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AI can be more useful when it understands a company’s products, terminology, processes and customer context. That does not mean proprietary data automatically produces better results or a lasting competitive advantage: the data must be usable, governed and matched to the right AI method. Most organisations can adapt existing models to their needs rather than build a foundation model from scratch.
Why a company’s own context can matter to AI
A general-purpose AI model may know broad facts but lack the details that shape a useful answer in a particular business: how that company names its services, handles a workflow, or explains a product. IBM Consulting’s Michael Choie puts it simply: “Every company has its own language,” (IBM Think). A customer-service assistant, for example, needs access to current product documentation to answer product-specific questions reliably.
Context can come from internal documents and records, as well as from staff knowledge and customer interactions. In a survey conducted in 2022–23, about 78% of sampled manufacturing and ICT enterprises reported collecting data internally from processes and staff, while 75% reported collecting it from customers and users. Those are historical findings summarized by the OECD, BCG and INSEAD in 2025, not a picture of every sector today; the report also notes that external data can supplement internal sources (OECD, BCG and INSEAD).
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Using company data to inform an AI response is not the same as allowing an external AI provider to train its model on that data. The distinction matters for privacy, confidentiality, contractual rights and governance. In the UK Business Data Survey 2026, 73% of businesses handling digitised data said they would feel uncomfortable with their business-owned data being used to train external AI models. That finding describes surveyed UK businesses, not attitudes everywhere (UK Department for Science, Innovation and Technology).
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What business leaders say—and what those figures do not prove
Recent IBM Institute for Business Value surveys show that many executives see proprietary data as strategically important, while also reporting gaps in readiness. The figures below are respondent views and experiences; they do not establish that data alone caused business performance.
| Finding | What respondents said | How to interpret it |
|---|---|---|
| CEO survey, 2025 | 72% viewed proprietary data as key to unlocking generative AI value; 68% identified integrated, enterprise-wide data architecture as critical for cross-functional collaboration. | These are surveyed CEOs’ assessments, not measured effects of a data strategy. IBM Institute for Business Value |
| CEO survey, 2025 | 50% said the pace of recent investment had left disconnected, piecemeal technology. | A reported architecture and coordination problem, not a measure of every organisation’s technology estate. IBM Institute for Business Value |
| CDO survey, fielded July–September 2025 | 84% said their unique data products had already provided significant competitive advantages; 26% were confident their organisation could use unstructured data in a way that delivers business value. | The first is respondent-reported experience; the second signals a readiness gap. The survey covered 1,700 senior data and analytics leaders across 27 geographies and 19 industries. IBM Institute for Business Value |
IBM Vice Chairman Gary Cohn wrote in the foreword to the 2025 CEO study: “When the business environment is uncertain, using AI and your enterprise data to identify where you have leverage is a competitive advantage.” That is a strategic view, not independent proof of a causal advantage (IBM Institute for Business Value).
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Three ways to give an AI system business context
Prompt context, retrieval-augmented generation (RAG) and fine-tuning solve different problems. The practical choice depends on task volume, how specific the desired behavior is, whether answers need fresh source material, and the effort available to prepare and maintain data. IBM’s descriptions are educational guidance, not a neutral cross-vendor performance or cost benchmark (IBM Think).
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Outbyte PC Repair FREERepair Windows errors before they cause bigger problemsFix Now →Outbyte Driver Updater FREEFix the driver behind crashes, sound loss and screen glitchesFind Drivers →| Method | How it works | Best fit | Key trade-off |
|---|---|---|---|
| Prompt engineering | Include relevant company information directly in each prompt. | Lower-volume or generic tasks where attaching context each time is practical, such as summarising a call transcript supplied with the request. | Repeatedly supplying context can become cumbersome at high volume; the information is provided for that request rather than made into a durable behavior change. |
| Retrieval-augmented generation (RAG) | Connect the model to a company knowledge source so it can retrieve relevant material while responding. | Questions that depend on source material that may change, such as a customer-service assistant retrieving product documentation. | The knowledge source must be prepared and maintained so the system can find appropriate information. RAG supplies context at answer time rather than changing the model’s parameters. |
| Fine-tuning | Use additional data to adjust some model parameters and adapt its behavior to a use case. | Specialized, repeatable tasks where consistent task-specific behavior is the goal; IBM gives insurance-claim processing as an example. | IBM describes it as requiring more upfront investment than prompting or RAG. It is not a substitute for providing changing facts at answer time. |
For instance, a business that needs answers grounded in its latest product specifications has a different need from one that wants a model to follow a specialized, repeatable claims workflow. The first points toward retrieval from maintained documentation; the second may call for a behavior-focused approach. A supplied transcript for an occasional summary may need no persistent connection or model adaptation.
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Why owning data is not the same as being ready to use it
Data can be present but still be difficult to apply. IBM’s 2025 CDO study identifies accessibility, completeness, integrity, accuracy and consistency as barriers. Unstructured information presents a particular challenge: only 26% of surveyed CDOs said they were confident their organisation could use it to deliver business value. IBM’s Ed Lovely, VP 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.” This is an attributed executive view, not independent validation of the outcome (IBM Institute for Business Value).
- Access: Can the system retrieve the information it is permitted to use?
- Quality: Are records accurate, complete, consistent and current enough for the task?
- Coordination: Can teams connect information across business functions instead of leaving it in disconnected systems?
- Rights and trust: Is the intended use consistent with privacy obligations, contracts, customer expectations and internal policy?
These questions apply whether a company supplies context in prompts, connects a retrieval system, or prepares data for fine-tuning. Governance should specify what information may be used, for which purpose, by which systems and under what retention or access controls.
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A practical way to decide where to start
- Choose a concrete task. Define the user, the question or workflow, and what counts as a correct, useful result. Avoid starting with “use all our data.”
- Identify the information the task needs. Separate stable instructions and terminology from facts that change, such as current product details or policies.
- Check rights, access and quality. Confirm that the intended AI use is permitted and that relevant information is accurate, complete and available to the system.
- Match the method to the need. Try prompt context for bounded, lower-volume tasks; consider RAG when responses must draw on maintained source material; assess fine-tuning for specialized, repeatable behavior.
- Evaluate against real work. Test representative questions and edge cases, inspect whether answers are supported by the intended source material, and monitor errors and changes in the underlying data.
IBM Consulting’s Shobhit Varshney describes the missing ingredient for a general model this way: “What they don’t have access to is your enterprise data. That piece of the puzzle is missing.” (IBM Think). The useful question for a business is therefore not simply whether it owns data, but whether it can give an AI system the right context safely and reliably for a clearly defined job.
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