AI purchasing does not usually follow normal procurement patterns. A team lead expenses a tool, a vendor offers a free tier that quietly becomes standard, or a pilot gets approved and never formally reviewed. By the time finance or IT sees it, the tool is often already embedded.
The standard evaluation sequence can still happen, but it tends to skip the questions that determine whether the tool actually works once deployed. Not whether it can do the job. Whether it fits the way the organization actually runs.
Evaluating Your AI Vendor Stack - The main essay. First, ask where the user lives. Sales teams live in Salesforce, finance teams live in the ERP, and customer success teams live in the platform that holds customer records. For execution-layer users, an AI tool that makes them open another application creates a habit problem. The tools that stick deposit output where the user already works.
Second, remember that access is scoped to a use case, but use cases expand. A tool approved for read access can gain writeback features. A call-recording review tool can start summarizing emails. The person who understood the original boundaries may leave, and the reasoning leaves with them. Every AI access decision needs a review owner and a trigger.
Third, distinguish load-bearing from substitutable tools. CRM, ERP, ITSM, and systems with downstream integrations need slower, harder evaluation. Tools that handle one task and do not hold authoritative data can move faster. Running the same procurement process on both makes companies slow where they should be quick and casual where they should be rigorous.
Also in this issue:
- Signals This Week - Agentic adoption is outpacing governance. AI agent infrastructure is becoming a managed service. The software market is pricing AI disruption indiscriminately.
- Claude vs. ChatGPT - Ramp's AI Index showed Anthropic closing the corporate adoption gap, with Claude Code and developer-first strategy driving momentum.
- The Wire - Salesforce bet $18B that security is the trust layer for agentic AI. Atlassian rebuilt Rovo Dev around transparency after engineers resisted it. Companies are going back to more human hiring to counter AI-generated applications.
- Meanwhile... - Chess grandmasters are using intentionally suboptimal moves to escape memorized computer lines, a reminder that knowing when not to follow the machine can become the edge.
- What I'm Consuming - Claude Cowork tutorials, Claude Code tips, multi-agent architecture, and personal context portfolios.
The vendor review meeting is already scheduled. The question is whether anyone asks where the user actually works, who owns the access grant when the champion leaves, and whether the tool is holding data or merely touching it.