AI-Ready Master Data Starts with Better Business Participation

Artificial intelligence has moved rapidly up the business agenda.

Organisations are exploring how AI can improve customer service, accelerate decision-making, automate routine work and uncover insights from growing volumes of business data. Yet there is a fundamental issue that can easily be overlooked amid the enthusiasm for new technology.

AI is only as reliable as the data on which it depends.

If the underlying data is incomplete, inconsistent or out of date, AI will not solve the problem. It may simply process poor-quality data faster and at a much greater scale.

This is one reason why data quality and data governance have become increasingly important parts of the AI conversation. Gartner has warned that many organisations do not yet have the right data-management practices for AI and has predicted that a significant proportion of AI projects unsupported by AI-ready data will be abandoned.

The challenge is not only technical. Organisations also need to improve the business processes through which data is created, reviewed, corrected and maintained.

AI ambitions depend on trusted data

The term “AI-ready data” covers a wide range of requirements. Depending on the use case, organisations may need to consider data quality, completeness, context, lineage, metadata, privacy, security and governance. Different AI models will require different combinations of structured and unstructured data.

Master data is only one part of that wider picture, but it is an important part.

Master data describes the core entities on which business processes depend: customers, suppliers, materials, products, employees, cost centres, profit centres, general-ledger accounts and other organisational structures.

When these records are inaccurate, the consequences already appear throughout day-to-day operations. A supplier may be set up with incomplete payment information. A material may be classified incorrectly. A customer record may contain duplicate or outdated details. A cost centre may be assigned to the wrong hierarchy. An employee record may no longer reflect the person’s current role or organisational position.

Poor-quality data creates inefficiency even before AI enters the picture. It leads to manual corrections, delayed transactions, reporting inconsistencies and additional work for operational teams.

As organisations make greater use of AI, the importance of trusted master data increases. AI tools need dependable information if they are going to support business users with useful recommendations, identify meaningful patterns or help automate decisions.

Master data governance depends on business users

It is tempting to treat master data governance as the responsibility of a central data-management team.

In practice, the central team rarely has all the information needed to make every decision.

A request to create or change a supplier may need input from procurement, finance, tax, risk and the business owner. A customer request may need confirmation from sales, credit control or compliance teams. A material-master change may require the knowledge of product specialists, operations teams or local business units. A new cost centre may need review by finance and controlling.

The central data team can define governance rules, coordinate the process and maintain oversight. However, many decisions still rely on people across the business.

These people are often not regular users of the master data system. They may only need to review a request occasionally. They may have competing priorities and limited time. They may receive a notification by email but postpone logging into a specialist application until later.

This creates a familiar bottleneck: the technology is capable of managing the process, but the process still depends on people responding.

Why master data requests slow down

Master data requests do not usually become delayed because someone has made a deliberate decision to block progress.

More often, the delay is caused by friction.

A request may be routed to an approver who does not regularly access SAP. The notification may arrive in an already crowded email inbox. The approver may not immediately understand what has changed or why their input is required. They may need to open a separate application, authenticate again and navigate through several screens before reaching the relevant information.

If the approver has a question, the process may move outside the system into an email exchange or a separate chat message. If additional information is required, someone may create a spreadsheet to collect responses. If the request becomes urgent, the data team may spend time chasing individuals manually.

None of these activities is unusual. Taken together, however, they slow down the process and make it harder to maintain a clear audit trail.

The result is not simply an administrative inconvenience. Delayed master data decisions can hold back wider business activity.

A supplier cannot be used efficiently until the relevant record is complete and approved. A product launch may be affected if material data is missing. Reporting may be delayed if financial master data is incomplete. A downstream transaction may fail or require correction if a record is inaccurate.

The quality of master data depends partly on the quality of the approval process around it.