I Accidentally Started Comparing AI Strategy to Dating

Over the past few weeks, I attended Xebia’s AI Leadership & Strategy training with Sander. Today was the final day.

The funny thing is that somewhere along the way, I stopped thinking about AI.

I started thinking about dating.

Not because the course was bad.

Actually, quite the opposite.

The more we explored strategy, stakeholder management, business cases, communication, resistance, adoption, and organizational change, the more I found myself asking:

“Why does this all feel so familiar?”

Then it hit me.

I’ve had these conversations before.

Not about AI.

About Agile.

About DevOps.

About cloud transformations.

About product operating models.

About organizational redesigns.

And, strangely enough, about relationships.

Because every transformation starts the same way.

There is excitement.

Possibility.

A shiny new future everyone can imagine.

In dating, it’s chemistry.

In organizations, it’s technology.

The demo is impressive.

The potential feels enormous.

Everyone is optimistic.

Everyone can already see what could be.

Then reality arrives.

Do we want the same future?

Can we communicate honestly?

Can we handle disagreement?

Can we navigate uncertainty?

Can we build trust?

Can we align around a shared vision?

Can we change our behavior when old habits pull us back?

Suddenly, it isn’t about the technology anymore.

It’s about the relationship.

The Pattern I Couldn’t Unsee

Near the end of the training, I found myself saying:

Replace AI with Agile.

Replace AI with DevOps.

Replace AI with Cloud.

Replace AI with almost any transformation trend from the last twenty years.

The sentence surprised me as much as anyone else.

Because I wasn’t trying to be cynical.

I wasn’t saying AI doesn’t matter.

It does.

The technology is extraordinary.

What struck me was something else.

The further we got into AI strategy, the less we seemed to be talking about AI.

Instead, we were talking about vision.

Trust.

Stakeholders.

Politics.

Resistance.

Communication.

Buy-in.

Fear.

Identity.

Leadership.

Human behavior.

The things that always show up when people try to move toward a future that doesn’t exist yet.

One of the exercises focused on stakeholder mapping.

Who influences whom?

Who has access to decision makers?

Who shapes opinions behind the scenes?

Who do people trust when uncertainty shows up?

At first, it felt political.

Then I realized something.

Organizations don’t change through org charts.

They change through relationships.

The official structure tells you who reports to whom.

The real structure tells you who trusts whom.

And when change arrives, trust travels faster than authority.

So does fear.

The Hidden Question Beneath Resistance

As the course progressed, I started noticing something.

Whenever someone described resistance, my instinct was to ask:

Resistance to what?

The tool?

Or what the tool means?

Because those are not the same thing.

An executive resisting AI might be resisting loss of control.

A manager might be resisting loss of certainty.

An engineer might be resisting loss of mastery.

A team might be resisting yet another transformation that promises everything and leaves them exhausted.

The AI is visible.

The thing underneath often isn’t.

Just as I stated before, every major transformation I have been part of over the last twenty years.

The debates always sound different.

But underneath, the questions are remarkably similar.

Do I still matter?

Will I succeed in this future?

Can I trust the people leading us there?

The Mistake We Keep Making

I think organizations make the same mistake over and over again.

They treat the visible thing as the problem.

The framework.

The methodology.

The process.

The technology.

And then they try to solve it there.

But if the real challenge lives underneath, no amount of optimization at the surface will fix it.

You can buy a better AI platform.

You can create governance boards.

You can redesign operating models.

You can launch another transformation.

And still struggle.

Because the challenge was never primarily technical.

It was human.

What I Am Taking Away

Walking into this training, I expected to learn more about AI strategy.

And I did.

But that wasn’t the biggest thing I took away.

The biggest thing I took away was this:

Every transformation eventually stops being about the thing people think it is about.

The argument about Agile was rarely about Agile.

The argument about AI is rarely about AI.

Just like the argument about dirty dishes is rarely about dirty dishes.

Those things are often containers.

Places where deeper fears, hopes, frustrations, ambitions, and uncertainty reveal themselves.

Which leads to a realization I wasn’t expecting.

The most important question in an AI transformation may not be:

“How do we implement AI?”

It may be:

“What is this transformation revealing about us?”

Because the AI was never the most interesting thing in the room.

The people were.

Examples because I LOVE Examples!

This is where things get interesting.

1

Take an AI customer support chatbot. On the surface, two organizations may be implementing exactly the same solution.

In one organization, support teams are overwhelmed, response times are growing, and employees are spending most of their day answering repetitive questions. The chatbot becomes a way to improve customer experience and free people to focus on more complex issues.

In another organization, the real goal is cost reduction. Leadership sees customer support as a cost center, wants to reduce headcount, and hopes the chatbot will solve problems created by underinvestment.

The technology is the same. The story underneath is not.

2

The same is true for AI coding assistants.

In one company, engineering teams already have strong practices, good architecture, and healthy collaboration. AI helps remove friction and accelerate learning.

In another, leadership is frustrated with delivery speed, technical debt is growing, and nobody wants to invest in engineering fundamentals. The AI assistant becomes a way of avoiding the harder conversation.

One initiative amplifies excellence. The other amplifies dysfunction.

3

AI meeting summaries can reveal a similar pattern.

Sometimes they solve a real problem. People are overloaded with meetings, decisions are forgotten, and information gets lost between teams. Automated summaries create clarity and transparency.

But sometimes the organization has a different issue entirely. Nobody is making decisions. Meetings are attended out of habit. More summaries simply document confusion more efficiently.

4

Knowledge assistants provide another example.

In healthy organizations, knowledge is trapped in a few experts who are constantly interrupted. AI can help distribute information and reduce bottlenecks.

In unhealthy organizations, people avoid documentation, information is fragmented, and knowledge is used as a source of power. Leaders hope AI will magically organize years of accumulated chaos.

The assistant becomes a symptom treatment rather than a cure.

5

Sales copilots can either increase customer value or hide deeper problems.

Sometimes salespeople spend enormous amounts of time on administration and reporting. AI gives them more time with customers.

Other times the real issue is weak product knowledge, inconsistent sales skills, or unclear positioning. The organization buys technology instead of developing capability.

6

Forecasting and planning tools are particularly revealing.

In mature organizations, leaders understand uncertainty and use AI to improve decision-making.

In less mature organizations, leaders are searching for certainty itself. The hidden hope is that AI will eliminate ambiguity and remove difficult choices.

Unfortunately, no technology can solve a leadership team’s discomfort with uncertainty.

7

AI recruitment tools often follow the same pattern.

One organization uses them to manage large hiring volumes while keeping people accountable for final decisions.

Another uses them to reduce recruiting costs or avoid addressing existing biases in the hiring process.

The technology may look identical, while the intent behind it is completely different.

8

Performance review assistants can either strengthen leadership or weaken it.

Used well, they help managers prepare more thoughtful feedback and identify patterns they may have missed.

Used poorly, they become a shield behind which leaders hide from difficult conversations.

The real issue was never feedback quality. It was courage.

9

Executive dashboards are another example.

Sometimes leaders are drowning in information and need better visibility into what is happening across the organization.

Other times they are seeking control rather than understanding.

The dashboard becomes a way to monitor rather than lead. More information does not automatically create better decisions.

10

Process automation is often presented as a straightforward efficiency play.

In some organizations, repetitive work is genuinely reduced and employees are freed to focus on higher-value activities.

In others, automation is a hidden headcount reduction strategy. Employees sense the difference immediately.

The technology remains the same, but trust and engagement move in opposite directions.

11

Even AI strategy assistants reveal underlying beliefs.

Some leadership teams use them to explore possibilities, challenge assumptions, and stimulate discussion.

Others quietly hope the tool will tell them what to do.

One sees AI as a thinking partner. The other sees it as a substitute for thinking.

And I could. One up with endless example tbh …

That is why I have become increasingly interested in a different question.

Not:

“Is this a good AI initiative?”

But:

“What is causing us to believe this is a good AI initiative?”

Because the answer often tells us far more about the organization than about the technology.

The AI initiative is visible.

The story underneath usually isn’t.

And that hidden story often determines whether the initiative succeeds, struggles, or quietly becomes another expensive lesson. 💚🔥🐉