Crossing the AI Hype Chasm

I just read through this MIT report on "The GenAI Divide: State of AI in Business 2025" (link in comments). While I'm not entirely surprised at the data, it's still an eye opener and a good reminder that while we're knee (neck?) deep in an AI hype cycle, it's far from delivering on its promises—for most organizations, anyway. 

TL;DR - Be patient.

The study, based on interviews with 52 orgs, surveys from 153 leaders, and analysis of 300+ AI initiatives, paints a picture of massive investment ($30-40B in enterprise GenAI) but shockingly low returns: 95% of companies are seeing zero measurable P&L impact. Tools like ChatGPT are everywhere—80% explored, 40% deployed—but they're mostly boosting individual productivity, not transforming businesses. 

The money quote -

"Enterprise grade systems, custom or vendor-sold, are being quietly rejected. Sixty percent of organizations evaluated such tools, but only 20 percent reached pilot stage and just 5 percent reached production. Most fail due to brittle workflows, lack of contextual learning, and misalignment with day-to-day operations."

The outcome from $40B invested (so far) - stalled pilots and a "shadow AI economy" where employees sneak in personal tools for real gains but enterprise-wide implementation remains elusive.

Not surprisingly, the media industry is moving the fastest toward broad adoption given the incredible pace of improvement in GenAI media tools, but most of the rest of the economy is so far showing minimal disruption. Budgets skew mainly toward sales/marketing automation(50%+), while back-office and ops — where ROI could be huge — remain largely overlooked. 

Reminds me of the early days of cloud computing. I'm old enough to have lived through & remember the hype when AWS launched in 2006 but concerns over security, data sovereignty, and integration meant a slow evolution from on-prem to the cloud. It took about a decade for cloud to hit critical mass: by 2015-2016, market share started tipping (AWS alone crossed $10B revenue). 

The lesson? Disruptive tech needs time to mature - refining models, building trust, and proving ROI - before it reshapes markets.

GenAI feels similar: we're in that frothy, experimental phase, but the report highlights paths forward for "crossing the divide." So, while the hype is real and the returns aren't (yet), like cloud, GenAI will redefine efficiency and innovation once we get the fundamentals right.

What do you think? Have any war stories or cautionary tales to share? Let me know.