AI Is Failing Companies. It Is Making Individuals Rich
14 Aug 2026
AI is failing companies. And making individuals rich.
AI is failing companies. And making individuals rich.
Same technology. Opposite results. Here is what the data actually says. (Multiple data sources actually, I link them below.)
→ Nine out of ten senior executives saw no measurable productivity gain from AI in three years. That comes from a survey of nearly 6,000 executives, quoted in Harvard Business Review last month. The author's diagnosis is simple. Leaders buy AI to fix whatever is loudest in the business. Cost, speed, the backlog. Almost nobody asks what it is for first.
→ MIT puts it more bluntly. 95 percent of generative AI projects showed no measurable return.
Now the other side.
→ PwC read more than a billion job ads across 27 countries. Workers with AI skills earn a 62 percent wage premium, up from 57 a year ago. Jobs asking for AI skills grew 69 percent. The whole job market grew 9. The premium is not spread evenly. It runs as high as 118 percent in consumer markets and 16 percent in government. Wherever your industry sits, it is moving in one direction.
The difference is who owns it. Companies rolled AI out as a project. Individuals picked it up as a skill.
It is fascinating to see what happens to a job when AI enters it. The routine part shrinks. The human part grows, and gets paid more.
AI raises the price of the human part.
What have you won so far with AI?
Sources: Harvard Business Review, https://hbr.org/2026/07/when-developing-an-ai-strategy-beware-the-urgency-trap. MIT via The Hill, https://thehill.com/policy/technology/5460663-generative-ai-zero-returns-businesses-mit-report/. PwC, https://www.pwc.com/gx/en/news-room/press-releases/2026/pwc-2026-ai-jobs-barometer.html.
Want to think through what AI means for your own leadership? My Private AI Advisory is here.
Why the same technology produces such different results
The HBR diagnosis is the one worth sitting with: leaders buy AI to fix whatever is loudest, cost, speed, a backlog, without asking what it is actually for first. That is a strategy failure, not a technology failure, and it explains why company-level ROI stays flat while individual wage premiums climb. A tool bought to silence a complaint rarely gets used with any real intention behind it.
In the leaders and teams I work with, the ones seeing something real from AI almost never started with a company-wide rollout. They started with one person, often a mid-level manager, deciding to genuinely learn a tool on a real problem, then having that spread by example rather than by mandate. The wage premium data backs this up: it rewards the individual who actually built the skill, not the department that bought the license, close to a real advisory case study on moving from AI pressure to AI confidence.
If you want to be on the right side of this gap, whether as a company or as an individual:
→ Pick one real, recurring problem before you pick a tool
→ Let the person doing the work build the fluency, don't outsource that to a rollout plan
→ Measure the thing that actually mattered before AI touched it, not just usage numbers
If you want a place to start, I have a free AI Tools & Tips Guide with the tools I actually use as a coach and business owner, and how I use them. Leave your email here and I will send it over: https://www.thechangerepublic.com/free-resources