Laverah Clara

77% of enterprise leaders say raising their team's AI skills is urgent.

In most departments at those same organizations, fewer than half of employees are receiving structured AI training.

Those numbers come from the Zapier AI Skills Crisis Survey published in April 2026, based on responses from over 500 C-suite executives at organizations with 1,000 or more employees.

Large organizations with dedicated HR teams and significant training budgets are struggling to close the gap between urgency and action.

If they are struggling, most organizations are.

The ones getting this right are treating capability building as part of the deployment plan, not something that comes after it.

July has been about one question.
Are you measuring whether your AI transformation is actually working for your people?

Not the technology side. The human side.

Whether employees are aware, capable, confident, and using AI in ways that genuinely change how they work.

Most organizations do not have a consistent way to answer that. And what I have heard in conversations this month, both here and in advisory work, is that leaders feel that gap. They just have not had a way to address it.

If this is something your organization is navigating, let's have a conversation.
I would love to help you think through it.

You cannot close a gap you cannot see.

Fifteen years of leading transformation taught me that the initiatives that stall are almost never short on intention or budget.
They are short on visibility.

Leaders are making decisions about AI every day. Where to invest next. Which teams need more support. Whether the rollout is working. But most organizations do not have a clear way to see what is actually happening at the people level.
So they guess.

And the gap between what leadership assumes is working and what employees are actually experiencing stays open.

The organizations moving past this are not doing anything complicated. They have found a way to measure what is happening on the human side of their AI adoption, not just the technology side.

That visibility changes everything.

Most organizations measure whether people have access to AI. Almost none measure whether that access is creating value.

Those are very different numbers.

Utilization tells you how many people opened the tool and how often. It does not tell you whether those interactions changed how they work, improved the quality of their decisions, or produced anything measurably better.

Effectiveness is the harder number. It asks whether AI is genuinely building capability, changing behavior, and delivering outcomes.

The organizations that have shifted from measuring utilization to measuring effectiveness have something the others do not.
They know where to invest next.
They can see which teams are genuinely benefiting and which ones are stuck.
They can show leadership that the investment is producing value, not just activity.

Without effectiveness data, every AI investment decision is an educated guess.

The good news is that effectiveness is measurable. It just requires measuring different things than most organizations are currently tracking.

I would love to hear your thoughts on if your organization is tracking utilization.

Most organizations track AI usage. Very few track whether that usage is translating into genuine adoption.

Those are not the same thing. And the difference matters more than most dashboards are showing.

Swipe through and save this for your next conversation about AI adoption progress.

Your organization has an AI strategy. The harder question is whether it has reached the people responsible for delivering it.

Most leaders assume it has. Most employees are still working out what it means for their specific role.

That gap is common. And it is almost never measured.

When I work with organizations on AI transformation, one of the first things I look for is what signal, beyond training completion rates and all-hands attendance, tells leadership whether the strategy is landing at the team level.

In most cases there is no signal.

Without one, organizations assume the strategy is working when it is not. Or they over-correct on something that was actually fine.

Strategic clarity in the AI era is not just about having the right strategy. It is about knowing whether it is reaching the people who need to carry it.

That requires a different kind of measurement than most organizations are currently taking.

What signals is your organization using to know whether the strategy is landing?
I would genuinely like to hear.

The leaders navigating AI transformation well are not the most technically capable.

They are the clearest communicators, the most trusted, and the best at helping people stay grounded when the ground is shifting.

Those are learnable capabilities. And right now most organizations are not investing in building them.

Swipe through.
And If this is where your organization needs support, I would love to be part of that conversation.