No, but the data is sobering. In Matt Britton's analysis, only 26% of Fortune 500 companies successfully scaled AI enterprise-wide between 2018-2022. That means 74% of the world's largest companies — with unlimited budgets and top-tier talent — couldn't move AI beyond pilots.
The failure isn't technical. It's structural.
After leading AI transformation at £3B+ CPG pladis and shipping 30+ AI products per year, we've identified three critical gaps that kill enterprise AI scaling. The companies in the 26% figured these out. The 74% didn't.
What Separates the 26% Who Scale from the 74% Who Stall?
The successful companies integrate data and workflows from day one. Take Conagra's Project Catalyst — a standout example from the research. They didn't just build demand forecasting models. They integrated AI directly into product development cycles and operational workflows.
The difference between pilot success and enterprise scaling comes down to three factors:
Strategy Connected to P&L
Most AI pilots optimize interesting metrics that don't move commercial needles. Conagra succeeded because they targeted demand forecasting — directly connected to inventory costs and revenue planning.
We've seen this pattern across three £500M+ CPG businesses. The pilots that scale answer one question: "How does this improve EBITDA?"
Integrated Data Foundation
Pilots run on cleaned datasets. Production systems need live, messy, real-time data from multiple sources.
At pladis, we learned this the hard way. Our promotional ROI model worked brilliantly in isolation. Scaling required integrating S&OP data, trade spend systems, and retailer feeds. The model was 20% of the work. Data integration was 80%.
Change Management from Week One
The 74% treat change management as an afterthought. The 26% embed it from the first workshop.
This isn't about training sessions. It's about designing AI systems that fit how your teams actually work — not how you think they should work.
The Three Gaps That Kill AI Scaling
Here's what we observe when we audit stalled AI initiatives:
| Gap | Pilot Phase | Scaling Reality | Business Impact |
|---|---|---|---|
| Strategy Gap | Impressive KPI improvements | No connection to commercial metrics | Board loses confidence |
| Data Gap | Works on historical datasets | Breaks on live, messy data | Operations can't adopt |
| Adoption Gap | Data scientists love it | End users find workarounds | System gathers dust |
The Strategy Gap
Your pilot improved forecast accuracy by 15%. Impressive. But did it reduce inventory holding costs? Improve case fill rates? Impact margin per SKU?
We've audited £200M+ logistics companies where AI pilots delivered technically sound results with zero commercial impact. Perfect models. Wrong problems.
The Data Gap
Pilots run on six months of cleaned historical data. Production systems need yesterday's shipment data, this morning's inventory levels, and real-time carrier updates.
The gap isn't technical capability. It's data engineering discipline. Most companies underestimate this by 400%.
The Adoption Gap
Your S&OP team has been running demand forecasting for eight years. They trust their spreadsheets more than your algorithm — until you prove the algorithm makes their job easier, not harder.
Change management isn't training people to use AI. It's designing AI to augment how people already work.
How the 26% Bridge These Gaps: Three Non-Negotiable Principles
Principle 1: Start with the margin leak, not the technology.
Conagra's Project Catalyst succeeded because they identified demand forecasting as a critical business lever first. The AI was the solution to a commercial problem, not a technology looking for an application.
Every successful scaling story we've seen starts the same way: "We're losing £X per month because of Y process inefficiency. AI can fix this."
Principle 2: Build the data foundation before the algorithm.
The 26% spend 70% of their time on data engineering and 30% on model development. The 74% flip this ratio.
At pladis, our breakthrough came when we stopped optimizing algorithms and started optimizing data pipelines. Real-time S&OP integration took eight months. The promotional AI took six weeks.
Principle 3: Design for users, not use cases.
Successful AI doesn't replace human judgment. It augments it.
We've built demand forecasting systems that suggest adjustments instead of automatic overrides. Trade spend optimization that highlights opportunities instead of automating decisions. The human stays in control. The AI handles the heavy lifting.
The Navigate-Execute-Land Framework: From Pilot to Production in 30 Days
Based on patterns from the 26%, we developed SCALE AI™ methodology. Three integrated pillars:
Navigate: Strategy Connected to Commercial Outcomes
- Map AI opportunities to P&L impact
- Prioritize use cases by ROI potential
- Align C-suite expectations with delivery timelines
Execute: Data Engineering Foundation First
- Audit existing data quality and accessibility
- Build robust pipelines for live data integration
- Ship working AI systems, not prototypes
Land: Adoption Through Augmentation
- Embed AI into existing workflows
- Design systems that enhance human judgment
- Measure adoption rates alongside accuracy metrics
What This Means for Your AI Strategy
If you're in the 74%, you're not alone. The challenges you're facing — data integration complexity, user adoption resistance, unclear ROI measurement — are universal.
If you're planning your first AI initiative, learn from the patterns. The 26% didn't succeed because they had better technology. They succeeded because they addressed strategy, data, and adoption as an integrated system.
If you're a PE Operating Partner, this data validates the opportunity. Even Fortune 500 companies struggle with AI scaling. Mid-market companies with the right methodology can move faster.
Your Next Step: Audit Before You Scale
Before investing in AI scaling, audit your current position:
- Strategy: Can you connect each AI use case to a specific P&L impact?
- Data: Do you have real-time access to the data your AI systems need?
- Adoption: Are your end users asking for AI tools, or avoiding them?
Most companies discover they're missing 2-3 foundational elements. Fixing these first accelerates everything else.
Ready for an honest assessment? Book an AI FlightCheck — we surface your highest-ROI opportunities and identify scaling blockers in five working days. No pitch. Just the roadmap from pilot to production.
The 26% figured this out. Your turn.