Back in June, I said there were four camps emerging in the AI-for-ITAM race: table stakes, aggregators, disruptors and one smart bet.
The technology I’m looking at today currently sits in the disruptor camp, largely because it is early to market. But I don’t think it will stay there. Ultimately, I think this kind of capability will become standard across the ITAM tools market. If vendors don’t develop it, I’m not convinced they’ll be around for very long. What looks disruptive today will become table stakes surprisingly quickly.
I recently took a look at Asset Uno, an ITAM platform that is starting to tackle this problem directly: discovering AI use across the enterprise, tracking consumption and cost, and giving ITAM teams a clearer view of who is using what.
So why cover this particular technology?
There are three reasons:
- First, they’re early. I imagine plenty of ITAM vendors are already working on similar capabilities, but this is the first implementation I’ve seen at this level of detail. If you’re already building technology in this area, I’d genuinely like to hear from you.
- Second, the people behind it have credibility. Kerim, MOS and the team behind it have previously won ITAM Forum Excellence Awards, and they’ve also taken a company through ISO certification as a partner. In other words, they understand ITAM at a serious operational level.
- Third, this provides a useful early example of where I think the market is heading.
I’m certainly not suggesting they’ll be the only company doing this. Quite the opposite. I expect we’ll see a lot more activity in this space over the next twelve months.
The ITAM tool console isn’t the interesting bit
Looking at the technology itself, the console is fine. Nothing particularly remarkable. I mean this with no disrespect to the AssetUno team, but coding in 2026 means the new benchmark for software interfaces is increasingly what one reasonably motivated person, with very little traditional technical expertise, can build using AI over a weekend. Attractive dashboards and interfaces are becoming much easier to create.
The interesting part here is what sits behind the console.
The real value is in the connectors into enterprise AI usage and the interpretation of that data into views that ITAM teams can actually use.
That plumbing here is the innovation. It’s why tools like these should also consider the MCP route, as I mentioned in my previous article.
Why AI consumption is becoming an ITAM problem
Many organisations are already using agentic AI, or actively exploring it.
The amount of money flowing into enterprise AI is significant. For example ServiceNow confirmed AI crossed $1 billion in annual contract value in Q2 2026:
But alongside sanctioned enterprise AI comes another problem: shadow AI.
Employees are opening their own ChatGPT accounts. AI functionality is quietly appearing inside existing SaaS applications. Development teams are adopting coding assistants. Departments are experimenting with specialist AI tools without necessarily involving central IT.
All of this creates another visibility gap.
And visibility gaps are exactly the sort of thing ITAM should be helping organisations close.
So what is this technology actually doing?
In short, it combines AI inventory and cost tracking across the enterprise with technology designed to keep those connections working as AI vendors change their APIs.
There are three particularly interesting elements.
Discovering shadow AI
First, it identifies AI products and capabilities already being used across the organisation. That might include personal ChatGPT accounts, AI features quietly activated inside SaaS products, coding assistants or other AI tools the IT department may not even know exist.
Understanding consumption and cost.
Second, it tries to understand what that AI usage is actually costing, and who is responsible for it.
It’s not enough to know:
“We spend $10,000 with OpenAI.”
The useful questions are:
Which team is spending it?
Which users?
Which projects?
How many tokens are being consumed?
How is consumption changing?
By connecting this information with services such as Entra and Active Directory, AI spend begins to move from a single invoice into something much closer to a genuine ITAM consumption dataset.
Keeping the plumbing alive
The third capability may be the least glamorous, but technically it could be the most important.
AI providers are moving extremely quickly. APIs change. Services evolve. New models and capabilities appear.
The platform therefore monitors those changes and attempts to keep its own integrations working as the underlying AI services evolve.
Again, this is why I don’t think the interesting innovation is the dashboard.
It’s the plumbing behind it.
The question for the ITAM industry
This raises some interesting questions for the ITAM community.
Are you already working with technology in this space?
Are you using anything to discover and monitor AI consumption across your organisation?
Or have you only just started exploring it?
And particularly for organisations signing substantial enterprise AI agreements, ServiceNow springs immediately to mind, what are you putting in place to measure actual consumption?
Do you have independent technology capable of monitoring usage?
Or are you relying on the vendor to provide the granular consumption data you’ll need when the next renewal arrives?
Because if AI expenditure continues growing at its current pace, understanding who is consuming what, where, and at what cost is going to become a fundamental ITAM requirement.
Today, that looks innovative, I suspect very soon it will simply be expected.
I’d love to hear what you’re seeing.
