AI pilots are everywhere, and that’s the problem.

In 2024 alone, over 70% of Fortune 1000 companies ran at least one AI pilot. But fewer than 25% of those pilots scaled across the enterprise. Most stall. Why? Because companies treat pilots as proofs of concept, not as stepping stones to capability.

Scaling AI is not about volume, it’s about velocity, repeatability, and cultural readiness.

Let’s look at how to break out of pilot purgatory and scale with purpose.

1. Start with Use Case Quality, Not Just Quantity

In The AI Adoption Roadmap, I talked about the importance of identifying operational AI leverage early. That means selecting use cases with:
– High business value (e.g., churn reduction, yield optimization)
– Strong data maturity
– Willing internal partners
– Measurable ROI

For instance, one mid-market logistics company started by optimizing route planning with a 4% cost savings. But they only scaled it after they created a feedback loop with ops managers, showing how decisions improved weekly. That buy-in created scale.

2. Design for Repeatability

Scaling doesn’t mean building 50 models. It means creating one model and deploying it 50 times, with consistency.

That requires playbooks, shared architecture, and AI enablement roles across business units. In Designing an Organization for AI Execution , I walk through how org structure and tools must evolve in parallel.

If your AI projects can’t be picked up and reused across teams, they’re not scalable, they’re isolated.

3. Empower Translators and Champions

AI is not an IT rollout, it’s a team sport.

You need AI translators embedded in product, finance, operations, and HR. These aren’t data scientists. They’re business people who understand the technology enough to frame problems, challenge outputs, and champion adoption.

Salesforce has embedded “AI advocates” across customer success teams to drive scaled personalization. They don’t code, but they create trust. That’s what moves AI from tool to team behavior.

4. Build Your Scaling Infrastructure Early

You don’t need a massive platform, but you need shared infrastructure.

That includes:
– Central model registry and version control
– Standardized privacy, governance, and logging protocols
– Self-service data access with proper guardrails
– Toolkits for business users (AutoML, prompt libraries, dashboards)

This foundation helps companies move from exploration to execution faster.

5. Create a Flywheel of Outcomes and Narratives

Scaling AI isn’t just technical, it’s emotional. People buy into what they understand and believe.

That’s why storytelling matters. Every pilot that delivers impact should be documented, shared, and celebrated. Tie AI success to company values and strategic goals. Publicize wins in all-hands meetings.

At a $900M consumer goods firm I advised, a simple workshop we did highlighting efficiencies from AI scheduling sparked four more departments to initiate their own pilots, with guidance.

AI starts small, but scale comes from momentum.

Final Thought

Pilot purgatory is real. But it’s not a technology problem, it’s a leadership and structure problem.

If you want AI to scale, you must treat it not as a project, but as a capability.

Start with business impact. Design for replication. Empower people at the edges. And keep telling stories that connect minds to results.

Need a blueprint to go beyond pilots? Explore How to Align Your Leadership Team.

Mahesh M. Thakur
AI & Leadership Advisor | CEO, Decisive AI
https://www.linkedin.com/in/maheshmthakur | https://MaheshMThakur.com