Why 95 Percent of AI Pilots Fail—And How to Avoid It Happening to You

The corporate world's artificial intelligence revolution has a secret: It's not going well. Executives are flocking to implement AI, but the vast majority of these initiatives collapse under their own weight. Marc Malott witnessed this firsthand, running into an invisible wall as he tried to lead an AI transformation at a consultancy. But there’s hope—studying the rare organizations that have successfully rewired their operating systems for the AI age, Marc uncovers the blueprint for sustainable transformation that doesn't just deliver ROI, but reimagines what's possible.—Kate Lee

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Companies everywhere are racing to integrate the world-changing power of AI into their businesses—and 95 percent of them are failing.

Such were the headlines as a study out of MIT went viral late last month that seemed to puncture the exuberance around corporate AI adoption.

I spent the last two years spearheading an ambitious AI transformation program at a mid-sized consulting firm. I don’t claim to know better than the study’s authors. But from what I’ve seen, a 95 percent failure rate is believable. A McKinsey report from earlier this year is similarly bleak: More than 80 percent of executives surveyed said generative AI has not yet moved enterprise-level earnings in a tangible way. Within companies, the employees leading the AI charge often hate their jobs the most: A study by Upwork published last month found that the most productive AI users are twice as likely to quit, and most don't understand their own firm's AI strategy. Eighty-eight percent reported feeling burned out.

All of these datapoints are due to the same root cause. Companies that are investing in AI are so eager to make their money back that they are sacrificing long-term payoff.

I learned this the hard way. My team started tailoring our organization’s AI adoption plan in 2023—we launched targeted pilots, overhauled workflows, and thought deeply about how to manage the change. We unlocked 40,000 hours of human capacity. Clients were thrilled.

But when we started chasing a hard return on investment, progress stalled.

The good news is that when AI transformation stalls, it often follows a pattern. It can be hard to spot, but I’ve managed to tease out some common traits, and found a recipe for how anyone can identify the warning signs and set a course toward lasting change.

Why success triggers failure

Last year, the 300-person consulting firm I was working at had strong momentum following our aggressive, early AI implementation. We implemented what has become a best-in-class tool for surfacing insights from research calls. It was like having a junior employee—it automated the preparation of transcripts and made client-ready summaries of our research. Teams were able to focus on the work that mattered—like overhauling critical project workflows and distilling actionable insights faster. We were delivering higher quality work and wowing clients.

We believed our success would speak for itself, validate AI’s potential, and accelerate progress across the company. Instead, progress stalled.

This is not a critique of my former company. It’s an illustration of the powerful systemic forces affecting nearly every legacy business trying to adapt to AI.

To reflect our shop’s newfound AI efficiency, we were told to charge clients a higher hourly rate. This would naturally entail fewer hours spent per project. But that made sense—we’d get the job done more quickly and better, and move on to the next project faster. It was an easy way to capture ROI from the hard implementation work we had done.

But the decision to begin harvesting ROI resulted in an unforeseen shift. After we raised performance targets and hourly prices, progress became exponentially more difficult. Slack for innovation vanished as everyone focused on hitting their new numbers.

The pressure to hit near-term targets created immense friction. The bar for approving any new expenditure was raised, slowing decisions and timelines to a crawl. The rollout of a key AI-enabled product expansion stalled for nearly a year because teams felt too overwhelmed to commit to more work.