AI is more useful when it can work with the language, information and workflows specific to your organisation. That does not mean proprietary data guarantees better results: the data must be usable, governed and matched to the task. In practice, organisations can add context to existing models through prompts, retrieval or fine-tuning rather than building a foundation model from scratch.
What does it mean for AI to know your business?
A general-purpose model may know common facts but lack your product names, internal definitions, service policies and operating procedures. Michael Choie, Senior Managing Consultant for AI and Analytics at IBM Consulting, put it this way: “Every company has its own language,” (IBM Think).
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That context can make the difference between a plausible answer and one that fits your organisation. For example, a customer-service system answering questions about a product needs accurate documentation and an understanding of the company’s terminology. Shobhit Varshney, Vice President and Senior Partner, Americas AI Leader at IBM Consulting, said: “What they don’t have access to is your enterprise data. That piece of the puzzle is missing.” (IBM Think)
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1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsThe strategic case is reflected in executive surveys, but these figures describe respondents’ views and reported experience—not proof that proprietary data caused superior business performance. In its 2025 study, IBM’s Institute for Business Value found that 72% of surveyed CEOs viewed proprietary data as key to unlocking generative AI value, and 68% identified integrated, enterprise-wide data architecture as critical for cross-functional collaboration (IBM, 6 May 2025). Gary Cohn, IBM vice chairman, wrote in the study’s foreword: “When the business environment is uncertain, using AI and your enterprise data to identify where you have leverage is a competitive advantage.”
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Why owning data is not enough
Data possession and data readiness are different things. Information can be inaccessible, incomplete, inaccurate, inconsistent or hard to interpret—especially when it is unstructured, such as documents, emails, audio or images.
In IBM’s 2025 survey of 1,700 senior data and analytics leaders across 27 geographies and 19 industries, fielded from July to September, 84% said their unique data products had already provided significant competitive advantages. Yet only 26% were confident their organisation could use unstructured data in a way that delivers business value. These are self-reported views, not an independent measurement of business outcomes (IBM, 13 November 2025).
Integration is another constraint. In the 2025 IBM CEO study, 50% of respondents said the pace of recent investment had left them with disconnected, piecemeal technology. A model connected to fragmented systems may struggle to draw on the right information or support work across teams (IBM, 6 May 2025).
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Prompt engineering, retrieval augmented generation (RAG) and fine-tuning add context in different ways. Choose according to how often the task occurs, whether facts must be current, how specific the desired behaviour is, and what effort the organisation can support. IBM’s descriptions provide qualitative guidance, not a neutral cross-vendor cost or performance benchmark (IBM Think).
| Approach | How context is supplied | Best fit | Key trade-off |
|---|---|---|---|
| Prompt engineering | Include the relevant information in each request. | Lower-volume, general tasks where it is practical to attach the context every time—for example, summarising a call transcript supplied with the prompt. | Information must be selected and provided for each request; this is less convenient for frequent tasks or large bodies of material. |
| Retrieval augmented generation (RAG) | Connect the model to a proprietary information source so it can retrieve relevant material when answering. | Tasks that need source material at response time, such as a customer-service chatbot looking up product documentation. | The information source must be prepared and maintained so retrieval can surface useful material. |
| Fine-tuning | Use additional data to adjust some model parameters and adapt its behaviour. | A specialised, repeatable task such as insurance-claim processing, where a more durable change in task behaviour is wanted. | IBM describes it as requiring more upfront investment than prompting or RAG; changing the underlying data or task may require further work. |
These methods are not interchangeable. If an answer needs current information from changing documents, retrieval is a more direct fit than relying on behaviour learned during fine-tuning. If a task is occasional and its context is short, supplying that context in the prompt may be simpler. Fine-tuning is aimed at adapting how a model behaves for a specialised task, rather than serving as a substitute for retrieving fresh facts.
Should business data be used to train external AI models?
Using internal information to ground responses and allowing an external provider to use that information to train its models are distinct data-use questions. A company can connect a model to its own documents for retrieval without treating that as permission for the provider to train on those documents; the actual terms and system design need to be checked.
In the UK Business Data Survey 2026, 73% of UK businesses handling digitised data said they would feel uncomfortable with their business-owned data being used to train external AI models. This is a UK survey measure of reported comfort, not a universal attitude or a finding about every form of AI data access (UK Department for Science, Innovation and Technology, UK Business Data Survey 2026).
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Before connecting business information to an AI system, establish what data it will access, whether the provider may retain or reuse it, who can see the outputs, and how sensitive information is protected. Rights, permissions and trust are part of whether the data can be used responsibly—not details to settle after deployment.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Where outside data fits
Knowing your own business does not mean ignoring the world beyond it. Internal data can explain your processes and customers; external data may add market, technical or other context. In a survey conducted in 2022–23 and reported in a 2025 OECD, BCG and INSEAD study, roughly 78% of sampled manufacturing and ICT enterprises said they collected data internally from processes and staff, while 75% reported collecting data from customers and users. These are historical, sector-specific survey findings, and the report notes that external data can supplement internal sources (OECD, BCG and INSEAD, 2025).
A practical way to decide what to do first
- Choose a concrete task. Define the users, desired outcome and what counts as a correct, useful response. A narrow workflow is easier to assess than a broad goal such as “use AI across the company.”
- Identify the context the task needs. Separate stable instructions and terminology from facts that change, such as current product details or policies. Decide which information is authoritative and who owns it.
- Check data quality and access. Confirm the material is accurate, complete enough for the task, consistent and available to the system. Resolve permissions and sensitive-data controls before connecting it.
- Match the method to the task. Use prompt context for occasional requests with manageable input, RAG when the system must look up relevant current material, and consider fine-tuning for specialised, repeatable behaviour when its added upfront investment is justified.
- Test against real cases. Check answers against authoritative sources, including cases where information is missing, contradictory or outdated. Decide how the system should show its sources, express uncertainty or hand a question to a person.
- Maintain the system. Assign responsibility for updating source material, reviewing access and monitoring failures. A model is only as dependable as the information and processes around it.
The business opportunity lies not in possessing the most data, but in making relevant knowledge usable for a defined job. Ed Lovely, IBM vice president and chief data officer, said: “Organizations that get this right won’t just improve their AI, they’ll transform how they operate, make faster decisions, adapt to change more quickly and gain a competitive edge.” That is an attributed IBM view; whether it holds for a particular organisation depends on its data, implementation and outcomes.
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