Enterprises are spending billions on AI, but the returns don't match the investment. A recent Bain report found that nearly 40% of companies that measured their AI cost savings achieved less than a 10% return, despite targeting 11–20%. Even Uber has been publicly wrestling with how to justify its AI spend. Across industries, the pattern is the same - ambitious pilots, disappointing scale.
The standard explanations point to model capability, budget, or change management. Our diagnosis is more specific - this is a context problem. AI doesn't have organizational memory. It doesn’t know how decisions actually get made, who the real approvers are, or what the workarounds look like. Without that, AI agents can't be useful regardless of how capable the underlying model is.
Enterprises have a missing data layer
Look at what's actually happening inside most large organizations right now. On one floor, a tangle of agents bumping into each other, duplicating work, routing requests in circles. On another, nothing - because no one could figure out what to build or where to start. The chaos and the paralysis share the same root cause.
Every enterprise has work that's done entirely by humans, work that could be split between humans and agents, and work that agents could own outright. Figuring out that split and building agents that can actually execute their piece of it requires knowing how work moves through the organization in the first place. Most companies don't have that knowledge compiled in one place or system.
Enterprise systems hold a lot of information. CRMs track deal stages, ERPs log financial transactions, and HCMs store org charts and compensation data. AI has also gotten good at making unstructured data (e.g., Slack messages, emails, documents, meeting transcripts) searchable and workable. What none of these capture is how work actually happens - the approval routed through Slack because the formal system takes three days, the regional spreadsheet tracking vendor commitments the ERP can't handle, the reconciliation process a finance team built in parallel because the last transformation missed their requirements entirely.
That missing layer is organizational memory - what agents need to do real work - and until now it hasn't existed in any system of record. At Klarity, we've built the Company Brain, a continuously updated map of how work actually moves through an organization, built through direct observation rather than interviews or process documentation. But mapping how work happens is only part of the equation. Buying AI tools is the easy 20%. The other 80% is change management - getting that knowledge into the hands of the people and systems that need it, in a form they can act on. That's the part nobody has built infrastructure for. Today, we're announcing four new products that do exactly that, extending the Company Brain across the enterprise.
What we're building
Our Generative Dashboards connect directly to the live process index and surface answers to questions most organizations have never been able to answer: where the real bottlenecks are, which policies are actually being followed, where time is going, where shadow IT is showing up. Every data point drills back to its source and updates continuously as the underlying work changes.
The Klarity MCP connects the Company Brain to the AI tools your teams already use, including Claude, ChatGPT, Copilot, Gemini, and others. Once running, the model figures out when to use it. An employee asking how a process actually works gets an answer grounded in organizational reality. A leader asking whether a new software purchase duplicates something already in use gets a real answer. No one has to learn a new tool.
Our Skills Factory generates the instruction sets agents need to execute specific tasks, grounded in how your organization actually handles that work rather than generic best practices. It surfaces which skills will have the most impact, shows estimated ROI before any build decision, and produces the skill files in minutes. One of our enterprise customers identified 76 buildable agents and had several running in production within a day.
The Agent Development Kit takes your teams from a business objective to a production-ready agent, delivering current-state analysis, agent recommendations mapped to your actual tech stack, governance documentation, and the code itself. Most teams building agents today do so without a standardized methodology, but this provides one.
If your organization is ready to give agents the context they need to actually work, reach out for a demo to see how Klarity maps your enterprise and turns it into infrastructure your AI can use.
