Every organisation wants its AI use case. Few want to talk about data quality. Yet the result of any AI tool, whether it predicts churn, scores leads or drafts customer answers, depends directly on the data it is given.
Why data comes first
AI amplifies what is already there. A lead scoring model trained on incomplete opportunity data learns the wrong patterns. A generative assistant grounded on outdated product information produces polished but wrong answers. The more convincing AI output becomes, the more dangerous poor data is.
The foundations to put in place
A clear data model. Know which system is the reference for customers, products, prices and orders, and how records relate to each other across systems.
Ownership. Every key data domain needs an owner in the business, not only in IT, who defines the rules and is accountable for quality.
Quality rules and monitoring. Mandatory fields, validation rules, duplicate management and regular quality dashboards make problems visible before they reach an AI model.
Access and privacy. Decide which data AI tools may use, for which purpose and under which conditions, in line with GDPR and your internal policies.
Start with the use case, fix the data it needs
Cleansing programmes that try to fix everything tend to lose momentum. A more practical route is to choose one AI use case and work back to the data it requires. Clean, enrich and govern that data first; the next use case will build on it.
Investing in data is less visible than launching an AI pilot. It is also what makes the difference between an impressive demo and a tool your teams can rely on.
