Training Wasn’t the Problem — Governance Was

Intro

This is Field Notes—real-world observations from inside living systems where AI meets leadership, governance, and human judgment.

These are not theories, tools, or trends. They’re moments from practice that reveal how AI actually behaves once it enters an organization.

Let’s get into today’s Field Note.

The Field Moment

A leadership team recently told me they were confused by their AI results.

They had done what most organizations do “right.”

They selected strong tools.

They invested in training.

They rolled out clear usage guidelines.

Participation was high. Engagement looked positive. On the surface, adoption appeared successful.

And yet—outcomes barely moved.

People used the tools, but decisions didn’t improve.

Work sped up, but clarity didn’t.

Confidence declined instead of increasing.

The leadership team kept asking the same question:

Why isn’t the training working?

The System Signal

The answer was uncomfortable.

Training wasn’t the problem.

Governance was.

Educators, managers, and staff had learned how to use AI—but no one had clarified the rules that mattered once AI entered the system.

No one had defined:

• When AI guidance should be followed
• When it should be challenged
• Who had authority to override it
• What accountability looked like when AI was involved

People weren’t confused about the tools.

They were confused about boundaries.

In the absence of governance, individuals were left to make private decisions about risk—when to trust AI, when to ignore it, and when to quietly work around it.

That’s not adoption.

That’s fragmentation.

#Artificial Intelligence

#Technology Integration

#AIinEducation

#AIforProductivity

#Digital Transformation

#Workforce Development

#Future of Work