Almost everyone is using AI to some degree in their daily lives, from habitually using Google, which now yields the new AI results experience when searching, to power users building entire autonomous AI workforces.
When it comes to companies, some conservative organizations are bearish on AI, mitigating risk by avoiding it until it’s mature enough to meet their standards. Others have been early adopters for years, with some even going as far as implementing token leaderboards in their companies (see ‘Tokenmaxxing’).
In any case, a prompt entered into an AI chat will usually yield a result—at least eventually. AI is generally designed to produce what I call a “dust-your-hands-off” result: an answer that appears to finish the task or dismiss the problem, even when it may not fully account for the context behind it.
At this point in time, AI can be highly effective for small, direct prompts where the desired outcome is clear and the consequences of error are low. That doesn’t mean its answers should always be taken as gospel, even if the AI digs its feet in and remains steadfast and confident in its answer.
Where this becomes problematic is that these black boxes can produce convincing answers without fully understanding your organization, its goals or the circumstances surrounding the question. The speed and confidence of the response can create the impression that the problem has been properly understood when it hasn’t (see ‘Fast answers can create the illusion of understanding’). Fast answers may be economically better for AI providers, given the costs of running data centres, but commercially worse for you.
Imagine this happens once or twice in executive meetings.
Now imagine it happens hundreds of times a day, across every level of your organization.
Does this mean that you shouldn’t use—or should stop using—AI in your organization? Absolutely not. Avoiding it altogether could put you at a commercial disadvantage. The greater risk is adopting AI without giving it access to the shared organizational knowledge it needs to produce relevant, consistent answers.
An AI Context is a reusable package of trusted information and instructions that helps an AI understand your organization, its terminology, processes and preferred ways of working.
Without shared Contexts, people across the same organization may ask similar questions and receive wildly different answers based on how much background they happen to include in each prompt. AI adoption without shared Context creates organizational inconsistency. Contexts turn scattered knowledge into reusable infrastructure for better AI work.
A shared AI Context system can help mitigate poor or incorrect answers by giving people and AI tools a consistent foundation to work from.
AI Contexts can:
Give AI trusted information about your company, people, processes, competitors, workflows and ways of working.
Help teams receive more consistent answers without rebuilding the same background in every prompt.
Create a reusable source of organizational knowledge that can be maintained and shared.
Reduce repeated input and, when combined with techniques such as context caching, potentially reduce token usage and associated costs. Some implementations have reported savings of 30–40% using context caching, reusable snippets and structured prompts.
Using AI effectively across an organization isn’t simply a matter of giving everyone access to the tools. It means giving those tools the right foundation so people can work from a shared understanding rather than starting from scratch every time.
That’s the problem Oi is here to help companies solve: turning organizational knowledge into reusable Contexts that can help people get more relevant and consistent results wherever they use AI. If you’d like to see how it works, please reach out.