The ROI Trap That's Stopping AI Pilots From Scaling
The ROI Trap That's Stopping AI Pilots From Scaling
In last month's Clarity Notes, I shared 3 questions every leader should ask before adopting AI.
But after analyzing Google's latest study on the ROI of AI, I realized question #2 was fundamentally wrong.
"What pilot could we launch in 30 days to prove ROI?" assumes ROI calculations help you choose pilots.
They don't. In fact, they're killing AI initiatives before they start.
The Study That Changed My Mind
Google just released their 2025 "ROI of AI" study surveying 3,466 business leaders. The headline stat? 88% of "agentic AI early adopters" see positive ROI.
Sounds encouraging, right?
But here's what the study doesn't tell you: 90% of AI pilots still fail to scale beyond proof-of-concept.
How can both things be true? Because ROI measurement and pilot success are completely different challenges.
After leading technology transformations affecting thousands of employees at Google, Meta, VMware, and Upwork, I learned that the ROI obsession is exactly why most AI initiatives fail before they start.
The ROI Mirage Problem
Google's study focuses heavily on measuring returns:
74% report ROI within first year
53% see 6-10% revenue gains
$250K average annual benefits per 1,000 employees
But ROI calculations come AFTER successful implementation. They don't help you choose which pilots to start with.
This is like using a speedometer to decide which route to take. You're measuring the wrong thing at the wrong time.
What This Means for You
Reading between the lines, the study reveals three critical points about why the traditional approach fails:
Success requires strategic foundation. The report emphasizes that ROI "continues to need C-suite sponsorship" and organizations need "comprehensive executive alignment."
Translation: Success isn't about the technology—it's about organizational readiness and strategic focus.
Implementation complexity is underestimated. Top challenges identified include data privacy and security (37%), integration with existing systems (28%), and cost management (27%).
These aren't ROI problems—they're pilot selection and preparation problems.
Generic approaches fail. The study shows AI adoption varies dramatically by industry and use case. Yet most consultants offer one-size-fits-all ROI assessments.
The Revenue Impact Assessment Alternative
Instead of starting with ROI calculations, I've developed what I call Revenue Impact Assessment—a systematic approach that works backward from business outcomes to technology selection.
The fundamental shift: Instead of asking "What's the ROI?" ask "Where does AI create the most business leverage?"
Here's the framework:
Step 1: Revenue Architecture Mapping
Step 2: AI Leverage Point Identification
Step 3: Implementation Readiness Scoring
Step 4: Strategic Pilot Portfolio Design
A Real Example: Why This Works
Let me show you the difference with a real SaaS company case:
Traditional ROI Approach:
Identified 15 potential AI use cases
Calculated ROI: Customer service chatbot (300% ROI)
Implemented chatbot first (seemed like highest return)
Result: 6 months later, 15% adoption rate, project stalled
Revenue Impact Assessment Approach:
Mapped revenue: 70% from new customers, 30% expansion
Identified bottleneck: Lead scoring (manual qualification losing 40% of qualified leads)
Assessment showed: High impact + confidence, low effort
Result: 25% improvement in lead-to-customer conversion in 90 days
The Critical Distinction
ROI methodology asks: "What's the financial return?"
Revenue Impact Assessment asks: "What's the revenue leverage?"
This distinction matters because:
Revenue leverage is measurable at current state
Financial returns are speculative until after implementation
Revenue mapping reveals organizational readiness gaps
ROI calculations ignore capability building requirements
What Successful Companies Actually Do
The companies Google studied didn't succeed because of better ROI calculations. They succeeded because they intuitively followed Revenue Impact Assessment principles:
82% have 10+ AI agents (portfolio approach, not single pilots)
39% of IT budget allocated to AI (systematic investment, not experimental)
78% have C-suite sponsorship (business outcome alignment, not technology enthusiasm)
They treat AI like systematic business transformation, not science experiments.
What Revenue Impact Assessment Looks Like
Here’s a simplified example of how this methodology maps AI opportunities to business outcomes. Download the PDF here.
This shows the systematic connection between:
AI implementations (your solutions)
Business processes (operational impact)
Revenue drivers (financial outcomes)
Total revenue (ultimate goal)
This is just a conceptual example. The real power comes from industry-specific Revenue Trees that map to your exact business model.
I’ve developed detailed Revenue Trees for 10+ industries, each showing precisely where AI creates leverage for that specific type of business.
Want your industry’s Revenue Tree?
I’ll be releasing these as resources for executives who are serious about systematic AI adoption.
Your Action Plan
Before your next AI initiative, answer these three questions:
Can you draw your revenue architecture on a whiteboard?
Do you know which revenue drivers have the highest AI leverage potential?
Are you selecting pilots based on learning velocity or just financial projections?
If you can't answer these specifically, you're not ready for AI pilots yet. You need Revenue Impact Assessment first.
Looking Ahead
Next month's Clarity Notes will dive deep into the complete Revenue Impact Assessment methodology with worksheets and templates you can use immediately.
I'll show you exactly how to map your revenue architecture and identify your highest-leverage AI opportunities.
Until then, focus on understanding where your revenue actually comes from. Most executives can't draw their revenue tree—and that's exactly why their AI pilots fail.
Ready to Apply This?
If this framework resonates and you'd like to explore how it applies to your business, I have a few Executive AI Fit calls available this month.
This is a 45-minute conversation where we'll discuss your AI priorities and I'll share initial thoughts on where Revenue Impact Assessment might help you avoid the 90% pilot failure rate.