{"id":24630,"date":"2026-09-22T20:31:30","date_gmt":"2026-09-22T18:31:30","guid":{"rendered":"https:\/\/netsurit.com\/en-za\/?p=24630"},"modified":"2026-09-25T12:30:12","modified_gmt":"2026-09-25T10:30:12","slug":"why-ai-pilots-fail","status":"publish","type":"post","link":"https:\/\/netsurit.com\/en-za\/why-ai-pilots-fail\/","title":{"rendered":"Why AI Pilots Fail: What the 95% Statistic Really Means"},"content":{"rendered":"\n

By Dr Jan van Niekerk, SVP Data & AI Strategy, Netsurit  |  6 August 2026<\/em><\/p>\n\n\n\n

Most AI pilots labelled \u201cfailures\u201d did not fail to work. They failed a strict test: no measurable P&L impact within six months, which is exactly what the widely quoted 95% statistic measures. In August 2025, a Fortune headline did the rounds of every board pack I saw that quarter: “95% of generative AI pilots at companies are failing.” I watched a steering committee quote it as the reason to freeze a programme that was, by its own numbers, working.<\/p>\n\n\n\n

The stat was real. The conclusion drawn from it was wrong. That gap, between a true number and a false conclusion, is what this first issue is about.<\/p>\n\n\n\n

What the Study Actually Measured<\/h2>\n\n\n\n

The number comes from MIT’s Project NANDA<\/a>, and the methodology deserves more attention than the headline got. The team reviewed more than 300 AI initiatives, interviewed people at 52 organisations, and surveyed 153 senior leaders. The famous 95% refers to custom, workflow-specific generative AI tools that showed no measurable P&L impact within six months.<\/p>\n\n\n\n

Read that definition slowly. Custom builds. Six months. Visible in the P&L.<\/p>\n\n\n\n

That is a strict test, and most transformation spending of any kind would fail it. Hold your ERP programme, your culture initiative, or your last reorganisation to “move the P&L within six months, measurably” and watch what happens to their success rates. Several analysts made this point within weeks of publication. The more interesting story was sitting in the authors\u2019 own data.<\/p>\n\n\n\n

Where the Value Actually Went<\/h2>\n\n\n\n

The same MIT report found that roughly 90% of employees use personal AI tools daily for work, while only about 40% of companies hold official enterprise subscriptions. The researchers called it a shadow AI economy.<\/p>\n\n\n\n

Value was flowing the whole time. It was simply flowing through personal accounts, unmeasured and unsanctioned, while the official pilot with the steering committee and the six-month clock stalled in review cycles.<\/p>\n\n\n\n

There is a second buried finding. Externally bought tools succeeded about 67% of the time in MIT’s data. Internal builds succeeded half as often. Put those together and the failure headline becomes a story about how enterprises buy, build, and measure, and much less a story about what the technology can do.<\/p>\n\n\n\n

Three Studies, One Number<\/h2>\n\n\n\n

If MIT’s method bothers you, use someone else’s. BCG<\/a> surveyed more than 1,250 decision-makers in 2025 and found 4 to 5% of companies capturing substantial value, with that group pulling away from the pack at roughly 1.7 times the revenue growth of peers. McKinsey’s 2025 State of AI<\/a> (n=1,993) found 88% adoption, 39% reporting any EBIT impact, and 5.5% attributing more than 5% of EBIT to AI.<\/p>\n\n\n\n

Three teams. Three methods. One answer, within a rounding error. About one organisation in twenty is getting real money out of AI, while nearly all of them are using it.<\/p>\n\n\n\n

When independent methodologies converge like that, the message is structural. Adoption stopped being the constraint. Converting adoption into money is the constraint now.<\/p>\n\n\n\n

What the Converters Do Differently<\/h2>\n\n\n\n

Across the AI programmes I’ve been close to, on four continents and in a dozen industries, the ones that crossed into production shared four habits. None of them involve model selection.<\/p>\n\n\n\n