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A senior partner at one of the top-three strategy consulting firms said this to me recently. Provocative? Sure. But he had a point.
He clarified: the penetration of true generative AI into meaningful enterprise use is so minimal that it’s almost a rounding error.
His reasoning? The builders of cutting-edge AI technologies are, understandably, focused on pushing technological frontiers, not contextualizing their innovations for enterprise workflows.
And that makes sense. With the consumer AI market racing toward a trillion-dollar TAM, it's natural that tech creators would target capabilities rather than enterprise customization. Product teams are prioritizing capabilities over customization.
The result? Most enterprises are still at the absolute starting line of their AI journeys.
But let's be clear: this isn’t a bad thing. Enterprises aren't emblematic of individual consumers; they're complex ecosystems composed of diverse internal and external stakeholders. AI's true potential—and complexity—comes alive when it's applied across these multifaceted organizational landscapes, not simply when automating single-user tasks.
So, what does this mean practically? It means enterprises shouldn't mistake the act of deploying shiny new technology for genuine AI maturity. Rather, there's an essential preceding step—AI readiness.
Deploying generative AI doesn’t mean your organization is mature in its use of it. Organizations must first build a clear roadmap that answers why they're adopting AI in the first place.
I've said this before: strategy comes before stack. It still applies.
To get ready for AI, organizations must avoid "boiling the ocean".
Start small. Pick a few practical experiments. Then scale with purpose.
Remember, the journey of a thousand miles starts with a single step—one you take deliberately and strategically. For more on this, click here to read the post I wrote earlier this month.
As enterprises consider "how" to approach AI, several challenging questions arise. This is where experience becomes incredibly valuable.
Likely not. The pace of change in AI—new large language models (LLMs), frameworks like model context protocol (MCP) and Agent2Agent (A2A), and technologies we haven't even seen yet—makes hard-coding brittle and costly.
Also no. Deep lock-in can hurt you. Hyperscaler bundles might seem convenient, but at a high cost. Locking into a single vendor's ecosystem or data centers could significantly limit flexibility and performance, creating unpredictable cost and scaling challenges.
Absolutely. AI model commoditization is inevitable. An agnostic approach lets you switch tools and components, reduce switching costs, and stay future-proof.
But here’s the catch:
Flexibility creates new complexity—especially around:
You need a governance layer that scales with your AI architecture. I’ll dive deeper into this in a future post.
Tech doesn’t adopt itself—people do. The workforce preparation challenge isn't new—it's inherent in any tech transformation. But the degree to which day-to-day roles will shift with AI is unprecedented. Organizational readiness for this magnitude of change is non-negotiable.
Enterprise leaders must help employees see AI as an enabler, not a threat. That means:
This is a transformation. You can’t assume your workforce will adapt by default. You have to help them get there.
Your platform, provider, and partner decisions, the "whats", should ladder back to your strategic “why.” How do you make choices today that future-proof your organization tomorrow?
Questions to consider:
Re-underwriting your approach from first principles is critical. AI is going to be pervasive in business operations and workflows in a more native way than technology has ever been before.
Don’t get caught reacting to every new release from the tech giants. Proactively position your enterprise to handle continuous evolution—with clarity, governance, and flexibility.
Here’s the bottom line:
Deploying AI ≠ Adopting AI.
True enterprise AI adoption can only follow comprehensive AI readiness. That means a thoughtful alignment of
My advice? Start with AI readiness. Then climb the maturity curve with intent. Trust me—it’s a far smoother, and more valuable, journey.
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