An AI assistant can give you a convincing answer in seconds. Knowing whether you can trust it takes more work.
For businesses investing in AI, that gap matters. A model might summarise a document beautifully, yet miss that two systems define the same term differently. It might combine thousands of records without recognising that several describe the same customer, asset or counterparty.
The answer looks credible. The underlying data tells a different story.
In February 2025, Gartner predicted that, through 2026, organisations would abandon 60% of AI projects unsupported by AI-ready data. It also reported that 63% of organisations either lacked the right data management practices for AI or were unsure whether they had them. That is a forecast, rather than a measured failure rate, but it makes the investment risk clear.
Businesses are spending on models, platforms, integration and specialist teams. Protecting that investment means looking closely at the information those systems will use—and whether its meaning survives the journey from one system to another.
Consider a specialty insurer using AI to assess exposure across its portfolio.
One coverholder’s bordereau records a property’s value as “TIV”. Another uses “sum insured”. A third separates buildings and contents. Those fields may overlap, but they cannot safely be treated as interchangeable without checking their definitions and scope.
Construction and occupancy introduce more variation: different coding schemes, abbreviations and free-text descriptions. Policy wording adds another layer. One contract may include storm surge within flood cover; another may exclude it.
An experienced underwriter knows where to look and when to ask questions. An AI system needs access to those distinctions, along with instructions and checks that ensure it uses them correctly. Otherwise, it can produce an accumulation figure that looks authoritative while combining values that should never have been added together.
A bank faces a similar problem when asking AI to calculate exposure to a counterparty. Subsidiaries, trading names and legacy records must be connected correctly. Definitions such as “default” or “non-performing” must retain the context in which they apply.
These are problems of identity, meaning and context. A more capable model may help spot them, but it cannot be relied on to resolve information the organisation itself has left ambiguous.
Grounding a large language model means connecting its responses to relevant, verifiable information for the task at hand. In a business setting, that might include approved documents, governed databases, product specifications or policy wording.
Retrieval-augmented generation, or RAG, is one way to provide that information. It retrieves material for a model to use when answering a question.
But retrieval alone does not establish whether the source is accurate, current or appropriate. A system can retrieve the right document and still misinterpret a field within it. It can also retrieve conflicting definitions without knowing which applies.
That is why grounding needs more than access to information. It needs information whose meaning is clear.
A governed business vocabulary gives people and systems a common reference point.
A term can be linked to a defined concept, a persistent identifier and an accountable source. Attributes can carry explicit units, currencies and scope. Abbreviations, translations and alternative names can be mapped to the same concept where they genuinely mean the same thing.
Just as importantly, different meanings can remain separate.
“Sum insured” should not automatically become “total insured value” because the labels look similar. A definition may depend on a policy, jurisdiction, business process or reporting date. Good semantic governance preserves those distinctions.
This is where master data management and semantic standardisation support AI grounding. They help establish what something is, how it is described and which interpretation applies.
They also make verification more practical. When an answer references a particular entity, attribute or definition, a reviewer has a clearer route back to its source.
That does not make every answer correct. Models still need evaluation, appropriate access controls and human oversight. It does give them—and the people checking their work—a firmer foundation.
A pilot can hide data problems. It may use a carefully selected dataset, familiar questions and a small group of knowledgeable users.
Production is less forgiving.
Different departments bring different terminology. New suppliers introduce unfamiliar descriptions. Records change, definitions evolve and users ask questions nobody anticipated during testing.
The consequences can include incorrect analysis, repeated reconciliation and decisions that need to be revisited. There is also the harder-to-repair cost of lost confidence. Once users learn that a system can be confidently wrong, they begin checking everything—or stop using it.
Preparing data for AI means making those weaknesses visible before they become embedded in a business process.
Start with a specific use case. Identify the entities, definitions and relationships it depends on. Resolve the ambiguities that could change an answer. Establish who owns the data, how changes are approved and how quality will be monitored.
The aim is to make the data fit for the decisions the AI will support, then maintain that fitness as the system and its sources change.
Is your data ready to support AI you can trust? Discover how KOIOS Master Data can help you build a trusted data foundation for AI.
The KOIOS–PIDX collaboration offers a practical example of this approach.
The Petroleum Industry Data Dictionary, or PIDD, provides shared templates and attributes for describing products used across the oil and gas industry. PIDX describes it as a resource built through industry collaboration, giving trading partners a common basis for exchanging product information.
Through the collaboration, PIDX members can map and validate their data against PIDD using the KOIOS platform.
That matters when an operator and its suppliers describe the same component differently. Mapping local descriptions to shared concepts helps establish which items are comparable and which attributes distinguish them.
For an AI assistant supporting procurement or analysing equipment data, that creates a more dependable starting point. The system has explicit definitions to work with, reducing its reliance on interpreting each company’s shorthand.
Insurance and financial services face a comparable challenge: many organisations exchanging information about the same risks, entities and transactions, each with its own systems and terminology.
The sector changes. The need to preserve meaning remains.
Model selection deserves attention. So do integration, adoption and the business case.
Data deserves the same scrutiny.
Before approving the next phase of an AI programme, ask: can we show what this data means, where it came from and whether it is fit for this decision?
If the answer is unclear, that is a useful place to start. Resolving it can prevent an expensive system from turning an old data problem into a much faster business problem.
LLM grounding connects a model’s responses to relevant, verifiable sources, such as approved documents or governed business data. It helps make answers more relevant and easier to check. Its effectiveness depends on the quality of the sources and how accurately the system retrieves and uses them.
Grounding is the broader practice of anchoring AI responses in reliable information. Retrieval-augmented generation, or RAG, is one technique for doing this: it retrieves source material for the model to use. RAG supports grounding, but retrieval alone does not resolve inaccurate records, conflicting definitions or missing context.
Start with what you want AI to help you do. Then check that the data it will use is accurate, up to date and clear enough to interpret correctly. Can different systems recognise the same product or organisation? Do terms mean the same thing across teams and languages? Shared, standards-based definitions help resolve those differences, giving AI clearer context. Make sure you can trace the data back to its source,and ensure it is maintained as things change. That gives AI a stronger foundation and makes its answers and actions easier to check and trust.
Is your data ready to support AI you can trust? Discover how KOIOS Master Data can help you build a trusted data foundation for AI.