Is your organisation ready for AI? The 4 prerequisites to get started.

AI is on the agenda at virtually every organisation. But how do you know whether your organisation is truly ready to start a structured rollout of AI initiatives? There are four prerequisites you need to have in place in order to unlock the full promise of AI.

1. AI-ready data – the foundation for reliable insights

The output of any AI model stands or falls with the quantity and quality of the data it uses. Many companies have data locked away in their systems, but that data is not exposed, not structured, or not properly connected. AI-ready data means that your data is complete, clean, and well connected.

Think of cleaning up date formats (such as converting Julian to Gregorian), linking sales orders to customer data, and building in automatic quality checks. This way, you can be confident that the outcomes of your AI initiatives are reliable and that everyone is working from the same version of the truth. At Cadran we have developed Analytics4NS and Analytics4JDE to help you quickly meet this prerequisite..

2. The right infrastructure – stable, scalable, and secure

A strong foundation requires the right technical infrastructure. From JD Edwards or NetSuite you connect multiple data sources such as Excel, other ERP systems, or operational data.

By using modern data platform technologies like Databricks, you can bring all these sources together without putting a strain on your ERP. With MLOps, you can then build, manage, monitor, and safely evolve your machine learning models. This ensures that your IT infrastructure grows in step with your organisation’s AI ambitions.

3. Insights via BI – knowing where AI creates value

A BI tool with solid reporting is crucial to get started. It helps you identify opportunities before you begin, and you can use it to understand the output of your AI models. With tools like Tableau or Power BI, you gain insight into margins, inventory, service levels, or bottlenecks in the supply chain.

These insights help you determine which use cases are the most valuable and feasible. Think of inventory optimisation, predictive maintenance, or commercial analysis. AI builds on BI – not the other way around.

4. AI ambassadors – support and ownership

AI projects need someone to take the lead. An internal AI ambassador creates a bridge between business and IT and understands where the opportunities lie. This person has the mandate to drive change, understands the value of data and AI, and knows how to enthuse colleagues and bring them together. Without such an ambassador, AI often remains an IT initiative that never really takes hold in the organisation.

Getting started – from idea to first results

Once the four prerequisites are in place, you can move on to the next step: a benefit assessment. Use a benefit assessment to determine where the value lies: high impact, relatively easy to implement.