Toolkit

Enterprise AI Platform Evaluation Worksheet

Most AI platform decisions are made on demos, not data. A vendor runs a polished pilot on their best use case, your team is impressed, and six months later you are locked into a contract built around a proof of concept that never reflected your real workflows. This checklist exists to prevent that.

Enterprise AI spending has crossed $300 billion globally, yet fewer than 1 in 5 organizations report that their AI investments have reached meaningful scale. The problem is rarely the technology. It is the evaluation process. Teams compare feature lists instead of outcomes. They score demos instead of running structured pilots. They pick a vendor before they have defined what winning looks like.

The 11 categories that determine whether an AI platform will actually work for your organization

This checklist gives each evaluation category a recommended weight out of 100, so your team can score vendors on what matters most — not what sounds best in a pitch deck. Assign weights, score 1 to 5, multiply, and let the data make the decision.

  • Data Connectivity and Permissions
  • Governance and Admin Controls
  • RAG and Retrieval Quality at Scale
  • Context Management
  • Workflow Automation and Orchestration
  • Time-to-Value at Two Weeks or Less
  • Security and Compliance
  • Builder Experience
  • Observability and Output Quality
  • Model Strategy and Portability
  • Commercials and Predictability

How to run a two-week pilot that actually tells you something

Start with two real workflows, not demo workflows. In this worksheet we provide two: one that is a reliable first tests because the quality bar is obvious and the time savings are immediately visible. And a reliable second test because it exposes retrieval quality, permissions enforcement, and governance in a single run.

Three numbers define a successful pilot. Accuracy of 80 percent or higher, measured by outputs your team accepts with light edits rather than rewrites. Latency of 10 seconds or less on typical queries, which is the threshold where AI stops feeling like a bottleneck. And 10 or more active users with repeat usage by week two, which is the only adoption signal that means anything. Demos do not count. Governance must also be demonstrated, not described — audit trail and role-based access control working in a real environment, not a sandbox.

What to request from every vendor before you score them

An admin and policy walkthrough, not a recorded demo. Any vendor who cannot or will not provide these items listed in the worksheet before you commit is telling you something about how they treat customers after the contract is signed.

Download the worksheet to get the full weighted scorecard, recommended default weights across all 11 categories, the complete two-week pilot script, and the vendor artifact request list — everything your team needs to run a rigorous evaluation and make a decision you will not regret.

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Toolkit
Enterprise AI Platform Evaluation Worksheet

Most AI platform decisions are made on demos, not data. A vendor runs a polished pilot on their best use case, your team is impressed, and six months later you are locked into a contract built around a proof of concept that never reflected your real workflows. This checklist exists to prevent that.

Enterprise AI spending has crossed $300 billion globally, yet fewer than 1 in 5 organizations report that their AI investments have reached meaningful scale. The problem is rarely the technology. It is the evaluation process. Teams compare feature lists instead of outcomes. They score demos instead of running structured pilots. They pick a vendor before they have defined what winning looks like.

The 11 categories that determine whether an AI platform will actually work for your organization

This checklist gives each evaluation category a recommended weight out of 100, so your team can score vendors on what matters most — not what sounds best in a pitch deck. Assign weights, score 1 to 5, multiply, and let the data make the decision.

  • Data Connectivity and Permissions
  • Governance and Admin Controls
  • RAG and Retrieval Quality at Scale
  • Context Management
  • Workflow Automation and Orchestration
  • Time-to-Value at Two Weeks or Less
  • Security and Compliance
  • Builder Experience
  • Observability and Output Quality
  • Model Strategy and Portability
  • Commercials and Predictability

How to run a two-week pilot that actually tells you something

Start with two real workflows, not demo workflows. In this worksheet we provide two: one that is a reliable first tests because the quality bar is obvious and the time savings are immediately visible. And a reliable second test because it exposes retrieval quality, permissions enforcement, and governance in a single run.

Three numbers define a successful pilot. Accuracy of 80 percent or higher, measured by outputs your team accepts with light edits rather than rewrites. Latency of 10 seconds or less on typical queries, which is the threshold where AI stops feeling like a bottleneck. And 10 or more active users with repeat usage by week two, which is the only adoption signal that means anything. Demos do not count. Governance must also be demonstrated, not described — audit trail and role-based access control working in a real environment, not a sandbox.

What to request from every vendor before you score them

An admin and policy walkthrough, not a recorded demo. Any vendor who cannot or will not provide these items listed in the worksheet before you commit is telling you something about how they treat customers after the contract is signed.

Download the worksheet to get the full weighted scorecard, recommended default weights across all 11 categories, the complete two-week pilot script, and the vendor artifact request list — everything your team needs to run a rigorous evaluation and make a decision you will not regret.

A free weighted scorecard to evaluate any enterprise AI platform in under 2 weeks. Score vendors across 11 categories — run a pilot that gives you real data, not sales theatre.
Download

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