ChatGPT Enterprise for AI-enabled work: connect each use case to its controls and evidence

Describe the task before choosing the feature

Start with the work decision. Record who performs it, the inputs they need, the output they produce, who relies on that output, the cost of a material error, and which information should not enter the workflow. Then identify the smallest ChatGPT capability needed for that task.

This order matters because workspace capabilities and controls change over time. OpenAI's current workspace settings documentation describes administrative surfaces for members, feature access, GPTs, apps, analytics, and identity controls. A team should confirm the plan, role, permissions, settings, and connected services actually in use rather than copying a generic checklist.

OpenAI's business data page states its current default treatment of business inputs and outputs, encryption, data controls, and enterprise administration features. Those are provider statements about the service. They do not decide whether a particular organization's documents, retention duties, or connected systems are appropriate for a proposed workflow.

Sources: [1] [2]

Build the use-case evidence map

Create one row per use case, not one row per department. Include:

Use explicit dispositions: ready for a bounded pilot, configuration change required, specialist review required, or not approved. Adoption is not a disposition. A workflow can have many active users and still lack a defined review or data boundary.

• task, accountable owner, user group, and launch scope;
• input categories, data classification, approved source locations, and excluded data;
• ChatGPT features used, including file upload, apps, GPTs, browsing, data analysis, or other enabled capabilities;
• workspace, group, seat, role, and feature permissions;
• output destination, required human review, and decision boundary;
• retention, export, monitoring, support, and incident route;
• evidence that configuration and workflow tests passed;
• review date, change triggers, and current disposition.

Worked artifact: a synthetic procurement brief

A fictional procurement team wants analysts to use ChatGPT Enterprise to summarize public supplier documents and draft a comparison brief. The first proposal says only, “Use the enterprise workspace and verify the answer.” The evidence map turns that into a testable workflow.

The owner limits inputs to public supplier pages and an approved synthetic request for proposal. Confidential bids, personal data, contract drafts, and credentials are excluded. Users belong to a pilot group with the minimum features needed for document review. The output is a draft evidence table, not a supplier selection. Every material statement needs a source link and a reviewer must open the cited passage.

During testing, an analyst uses an enabled app that can search an internal repository. That capability is unnecessary for the pilot and creates an input path outside the approved source set. The team changes the group's feature access, repeats the test with a member account, and records screenshots of the effective settings. A second test finds that one comparative sentence is not supported by the cited supplier page. The reviewer removes it and logs the failure mode for training.

The final artifact contains the use-case ID, pilot group, effective permissions, source boundary, prompt task, sample inputs, expected table fields, reviewer checks, failed test, corrected configuration, approval, and expiry date. The example is synthetic. It does not establish that the configuration is suitable for a real organization's requirements.

Verify the effective experience

Do not stop at an administrator screenshot. Test the workflow with a representative member account because effective access can depend on plan, seat, role, group, workspace default, custom role, and feature eligibility.

Run at least four checks:

The goal is not to prove that every future output will be correct. It is to establish that this bounded task has an owner, a known configuration, an observable review, and a response when something fails.

• Access check: confirm that the pilot user can reach only the intended features and connected sources.
• Data-boundary check: attempt a clearly excluded input and confirm that the operating process handles it as designed.
• Output check: seed the material with a contradiction, stale fact, or unsupported comparison and observe whether the reviewer catches it.
• Evidence check: verify that the team can reconstruct the configuration, test input, output, review, and disposition later.

Read analytics for the question they answer

Workspace analytics can help programme owners understand adoption and engagement patterns where those views are available to the relevant roles. They do not by themselves show whether a participant framed the task well, used the right evidence, noticed an unsupported claim, or delivered a sound business decision.

Keep configuration evidence, usage evidence, and work-quality evidence separate. Use each for its proper question. Review the use-case map when the task, data class, user group, model or feature, app, retention setting, source system, or provider documentation changes.

Two Prune can assess how a participant handles a realistic task with AI support inside a controlled simulation, including evidence use, verification, judgment, and communication. It does not administer ChatGPT Enterprise, certify a deployment, inspect a customer's live workspace, or make employment decisions.