The analysis layer SAM tools never built.
Over a hundred software rationalisation meetings. At some point, every single one opens a spreadsheet. That’s not a process failure. It’s a tool gap.
Over a hundred software rationalisation meetings. One thing almost every single one has in common: at some point, someone opens a spreadsheet.
It doesn’t matter if the organisation is paying £200k a year for Flexera. It doesn’t matter if Snow or ServiceNow is running with the consolidation module live. When the actual decision needs to be made, the data gets pulled, pasted into Excel, and the Googling begins.
The ritual every programme team recognises
The spreadsheet isn’t laziness. It isn’t budget failure. It is the only tool available that lets people ask the questions that actually matter at the point of decision.
Do we already own something that does this? What in our estate could replace this renewal? Is this covered by an agreement we signed three years ago?
No SAM platform answers these. They weren’t built to. They were built to track what you own. The analysis layer, the thing that connects the data to the decision, was never built into any of them.
What SAM tools were built to track
Flexera, Snow, ServiceNow with the SAM module. These are entitlement engines. They record what licences you hold, what contracts are live, what’s due for renewal. That data has genuine value and, usually, considerable cost to maintain.
The limitation isn’t the data they collect. It’s what happens the moment someone needs to act on it. SAM tools catalogue capabilities as product names, not as functional descriptions. You cannot ask Flexera whether Tool A already covers the use case that Tool B is being evaluated for. You cannot ask Snow whether a recently acquired entity’s Datadog contract makes your enterprise renewal redundant.
The data sits there. The decision still needs a human to make the connection, usually in a spreadsheet, usually under deadline.
Why generic AI can’t fill the gap
The obvious move is to try Copilot or ChatGPT. Both handle general software questions competently. Neither has any visibility into what your organisation actually owns.
Generic AI hallucinates on product capabilities at meaningful rates. More importantly, it has no awareness of your contract terms, your data residency requirements, or the 4,200 Microsoft 365 seats your enterprise agreement already covers. When it answers, it answers in a vacuum. A confident answer in a vacuum is worse than no answer, because someone will act on it.
That is not a model quality problem. It is a context problem. The model is being asked to make a decision it doesn’t have the information to make. Read more about why generic AI can’t safely decide.
The question that stalls every review
The questions that slow rationalisation programmes aren’t exotic. They repeat across every request, every renewal, every category review.
“We’re evaluating a new collaboration tool. Do we already own something that covers this use case, and who owns it?”
Without a system that knows your estate and understands software capabilities, a question like this takes days. Someone pulls the SAM export, cross-references procurement data, checks with Enterprise Architecture, and maybe runs a survey. By the time the answer arrives, the requestor has found a workaround or the vendor has moved the conversation forward on their terms.
That delay has a cost. Not just the time. The purchases that slip through while the process is running.
We’re about to renew our Zoom licence for 3,200 seats. We already have Microsoft Teams deployed across the estate. Is this renewal justified?
Your estate holds 4,100 active Microsoft Teams licences under EA #3812, renewed until March 2027. Usage telemetry shows 91% of Zoom sessions involve users who already hold active Teams licences. Renewing Zoom at current volume is not justified. Recommend replacing with Teams for standard collaboration and restricting remaining Zoom spend to documented specialist exceptions only.
The layer that was missing
Samplify sits between the data you already have and the decisions you need to make. Not a new dashboard to maintain. Not another place to store your catalogue. The intelligence layer that reads your estate and evaluates every request against it in full context.
The input is what most organisations already hold: SAM exports, procurement data, existing spreadsheets. Samplify normalises these into a structured catalogue and evaluates every request, renewal, and comparison against it. Natural language, at the point of decision, via email. No agents to install. No new tool for requestors to learn.
Organisations running this in production have prevented $2-3M per month in unnecessary spend, removed 700 tools at a major global technology company in under two months, and evaluated 92% of over $120M in annual spend with a defensible, source-backed answer for each one.
The data you need already exists. SAM exports, procurement records, renewal schedules. It’s always existed. It just needed the right layer on top of it. If you want to see what that looks like against your own estate, the 30-day proof of value starts with the data you already have, imperfect as it is.
The 30-day proof
Run Samplify on your stack, your questions, your inbound flow.
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