The answer isn’t about technology – it’s about data. In this blog post, we’ll explore why a structured approach to data is not only useful, but absolutely essential for businesses aiming to succeed with AI implementation, and how you can ensure your organisation is well prepared.
AI must be the end product.
There are significant potential gains from adopting AI. But before you get that far, you need to take control of what gives AI its value: your own data. It’s not just about having data, but about having it collected, structured and accessible in a way that makes it usable.
We see many organisations starting at the wrong end. They begin with tools and models, and hit a wall when it turns out that:
- the data is scattered across different systems and Excel sheets,
- no one knows who “owns” the data,
- and the quality is too poor to be used for anything meaningful.
The result is that solutions don’t work as intended, employees lose trust, and initiatives end up shelved.
Must solve a specific problem
You get the greatest impact when AI is used as a natural part of the organisation’s workflows. But to achieve that, the data must be accessible, understandable and quality-assured. By structuring the data in a modern data platform, you achieve exactly that.
What does that mean in practice?
Collecting and structuring data doesn’t mean you have to invest millions in new technology. It means:
- knowing what data exists and where it is,
- cleaning up data that is outdated, duplicated or incomplete,
- gathering it in one place where it can be used – by both people and machines.
But perhaps most importantly, doing it in the right order.
Right from the start
When we help organisations with their AI strategy, our most important advice is: Start by gaining control of the data foundation. It’s time-consuming, complex and expensive to change structure, fix errors and improve data quality after an AI system has been implemented.
Here’s how to get started with collecting and structuring useful data:
- Identify the opportunities for AI use in your organisation.
- Based on the Opportunity Analysis, you’ll know what data is needed for the prioritised use cases.
- With this list as a foundation, the work of collecting and structuring the data in a data platform can begin.
Smooth technology adoption
Organisations that have control of their own data get started with new technology faster. They avoid spending time and resources cleaning up afterwards – and can instead focus on experimenting, learning and scaling.
We’re now seeing a new generation of AI tools that can perform tasks on behalf of employees – so-called AI agents. They can automate routines, draft documents, retrieve information, send emails and more. But such tools only work well if they have access to the right and relevant data.
That’s why clean data is not just a technical necessity, but a strategic advantage. You’re not just enabling AI – you’re making the organisation more agile, more willing to learn, and better equipped to adopt new technology.
By investing in cleaning, collecting and making data accessible today, you’re building a foundation that makes future innovations both easier and safer.
The road to successful AI doesn’t start with algorithms. It starts with data.