Anyone getting started with AI quickly looks at the application. Which copilot are we going to use? Can we build our own AI assistant? And where will AI agents soon be able to take over work?

Logical questions. But they are actually starting at the wrong end. Because before AI can do anything useful, quite a lot needs to be in order behind the scenes. The right data must be available, systems must be able to communicate with each other, and you need to know who gets access to which information. If that is not properly arranged, a better AI model is not going to solve the problem.

Good AI therefore does not start with AI. It starts with the data and systems that AI must be able to trust.

A good AI model does not fix bad data

You can choose the best AI model available. If the information behind it is outdated, fragmented or poorly accessible, the results will continue to disappoint. After all, the model can only work with the information it is given.

That may sound obvious, but in many organisations the reality is stubborn. Data is scattered across databases, cloud environments and various applications. Documents are located in yet other places. Over the years, systems have been linked together or, conversely, function largely independently of each other. And it is far from always clear which information is up-to-date or where the most reliable version is.

Putting AI on top of such an environment simply carries those problems over. AI does not make bad data any better and will not automatically solve a fragmented IT landscape.

The correct data must also be accessible

Data quality is therefore important, but good data alone is not enough. AI must also be able to access that data at the moment it is needed.

Here it comes Data Engineering & Integration looking ahead. How do you unlock information from different sources? How do you ensure that systems exchange data with each other in a usable way? And how do you prevent having to build all kinds of separate connections from scratch for every new AI application?

Those questions are becoming more important as AI plays a greater role in daily processes. An AI assistant that only uses information from a single document environment is different from an application that combines data from multiple enterprise systems to support an employee. In that case, it must be clear behind the scenes where the information comes from, how it is processed, and whether that information is still up to date.

Making AI agents increases that dependency even further

With AI agents, this becomes even more concrete. A AI agent doesn't just answer a question, but can also carry out tasks independently. For example, gathering information, checking data, starting a process or performing an action in a business system.

For that to happen, such an agent must be able to access the right systems. For example, an agent tasked with handling a customer query will not get much benefit from access to documents alone if relevant information is also held in the CRM or ticketing system. And when the agent subsequently needs to make a change or initiate a follow-up action, a secure connection to those systems is essential.

An AI application certainly does not need access to everything for this. In fact, you want to determine in advance which information is needed, which systems are accessible, and which actions AI is allowed to perform autonomously.

Security and governance must be included from the start

This automatically raises questions about security and governance. Who is allowed to see what information? Is an AI application allowed to use the same information as any employee? What happens to sensitive data? And what actions is an AI agent permitted to perform autonomously?

Those are not questions to be answered only when an AI application is ready for use. They belong at the very beginning of the process. Certainly when AI is being integrated ever more deeply into existing processes and gaining access to commercially sensitive information.

Reliable AI therefore doesn't just mean that an answer is factually correct. It also means that it is clear where information comes from, that access rights are respected and that the organisation maintains control over what AI is and isn't allowed to do.

Look beyond the AI application

The discussion about AI understandably often focuses on what employees will soon see on their screen. But a large part of the success is determined by what happens behind the scenes.

Search, Data Engineering, Integration, Security and AI are increasingly intertwining. Enterprise Search can ensure that AI finds relevant information from various sources. Data Engineering and Integration ensure that data is available and usable. Security and governance then determine the boundaries within which AI is permitted to work with it.

That requires a broader perspective than simply choosing an AI model or tool. Starting a pilot can be relatively straightforward. Ensuring that AI subsequently works reliably with the organisation's data and processes demands more.

At PuurData, we therefore look beyond the AI application itself. We help organisations make data accessible, connect systems and information sources, and set up the environment in such a way that AI can be applied reliably. In doing so, we build on our expertise in search, data and security, and ensure that AI is not treated as a standalone technology superimposed on the organisation.

Is your organisation ready for AI?

Good AI therefore starts even before the first model is chosen. With reliable data, accessible systems, good integrations and clear agreements on security and governance.

Do you want to know what it takes to implement AI reliably and scalably within your organisation? Get Contact Get in touch with PuurData and find out how we can help you get the data, systems and technology behind AI in order.