An estate that grows between reviews
Traditional software sprawl was at least slow: procurement saw most of it, and inventory drifted over years. AI sprawl is different. Capability appears inside tools you already own, seats are assigned in bulk because they are cheap relative to the promise, and teams stand up model-API workloads without touching a purchase order.
The result is a familiar IT position with an unfamiliar pace: you are accountable for an estate whose shape changes faster than your ability to document it, and the questions arriving from the board, from finance, and from risk all assume you already have the answer.
Cross-platform inventory
AI platforms in use across the organization in one list, with automated, manual, and CSV sources each labeled for what they are.
Who is actually using them
Active users per platform resolved to people and, where the vendor exposes directory data, to departments.
Adoption over time
Retained historical series rather than point-in-time snapshots, so a plateau or decline is visible rather than inferred.
Coverage transparency
Explicit statements of where data is missing, so an incomplete inventory is never presented as a complete one.
Governance visibility
A portfolio view that shows what is approved, what is in use, and where those two lists disagree.
Executive reporting
One AI estate narrative for the board and finance that IT does not have to reassemble each quarter.
What IT leadership has to decide
- Standardize or allow
- Whether overlapping AI platforms get consolidated onto a standard, or whether the duplication is justified by genuinely different use.
- Where enablement goes next
- Which departments have licenses but no adoption, and therefore need enablement rather than more licensing.
- What to bring under governance
- Which tools discovered in use should be formally onboarded, and which should be retired.
- What to tell risk and finance
- A defensible account of the AI estate, including its known blind spots.
- Which platforms to instrument first
- Where automated connectivity buys the most visibility for the least effort.
Every vendor reports only itself
Microsoft reports Microsoft. GitHub reports GitHub. Google reports Workspace. Each console is authoritative inside its own boundary and blind outside it — and none of them knows your contracted price, your other vendors, or the tools a department bought last month. Nobody publishes the cross-vendor view because no vendor has an interest in producing it.
Midgentic reads those admin and analytics APIs directly where connectors exist, accepts manual and CSV data everywhere else, and normalizes the result into one portfolio. See integrations for exactly what is ingested from each platform.
The IT-relevant AI metrics
- AI platforms in use, and which have automated versus manual data
- Assigned seats against active users, per platform
- Adoption by department, and which departments never started
- Adoption trend rather than a single current number
- Overlapping capability across platforms covering the same users
- Tools in use that are not on the approved list
- Coverage gaps: where the inventory is known to be incomplete
An intelligence layer, not an enforcement layer
Midgentic does not block tools, inspect content, run an endpoint agent, proxy traffic, or enforce policy. It does not monitor individuals for performance. Claiming otherwise would misrepresent what it does and would not survive first contact with your security team.
What it does is close the knowledge gap those teams work around today: a current, cross-vendor account of what AI the organization runs, how heavily, by whom, and at what cost — the input layer that governance and enforcement decisions have been missing. Related reading: shadow AI and AI adoption analytics.
Common questions
Does Midgentic block or control AI tools?
No. It has no enforcement capability: no blocking, no DLP, no endpoint agent, no proxy, no policy enforcement. It is the intelligence and visibility layer that tells you what is in use and how heavily, so your existing security, identity, and endpoint platforms can act with better information.
Does it replace our identity provider or CASB?
No. Identity platforms govern access and CASBs enforce policy on traffic. Midgentic reads administrative and analytics data from AI platforms themselves, then joins it with license and value data those systems do not hold. The concerns are complementary.
How complete is the AI inventory?
As complete as the sources you connect and record. Six automated connectors are implemented and in beta, and every other provider can be tracked manually or by CSV. Coverage gaps are shown explicitly rather than implied away, so the inventory is honest about what it does not yet see.
What data does Midgentic actually read from connected platforms?
Administrative and analytics endpoints only: seats, active users, activity counts, and where the vendor exposes it, metered spend. Prompts, responses, message bodies, documents, and source code are never accessed.
Can adoption be attributed to departments?
Yes, where the connected platform exposes directory data. Users are resolved to people in your workspace so adoption can be read by department or team rather than as one organization-wide average.