Is This AI Worker Ready for the Role? A Readiness Checklist for Operators Comparing Vendors

Is This AI Worker Ready for the Role? A Readiness Checklist for Operators Comparing Vendors

If you're comparing AI worker vendors, the hardest question isn't "which one is smartest?" It's "is this one actually ready to do *this specific job* in *my business*?"

Demos don't answer that. A polished demo shows the vendor can handle the five inputs their sales team rehearsed. It tells you nothing about input six — the customer who types something unexpected, uploads a malformed file, or asks the agent to do something it was never scoped for.

This is a draft framework for evaluating readiness, organized around two sides of the same question: **is the vendor's agent ready**, and **is your business ready to put it in that role**?

Part 1: Define the role before you judge the candidate

You wouldn't hire a person against a vague job description. Do the same for an AI worker.

Write down, in plain language:

- **The role** — what the agent is accountable for, day to day, not the technology it uses - **The boundary** — what it must *not* do, and where it escalates to a human - **The definition of done** — what a correct outcome looks like, and how you'd know - **The stakes** — what happens downstream when it's wrong, and how expensive or visible that is

Most "readiness" problems are actually scoping problems. If you can't write this down, no vendor evaluation will give you a useful answer.

Part 2: Test the agent the same way you'd test a hire — comparably

The recurring advice across vendor evaluation guidance is to **give every vendor the same knowledge base, the same test conversations, and the same scoring rubric**. Without that, you're comparing rehearsed performances, not capabilities.

A practical comparison setup:

1. Pick 10–20 real scenarios from your actual backlog — including a few ugly ones. 2. Run every vendor through the identical set, on the identical documents. 3. Score against one rubric you wrote before you saw any of them.

Microsoft's evaluation research is often cited on this point: no single metric tells you whether an agent truly works well. So score along several dimensions rather than one headline number.

What to score

- **Accuracy on your real inputs** — not on their sample data - **Handling of failure** — see below; this is where the real signal is - **Consistency** — same input, same quality, on Tuesday and on Friday - **Recoverability** — when it's wrong, how quickly can you find out and fix it? - **Cost per correct outcome** — not cost per token or per seat

The failure test

Score vendors on **how they handle failure, not on their demo.** Specifically ask:

- What happens when the agent doesn't know the answer? - What happens when the input is malformed, off-topic, or outside scope? - Does it try to bluff, or does it stop and hand off? - Who gets told when it fails, and how fast?

An agent that fails loudly and cheaply is often more useful than one that fails rarely but silently.

Part 3: The vendor questions where vague answers are an answer

A useful posture from enterprise evaluation guidance: if a vendor cannot answer basic questions plainly, that itself is your answer. Ask these directly and note how comfortably they respond.

**Deployment and control** - Where does the agent run, and where does our data go? - Can we see and edit what it "knows"? - How do we change its behavior without filing a ticket with you? - What happens to our configuration if we stop paying you?

**Security and compliance posture** Compliance shouldn't be a final checkpoint before signature — evaluate it early. Depending on your business and workflows, the relevant questions include whether the vendor holds **SOC 2 Type II**, whether a **HIPAA BAA** is available if health data is in scope, whether they offer **GDPR data processing agreements**, and whether **PCI-DSS** applies to any payment-adjacent workflows you'd hand over.

Not every business needs every one of these. But you should know which apply to *you* before the conversation, so you can tell a straight answer from a deflection.

**Governance and accountability** - Who is responsible when the agent causes a problem? - What logs exist, and can we get them? - How do we audit what it did and why?

**Commercial terms** - What's the minimum commitment, and how do we exit? - Is pricing tied to usage we can't predict? - What's included in support, and what's an upsell?

Part 4: The readiness questions about *you*

An agent can be ready while your business isn't. Readiness guidance for businesses repeatedly lands on the same handful of preconditions — treat these as a self-check, not a vendor scorecard.

- **Data readiness** — Is the information the agent needs actually accessible, current, and clean? Surveys cited in readiness research note that a meaningful share of employers hold off on AI because their data isn't ready. If that's you, that's the first project. - **Process clarity** — Is the task well-defined enough to be delegated, or does it live in someone's head? - **Skills and training** — Will the people working alongside the agent be trained for the changed workflow? Research on digital work trends has found more than half of employees don't feel adequately trained to work with AI. Budget for that, not just for the tool. - **Alignment** — Does the role map to an outcome you already care about, or is it a solution looking for a problem? - **Change management and measurement** — Who owns the rollout, and how will you know in 90 days whether it worked?

If several of these are shaky, the honest move is to fix them first. Readiness doesn't happen overnight, and a rushed rollout costs more than a delayed one.

Part 5: A short scorecard you can actually use

Before you shortlist, write one row per vendor and fill it in from evidence, not impressions:

| Dimension | Question | Evidence required | |---|---|---| | Role fit | Can it do *our specific* task at our volume? | Passed test set | | Failure behavior | Does it fail safely and visibly? | Documented failure cases | | Data control | Can we see, edit, and export what it knows? | Live walkthrough | | Compliance | Does its posture match our regime? | Certifications / agreements | | Governance | Can we audit and assign responsibility? | Logs and terms | | Economics | Cost per correct outcome, not per token | Worked example | | Exit | Can we leave without losing the work? | Contract language |

Where a vendor can't supply the evidence, leave the row blank. Blank rows are informative.

The short version

Judge the agent against a defined role, test every vendor on the same scenarios, weight failure handling as heavily as capability, and check that your own data, process, and people are ready to receive it. Do that, and the comparison mostly makes itself.

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*This is a draft for human review. Details about specific certifications, pricing, or vendor capabilities should be verified against current, primary sources — and against what any given vendor will actually commit to in writing.*