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No longer a fringe curio, AI has evolved from futuristic concept into an all-consuming technology. And while the first wave of viable AI learned to walk with chatbots and basic automations, AI is coming of age as organisations seek to fuse it with real-time data to unlock efficiencies and opportunities. This is the era of dynamic data, and companies are beginning to realise just how critical data maturity is to make it reality.

If the first wave of viable AI was all about chatbots—linear automation that answered simple queries framed as preset questions based on predefined data—businesses are now looking to flex beyond set outcomes to integrate AI with real-time operational data. Think retail companies merging AI with live sales data, or member organisations feeding AI with member information to personalise experiences and deliver timely insights.

In essence, businesses are realising data is at its most potent when it’s current. While point-in-time data provides useful historical context, real-time data is the fuel AI needs to make accurate predictions and deliver timely insights. Instead of relying on last quarter’s sales reports, AI can now access live sales, giving businesses the ability to tweak operations on the fly. And it’s this shift that is redefining how businesses interact with their data.

Where are you? Modernising or innovating.

To fully realise the benefits of AI, businesses need to move beyond basic data storage and management. They have to strive for data maturity.

Which means that, broadly speaking, businesses are either modernising or innovating. For the modernisers, it’s about shedding siloed systems and centralising data onto a modern cloud platform. For the innovators, the next step is to harness the power of AI, advanced analytics, or data science to create real value.

Wherever you sit, the success of both approaches hinges on one critical element: governance. Data without governance is chaos, and chaos doesn’t scale well.

Data governance. The key to scalable AI.

Governance—an often-overlooked aspect of data management—is critical for businesses looking to scale AI. At its core, data governance ensures data is properly managed, secured, and accessible to the right people. In the AI era, governance also plays a crucial role in helping AI understand and interpret data correctly.

Organisations successfully governing their data find it becomes much easier to scale their AI ambitions. By treating each dataset as “endorsed”—complete with tagging covering such things as data owner, subject matter expert, along with clearly defined security roles—businesses can create a consistent structure across their entire data ecosystem. This structure not only improves data management but also ensures AI can operate efficiently and effectively.

Practical steps for implementing data governance.

Implementing data governance needn’t be overwhelming. In our experience, applying a few key principles to data subsets before scaling up works better than an all-in, fix-all approach. Here’s a simple roadmap for getting started:

1. Identify key data owners and stakeholders: Start by assigning clear dataset ownership. These individuals will be responsible for data accuracy, quality, and security. Ensure you have subject matter experts who can oversee specific areas, like finance or customer data, to help maintain the quality of these datasets.

2. Define security and access controls: It’s critical to establish who can access your data and what they can do with it. Create role-based access controls, ensuring that only authorised individuals can view, modify, or export sensitive information. This step is especially important when integrating AI, as AI needs to adhere to the same security and access controls.

3. Establish metadata and tagging practices: Metadata provides critical context to your data, helping both humans and AI interpret it accurately. By tagging data with relevant information—timeframes, sources, subject areas—you can make it easier to manage, search, and use for AI-driven insights. Metadata also helps AI understand how to interpret or make recommendations based on the context of the data.

4. Adopt a consistent governance framework: Once you’ve established governance to a subset, apply the same framework to the rest. This ensures consistency and scalability, making it easier to introduce new datasets or expand AI applications.

5. Monitor and adjust: Data governance is not a “set-and-forget” strategy. Regularly review your processes and adjust as your business evolves. AI applications may demand different types of data or more frequent access to real-time information, so your governance model should adapt accordingly.

The benefits of prioritising data.

When data is prioritised and governed correctly, organisations experience a shift. They move from reactive problem-solving to proactive planning. Data starts to drive business strategy, leading to more informed decisions and greater alignment between business and technology. Teams know what’s happening now, can predict what’s coming, and can feel confident in their data’s accuracy.

Moreover, when data strategies are aligned with business goals, everyone wins. It’s a calmer, more coordinated way of working, where both people and AI have a clear understanding of the data’s structure and purpose. When data strategies are disconnected from business strategies, even the most cutting-edge data platform will stagnate. It’s like a ship without a rudder—data solutions veer off course, while business leaders tack on in a different direction. When data is prioritised and aligned with where the business is heading, organisations are more likely to stay focused and calm, even during times of disruption or rapid technological change.

What C-Level executives need to know about AI and data.

For CDOs, CTOs, or other executives responsible for data, understanding the strategic, tactical, and practical approaches to data management is critical.

Strategically, executives should ensure their organisation is on a modern data platform, should engage in proactive conversations about the implications of AI (how might AI disrupt your industry, how can you stay ahead of those changes in the next 12 months, three years, or even the next five?), then have a clear roadmap for how AI fits into your business strategy.

Tactically, a governance framework is non-negotiable. Even if your organisation isn’t ready to fully embrace AI, setting up the right governance principles now will pay off down the line. It’s easier to introduce AI when the underlying data is clean, secure, and well-governed.

Practically, executives should look for specific processes or areas where AI can be introduced to deliver immediate value. Whether it’s improving customer service, streamlining operations, or identifying new revenue opportunities, AI should be seen as a tool that can unlock real, measurable benefits when applied thoughtfully.