Prefer to watch? The full video is above. The post goes deeper on the data and case studies.
What MIT Actually Found
Before reacting to the headline number, read what the study actually measured.
The MIT NANDA "State of AI in Business 2025" report is the source behind the 95% figure everyone's citing. I read it in full when it came out — and the first thing worth knowing is what it actually studied.
MIT NANDA — Study Methodology
The report's own disclaimer: the data is "directionally accurate" — it tells you which way the wind blows, not a precise number to four decimal places. That caveat matters, but direction is what matters most here.
The researchers are upfront: the sample isn't statistically representative. A Wharton professor noted there's limited support for how exactly they derived the 95% figure. That's a legitimate point. But here's the thing — when you stack this against seven other independent studies, the direction doesn't change.
The hidden adoption story
The enterprise spent millions on a sanctioned AI platform. Most employees are ignoring it and using their personal Claude or ChatGPT account instead. Value is happening — it's just not the kind anyone bought, measured, or reported back.
"The 95% figure has been contested. That's a legitimate point about methodology. But when you stack this against seven other independent studies, the direction doesn't change. Something is genuinely broken."
8 Studies. Same Direction.
MIT's methodology is contested. The cross-source pattern is not.
McKinsey surveyed nearly 2,000 organizations. Only one in twenty reported significant financial impact from AI. Eighty-eight percent use it. Six percent win from it.
BCG ran their study across a thousand executives in nearly sixty countries. Three quarters say they struggle to scale AI value. Only four percent — four — have what BCG calls genuine, cutting-edge AI capability.
IBM interviewed two thousand CEOs directly. Three quarters said their AI initiatives failed to deliver expected ROI within three years.
S&P Global — AI Project Abandonment Rate
The failure isn't slowing down. It's accelerating.
2.5× increase in one year. More companies run AI experiments without a clear framework — and walk away faster when they don't see results in the first six months.
Why some studies contradict each other
Productivity ≠ revenue. Meeting expectations ≠ positive ROI. The studies that show "AI is working" and the studies that show "AI is failing" are measuring entirely different things. The ones measuring financial impact all point the same direction.
The 3 Real Failure Modes
Not abstractions — patterns I've watched happen in real projects with real clients.
Failure 1 — Wrong Timeframe
Deloitte's longitudinal research found that significant ROI from AI materialises in two to four years. The MIT study measured at six months. When you measure at six months, you'll always see 95% failure — because you're measuring before the return exists.
Measuring AI ROI at six months is like measuring whether a new hire was worth it after their first week. Nobody manages a business like that — except when it's AI.
Efficiency Metrics
Operational Metrics
Financial Metrics
I've seen this pattern kill good projects. One client was three months into a document automation system that was genuinely transforming their back-office workflow — leadership wanted to shut it down because the ROI report didn't look right. The efficiency gains were real and measurable. The financial returns hadn't compounded yet. They almost pulled it.
Failure 2 — Wrong Use Case
More than 50% of enterprise AI budget flows to sales and marketing. Which, according to the same MIT data, delivers the lowest return on investment of any use case. Meanwhile, back-office automation — replacing repetitive, high-volume document processing — consistently delivers the highest returns.
Where the budget goes vs. where the ROI is
Here's a real example. DG Financial came to us needing to search across 80,000 documents spread across multiple cloud platforms — multiple formats, scanned PDFs, handwritten notes. We built a pipeline that made all of it searchable in under a second. Their team was spending hours every week manually hunting through files. That time disappeared almost immediately.
DG Financial — Document Search Pipeline
"Nobody puts document search on a conference slide. Everyone wants the AI that writes personalised emails. But the document search is what makes money. Back-office, not front-office. High-frequency, repetitive, boring — and that's exactly why the ROI is there."
Failure 3 — Wrong Approach
The MIT finding here is explicit: pick one pain point, execute well, partner smartly. Only one in five companies actually redesigns their workflows around AI. The rest layer AI on top of existing processes and wonder why nothing changes.
The 95% Approach
The 5% Approach
Angel Protection came to us with one specific problem: weapon detection across hundreds of live camera feeds simultaneously. Not "AI for security." One detection task, one hardware constraint, one latency target. That system now runs at sub-300ms latency and is part of their core product. Narrow scope. Deep integration. Measurable outcome.
The 5% Blueprint
Three questions to answer before you start — or before you kill — any AI initiative.
The 5% Blueprint — 3 Questions
What is the one workflow where a manual task takes 2+ hours and happens 3+ times per week?
Not 'where can AI help?' — that question gets you 15 answers and no decisions. Name one specific, recurring, painful task.
Are we measuring the right thing in the right timeframe?
Month 1–3: efficiency metrics. Year 2–4: financial metrics. Budget for the longer horizon and set expectations accordingly.
Build, buy, or partner — and does our team have the capacity to own whichever path we choose?
Vendor solutions succeed at roughly 2× the rate of internal builds. Be honest about your team's capacity to maintain what gets built.
If you can't name one specific workflow in 30 seconds...
...you are not ready to start an AI project yet. That's not an insult — it's the most useful thing I can tell you. The first step isn't technology. It's clarity about the problem.
Question 3 expanded: Build, Buy, or Partner?
Build
High riskCustom requirements + your team can own and maintain it long-term
Buy
Low riskGeneric use case — a vendor has already solved it well
Partner
Medium riskProprietary data + custom workflows where off-the-shelf can't go
The SMB Advantage
The enterprise failure story gets one thing wrong about your company.
The companies in those stats are mostly huge: broad rollouts, committee-approved tools, year-long deployment timelines. Bureaucracy stops them from doing exactly what the 5% actually does. SMBs map directly onto the success playbook.
Path to ROI — Enterprise vs. SMB
Same destination. SMBs get there faster because they can go narrow, deep, and iterate without committee approval.
Speed
One use case and a working prototype in weeks — not months of vendor approval and procurement theatre.
Depth
A small team with AI embedded in their real workflow outperforms 1,000 people ignoring a sanctioned tool.
Iteration
Feedback comes directly from the person doing the work — not filtered through five layers of management.
The stat the failure reports missed
69% of organizations report employees using unauthorized AI tools at work.
Bypassing whatever official tool the company paid for. The grassroots adoption story isn't in the failure statistics — but it tells you exactly how much demand exists once the right use case is in front of the right person.
DG Financial, Angel Protection, Salibo — all small-team builds by enterprise standards. No procurement theatre. One problem, one focused build, fast iteration. The 5% aren't the biggest companies. They're the most focused.
The whole game — in three lines
That MIT number — the one everyone's using to say AI is broken — isn't a reason to wait. It's a blueprint. The useful question isn't "will this AI project succeed?" It's: are we doing the three things that separate the 5% from the 95%?
Gradient Insight
AI project not delivering ROI yet?
We help SMBs identify the one high-ROI workflow, build it right, and measure it correctly. Average client ROI: 300%+ in the first year. Free discovery call, no commitment.