Most leadership teams today aren’t debating whether to use AI, they’re stuck deciding where to start.
Some are juggling a hundred disconnected experiments; others are managing pilots that never scale. The result is the same: high activity, low value creation.
That’s exactly what Bain’s 2025 Private Equity report reveals:
- 90% of companies are experimenting with AI.
- Fewer than 20% are creating measurable value with it.
The problem isn’t ambition, it’s focus.
From Experimentation to Allocation
The companies breaking through are the ones that treat AI not as a playground for innovation, but as a capital allocation decision.
They approach AI projects the same way they approach growth investments, by deciding what’s worth doing before they do it, and by holding those initiatives to measurable business standards.
A Rubric for Prioritizing AI Initiatives
Here’s a simple scoring framework I use with companies I advise.
Use it to cut through noise and focus on the AI projects that matter most.
AI Initiative Scoring Rubric (1–5 scale for each criterion)
1. Business Impact (30%)
Will it move a core business metric – revenue, margin, cost, or risk?
- 1 = unclear or no link
- 3 = 2–5% impact
- 5 = >10% or unlocks new revenue
2. Feasibility (20%)
Can it be delivered with current tools and talent?
- 1 = requires major new capability
- 3 = possible with external support
- 5 = achievable internally
3. Speed to Value (15%)
Can we demonstrate measurable results quickly?
- 1 = >12 months
- 3 = 3–6 months
- 5 = <60 days
4. Adoption Likelihood (20%)
Will people actually use it?
- 1 = requires major behavioral change
- 3 = likely with support/training
- 5 = fits naturally into existing workflows
5. Strategic Fit (15%)
Does it reinforce the company’s long-term differentiator?
- 1 = nice-to-have
- 3 = supports current goals
- 5 = critical to future positioning
Scoring Outcomes:
✅ 20–25: Prioritize and fund immediately
⚠️ 15–19: Viable, but refine scope
🛑 <15: Kill or redesign
Proof in Performance
When AI investments are tied to outcomes, results follow.
In Bain’s examples:
- Vista Equity Partners bakes this discipline into its annual planning cycle.
- Apollo mandates 3–5 high-scoring AI use cases directly tied to measurable business outcomes.
The results speak for themselves:
- 40% reduction in costs
- 65% improvement in response time
- +$5M new revenue in the first year
Start with Selection, Not Scale
Most companies are drowning in AI experiments. The ones that win are ruthless about selection.
Start small. Pick one initiative. Score it honestly.
Then build the internal muscle that turns AI from theatre into enterprise value.
That’s how you move from testing AI to transforming through AI.