You Can't Measure AI ROI Without Vendor Visibility First

Nearly half of large organizations can't reliably answer a question their boards are now asking every quarter. According to Everest Group research cited by CloudNuro, 49% of enterprises report they cannot accurately calculate AI ROI because their data and metrics sit in silos and depend on manual spreadsheets. That gap has stopped being a reporting inconvenience: 81% of CIOs now say demonstrating clear ROI is the top priority for new AI initiatives, and more than 70% of CFOs rank AI investment return among their top three planning metrics for the coming fiscal year. The article's framing is direct — the gap between AI expectation and AI measurement has become a board-level risk.

The three root causes it identifies are worth sitting with, because none of them are actually about AI. They're about vendor and contract visibility.

The Real Problem Sits One Layer Below "AI ROI"

The analysis names three structural issues behind the 49% figure: a fragmented SaaS and AI footprint with no single system tracking where AI is used or by whom; unclear ownership, since AI initiatives get sponsored by IT, a business unit, marketing, or operations with budget responsibility rarely aligned to any of them; and manual, ad hoc measurement, where ROI gets reconstructed in a slide deck once a year instead of tracked continuously.

Read those three again without the word "AI" in front of them. A fragmented footprint with no single system of record. Unclear contract ownership. Manual, once-a-year reconciliation instead of continuous tracking. That's not a new problem AI created — it's the exact same failure mode

FinOps teams have been fighting in vendor and SaaS contract management for years, just with a new line item riding on top of it. AI features now ship embedded inside existing SaaS subscriptions as usage-based surcharges or premium tiers, on top of entirely new AI-specific tools that show up through shadow IT and never touch procurement. If a company couldn't reliably track its 130-plus SaaS contracts before AI pricing got layered in, it has no chance of isolating what an AI feature specifically costs, let alone what it returns.

Why the AIM Loop Stalls at Step Two

The proposed fix — CloudNuro's AIM Loop of Align, Instrument, Monitor — is a reasonable framework, and the "Instrument" step is where most organizations actually get stuck. Instrumentation requires centralizing a SaaS and AI inventory, tagging which subscriptions carry AI-related charges, and linking usage events to outcomes. That's a lot to ask of a team that still can't produce an accurate, current list of who owns each vendor contract, when it renews, or what's actually included in the pricing tier they're paying for.

This is the practical sequencing problem: Align defines what to measure, Monitor reports on it, but Instrument depends entirely on having trustworthy vendor and contract data underneath it. A ROI dashboard built on top of a spreadsheet nobody has updated since Q1 isn't instrumented — it's decorated. The Everest Group finding that 49% of enterprises can't calculate AI ROI is really describing the downstream symptom of a vendor-data problem most of those same enterprises haven't solved yet either.

Where Asozal Fits — Before the ROI Conversation Starts

Asozal isn't an AI ROI calculator, and it doesn't try to link AI usage events to revenue or churn — that's genuinely finance and analytics work that has to happen close to the business outcome. What it does solve is the layer underneath: the vendor and contract visibility that any AI ROI framework has to assume already exists before "Instrument" can mean anything.

Automated vendor intelligence enrichment gives a team the single, current inventory the CloudNuro analysis says most enterprises lack — including catching pricing tier details and AI-related surcharges buried inside contracts nobody has reread since signing. Assigning a named owner of record to every vendor closes the "unclear ownership" gap directly; an AI subscription with no owner can't be held accountable to any ROI target, no matter how good the dashboard downstream looks. And continuous renewal and usage tracking replaces the once-a-year PowerPoint reconstruction with data that's current when finance actually asks for it — not reconstructed under deadline, the same problem this analysis flags as its third root cause.

None of that produces an AI ROI number by itself. It produces the thing an AI ROI number is built on top of: an accurate answer to what's being paid for, who owns it, and whether it's actually being used — for every vendor contract, AI-related or not.

The Sequencing Matters

The organizations Everest Group counts among the 49% aren't failing at ROI math. They're trying to calculate a ratio where the denominator — total AI-related cost, spread across a fragmented, under-owned vendor footprint — was never reliably known in the first place. Fixing the measurement framework without fixing the vendor data underneath it just produces a more sophisticated-looking version of the same unreliable number.

If your team is trying to get ahead of the AI ROI conversation before your next board or budget review, the place to start isn't a new dashboard — it's making sure every vendor contract, AI-enabled or not, has an owner, a current cost, and a visible renewal date.