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Before You Read

AI Isn't Creating Your Culture. It's Revealing It.

I’ve spent a significant part of my career around technology, and I recognize something familiar in the way we’re talking about AI. A powerful new technology arrives, companies rush to adopt it, and we begin attributing almost magical qualities to the technology itself. It’s going to transform productivity, reinvent industries, change how people work, and, depending on which headline you read, either liberate employees from meaningless work or eliminate enormous numbers of jobs.

AI will almost certainly change work in profound ways. I’ve already argued on On the Shoulders of Giants that sales is being fundamentally reshaped because buyers no longer need sellers primarily for information. The transactional seller is becoming easier to replace while the person who can help a buyer make sense of complexity, exercise judgment, and reach a confident decision becomes more valuable.

I think something similar is about to happen across organizations, but there’s another dimension to it that deserves more attention. AI doesn't enter a company on its own. It enters an existing culture with existing levels of trust, capable or incapable managers, clear or unclear priorities, and employees who have been paying attention to how leadership behaves when difficult decisions have to be made.

The technology may be new. The organization receiving it isn't.

This Week’s First Principle

The August 2026 Gallup research offers a fascinating window into what’s happening. Twenty-four percent of employees say AI has improved their workplace culture, while 25% say it has made their culture worse. Nearly the same technology is entering Corporate America at the same time, yet employees are having radically different experiences with it.

That raises a question I find far more interesting than whether AI is good or bad for workplace culture. How can the same technology make one organization better and another one worse?

I think the answer has less to do with the technology than we want to admit.

Put AI inside an organization where people trust leadership, understand where the company is going, and believe their manager has their interests in mind, and employees may see the technology as an opportunity. It can remove tedious work, increase their capabilities, and create more room for judgment, relationships, creativity, and problem-solving.

Put the same technology inside an organization where people don’t trust leadership, managers are already overwhelmed, and employees aren’t sure where they stand, and every conversation about “efficiency” takes on a very different meaning. People begin wondering whether productivity means making their jobs better or simply making fewer people responsible for more work.

Technology didn’t create either culture.

It revealed what was already there.

The Same Tool Can Tell People Two Different Stories

Imagine two organizations introducing essentially the same AI technology.

In the first, leadership explains why the company is adopting it and what problems they’re trying to solve. Managers are given enough context to understand the strategy before being asked to explain it to their teams. Employees understand where AI can eliminate repetitive work, where human judgment remains important, how roles may change, and what the organization hopes people can do with the capacity the technology creates.

In the second organization, leadership announces an AI initiative with familiar language about innovation, productivity, and efficiency. Managers learn about it shortly before everyone else and are expected to answer questions they can’t answer. Employees are encouraged to embrace AI while simultaneously reading headlines about companies using the technology to reduce headcount.

Same technology. Very different human experience.

Gallup’s research reinforces how important the manager is in determining which experience employees have. Among employees in organizations integrating AI, only 36% strongly agree their manager supports their team’s use of the technology. Yet employees who have that support are substantially more likely to use AI frequently and to believe it is improving how work gets done.

That shouldn’t surprise us. Technology may change the work, but people still experience much of the organization through the people leading them.

We Keep Calling Human Problems Technology Problems

One of the recurring mistakes I’ve watched organizations make throughout my career is expecting technology to solve problems that were never really technology problems.

We buy a new platform because teams aren’t collaborating effectively. We introduce another communication tool because information isn’t flowing through the organization. We build dashboards because people aren’t making good decisions. When the results disappoint, we assume the implementation failed or we chose the wrong technology.

Sometimes that's the case. But sometimes the technology is working perfectly and the organization isn't.

AI won't make an unclear organization clear. It won't make an untrusted leader trustworthy or teach an incapable manager how to develop people. It won't resolve competing priorities that senior leadership has been unwilling to choose between, and it won't create a healthy culture inside an organization that treats people primarily as a cost to manage.

In fact, AI may amplify those problems because it allows organizations to move faster without necessarily thinking better.

That distinction matters.

Gallup reports that AI adoption is accelerating rapidly, yet only a quarter of employees strongly agree their organization has communicated a clear AI strategy. We’re moving quickly on capability while moving considerably more slowly on understanding what we intend to do with it.

If our recent conversation about clarity taught us anything, it should be that those two things are not the same.

Productivity Is Where the Conversation Gets Interesting

I understand why executives are focused on productivity. Businesses exist to create value, and if a technology allows people to accomplish in thirty hours what once required forty, leaders have a responsibility to understand what that means economically.

But productivity is where the conversation should begin, not where it should end.

Suppose AI delivers exactly what we’re being promised. A manager gets five hours of administrative work back every week. An employee can analyze information in minutes instead of hours. A team can produce significantly more with the same number of people.

What do we do with the capacity?

That may become one of the defining leadership questions of the AI era.

We could give managers some of that time back to coach their people, think more deeply, talk to customers, develop talent, and make better decisions. We could give employees greater opportunities to solve harder problems, develop new capabilities, and do more of the work that requires human judgment.

Or we could simply raise the target.

If someone can produce ten units instead of seven, ten becomes the new seven. If AI gives someone five hours back, we fill those five hours with additional work. If a team becomes substantially more productive, efficiency becomes primarily an opportunity to reduce headcount.

There will be situations where reducing costs or eliminating roles is the right business decision. Caring about people doesn’t mean protecting every job from technological change, and pretending otherwise doesn’t help anyone.

But the choices leaders make about the productivity AI creates will tell employees something important about what the organization values.

That’s a culture decision disguised as an efficiency decision.

AI Raises the Bar for Being Human

There’s a comforting version of the AI conversation that says as machines become more capable, human skills will automatically become more valuable. I think that’s only half right. AI may make distinctly human capabilities more valuable, but that doesn’t mean people automatically become more valuable simply because a machine can’t replicate everything they do.

We’re already seeing this distinction in sales. Buyers no longer need sellers primarily for information because information has become abundant. The traditional seller who memorizes the product, delivers the pitch, handles objections, and relies on closing techniques is watching much of that value become commoditized. But the seller who can help a buyer make sense of complexity, challenge assumptions, exercise judgment, build trust, and move from information to a confident decision may become considerably more valuable.

Leadership is heading toward the same place.

Being human doesn’t automatically make someone good at judgment, communication, empathy, courage, creativity, or relationships. People can exercise terrible judgment. Managers can communicate poorly. Leaders can destroy trust faster than any algorithm ever could.

Those capabilities have to be developed.

As machines become better at the informational and transactional parts of work, we may discover that many of the things we historically dismissed as “soft skills” are anything but soft. Judgment, trust, courage, wisdom, curiosity, relationships, and the ability to help another person make sense of complexity may become some of the hardest—and most economically valuable—capabilities an organization can develop.

AI isn't making human capability less important. It's raising the standard for what valuable human capability looks like.

People Matter Most Gets Harder When Technology Gets Better

At High Achiever, one of our foundational principles is People Matter Most. Principles like that are easy to embrace when taking care of people and maximizing short-term economic efficiency point in the same direction. They become far more revealing when those interests collide.

AI is going to create those moments.

Technology will eliminate work. Some roles will disappear while others change significantly. Organizations will have legitimate decisions to make about cost structures, staffing, investment, and where human beings continue to create value.

I don’t think a people-first organization avoids those decisions. I think it approaches them differently.

People watch what happens when values become inconvenient. They notice whether leadership communicates early or waits until rumors force the conversation. They notice whether managers are equipped to answer questions or left to absorb the anxiety created by decisions made somewhere above them. They notice whether productivity gains create opportunities for people to grow or simply increase the amount expected from them.

Culture isn't the values printed on the wall when everything is going well. It’s what remains when living those values becomes expensive.

AI may give us an unusually clear view of the difference.

The Manager Is Still Where Strategy Becomes Reality

There’s another connection to the manager conversation we’ve been having that I don’t think leaders should miss.

Executives can establish an AI strategy, approve investments, and announce ambitious productivity goals. Employees will experience much of that strategy through their manager. Gallup reports that while virtually every CHRO it surveyed considers AI important to organizational strategy, half aren't confident their managers are prepared to guide employees in using it.

Think about the contradiction.

Organizations are making AI strategically important while lacking confidence in the people responsible for translating that strategy into daily work.

If managers don’t understand where AI should be used, where it shouldn’t, what the organization is trying to accomplish, or what the implications are for their people, no amount of enthusiasm from the executive team will solve the problem. Once again, the manager becomes the place where corporate ambition collides with human reality.

AI doesn’t diminish the importance of good management.

It raises the cost of bad management.

Carry This Into Your Week

As AI enters more of your work, resist the temptation to measure its value only by what it allows you to produce. Pay attention to what the technology is revealing about the organization around it. Notice where people trust leadership enough to experiment, where managers have enough clarity to guide their teams, and where anxiety about AI is actually exposing uncertainty that existed long before the technology arrived.

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