Generative AI is everywhere in business headlines. But for most companies, the results aren’t living up to the hype. According to new research from MIT’s NANDA initiative, nearly all GenAI pilots launched inside large companies are still falling short.

The headline stat is stark: according to reporting from The Register, only about 5% of enterprise GenAI pilots show meaningful business impact. Most never make it past the pilot phase, and even fewer reach production or deliver measurable improvements to revenue or cost.

Why is this happening?

And more importantly, what are the 5% doing differently?

Let's break down what MIT’s research actually says, why most GenAI projects stall out, and how you can design a pilot that survives and scales. If you’re involved in AI planning, marketing ops, or digital transformation, this is your chance to learn from what’s working — and skip the mistakes others are making.

TL;DR

MIT’s NANDA initiative says only about 5% of enterprise GenAI pilots show real business impact. Most stall before production or deliver no measurable P&L effect. Success correlates with tight workflow integration, memory and learning, and focused use cases.

What MIT actually found

• The study reports a “GenAI Divide”: roughly 95% of companies in the dataset are getting zero return from GenAI pilots, and only 5% reach production at scale or drive rapid revenue acceleration.
• Coverage highlights a key nuance: the failure is less about base models and more about a “learning gap” and weak integration with real work.
• Sectors seeing the clearest impact so far: Technology, Media and Telecom. Others remain flat.
• Related context from the same research stream: firms are cutting costs by not renewing BPO and agency contracts, and about half of AI budgets go to sales and marketing.

Why most pilots stall

• No durable memory or adaptation to workflows, so tools can’t learn from repeated tasks.
• Fuzzy objectives and KPIs, poor data plumbing, and shallow integrations with systems of record.
• “Generic chatbots” are easy to try but rarely meet safety, latency, and audit needs in critical workflows.

Want to avoid the “pilot purgatory” most companies hit? The Generative AI for Executives and Business Leaders course gives non-technical leaders a step-by-step playbook to choose high-ROI use cases, set real KPIs, and build a governance-ready rollout plan in about 10 hours—so your pilot actually makes it to production. Start building your executive-ready GenAI plan today.*

Vendor vs in-house: what’s working

Press summaries emphasize that off-the-shelf vendor tools and partnerships tend to outperform internal custom builds on production rates and business results. Treat this as directional until the full report is public, but the pattern is consistent across coverage.

So what does it take to run a GenAI pilot that actually works?

Here is a 6-week rollout plan designed to mirror what MIT’s research suggests successful teams are doing. It’s built for speed, realism, and business impact - with clear gates at every step.

Use this structure to avoid the common traps (like vague goals, bloated use cases, or lack of feedback loops) and get to production faster with fewer surprises.

A 6-week plan to cross the GenAI Divide

Week 1: Pick one workflow with high volume, clear acceptance criteria, and low exception rates. Define two KPIs that move P&L.

Week 2: Configure a proven vendor tool plus your data layer. Add human-in-the-loop and an approval gate for exceptions.

Weeks 3–4: Shadow mode in production. Track cycle time, first-pass yield, defect/rework rate, SLA hits.

Week 5: Limited rollout to opt-in champions. Add session memory, feedback prompts, and weekly policy or prompt updates tied to KPI deltas.

Week 6: Expand or stop. Continue only if both KPIs improved and no critical errors occurred.

Why this works: it aligns with MIT’s findings on memory, learning, and real workflow integration rather than “demo-ware.”

Buyer checklist for CMOs and Ops leaders

• Start with one data source, one SSO path, one approval rule.
• Require audit trails, red-team tests, and latency budgets.
• Insist on weekly learning loops: prompt/policy diffs plus KPI movement.
• Aim first at measurable hour-reduction use cases like agency copy iterations, RFP boilerplate, product taxonomies, contract intake, or claims triage. That mirrors where early savings appear.

FAQs

Q: What is MIT NANDA?

A: MIT NANDA is a media lab program studying decentralized and agentic AI. It hosts the “State of AI in Business 2025” access page.

Q: Does “95% failing” mean GenAI never pays off?

A: No. It means most current pilots don’t reach production or P&L impact. A minority do, especially where tools learn from the workflow and are integrated tightly.

Q: Where is GenAI having workforce impact?

A: Early effects are mostly in outsourced or off-shore tasks. Companies are saving by not renewing BPO contracts. Long-term exposure could reach far more roles.

Q: Should you build or buy?

A: Coverage suggests vendor tools plus targeted customization outperform fully in-house builds on time to value and production success.

Once your GenAI pilot is underway, it helps to track progress in a consistent format that works across teams. This quick-reference scorecard keeps everyone aligned on what’s being tested, what metrics matter, and whether the project is delivering results. It’s designed for ops leads, marketing owners, or AI project sponsors to update weekly - no dashboards required.

Copy, paste, and fill it in as you go.

Copy-paste pilot scorecard (Notes-friendly)

1 - Use case: [task]

2 - Owner: [name]

3 - Data source: [system of record]

4 - Guardrails: [PII filter, approval step]

5 - KPIs: [cycle time], [first-pass yield]

6 - Baseline: [value], [value]

7 - Week 2 shadow results: [value], [value]

8 - Week 4 rollout results: [value], [value]

9 - Errors: [critical/none]

10 - Go or no-go: [decision + reason]

Ready to join the 5% that see real business impact? The Generative AI for Executives and Business Leaders course helps you align strategy, data, and compliance to produce a concrete integration plan your team can execute. Enroll now and turn your next pilot into production-grade results.*