Your AI Partner Has the Badges. Can the Team Do the Work?

Move from the corporate total to the proposed team

Imagine a hypothetical B2B software company choosing a partner to build an AI assistant for customer-service staff. The presentation is polished. The provider has trained thousands of employees. The proposed assistant produces a fluent answer to a routine account-access question.

Then the operations director introduces a less cooperative case: an account has conflicting support entitlements, and the customer wants a firm service commitment. Now the useful demonstration begins. What does the team check? Which uncertainty does it preserve? Who decides whether the answer can go to the customer?

This is an illustrative buying exercise, not a reported Cognizant demonstration. Its purpose is to make the supplier’s capability visible at the level where the buyer will depend on it. The relevant evidence concerns the assigned people, the task and the working conditions, rather than the size of the training program.

OECD’s June 5 brief on AI and skills emphasizes that workplace needs extend beyond coding to analytical, managerial and problem-solving skills. It synthesizes earlier evidence, with the newest underlying data from late 2024. It supports a broader view of capability, not a current ranking of providers. My application is to ask for evidence of judgment alongside tool familiarity. [2]

A good answer can conceal a learning gap

There is a reason to look beyond the finished output. In research published January 29, Anthropic studied 52 mostly junior Python engineers learning a new coding library. The AI-assisted group scored lower on an immediate knowledge quiz than the unassisted group. This was vendor-conducted research on a particular learning task, not evidence that experienced AI practitioners are unqualified or that certification fails. [4]

Independent research offers a related, narrower caution. An August 24 preprint used short logic puzzles and simulated, perfectly correct assistance. Assisted performance could overstate subsequent unassisted performance. That laboratory setting is far from an enterprise implementation, and the study did not test a commercial AI system or a credential. [5]

Together, these sources give a buyer a reason to separate the quality of an assisted output from what the person understands. They do not establish a hiring rule. For our fictional software company, I would ask the team to explain why it accepted one entitlement record, rejected another and stopped short of making a promise.

The objective is not an elaborate examination without tools. The job may properly depend on tools. It is to see whether the people responsible can recognize a problem, explain their reasoning and choose an appropriate next step when the easy answer is unavailable.

Keep enough expertise on your side of the table

The buyer also needs someone capable of judging the audition. OECD’s January 19 report on building an AI-ready public workforce discusses internal skills and procurement information asymmetry. Its public-sector guidance is not a tested private-sector purchasing method. Applied here, it suggests retaining enough internal understanding to challenge a proposal and evaluate the delivered work. [3]

For the software company, that means involving the service manager who understands customer commitments, together with a technical reviewer. Neither should have to accept a fluent demonstration as the sole definition of success. Outsourcing implementation does not remove the need to know what a useful result looks like.

I would begin with one bounded, paid evaluation where appropriate, using synthetic or suitably authorized data. Agree beforehand what the team must demonstrate and what evidence would justify a wider assignment. This is my proposed application of the role-specific skills guidance and the studies’ distinction between output and understanding, not a validated procurement framework. [2][3][4][5]

• Ask what the credential measures: completion, assessed knowledge or demonstrated performance. Keep the answer separate from claims about this team’s experience. [1][2]
• Observe one relevant exception: ask the proposed team to explain its checks, limits and escalation decision, with the tools it expects to use. [2][3][4][5]
• Name the buyer’s evaluator: agree who can judge the work and what knowledge must remain inside your organization. [3]

Give the badge a useful job

Back at the fictional software company, the supplier may handle the difficult case well. That is good news. The buyer now has a more concrete basis for the next conversation: scope, staffing and what the first delivery must demonstrate.

Training credentials deserve a place in that discussion. They should help executives ask better questions, rather than excuse them from asking. Before the next AI partner presentation ends, move from “How many people have you trained?” to “Show us how this team will handle our work.”

Questions for executives

1. What does the proposed team’s credential actually assess?
2. Who inside our business can evaluate its work?
3. Which realistic exception would make its judgment visible?