Most AI pilots don’t fail because the tech doesn’t work. They stall because they’re disconnected, from strategy, from systems, from anything resembling a roadmap.
I’ve seen this firsthand: a promising prototype gets attention, maybe even a solid demo… and then quietly fades. No integration. No scaling path. Just another “innovation” that never made it out of the lab.
Lately, I’ve been working through The AI Innovation Roadmap Playbook, and it’s been a useful lens. What stood out to me wasn’t just the frameworks, but the way they emphasize patience, structure, and long-game thinking.
Here are a few notes that resonated:
- Think in Horizons
• 0–6 months: Quick wins (support automation, data tagging)
• 6–18 months: Building pipelines, MLOps, team maturity
• 18+ months: AI products that reshape workflows or unlock new value
Short-term wins are great, but they should sit inside a broader arc. - Sequence Matters
Early projects should earn trust, but also quietly build the foundations that make future work easier. - Balance the Portfolio
The 70/20/10 model (core/adjacent/transformational) is a helpful check. Are we just automating, or also creating space for new thinking? - Build Capability, Not Just Output
AI that scales tends to come from places that invest in data foundations, responsible governance, and cross-functional trust. The human side carries more weight than it gets credit for.
If there’s a takeaway here, maybe it’s this: pilots are easy. Roadmaps are harder, but they’re what turn effort into impact.
