In the spring of 2023, I found myself sitting across from "Kathy" (not her real first name), a brilliant technologist who had just witnessed her AI project implode at a Fortune 500 company.
"The funny thing is," she said, stirring her coffee thoughtfully, "we thought we were ready. We had ticked all the obvious boxes. But when we dug deeper, we discovered gaps in places we never thought to look."
That conversation sparked a realization that would reshape my understanding of organizational AI readiness. After several years implementing AI initiatives across industries, I've discovered that "AI readiness" isn't a checkbox — it's a complex interplay of five critical pillars, as explained by Kavita Ganesan, in her book The Business Case for AI .
The Data Paradox: Having Isn't Enough
The first pillar, identified by Kavita — data — seems straightforward. Most organizations think they're ready because they're collecting data. But here's the catch: having data and having usable data are entirely different things.
I recently worked with a company that proudly proclaimed they had years of customer interaction data. But when we dug deeper, we found they couldn't centrally access most of it.
Their data was like having a library where all the books are locked in different rooms, and nobody has all the keys.
As Kavita insists, the real measure of data readiness isn't in the volume of data you're storing — it's in your ability to:
Clearly identify and understand your data sources
Effectively capture and store operational data
Log meaningful customer interactions
Access your data centrally when needed
Cultural Shift: Beyond the Buzzwords
The second pillar she proposes — organizational culture — is where things get interesting. Kathy's company, like many others, thought they were culturally ready because they used terms like "data-driven" in their meetings.
But true cultural readiness goes deeper.
The organizations that succeed with AI share some surprising cultural traits, insists Kavita Ganesan. They're comfortable with uncertainty. Their executives don't just nod along in AI strategy meetings — they genuinely understand AI's potential and limitations.
They've learned to work across departmental boundaries, bringing together business leaders and technical experts in ways that would have seemed strange just a few years ago. I have the same experience.
Infrastructure Reality Check
Infrastructure is the third pillar suggested by Kavita. Here is where many organizations face their moment of truth. You can have the best data and the most forward-thinking culture, but without the right infrastructure, your AI initiatives will stall.
It's like trying to build a skyscraper without first laying the foundation.
The hard truth I've learned: successful AI deployment isn't just about having infrastructure — it's about having the right infrastructure. Many organizations I've worked with needed to adapt their technical foundation before they could successfully implement AI solutions.
The Skills Gap Nobody Talks About
The fourth pillar described by Kavita Ganesan — skills — is perhaps the most misunderstood. Many organizations think they can solve this with a few hiring moves or quick training sessions. But true AI readiness requires a more nuanced approach to skill development.
Organizations need three kinds of AI literacy:
Executives received AI training
Deep technical knowledge for key innovators
Practical implementation skills for implementers
Budget Reality: Beyond the Bottom Line
The final pillar she mentioned — budget — seems straightforward but hides surprising
complexities. It's not just about having money; it's about allocating it correctly across multiple dimensions she suggests:
Executive and innovator education
Technical talent acquisition or development
Infrastructure setup and maintenance
Data architecture improvements
Moving Forward: A Framework for Readiness
When I caught up with Kathy recently, she shared how her organization had transformed their approach to AI readiness. "We stopped treating it like a technical checklist," she explained, "and started seeing it as an organizational journey."
This shift in perspective is crucial. AI readiness isn't about ticking boxes—it's about building capabilities across all five pillars simultaneously, insists Kavita Ganesan. It's about understanding that gaps in any pillar can undermine success in all the others.
Think of it this way: If implementing AI is like preparing for a long journey, most organizations focus on packing their bags (the technical preparations) while ignoring whether they're physically fit enough for the trek (organizational readiness).
Both matter, and weakness in either will stop you short of your destination.
The organizations that succeed with AI aren't necessarily the ones with the biggest budgets or the most data. They're the ones that have thoughtfully developed capabilities across all five pillars: data accessibility, cultural readiness, infrastructure capability, skills development, and strategic budget allocation, suggests Kavita.
I strongly recommend you read her book. It is very practical and well structured.
As Kathy told me in our last conversation, "We finally got it right when we stopped trying to be ready and started trying to become ready." That distinction — between a state and a process — might be the most important insight of all.
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