Over the past two years, I’ve sat in Executive Briefing Center sessions and QBRs with multiple organizations, all wrestling with some version of the same question: we have a generative AI strategy — so why isn’t it in production yet?
The honest answer is that most GenAI strategies aren’t failing at the strategy layer. They’re failing at the alliance layer.
The gap nobody puts on the roadmap slide
Enterprises don’t lack ambition. They lack a clear operating model for how cloud infrastructure, hyperscaler partnerships, and AI platforms actually connect to deliver a production-grade outcome. I’ve watched well-funded GenAI initiatives stall for months — not because the model wasn’t good enough, but because nobody had aligned the cloud-native infrastructure, the security posture, and the hyperscaler GTM motion behind it.
This is where I spend most of my time: in the space between “we have an AI strategy” and “we have AI in production, at scale, with a defensible ROI story for the board.”
Most GenAI strategies aren’t failing at the strategy layer. They’re failing at the alliance layer.
Three patterns I keep seeing
Teams pick a cloud-native stack first, then bolt on GenAI ambitions later — instead of designing for both from day one. The result is expensive re-architecture six months in.
The organizations that move fastest treat their hyperscaler relationship (Azure, AWS, GCP) as a strategic lever for GTM and investment — not just a vendor contract to negotiate down.
Someone owns the model. Someone owns the infrastructure. Almost nobody owns the handoff between them — the compliance, the security architecture, the change management that turns a pilot into something regulated industries will actually run in production.
What’s actually working
The enterprises that get GenAI into production fastest do three things differently:
- Design cloud-native and AI strategy together from the start
- Treat hyperscaler alliances as a growth lever, not a cost line
- Put a single accountable owner — not a committee — in charge of pilot-to-production
The gap between AI ambition and AI in production isn’t a technology problem anymore. It’s an alliance and execution problem. And it’s solvable — but only if someone owns it end to end.
I’d love to hear how other leaders are closing this gap in their own organizations. What’s the biggest blocker you’ve seen between GenAI strategy and GenAI in production?
Disclaimer: These are my personal views and observations based on recent enterprise engagements and do not represent the views of my organization.