I remember a meeting where a client’s CEO leaned in and asked me, “So, we have tons of data… Why can’t we just add AI and call it a day?” He was excited—who isn’t these days? But my team and I just shared a knowing look. We had seen this story again and again: everyone wants the shiny AI solution, yet few realize how much work it takes to get the data ready. (If only it were that easy!)
In this post, let’s talk honestly about what it takes to get your enterprise data AI-ready—specifically GenAI-ready. We’ll walk through how this fits into an enterprise data maturity journey (is it Stage 5, or just the “deep end” of Stage 4?), why being AI-ready is about a lot more than just technology, and how focusing on data quality, governance, and architecture can make or break your AI aspirations. We’ll also look at examples of generative AI applications (think chatbots, document summarizers, intelligent search, and copilots) that all share one thing: they demand well-prepared data. And because I’m a fan of practical advice (and biased), I’ll highlight a couple of Azure tools – Microsoft Purview and Microsoft Fabric – that can help you on this journey.
So grab a coffee, and let’s dive in. By the end, you should have a clearer idea of how to start getting your data truly AI-ready.
Note: If you need a refresher on OpenAI and LLMs, check out my blogs Introduction to OpenAI and LLMs, Introduction to OpenAI and LLMs – Part 2 and Introduction to OpenAI and LLMs – Part 3, along with a presentation on that topic that I did for the Toronto Data Professional Community which you can view and download the slides.
From Stage 4 to Stage 5: Data Maturity Meets AI
Every organization progresses through some kind of data maturity model. You might have seen one of those charts (maybe the one with four stages in my book) that show a company’s evolution from basic reporting all the way to advanced analytics. Typically it goes something like:
- Stage 1 – Reactive analytics: Siloed data, manual reports, and ad-hoc analyses (basically just reacting to past events). Often Excel files are emailed all over (spreadmarts).
- Stage 2 – Informative analytics: A centralized data warehouse and BI provide a single source of truth with standard reports. Focus on stages 1 and 2 is historical reporting. Usually the size and types of data it can handle is limited and solution is not very scalable and data is ingested infrequently (i.e. every night).
- Stage 3 – Predictive analytics: Can handle larger quantity of data, different types, and more frequently (streaming). Data science and machine learning efforts begin, aiming to forecast trends and outcomes, and decisions can be made real-time.
- Stage 4 – Transformative analytics: Can handle any type of data, no matter the size, type, or speed. Advanced analytics (even some AI) is embedded in processes, and a data-driven culture is in full swing.
Now, along comes generative AI. It feels like a leap beyond what we used to consider “advanced analytics.” So where does GenAI fit? It could be a Stage 5 – AI-Driven Enterprise, or simply an evolution of Stage 4 (in reality, it’s a bit of both). If your company is still in Stage 2 or 3, you have foundational work to do before jumping to GenAI – you can’t just skip to the top. But if you’re solidly in Stage 4, getting AI-ready is the next natural step. The key point is that becoming GenAI-ready is a continuation of the data maturity journey, building on everything you’ve achieved so far in BI and analytics, and then going further.
What Does it Mean to Be GenAI-Ready?
Let’s cut through the buzzwords. Being “GenAI-ready” isn’t about having the latest AI tool; it’s about your data being ready to support those tools. It means you’ve set up the plumbing and prepared the ingredients so that an AI system can actually do something useful (and trustworthy) with your data.
How can you tell if your data is AI-ready? Here are a few signs:
- Clean, quality data: It’s accurate, consistent, and up-to-date – no “garbage in” to poison your AI’s outputs.
- Clear context and governance: Data carries business meaning (thanks to good metadata and definitions) and is properly governed, so everyone knows what it represents and how it can be used.
- Managed unstructured data: Documents, emails, and other unstructured sources are stored in accessible formats and tagged with relevant info, ensuring AI models can find and interpret them.
- Flexible architecture: Your data platform can easily integrate new sources and deploy AI models without major rework – it’s built to scale and adapt as AI use cases grow.
If you read that list and feel a bit overwhelmed, you’re not alone! Very few organizations check every box. The idea is to see where you stand and where you need to improve, because any weaknesses in your data will be magnified by AI. Remember, GenAI is not a magic wand – it will amplify whatever you feed it. So feed it well.
Building an AI Solution: As Complex as Building a Data Warehouse (or More)
All those fundamentals – data quality, governance, architecture – underscore one truth: building a robust AI solution is no less complex than building a data warehouse or any major analytics platform (in fact, it can be more).
Why more complex? For starters, GenAI projects often involve diverse data types (text, documents, images, chat logs) and require new infrastructure (like vector databases for embeddings or real-time data pipelines) that you might not have needed in a traditional BI project. Additionally, developing AI is an iterative process of training, fine-tuning, and validating models, which means your data pipelines must be flexible and your team prepared for a lot of experiment-and-learn cycles. In other words, it’s a different style of cooking – you add ingredients, taste, adjust, and repeat – not a simple follow-the-recipe dish.
The takeaway? Approach AI initiatives with the same rigor as your big data projects – if not more. Plan thoroughly, ensure the fundamentals are solid, and involve the right experts (data engineers, data scientists, domain specialists) from day one. It’s a marathon, not a sprint (with a few extra hurdles on the track), but with preparation and teamwork, you’ll reach the finish line.
GenAI Applications in the Wild: Why They Need AI-Ready Data
Let’s ground this in some real-world examples. What do we actually do with GenAI in a business setting, and why does data prep matter so much? Here are a few popular use cases:
- AI Chatbots and Virtual Agents: They need a curated, trusted knowledge base of information; otherwise the bot will either give wrong answers or be embarrassingly clueless.
- Document Summarization and Analysis: The documents must be machine-readable (not just scanned images) and organized, so the AI can find key points and accurately summarize them.
- Intelligent Search: This requires indexing and integrating your data (often via semantic or vector search). Without well-prepared data, the AI won’t retrieve the answers users are looking for.
- AI Copilots for Employees: These AI assistants (for coding, marketing, finance, etc.) rely on internal data. If that data is siloed, outdated, or poorly defined, the copilot’s guidance will be far less useful.
Across all these examples, the pattern is clear: the quality and readiness of data determines whether a GenAI application succeeds or face-plants. Even a state-of-the-art model can’t overcome messy, siloed, or untrustworthy data. I’ve seen brilliant technical prototypes get scrapped because the underlying data pipeline wasn’t sustainable or the outputs couldn’t be trusted by end users. Generative AI can do amazing things, but only if our data house is in order.
Tools for the Journey: Azure Purview and Microsoft Fabric
By now you might be thinking, “Alright, we need to improve a lot of things… where do we even start, and are there tools to help?” The good news is yes – there are platforms that align well with this journey. In the Azure ecosystem, two worth highlighting are Microsoft Purview and Microsoft Fabric:
Microsoft Purview: Purview is Azure’s data governance service. It helps you discover, catalog, and track the lineage of data across your organization. In short, Purview makes it easier to ensure your data is well-defined, trustworthy, and compliant – exactly what you need before unleashing AI on it.
Microsoft Fabric: Fabric is Microsoft’s unified analytics platform, combining data engineering, data warehousing, and data science tools in one place. It’s built with AI in mind – using OneLake to store all your data and integrating seamlessly with Azure AI services. This means you can develop and deploy AI solutions faster, without stitching together a dozen separate systems.
Of course, tools alone won’t magically make your data AI-ready. But leveraging platforms like Purview and Fabric can accelerate the process by reinforcing good practices (governance, single source of truth, scalable architecture) as you embark on your GenAI projects.
Conclusion: Begin Your AI-Ready Data Journey
If you’ve stuck with me this far, you know that getting data GenAI-ready is a journey, not an overnight task. The best way to start is by assessing where you are today. What stage of data maturity are you at, and where are the gaps? Maybe you have lots of data but little governance, or great dashboards but poor data quality. Identifying one or two key areas to improve is a great first step.
Next, consider running a pilot project that leverages GenAI on a small scale. Pick a use case that excites people but is manageable—perhaps an internal Q&A chatbot or an AI-generated report summary. As you execute it, pay attention to what’s blocking you. Are you scrambling to clean data or define metrics? Use those lessons to shore up your data foundations.
Also, remember to celebrate the “boring” work that enables AI – like setting up a data catalog, cleaning datasets, and defining business terms. These may not feel like innovation, but they directly boost your AI projects. And keep the collaboration going: getting data ready for AI isn’t just an IT task, so you need buy-in from leadership and participation from business teams. When everyone sees that better data leads to better AI, it’s much easier to get support for data quality and governance efforts.
If you’ve built a data warehouse or analytics platform before, you already know the playbook: define the goal, get the data in shape, build iteratively, and keep improving. GenAI is simply another chapter in that story.
So, roll up those sleeves and start laying the groundwork. Each improvement in data quality, governance, and architecture moves you closer to the day you can confidently say, “Yes—we’re ready for AI.”
The post Getting Your Data GenAI-Ready: The Next Stage of Data Maturity first appeared on James Serra's Blog.