AI becomes more useful to a business when it can work with the company’s own knowledge: its products, processes, customer information, and expertise. But simply owning data does not create an advantage. That knowledge must be accessible, trustworthy, properly governed, and connected to a real task—and even then, it can create an opportunity for differentiation, not guarantee a lasting competitive moat.
Why company knowledge changes what AI can do
A general-purpose AI model does not automatically know the current details of a particular company: how its products differ, which internal procedure applies, or what a team has learned from past work. Supplying relevant context can help it produce answers that fit the organization rather than generic responses. IBM’s Shobhit Varshney described the gap this way: “What they don’t have access to is your enterprise data. That piece of the puzzle is missing.” IBM explains the role of proprietary data in AI.
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Forrester’s public position is that proprietary knowledge and expertise can be put to work through AI models, applications, and agents. That is a strategic thesis, not proof that data ownership alone creates durable advantage. Competitors may have similar tools; what matters is whether a company can put its own knowledge to work effectively and keep doing so as its business changes. Forrester’s public summary.
How can a company use its own data and knowledge with AI?
The main options differ in where the company context comes from: the prompt, a knowledge source the model can consult, or examples used to adapt the model. IBM describes prompting, retrieval augmented generation (RAG), and fine-tuning as distinct approaches—not interchangeable settings.
#1 Best Overall
| Approach | How company context is used | Often fits | Tradeoffs to consider |
|---|---|---|---|
| Prompting | Relevant information is included in each request. | Lower-volume or generic tasks where the necessary context can reasonably be supplied each time. | People or systems must provide suitable context for each request; this may be inconvenient for repeated work. |
| Retrieval augmented generation (RAG) | The model retrieves relevant material from a connected proprietary information source while responding. | Tasks that need answers grounded in information that changes and should be looked up when needed. | Retrieval can improve accuracy and reduce hallucinations, but does not eliminate them; it can add response time. RAG does not permanently change the model’s parameters. |
| Fine-tuning | Additional examples are used to change model parameters for a specialized task. | A focused use case that needs consistent specialized behavior. | IBM describes more upfront investment than prompting or RAG. Fine-tuning is not a substitute for keeping frequently changing reference information current. |
IBM’s overview of these approaches does not set a universal winner. The choice depends on the work: how often source material changes, whether the task requires factual lookup or specialized behavior, what access restrictions apply, and how much response time is acceptable. Plan how to evaluate results in the actual workflow rather than selecting a method based on the AI label alone.
What has to be ready before company knowledge can help
Data can only ground an AI response if the right information can be found and used under appropriate permissions. Fragmented sources, stale material, inconsistent records, or unclear access rules can undermine an otherwise capable system. Governance and data readiness are therefore part of the AI project, not cleanup to postpone until after launch.
Rank #2
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- A Guide to the Project Management Body of Knowledge (PMBOK Guide) – Seventh Edition and The Standard for Project Management (ENGLISH)
A Hewlett Packard Enterprise (HPE) commissioned survey, published in April 2024, covered more than 2,000 IT leaders in 14 countries. HPE reported that 7% of surveyed organizations could run real-time data pushes and pulls, while 26% had data-governance models in place and could run advanced analytics. These figures describe that survey population, not all businesses. HPE’s Sylvia Hooks said the results showed both appetite for AI and “very real blind spots” that could stall progress. HPE’s survey release.
- Find the source of truth: identify which systems hold the current, authoritative information for the task.
- Check access: preserve permissions so an AI system does not expose information to people who could not otherwise see it.
- Assess quality and freshness: decide how incorrect, duplicated, or outdated material will be identified and corrected.
- Define success and evaluation: test whether answers are accurate and useful for the workflow, and monitor performance as sources or processes change.
- Plan adoption: involve the people who will use the system, provide support, and adapt the workflow rather than assuming the technology will change work on its own.
OpenAI’s 2025 report identifies organizational context, data readiness, continuous evaluation, reusable workflows, executive sponsorship, and change management as patterns in enterprise AI adoption. These are useful considerations, not a prescription that every company needs the same architecture. OpenAI’s State of Enterprise AI report.
What the reported productivity figures do—and do not—show
OpenAI’s 2025 report describes a survey base of 9,000 workers across almost 100 enterprises. In that report, 75% of surveyed workers said AI improved the speed or quality of their output. ChatGPT Enterprise users attributed 40–60 minutes saved per active day to AI use. These are survey findings and user-attributed outcomes reported by OpenAI, not a guarantee of time savings for any particular company. Results depend on the task, the quality of the implementation, and whether the saved time translates into better work or other business value. OpenAI’s report and methodology context.
What Tapestry’s internal knowledge assistant illustrates
AWS describes how Tapestry, the global retailer behind Coach, Kate Spade New York, and Stuart Weitzman, built an internal knowledge-management system to make information spread across business units and geographies easier for employees to query through a chatbot. AWS says the build, test, and deployment took four months; approximately 300 people across six teams used the system. It used single sign-on, and its knowledge base was automatically updated as new information was added. AWS reports that it reduced employee search time and the burden on subject-matter experts answering repetitive questions. AWS’s Tapestry case study.
This is a vendor-published implementation example, not an independent controlled study. It illustrates how a company can connect internal knowledge to a specific employee task; it does not establish independently measured productivity gains or financial return for Tapestry or other businesses.
When company knowledge can become an advantage
The useful question is not whether a business has proprietary data, but whether it can reliably apply that knowledge where it matters. A company may build a more relevant support experience, help employees locate procedures faster, or apply specialized expertise consistently. Those possibilities depend on fit between the information, the AI approach, and the workflow.
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AI capabilities alone are not a moat, and proprietary data does not guarantee one. The strategic case is strongest when a company has valuable knowledge that is difficult to reproduce, can make it usable without compromising quality or permissions, and can improve the system through evaluation and adoption. As OpenAI Chief Economist Ronnie Chatterji put it, “the next phase of enterprise AI will be shaped by stronger performance on economically valuable tasks, better understanding of organizational context, and a shift from asking models for outputs to delegating complex, multi-step workflows.” OpenAI’s 2025 report.
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