Enterprise AI: From Pilot to Scale
How enterprises scale AI from pilot to enterprise-wide. Critical success factors.
Critical success factors: executive sponsorship, change management investment, infrastructure readiness, governance scaling, talent strategy.
Common failure modes: pilot purgatory, infrastructure breakdown, adoption resistance, governance gaps, talent constraints.
Phases: Pilot (months 1-6) → Scale (months 6-18) → Embed (months 18-36) → Compete (year 2+).
Bottom line: Scaling enterprise AI requires deliberate strategy and investment beyond pilot.
Frequently asked questions
Why do enterprise AI pilots fail to scale?
Change management gaps, infrastructure breakdown, governance scaling issues, talent constraints. Pilot success doesn't guarantee scale success.
How long from pilot to scale?
12-18 months typical for substantial scaling. Compounds over multiple years. Patience required.
Resource requirements for scaling?
5-10x pilot resources for enterprise scaling. Change management, training, governance, infrastructure all scale up.
Should enterprise scale one AI initiative or many?
Sequential typically better than parallel. Build capability and confidence with first scale; expand from there.
What's the biggest scaling risk?
Change management gaps. Tools deployed without adoption. Enterprise-wide rollout without sufficient training and support.
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