] [12] Secure Vibe Coding Guide | Become a Citizen Developer | CSA

https://cloudsecurityalliance.org/blog/2025/04/09/secure-vibe-coding-guide

[2] [4] Vibe Data Engineering: What's Next After Vibe Coding

https://powerdrill.ai/blog/vibe-data-engineering-whats-next-after-vibe-coding

[7] [8] [9] Vibe Coding: The New Language Of AI-First Data Engineering

https://cloudtweaks.com/2025/07/vibe-coding-new-language-ai/

[11] Top 10 AI-Powered Tools for Vibe Coding in 2025 - Designveloper

https://www.designveloper.com/blog/vibe-coding-tools/

[13] [14] [32] 10 Data Governance Use Cases You Need to Know!

https://www.castordoc.com/data-strategy/data-governance-use-cases

[15] [16] [37] Why You Need Metadata for Smarter AI and Data Governance – Komprise

https://www.komprise.com/blog/why-you-need-metadata-for-smarter-ai-and-data-governance/

[17] [18] [27] [34] Metadata Management Gets Smarter with AI and NLP Datahub Analytics

https://datahubanalytics.com/metadata-management-gets-smarter-with-ai-and-nlp/

[19] [20] [21] [24] [25] [29] Data Governance and Security in Databricks: How to Build a Trusted Data Environment - ASB Resources

https://asbresources.com/data-governance-and-security-in-databricks-how-to-build-a-trusted-data-environment/

[22] [23] [26] [28] [30] [31] Manufacturing’s Data Backbone: Engineering Trustworthy Pipelines for Performance

https://manufacturing-today.com/news/manufacturings-data-backbone-engineering-trustworthy-pipelines-for-performance/

[33] [35] [36] How AI will disrupt data engineering as we know it | dbt Labs

https://www.getdbt.com/blog/how-ai-will-disrupt-data-engineering

“Metadata screams at you, ‘I am a graph!’” — Mark Beyer, Gartner (2023)

CTOs and enterprise architects are under pressure to unleash generative and agentic AI across the business, yet many still wrestle with the same stubborn reality: AI is only as smart, safe, and scalable as the data beneath it. When dashboards contradict each other or a model draws the wrong conclusion, we’re reminded that the true bottleneck isn’t the algorithm—it’s the trustworthiness of the data foundation.

Over the past several months I have argued that building an AI‑Ready, Trusted Data Environment is now the #1 strategic priority for technology executives. Below, I pull together the key insights from my latest whitepaper to show how three tightly‑woven capabilities—active metadata, data fabric, and an augmented data catalog—form the nervous system of that environment and clear the runway for digital workers to transform your processes.

Dive deeper by reading the full whitepaper!

1. Passive vs. Active Metadata: From Glossary to Engine Room

Traditional metadata behaved like a dusty card catalog: helpful if someone remembered to open the drawer. Active metadata, by contrast, moves: it refreshes automatically, detects anomalies, and triggers downstream actions in real time. When a daily claims feed skips a run, active metadata should fire an alert before a report misleads underwriting. Gartner calls this “converting passive into active metadata,” and it is the first step toward self‑healing data pipelines.

The message for leaders is clear: capture everything (schemas, lineage, usage, quality scores) and instrument it so machines—not analysts—spot issues at machine speed.

2. Data Fabric: Weaving Context and Control

A true data fabric is not another data lake; it is an architectural pattern that analyzes that river of active metadata and automatically optimizes how data is integrated, governed, and delivered. Picture an intelligent mesh that:

• finds linkages between siloed data sets,

• recommends the best source for a given request, and

• reroutes pipelines when schemas drift.

Insurance leaders use fabrics to create a real‑time Customer‑360 without “rip‑and‑replace” core systems; one bank slashed data prep time for AI models by 67 % after deploying a fabric overlay. And because a fabric sits above the physical storage layer, it pairs naturally with the emerging lakehouse pattern—letting you keep the open formats and elastic compute of a lake while layering on the governance, semantics, and real‑time metadata routing a warehouse provides. McKinsey’s 2023 study on modern data architecture found that companies marrying a lakehouse foundation with a metadata‑driven fabric delivered new data products 30–40 % faster than those running lakehouses alone. The takeaway? Stop coding brittle point‑to‑point feeds and start letting metadata drive adaptive integration—whether your data lives in a warehouse, a lakehouse, or both.

Subscribe now

3. The Augmented Data Catalog: Your Enterprise System of Record—for Context

If the fabric is the weave, the catalog is the lens. A modern, AI‑augmented catalog inventories every data product, tags its owners, displays quality metrics, and exposes lineage in a click. Crucially, it’s consumable by humans and machines alike. A digital underwriting agent can query the catalog API to verify that the “Claims_History” table is certified and 98 % complete, while a product analyst can search plain English to find the same data.

“Without trusted data, there’s no trusted AI.” — Precisely (2024)

Governance needn’t slow you down; a properly instrumented catalog enforces PII rules, tracks usage for auditors, and still leaves analysts free to self‑serve. More importantly, it becomes the nexus where human and digital workers collaborate: the catalog’s graph of active metadata allows an AI agent to (1) discover which data assets already satisfy its task, (2) surface gaps and automatically raise requests for new feeds, and (3) notify data stewards—and the human stakeholder it supports—about progress and quality status in real time. In effect, the catalog acts as a mission‑control dashboard for the digital workforce, constantly reconciling “data available” versus “data required” and orchestrating the stewardship backlog so that humans focus on high‑value interventions while machines handle the plumbing. That balance—speed with safety—and true human‑machine teaming is the executive sweet spot.

4. Six Pillars of the Trusted Data Environment

Bringing it all together requires sustained investment in six mutually‑reinforcing capabilities:

1. Data Observability – automated monitoring for freshness, volume, and schema anomalies.

2. Data Quality Validation – rules for accuracy, consistency, and timeliness executed continuously.

3. Master Data Management – golden records for customers, suppliers, and products.

4. Reference & Semantic Models – shared taxonomies (ACORD, ISO codes, etc.) plus domain‑specific views.

5. Domain Data Products – curated, self‑describing datasets ready for underwriting, claims, AML, or marketing.

6. Discoverability via Catalog & APIs – last‑mile access for people, BI tools, and AI agents.

Delivering on all six lets you move from “garbage in, garbage out” to “insight in, impact out.”

5. Agentic AI: When Data Trust Meets Digital Labor

“The agent has skills, tools, and workflows, and chooses the best route for the task at hand.” — ServiceNow Roundtable (2025)

Agentic AI—digital workers that decide, act, and learn—relies on three things: context, confidence, and orchestration. The trusted ecosystem supplies context (well‑described data), confidence (lineage and quality scores), and orchestration (event triggers from active metadata). Insurers already let AI straight‑through‑process small auto claims; banks deploy compliance bots that monitor trades in real time. In every success story, the heavy lifting happened below the waterline—clean master data, governed access, and a catalog the bots can read.

“Data is the differentiator between building a generic generative AI app and one that knows your customers deeply.” — AWS Architect (2024)

Your digital workforce will scale your expertise or your errors. A fabric‑powered catalog stacked on the six pillars ensures it scales expertise.

Subscribe now

Executive Playbook

So what should you do if you're an IT or business executive who's trying to figure out where to lay your first dollars down? Think about these steps to get your organization ready to roll for* any* analytics use-case, whether it's reporting, dashboarding, traditional AI, generative AI, or the bedrock of your digital workforce:

1. Prioritize Metadata First. Fund harvesting, graph analytics, and alerting projects in the next quarter.

2. Modernize Integration with a Fabric Mindset. Start by virtualizing two high‑value domains; measure cycle‑time gains.

3. Operationalize Governance in the Catalog. Embed PII tagging, lineage, and social collaboration—not as an afterthought but as product requirements.

4. Pilot an Agentic Use Case. Choose a data‑rich yet bounded process (e.g., claims triage). Instrument it end‑to‑end and showcase the ROI.

5. Educate the C‑Suite. Tie every AI win back to the investments in data quality and metadata to keep budgets flowing.

The Bottom Line

Data architecture is destiny. Transformative AI requires more than a clever model; it demands a living, breathing data nervous system built on active metadata, woven through a data fabric, and surfaced via an augmented catalog. Get that right and your digital workforce will not just automate tasks—it will compound value.

Now is the time to invest the money, energy, and leadership capital to turn data chaos into digital confidence. The organizations that do will find that their next breakthrough doesn’t come in spite of their data—it comes because of it.

Artificial intelligence is only as powerful as the data that fuels it. As AI capabilities advance – from predictive analytics to autonomous “agentic AI” systems – organizations are realizing that data readiness is a strategic imperative. In particular, metadata (data about data) has emerged as a critical …

Read more

📌 THE POINT IS: A well-defined purpose fuels motivation, enhances teamwork, and drives business impact. Teams with a shared mission don’t just function—they thrive. Purpose fuels motivation, collaboration, and impact. Leaders who create clarity around their team’s mission will unlock a new level of performance.

In my career, I’ve seen firsthand how purpose can transform a group of individuals into a high-performing team.

Whether at General Electric (GE) or Bank of America, I’ve led data and analytics teams through major transitions, and the one constant factor in their success has been a clear sense of purpose. When people understand why their work matters, they become empowered, emboldened, and more engaged.

But this isn’t just anecdotal—it’s backed by research and real-world applications across industries, including the corporate world and even the military.

The Science Behind Purpose-Driven Teams

A McKinsey study found that employees who derive meaning from their work are five times more likely to stay engaged and productive. They also found that during the Pandemic, 67% of employees started focusing on their life's purpose. This resulted in the Great Resignation, another global phenomenon, that highlighted people's need to find meaning and purpose in their work.

Similarly, Deloitte reports that organizations with a strong sense of purpose experience 30% higher innovation levels and 40% greater workforce retention.

One reason for this? Purpose creates alignment. When a team understands its mission, decisions become easier, collaboration is smoother, and obstacles feel like challenges rather than roadblocks.

This holds true beyond corporate settings. The U.S. Army’s research into unit cohesion shows that soldiers who understand their mission and feel connected to a larger purpose demonstrate greater resilience under pressure. Sergeant Major of the Army, Robert E Hall said,

Sense of purpose is the primary factor for high morale- the individual soldier's knowledge that he or she is making a difference.

Real-World Examples: Bringing Good Things, and Purpose, to Life

Building a People Analytics Function at GE

When I was tapped at GE to create the People Analytics function, I had to build a high-performing team from scratch—bringing together analysts from different parts of the company, many of whom had never worked together. The challenge? They came from different reporting functions, different countries!, and had varied skill sets.

The first step wasn’t just hiring the right people—it was giving them a mission. Together, we crafted a purpose statement:

🛠️ To be the premier analytics experts of GE HR data, shifting the paradigm from "show me your Excel" to "show me your PowerPoint."

That single statement reframed the team’s mindset. Instead of churning out data reports, we became strategic advisors helping leaders make better talent decisions. The results? Within our first year, we became the only shared service team to gain accolades across multiple business units, saving the company >$1 MM in cost and much more in work content redistribution (e.g. people got to work on more important stuff!).

Merging Data Scientists & Developers in Internal Audit

Later, when I moved into internal audit at GE, I was asked to build a data analytics practice. The challenge? Merging a team of data scientists and software developers into a cohesive unit. The existing team was stuck in a reactive, service-delivery model—taking on one-off projects rather than driving meaningful change.

We reset with a purpose:

🔍 To develop an analytics platform that would revolutionize how auditors detect financial anomalies.

This clarity transformed the team’s work. Instead of handling sporadic requests, we built a machine-learning model that automatically, successfully audited the fixed asset register—something that had never been done before. Purpose turned fragmented efforts into a structured, impactful initiative.

Bringing “a Little Love” to a Data Team at Bank of America

When I joined Bank of America, I quickly realized that my new data team was missing something fundamental: confidence. They had the technical skills, but they lacked the business context to understand how their work fit into the bigger picture.

I told my boss,

“The only thing this team really needs right now is a little love.”

And that “love” came in the form of purpose. We crafted a new mission:

💡 To build an automated decision engine that adds a human touch to collections scenarios.

This changed everything. Team members no longer saw their work as just processing data; they saw it as helping people navigate financial hardships with empathy. That shift fueled innovation and engagement in ways that a simple directive never could. It was especially powerful during the COVID-19 pandemic. When everyone could have been distracted by fear and the impact on their own worlds, they rallied behind making sure others' financial troubles were at least minimized. Now that's a purpose.

How to Instill Purpose in Your Team

If you’re leading a team—whether it’s a corporate department, a product group, or even a cross-functional initiative—consider these steps:

✅ Craft a Clear Mission Statement – Define your team’s purpose in a way that is inspiring yet practical.

✅ Align the Work with Business Impact – Show how everyday tasks contribute to the bigger picture.

✅ Foster Ownership – Give team members autonomy over how they achieve the goal.

✅ Communicate Purpose Regularly – Reinforce the mission in meetings, one-on-ones, and project planning.

✅ Celebrate Progress – Recognize milestones that tie back to the core purpose.

🤔 Final Thoughts: Purpose Fuels Performance

If there’s one thing I’ve learned, it’s that a purpose-driven team is an unstoppable team. Purpose is not a corporate buzzword—it’s the backbone of innovation, engagement, and retention.

When employees see where they fit into the big picture, they go beyond just meeting expectations—they exceed them.

Matt Brooks is a seasoned thought leader and practitioner in data & analytics, leadership development, and business transformation. Follow for insights on building high-performance teams and driving strategic change powered by your organization's data assets.

Subscribe now