According to an MIT study, in 2024 more than nine out of ten AI initiatives failed to generate any new revenue. That is a 30- to 40-billion-dollar problem, and because AI is now woven into the full fabric of the global economy, those failures ripple outward. When AI does not deliver results at scale, productivity stalls, growth slows, and entire industries begin to misallocate capital. In other words, AI's failure to produce real-world results is not just a business problem. It is a macroeconomic threat.
Why is this happening? The reason is clear: most organizations are still treating AI as an accessory -- something to bolt onto existing workflows, automate a task here or there, or make a legacy process run a little faster. That mindset guarantees limited returns.
The deeper issue is that AI is being forced to operate in the dark. Even the most capable model can only perform as well as the context it is given, and today that context is shallow, fragmented, and trapped inside documents, emails, meetings, and individual minds. Companies want intelligent outcomes while giving AI only a keyhole view of how they actually function.
KontextOS solves this foundational problem by placing AI at the center of operations rather than at the edges. It does this by transforming an organization's knowledge -- its training, policies, plans, workflows, conversations, and data -- into a coherent, continuously updated context layer, delivered through the most universal platform available: the web browser. By operating where people already work, KontextOS requires no specialized hardware or proprietary client software. This context layer becomes the operating system that mediates AI-driven decisions, learning, and action. (Want the architectural rationale behind KontextOS? Read about our architecture.)
As part of its near-term roadmap, KontextOS will natively integrate MCP (Model Context Protocol) as a first-class interface for delivering governed context to AI models. MCP standardizes how models access external resources and tools at runtime; KontextOS determines what context is exposed, how it is structured, and who controls it. Together, they ensure that models remain interchangeable while context remains durable, portable, and owned by the organization. (See the planned MCP integration for details.)
In other words: organizations do not just need AI. They need the missing infrastructure that makes AI effective. They need a contextual operating system.
They need KontextOS.
But what about the titans of the AI world?
They need KontextOS too. They all face the same constraint: without deep, accurate, organization-specific context, their intelligence is like a world-class business guru who knows everything except how your company actually works. Ask that guru a question and the first thing they will say is, "Tell me more. Give me the details." That is because no matter how advanced AI becomes, it still needs eyes and ears on the ground. Your eyes. Your ears. Only you can provide that.
This explains why AI providers are developing their own internal versions of contextual systems -- such as OpenAI's "company knowledge." But these systems come with a hidden cost: they require organizations to structure their most important operational data inside a single vendor's ecosystem -- inside a walled garden. In a rapidly shifting AI landscape, that creates a serious strategic vulnerability. If another provider -- be it Google, Anthropic, Meta, or an open-source model -- surpasses OpenAI on capability, price, privacy, or speed, companies that centralized everything inside one platform will face an expensive, time-consuming rebuild of their entire knowledge structure just to switch models.
Put simply: binding your institutional memory to a single provider means that every technological leap in the industry forces you to start over.
KontextOS eliminates that risk. It gives organizations an independent, portable context layer that works with any model. Use OpenAI today, Gemini tomorrow, and an in-house model next year, without losing your workflows, your preferences, or your accumulated knowledge. KontextOS lets your AI -- whichever AI you choose -- run your proprietary programs, learn from your protected datasets, and access the domain knowledge no outside vendor should ever control.
That is why organizations need KontextOS, and why even the biggest AI companies would benefit from it: they can build the intelligence, but only you can own and preserve the context.