Having guided companies through every major tech disruption, AI washing is a repeated error

(SeaPRwire) –   When Sam Altman noted earlier this year that some companies are using AI as a convenient justification for workforce reductions they might have made regardless, he was not mistaken. Every morning, my news feed presents another instance of this. I have spent over two decades guiding enterprise technology companies through the cloud transition, the mobile revolution, and the platformization of work itself. I recognize when a narrative outpaces the evidence — and this is precisely such a moment.

The typical “transformation” story unfolds like this: AI has arrived, headcount is a cost, and acting swiftly on both demonstrates leadership.

The data, however, tells an entirely different story.

The fundamental misread

When AI’s impact is measured at the task level rather than the job level, the picture changes completely.

Anthropic’s research team recently published one of the most rigorous early attempts to quantify AI’s labor market effects. They discovered that, even in occupations with the highest AI exposure — such as computer programmers, customer service representatives, and financial analysts — there has been no statistically significant increase in unemployment since ChatGPT’s launch.

At Cornerstone, where we serve more than 140 million workers across 186 countries, our workforce intelligence platform reinforces this from a different perspective. Tracking over 55,000 distinct skills across 1.3 billion job postings and 1 billion resumes globally, our data indicates positive demand growth across 15 of 16 occupational categories, regardless of AI exposure level. In nearly every category, demand outpaces supply by an average of 3.2 times. These are not the hallmarks of a displacement crisis but rather signals of a talent shortage that AI is accelerating.

AI is primarily eliminating tasks, not jobs. That distinction is not merely semantic — it has meaningful impact.

When AI absorbs the routine synthesis work in a financial analyst’s role, their job does not disappear. What remains, and what compounds in value, is the judgment to understand what the numbers signify, the instinct to ask the question the model did not consider, and the credibility to guide a board through a decision under uncertainty. AI handles the throughput. The analyst owns the thinking.

I have observed organizations getting this wrong during every major technology cycle of the past three decades. The pattern is consistent: a change in technology equates to a change in headcount. Those getting it right ask a better question: If AI absorbs these tasks, what does that liberate my people to do?

What workers are telling us

We recently surveyed 2,000 workers in the US and UK about how AI is reshaping their experience, and the findings should give any C-suite pause.

Nearly half (46%) of those using AI tools have never received formal training. Of those without guidance, 47% taught themselves through trial and error, 36% deliberately limit their AI use to avoid mistakes, and 17% simply pretend to use it when asked.

When questioned about which skills would matter most to their careers, workers ranked critical thinking, judgment, creativity, and resilience at the top. Technical AI knowledge came last.

These workers already understand something their organizations have not operationalized. The durable value in an AI-augmented workplace is the quality of human decision-making applied to the output. Their development gap is about thinking, not prompting.

Building an agile organization

In many ways, AI has presented organizations with a rare gift. It absorbs the work that can be the least interesting, least productive part of what people do. Treat it as a release valve — one that finally frees your people to operate at the level they have always been capable of — and you have a fundamentally better challenge on your hands.

The advantage comes from investing deliberately in four interconnected capabilities. None requires a transformation announcement — all compound over time.

1. Make your workforce visible to itself.

Most organizations know less about their people’s capabilities after five years of tenure than they knew from the resume on day one. Building a real-time picture at the skills level — not job titles, but actual capabilities — surfaces where people are developing, where gaps are forming, and which adjacent capabilities could be activated to meet new needs.

2. Close the distance between learning and work.

The model of learning as coursework was built for a world where skills had long shelf lives. The more durable approach is development embedded in the work itself, with AI agents surfacing the right guidance at the exact moment a gap appears, triggered by performance signals rather than calendar cycles.

3. Redesign roles around what AI cannot do.

Before any workforce decision, three questions deserve honest answers:

· Which tasks does AI handle well enough to absorb entirely?

· Which tasks improve when humans and AI work together?

· Which tasks become more valuable precisely because AI handles everything around them?

Organizations that map work at this granularity — a process AI itself can accelerate — make better decisions about where to invest in human capability and where to let technology carry the load.

4. Invest in managers as the connective tissue.

Technology can surface insights and personalize development. But managers control what work gets assigned, how feedback lands, and when someone is ready for a bigger challenge. Developing managers who recognize capability gaps and who coach toward judgment rather than task completion turns them into development multipliers for the entire organization.

What this requires

Every technology disruption I have led through has required the same starting point: get honest about the task, not the job. The answers are almost never “entire job eliminated.” They are almost always “this task absorbed, that task elevated, this new task created.” You cannot lead a transformation you haven’t mapped.

Make workforce intelligence your operating system. Build infrastructure to see your workforce as a dynamic portfolio of skills that can be developed, deployed, and directed toward what the business needs next.

Invest in the human layer. The capability gap workers say matters most — judgment, creativity, resilience — is the same asset that determines whether your AI investments compound or stall. Organizations that develop these will find their AI tools grow more valuable over time. Why? Humans are better equipped to direct them, interrogate outputs, and apply judgment to what the machine produces.

I have seen enough technology cycles to know that the organizations who win aren’t the ones who moved fastest on the tool. They’re the ones who invested, deliberately and sustainably, in the human capabilities that make the tool most valuable.

That’s not a threat to manage, but an opportunity to lead.