A team buys a coding assistant. Another starts using a research tool. Someone connects an agent to an internal knowledge base. Each decision can make sense on its own. The operational question is what those decisions add up to.
Which tools are becoming part of everyday work? Where do subscriptions overlap? What information is flowing into AI? Who can decide which uses to encourage, review, or restrict?
We think those questions belong in a shared operational view. That is the idea behind Themisto: an AI operations layer that helps organizations understand usage, optimize their investment, and put governance into practice.
Start with the activity, not the contract
A contract tells you what a company bought. An inventory tells you what it approved. Neither, on its own, tells you how people use AI in their work.
The useful starting point is an interaction with context: the tool involved, the application it came from, the team using it, and the policy that applies. A conversation in a browser and a coding assistant in an IDE may both be AI activity, but they represent different workflows and different decisions.
When those interactions connect over time, the picture becomes more useful. Teams can distinguish a short experiment from recurring use. An IT owner can notice a new tool. A procurement owner can bring adoption evidence into a renewal conversation.
One signal can answer several questions
Consider an employee using a coding assistant. That activity can inform more than a security review.
- Operations can understand which development tools are actually in use.
- Engineering leadership can see how adoption changes after a rollout.
- Finance can review whether multiple paid assistants serve the same people.
- Security can apply a policy when a request includes sensitive information.
These teams need different views of the same underlying activity. Sharing the context helps them make decisions together, instead of reconciling separate spreadsheets after the fact.
Separate observation from interpretation
Activity is evidence of use. It is not automatically evidence of value.
A high request count might reflect a productive workflow, an extended debugging session, or an automated process. Low usage might identify a forgotten license, but it could also reflect a specialized tool used for an important monthly task.
A good operational review connects usage to a business question. Before renewing a tool, ask whether the intended users still need it. Before expanding a rollout, ask what successful teams are doing differently. Before restricting an application, ask which workflow needs an approved alternative.
The goal is better decisions, with enough context to explain them.
Bring governance into the same conversation
Governance becomes more practical when it is connected to how work happens. An approved-tool policy needs an accurate tool inventory. An exception needs an owner and a reason. A sensitive-data event is easier to investigate when the originating application is known.
This is why security belongs inside the broader AI operations picture. The same infrastructure that helps apply a policy can also help an organization understand adoption and make better spending decisions.
Observe. Understand. Optimize. Govern.
These are the four jobs we are building Themisto around.
Observe AI activity across the organization. Connect it to the tools and workflows that give it meaning. Use that understanding to improve decisions about investment and adoption. Apply the organization’s policies where the activity happens.
As AI becomes part of more applications, a shared operational view becomes a useful foundation for the next decision and the one after that.
Make the bigger picture yours.
Explore how Themisto connects AI usage, spend, adoption, and governance across your organization.
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