You Can’t Spell FAIL Without AI

Greg Miller
11 minutes ago
3 min read

The Reality Check

A new MIT-linked report spotlighted by Fortune says 95% of enterprise generative-AI pilots aren’t delivering measurable ROI. That’s not a tech problem as much as an execution problem. Misaligned use cases, shaky data foundations, “DIY everything” mindsets, and weak change management all contribute. The winners are rigorous: start with business value, buy before you build, fix data, redesign processes, and measure outcomes from day one ^[1]

MIT’s Project NANDA “GenAI Divide” research, covered by Fortune, claims only ~5% of pilots make it to production with measurable value. Investors even reacted as AI-linked stocks wobbled on the headlines. Whether or not the “95% fail” framing is exact, the signal is clear: most AI pilots are not crossing the value chasm. ^[1]

S&P Global found the share of companies scrapping most AI initiatives jumped from 17% to 42% year over year. Gartner similarly projects 30% of GenAI projects will be abandoned after proof-of-concept by end of 2025. In other words: the failure pattern is real—and widely documented. ^[2][3]

Also notable: outcomes differ based on build vs. buy. Enterprises that purchase and integrate proven tools show better results versus those attempting to build everything in-house—an approach that’s costly, slow, and risky without modern data platforms and operational maturity. ^[1][4]

Why AI Pilots Stall

1. Solution-first, problem-second. Teams start with a model, not a business case. No baseline, no KPI, no A/B plan → no ROI. ^[5]

2. DIY bias. Custom builds chase novelty and rack up integration debt. Buying a mature tool for non-differentiating work often wins faster. ^[1][4]

3. Data isn’t AI-ready. Poor governance and data quality make models brittle, unsafe, or unscalable. ^[6]

4. Pilot purgatory. Experiments never graduate: security, privacy, and cost surprises halt rollout. ^[2][3]

5. Underestimating process change. AI without workflow redesign yields “digital duct tape.” The 5% that succeed re-engineer work. ^[5]

6. Cost unpredictability. Token/compute surprises kill the business case. ^[1]

7. Hype-driven bets. Immature stacks overpromise and underdeliver; many “agentic AI” projects won’t last. ^[3]

What the 5% Do Differently

· Start with a real business problem.

· Buy where you can, build where you must.

· Make your data AI-ready.

· Design the workflow, not just the model.

· Engineer for production from day one.

· Embrace friction. The winners treat guardrails and feedback loops as fuel for improvement, not red tape. ^[7]

· Own the unit economics.

A Practical 30-60-90 for ROI

Days 0–30: Baseline & guardrails- Pick one use case with clear KPIs.- Instrument current processes for throughput, cost, and error rate.- Stand up a sandbox with policy, logging, and budget caps. ^[5]

Days 31–60: Pilot to production path- Run an A/B with human-in-the-loop; publish weekly KPI deltas.- Resolve data quality and access issues.- Define change-management and support model. ^[6]

Days 61–90: Scale or stop- Gate release on KPIs, safety evals, and cost targets.- Expand to next cohort or stop and reprioritize.- Document learnings for the next use case. ^[2]

ROI Checklist

☐ Business + KPI targets defined

☐ Baseline metrics captured

☐ Buy vs. build documented

☐ Data governance in place

☐ Human-in-loop process ready

☐ Safety tests and rollback plan ready

☐ Cost per output/task monitored

☐ Training and adoption plan delivered

☐ Quarterly ROI review cycle

Bottom Line

AI fails not because the AI models are weak, but because the operating model is. Treat AI like any other transformation: start with value, prove it with data, and scale what works. The 5% club isn’t lucky. They’re disciplined!