This is Part 12 of The AI Reckoning: A Future of Trust Series
We have built an extraordinary amount of infrastructure for artificial intelligence.
Data centers stretching across hundreds of acres. Chips engineered for AI workloads. Power generation, cooling systems, fiber networks, cloud platforms, foundation models, APIs, applications, and now agents capable of acting on our behalf.
The investment is staggering. So is the speed.
At the same time, governments, researchers, and companies are wrestling with how to deploy that capability responsibly: governance, security, privacy, explainability, accountability. Much of it is still being figured out while the technology continues to advance.
But another layer of infrastructure seems to be missing: the infrastructure for the humans expected to live and work alongside it.
I don’t mean more training. Training matters. People need to know how to use the tools, but knowing where to click isn’t the same as being ready for what the technology is changing.
People are being asked to rethink how they work, how they make decisions, what expertise means, which parts of their jobs still belong to them, and increasingly, what they can trust. Leaders are being asked to guide organizations through changes they are still trying to understand themselves.
When we talk about keeping pace with AI, I don’t mean asking people to think as fast as machines or produce at machine speed. That’s the wrong race. I mean giving people what they need to adapt as the world around them changes faster.
We’ve spent enormous resources building the infrastructure AI needs to become more capable, and we’re beginning to build the infrastructure required to govern that capability responsibly.
Now we need to build the infrastructure that supports the people living with it.
The Reckoning Was Never Just About AI
When I started The AI Reckoning series, I knew I wanted to get to the human part of this story, but I didn’t want to start there.
Before we can understand what a system is doing to people, we need to understand the system itself. Its foundations. Its constraints. The assumptions we’ve built into it. The economics driving it. The dependencies we’ve created. And the secondary consequences that may not be obvious when we’re focused on what the technology can do.
That’s how I tend to approach complex problems. I want to understand the environment producing the experience before I try to explain the experience itself.
So we began underneath AI, with the physical infrastructure most of us never see: the energy, water, compute, and environmental costs hidden beneath something that can feel almost weightless on a screen.
From there, we looked at the economics behind the extraordinary investment, the risks of building businesses on intelligence someone else owns, and the fragmentation created by an explosion of disconnected tools.
I wanted to look at those things with eyes wide open, not because I believe they diminish the promise of AI, but because they shape the environment in which that promise has to be realized.
Then we could turn to the human side.
We looked at adoption and resistance, at what happens to judgment when information becomes abundant, and at what happens to trust when seeing is no longer believing or when an AI system can give us an answer no one can fully explain. And most recently at what happens when we become exceptionally good at solving problems without understanding the systems producing them.
The progression was intentional. People aren’t responding to AI in isolation. They’re responding to the technology and to the conditions surrounding it: the pace of change, uncertainty about work, questions of trust and authenticity, decisions they may not understand, tools that don’t always fit together, and organizations that are themselves still figuring out what comes next.
When we talk about keeping pace with AI, I don’t mean asking people to think as fast as machines or produce at machine speed. That’s the wrong race.
I mean giving people what they need to adapt as the world around them changes faster.
We can call those human problems. But that doesn’t mean the human is where the problem started. If we look only at the person struggling to adapt, we may conclude that they need more training, more resilience, or a better attitude toward change. If we look at the system around them, we may see something very different.
Across these eleven reckonings, the question underneath the series has become increasingly clear: What happens when technological capability advances faster than our ability to understand, govern, absorb, and trust its consequences?
The answer isn’t to ask humans to simply move faster. It’s to understand what they need in order to move through change well.
When Intelligence Is Abundant, What Becomes Scarce?
For most of human history, access to knowledge and expertise was scarce.
If you needed a legal opinion, you called a lawyer. If you wanted to understand a medical study, you needed someone who knew how to interpret it. Writing software or analyzing a financial model required either the expertise yourself or access to someone who had it.
AI is changing that equation remarkably quickly. Knowledge isn’t suddenly infinite, and AI certainly isn’t always right. But access to something that can explain, analyze, synthesize, create, and increasingly reason across enormous amounts of information is becoming available to almost anyone.
When something that was scarce becomes abundant, value tends to move somewhere else. If answers become easier to generate, knowing which questions to ask becomes more important. If content becomes abundant, discernment becomes more important. If analysis can be produced in seconds, judgment about what to do with it becomes more important. And if something can look and sound completely real without being real, trust becomes more important.
We’ve touched each of these questions throughout The AI Reckoning. Taken together, they point to something larger: The more intelligence we have access to, the more important our relationship with that intelligence becomes.
Can we evaluate it, question it, and put it in context? Can we recognize what it doesn’t know or what assumptions may be shaping it? Do we know when not to use it? Are we willing to take responsibility for what happens when we do?
Those aren’t primarily questions of machine capability. They’re questions of human capacity. And capacity is not the same as productivity. Much of the conversation about AI and people still centers on how much more humans will be able to produce. The productivity opportunity is real and potentially enormous. But if we define human readiness primarily by how efficiently people can use AI to produce more, we may be measuring the wrong thing.
We can become dramatically more productive without becoming more discerning. We can make decisions faster without making better ones. An organization can deploy AI everywhere without building greater trust in how it is being used. More capability doesn’t automatically create more capacity to use it well. In some ways, it demands more from us.
What happens when technological capability advances faster than our ability to understand, govern, absorb, and trust its consequences?
As AI becomes increasingly embedded in our work, the human capacities that once seemed difficult to measure or easy to dismiss as “soft” start to look much less optional: judgment, curiosity, discernment, adaptability, trust, the ability to sit with uncertainty long enough to ask another question, and the confidence to challenge an answer when everyone else is ready to move forward.
The better AI gets, the more these capabilities matter.
The Visibility Gap
At the end of the last article, I left you with a question:
What if the things leaders most need to understand are the very things they have the hardest time seeing?
Organizations have never had more data. Leaders can track productivity, performance, turnover, absenteeism, engagement, technology usage, customer behavior, and increasingly almost anything that leaves a digital trail. AI can find patterns across enormous amounts of that data and surface relationships a human analyst might never see.
But much of what determines whether people can actually adapt to change doesn’t leave a clean digital trail.
Are people struggling with a new technology because they don’t understand it, don’t trust it, or are afraid of what it means for their future? Is someone quiet in a meeting because they agree or because they’ve learned that speaking up isn’t worth the risk? Are people genuinely adopting a change, or complying until the attention moves somewhere else?
We can see the behavior. Understanding what’s underneath it is much harder.
That’s the visibility gap.
The more intelligence we have access to, the more important our relationship with that intelligence becomes.
Our data mostly tells us what happened after something became visible. Turnover tells us who left. Usage tells us whether a tool is being used. Productivity tells us what is getting produced. An engagement score tells us something about how people are responding.
By the time those signals move enough to command attention, the conditions producing them may have been developing for months. And some of the most valuable information may never enter the organizational data stream at all.
People filter. They decide what is safe to say, who it is safe to say it to, and how much of what they’re experiencing they want attached to their name.
That doesn’t mean leaders don’t care or aren’t asking. Even very good leaders have a structural problem: the fact that they are in a position to act on information can affect what people are willing to tell them.
Surveys, listening sessions, town halls, manager check-ins, sentiment analysis, and engagement measures can all provide useful information. But there is a difference between asking people what they think and creating the conditions in which they are willing to tell you.
There is also a difference between collecting responses and understanding the patterns connecting them. A trust problem may appear as an adoption problem in one part of the organization, turnover somewhere else, resistance in another, and declining productivity somewhere else again. If each function sees only its own metric, no one may recognize that they’re looking at different expressions of the same underlying condition.
That’s the visibility we’re missing. Not more information about what happened, but a better understanding of what’s happening underneath it. The insight has to be able to travel upward without the individual’s identity traveling with it.
What Human Infrastructure Looks Like
Human infrastructure has to work in both directions.
For the individual, change is personal.
An organization may describe an AI transformation in terms of productivity, efficiency, competitive advantage, or new capabilities. The person experiencing it may be wondering whether they’re still good at their job, whether the expertise they spent years developing still matters, whether they’re expected to use a tool they don’t trust, or what any of this means for the role they’re adapting to.
Those aren’t problems a training module can solve. People need somewhere private enough for candor, where they can question their assumptions, understand what may be triggering a response, work through a difficult interaction, make a decision, or simply get enough perspective to see what’s happening differently. That support has value even if the organization learns nothing from it. The individual cannot simply become a richer source of organizational data. Human infrastructure has to serve the human first.
But when many people have a trusted place to work through what they are actually experiencing, something else becomes possible. Patterns begin to emerge. What looks like resistance may actually be a trust problem. People may understand the technology but not understand how their roles are changing around it. A workload issue may be appearing across teams that look unrelated on an organizational chart.
Our data mostly tells us what happened after something became visible.
Visibility alone isn’t enough. We already have dashboards that tell us something is happening. Human infrastructure should help us get closer to why. This is where AI can be enormously useful. It can identify patterns across conversations and experiences, connect signals that would otherwise remain separate, and surface possibilities a leader may not have known to look for.
But an insight isn’t a decision. Someone still has to determine what it means, whether it makes sense in context, and what action is appropriate. The need for judgment doesn’t diminish as AI becomes more capable. It increases.
So when I talk about human infrastructure, I mean something that helps people navigate change while helping organizations understand what is happening across the people experiencing it. Something that can connect patterns to possible causes and paths forward without pretending complex human behavior can be reduced to a clean equation.
The individual gets support. The organization gets better visibility. Leadership has a better basis for action. And what happens next becomes new information about the system.
This gives change a better chance to take hold, and that’s when transformation becomes possible.
Trust Has to Be Part of the Infrastructure
None of this works without trust.
If we want to understand what people are actually experiencing, they have to feel safe enough to be honest about it. That becomes harder when the organization receiving the insight is also the organization that controls their job, their opportunities, and, in some cases, their future.
This is where privacy becomes fundamental. People want issues addressed. They just don’t want to be identified as the one who raised them, and that distinction is what makes candor possible. Trust doesn’t require that every concern be resolved or that everyone agree with every decision. But it does require people to believe that telling the truth doesn’t come with repercussions, whether immediate or delayed. When people believe that, the information leaders actually need, the kind that explains what’s really causing a problem, finally reaches them.
That is why trust can’t be something we hope emerges after the technology is built. It has to shape what we build, what we protect, what leaders can see, and what happens with what they learn.
Trust isn’t downstream of the AI transformation. It is part of the infrastructure that determines whether the transformation works.
Why I Built Parsley360
I didn’t start Parsley360 (Parsley™) because I wanted to build an AI company. I started because I wanted to solve a problem, one I had seen across my career and that became amplified in the wake of the COVID pandemic.
I had seen decades of progress seem to slide backward as global uncertainty and unrest took hold. This was a macro example of change that didn’t stick. But I had experienced the same pattern at smaller levels across organizations, teams, and individuals.
Organizations would devote enormous amounts of time, money, and energy to transformation. New strategies, technologies, processes, structures. People would move. Metrics would improve. For a while, it could look like the change had worked.
And then came the backslide.
I think of it like stretching a rubber band. Unless something fundamentally changes, there is a force pulling it back. It may never return to exactly its original shape, but the snapback is real. That was my frustration. Something that transforms doesn’t simply change for a while. It becomes something new.
So I kept asking the question I tend to ask about almost everything: Why?
Why didn’t the change hold? Why did one group adopt it while another resisted? Why could an organization successfully implement something without ever fully realizing the transformation it was intended to create?
I’m a systems thinker by nature. I tend to look beneath the visible problem and ask what is producing it, what else it is connected to, and what happens if we intervene in one place without understanding the rest. That made it increasingly difficult for me to look at adoption as an implementation problem.
Adoption is human.
Training can teach someone how to use a new system. Communication can explain why the organization is changing. Incentives can encourage behavior. Measurement can tell us whether usage went up. None of those things necessarily tells us whether the change has taken hold.
Someone can use technology without trusting it, follow a process without believing in it, or comply with a change while waiting for the opportunity to return to the old way of working. Implementation can happen without transformation.
For transformation to be fully realized, both the system and the people within it have to change in ways that can hold.
That’s the problem I wanted to solve more holistically. In my work with individuals, I’ve seen how much can change when someone feels seen, heard, and understood. I’ve also learned that support has to be available in the moments that matter. That requires something close to omnipresence. I’d done it with large teams, but only by being everywhere myself. That’s a recipe for burnout, not a business.
In organizations, I saw leaders making decisions with incomplete information. They were flying blind but often didn’t know it. They acted on the data they had and remained frustrated when the outcomes didn’t change.
For a long time, there wasn’t an obvious way to bridge those realities. Then AI changed what was possible.
I could imagine an environment available whenever someone needed it, where a person could privately work through what they were experiencing and begin connecting their own dots: what they’re feeling, what may be triggering it, the assumptions or patterns underneath it, and what they want to do next.
AI also created the possibility of understanding patterns across those experiences without exposing the individuals behind them. Leaders could begin to see that what appeared to be an adoption problem might actually be a trust problem, recognize something shifting before it became an outcome on a dashboard, and have a better path forward.
That became the idea behind Parsley360: Connect the dots and close the gaps.
That idea shaped the architecture as much as the mission. Early on, Craig and I faced the same question every builder in this space eventually faces: build on top of someone else’s model, or build the reasoning and empathy layers ourselves. We talked about this in the third piece of this series, the risk of building something essential on ground you don’t own. That risk was reason enough. But there was a second one that mattered just as much.
The reasoning and empathy layers are where a person’s private experience lives, the things they’re willing to say only because they trust it goes no further than it needs to. That kind of trust can’t depend on a platform that could reprice, restrict, or shift its priorities without warning. If the privacy we promised was only as durable as someone else’s roadmap, it was never really a promise.
So we built our own. Parsley360 runs on a proprietary reasoning engine designed for empathetic, long-form discourse, not a wrapper around someone else’s model. It was slower to build. It also means the thing at the center of this company, the part actually doing the work of understanding a person, answers to us and to the people using it, not to a platform whose interests may eventually diverge from ours.
The technology has evolved considerably since we began building it. The problem we’re trying to solve hasn’t.
How do we help people navigate change in a way that actually benefits them, while helping organizations understand what their people need to make that change sustainable?
How do we move beyond implementation to adoption, and beyond adoption to transformation that lasts?
That’s the human infrastructure we’ve spent the last several years building.
The Next Thing We Build
When I began The AI Reckoning, I said I was writing as a builder, not a critic. Twelve articles later, I still am.
I remain deeply optimistic about what artificial intelligence can make possible. But if we’re serious about building something transformative, we have to be willing to look at the whole system it will live inside. I don’t come away from this series believing we should build less. I come away believing we haven’t finished building.
We’ve spent extraordinary resources making artificial intelligence more capable, and we’re beginning to wrestle seriously with how to govern and deploy it responsibly. Now we need to bring the same intention to the humans living and working alongside it.
That means recognizing that transformation isn’t fully realized because a technology has been deployed or a metric has moved. It is realized when the system and the people within it have changed enough that we don’t simply snap back.
The next chapter of artificial intelligence will be shaped by what we build next.
Let’s make sure we build for the humans too.
Read more from The AI Reckoning: A Future of Trust Series
Part 1: The Hidden Cost of Intelligence, The Trust Story Hiding in Plain Sight
Part 2: The Reckoning Behind the Revenue: When the Numbers Don’t Add Up
Part 3: Rented Intelligence: Building on Borrowed Ground
Part 4: The Fragmentation Tax: Death by a Thousand Tools
Part 5: The Human Adoption Gap: We Built the Technology, We Forgot the Human
Part 6: Signal, Noise, and Judgment: The Trust Debt Nobody Is Measuring
Part 7: The Generation Saying No: Is Anyone Listening?
Part 8: When Seeing Is No Longer Believing: Trust Under Attack
Part 9: The Room Where Everyone Agrees With You
Part 10: The Black Box Problem: Why Explainability Is the Foundation of Trust
Part 11: Why We Keep Treating Symptoms Instead of System: What We Miss When We Only Solve What We Can See
I REALLY like your point that keeping pace with AI shouldn’t mean trying to think or produce at machine speed.. that feels like a weird race to volunteer humans for!
The more capable these systems get, the more I find myself caring about whether we’re getting better at judging, too!
Your distinction between capability and readiness stood out to me, Sheryl. As AI continues to change how we work, preparing the technology is only part of the work. I agree that preparing and supporting the humans who will live and work alongside it matters just as much. What especially stood out to me was the importance of psychological safety. People need to feel safe to ask questions, say they don’t understand, make mistakes, and learn. Taking the time to train and support people through change is something that remains important, from our schools to our workplaces.