Enterprise Readiness for AI: More Than Just Data

2025 is shaping up to be the year when AI stops being a set of pilots and experiments and starts becoming the backbone of enterprise operations. Over the years of leading Data and AI practices at Persistent, I’ve seen organizations move from curiosity to full-blown experimentation—but now we are at an inflection point: experimentation is no longer enough. Enterprises need to be ready for AI.

What Enterprise AI Readiness Really Means

When I talk about enterprise readiness, I emphasize it’s bigger than “cleaning up data,” it’s about building readiness across three interconnected dimensions:

1. Data Readiness – Data that is not just clean and available but truly fit for AI consumption. It needs to reflect the realities of the use case, capturing the patterns, the outliers, and even the unexpected events that an agent may encounter.

   Data will be consumed by traditional layers like BI tools and APIs but also by modern layers like Copilots and AI Agents that operate without human mediation. The non-human nature of Copilots and AI Agents raises the bar requiring the data to be even more clean, even more governed and the data platform must scale even better. This isn’t a one-time effort; it’s about continuously qualifying data for confidence, accuracy, and trust.

2. Platform Readiness – AI needs a scalable data platform strategy and a robust AI platform strategy. Scalable, secure, and interoperable data platforms that can handle exponential agent-driven workloads, without trade-offs in performance or cost. Having a clear AI platform strategy that provides the core building blocks—like LLM Ops, secure gateways, guardrails, evaluation frameworks, fine-tuning, and observability—needed to run AI at scale. Whether you buy or build, these components are essential to avoid fragmentation today and to stay ready for tomorrow’s AI innovations.
3. Workforce Readiness – Building AI literacy and adoption across the enterprise, so that people don’t just use AI tools, but trust them and embed them into how work gets done. And across all three sits digital trust—explainability, fairness, governance, lineage, and compliance. These aren’t just check-the-box items; they have to be designed in context of how AI consumes and acts on your data.

   In my experience, this is the real shift. Enterprise readiness for AI isn’t static hygiene, it’s a living discipline, continuously evolving as new AI use cases emerge and as agents start taking on more responsibility. And that’s why the discussion on data and platforms is so important.It’s tempting to see these dimensions as steps in a sequence. But enterprise readiness doesn’t work that way. They are parallel pillars, and the most important thing is to get started somewhere. For some organizations that means fixing their data foundation, for others it’s putting platform guardrails in place, and for many it’s about preparing their people. Momentum in one area almost always creates pull in the others.

Why “Just Data” Isn’t Enough

Of course, this doesn’t mean that data readiness is irrelevant. In fact, many organizations are still struggling to get this foundation right. But being data-ready alone will not guarantee success with AI.

For years, being data-ready meant having clean, accessible, and well-governed information. While that is still necessary and many organizations are struggling with that, that being the only factor doesn’t hold anymore. Today’s AI is not passive, it consumes, interprets, and acts on data.

I’ve watched copilots evolved from answering simple prompts to suggesting next best actions, to automating routine tasks, and now to stitching together workflows across domains. Very soon, we’ll see agents that span organizational boundaries, exchanging information and collaborating across ecosystems.

This progression means the bar for readiness keeps moving higher. Enterprises must think not only about data quality, but also about how data is consumed, trusted, and orchestrated in real time. This is why an enterprise AI platform strategy becomes critical.

Why a platform strategy is essential

For AI to succeed at enterprise scale, you also need a platform foundation—the scaffolding that supports, governs, and scales AI use cases.

An AI platform strategy isn’t about picking a single product; it’s about ensuring your enterprise has the core components that every AI use case will inevitably require. These include operational pipelines to deploy and monitor models (LLM Ops), secure gateways for connecting with multiple providers, guardrails for responsible use, frameworks to evaluate and benchmark model performance, reusable prompt libraries, fine-tuning capabilities for domain-specific contexts, and observability to keep costs and performance under control.

These aren’t optional. They’re the plumbing and guardrails of AI at scale. You can choose to buy them, build them, or adopt a hybrid approach—but you cannot ignore them.

The Roadmap to Readiness

Getting there requires a structured approach, but not necessarily a sequential one. On paper, readiness can look like a clean step-by-step journey, but in practice these activities overlap and reinforce each other.

My Takeaway

AI is not waiting, and neither should you! What matters is to start, build momentum, and expand from there. Copilots are already on every desktop. Agents are entering an increasing number of workflows. In my years of working with CIOs and CDOs, the ones who succeed aren’t those chasing the latest model, but those investing in being truly AI-ready, in data, in platforms, and in people.

The enterprises that thrive won’t just be the ones with the most data, but the ones most ready, ready for AI to consume, act, and orchestrate across their business. At Persistent, this readiness journey is one we live every day with our customers. It’s not just about keeping up with AI but it’s about building the foundations for trust, speed, and scale in a world where AI will increasingly act on our behalf.