Three weeks of research. The tool was already owned.
Forty inbound software requests a month. The same three questions every time. And a governance process that consistently arrives after the decision.
Forty inbound software requests a month is not an unusual volume for an enterprise procurement team. What is unusual is the consistency of the conversation each one starts. Who owns this space. What does the organisation already hold. Does the new tool overlap with anything in the estate. Three weeks of work. Every single time.
The same conversation, every time
The process is predictable. A request arrives. The SAM or procurement team starts pulling the catalogue. Category searches. Stakeholder interviews. Licence exports from three systems that do not talk to each other. A comparison document that takes two days to build and another three days to get reviewed.
At the end of it, a result that may or may not be conclusive. Because the tool that already covers what was requested is sitting somewhere in a 4,000-row spreadsheet. Category: correct. Description: absent. Nobody outside IT knows what it actually does at feature level. So the request gets approved. New contract. New vendor. Deeper sprawl. And in row 2,847: a product the organisation has been paying for since 2019 that does exactly what they asked for.
What the catalogue tells you, and what it cannot
SAM tools are excellent at what they are designed to do. Discovery, entitlement tracking, licence reconciliation, usage data. These are hard problems and solving them is genuinely valuable. The gap is not in what they capture. The gap is in what they cannot infer from what they capture.
A SAM tool tells you the category a product belongs to. It does not tell you what it does at feature level. Those are different things. Knowing you hold a contract for a project management platform does not tell you whether that platform covers the specific collaboration capability the new request is asking for. Only a feature-level comparison can answer that. And feature-level comparison is manual work, carried out by analysts, three weeks at a time, indefinitely.
SaaS management platforms track spend and usage. They do not compare capabilities. EA systems map architecture. They do not govern individual purchase requests. None of these tools sit above the estate as a decision layer. They catalogue the estate. They do not reason about it.
The tool that already covers what they need is sitting in row 2,847 of a 4,000-row spreadsheet. Category: correct. Description: none. Nobody outside IT knows what it actually does for the business.
The decision made in the corridor
Here is what makes the data structure problem worse. By the time three weeks of analysis returns a recommendation, the decision has usually already been made.
Enterprise software decisions do not wait for formal governance. They emerge from conversations. A budget holder says yes over coffee. A stakeholder's manager nods it through at an all-hands. A vendor has already been in for a demo and the team is enthusiastic. By the time the analysis lands, organisational momentum is already pointing in one direction. The recommendation becomes a rubber stamp, not a decision. That is the actual problem. Not speed. Not resources. The decision happens before the analysis is ready.
One sourced recommendation. Four possible answers.
What changes this is the time between the request and the recommendation. Not three weeks. Not three days. One hour.
A request comes in. Your team forwards it to sam@samplify.ai. Within the hour: a recommended action, one of BUY, RENEW, REPLACE, or REJECT, with the full workings attached. The decision layer is stack-aware, because Sam knows what you own. Feature-level, because it compares capability rather than category. Evidence-backed, because every answer includes sources your team can put in front of a stakeholder or an auditor. The recommendation arrives before the corridor conversation, not after it.
Marketing are requesting Notion, 200 seats, at roughly £18 per user per month. We have an active Adobe Workfront contract. Do we need to buy this?
Your Workfront agreement (EA #4471) covers 380 seats organisation-wide, with 147 active as of March 2026. At feature level, Workfront includes project management, content collaboration, and task workflows that directly cover Notion's stated use case for the marketing team. Purchasing Notion would create a capability overlap with an active contract. Recommend routing marketing to Workfront onboarding and reviewing utilisation on the 233 dormant seats before opening a new vendor relationship.
The 30-day proof of value is structured for organisations with imperfect data. No integration required. No data upload. Sam learns as you use it. One avoided duplicate purchase covers the annual investment. One bad renewal caught is £250,000. One category consolidation across a fragmented estate is a seven-figure saving. The case for the decision layer does not depend on a long list of marginal improvements.
Why generic AI is not the answer
The obvious question is why an existing AI tool cannot solve this. Paste the spreadsheet into Copilot. Ask ChatGPT whether the new tool overlaps with anything. You will get an answer.
The answer will not know whether the tool in your specific estate already covers the capability at feature level. Generic AI does not know your stack. It does not know your renewal calendar, your ownership history, your contract structure, or your internal policy. And it cannot tell you whether the response it returns is grounded in your estate data or synthesised from general training. The answer looks confident. It is not sourced.
Sam returns one of four answers. BUY, RENEW, REPLACE, or REJECT. With sources. Every time. Before the request has had time to gather corridor momentum.
The 30-day proof
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