The useful test of an AI colleague is whether it helps someone investigate a business problem they could not easily resolve alone. Ask it to explain an unexpected cost, find the records behind its explanation, and identify what still needs a person’s judgment. A faster report is useful. A working partner should help you decide what to examine next.

That is why the finance example in OpenAI’s October 27 Oracle announcement deserves attention. Oracle is developing AI Workspace to connect its business applications and data to ChatGPT. The example follows a new finance employee investigating costs, understanding purchasing decisions, and identifying unfinished month-end work across finance, procurement, and planning.

It describes an intended experience, not a measured customer result. But it gives leaders a better demonstration to request.

In brief: Evaluate AI on the investigation, not just the answer. Give a person and an assistant a bounded business question, require traceable evidence, and see whether their work together improves the next human decision. Keep permission to investigate separate from permission to change records.

The question behind the report

Consider a hypothetical department manager who sees that outside-service spending has risen. The report shows the increase. It does not explain whether a supplier raised prices, an annual invoice arrived early, or the department bought more work.

A useful colleague would help distinguish those possibilities. They might compare invoices, locate a purchase approval, and notice that two transactions cover different service periods. They would also know when to ask the budget owner what actually happened.

That is the assignment I would give an AI collaborator. Not “summarize this dashboard.” Help me understand the change well enough to have a productive conversation with the person responsible.

Oracle’s announcement says Workspace is designed to connect Fusion Applications, NetSuite, Oracle AI Database, and other enterprise systems through ChatGPT plugins, with approvals and controls retained. Connecting those records could support the investigation above. Whether it does so reliably is what a customer would need to establish.

The release also carries Oracle’s future-product disclaimer: functionality, timing, and pricing can change. Do not confuse a compelling assignment with a product you have verified is available for your team.

Oracle has a narrower result worth examining

There is already a reported example of AI helping people prepare for a business conversation.

In a customer account published by OpenAI, Oracle describes a talent-market research tool built with ChatGPT Work. It takes a job description and researches comparable roles, compensation, and the available talent pool. Oracle’s recruiting leader says preparation that previously took two to four days now takes about 15 to 20 minutes.

Those are vendor-published customer claims, not independently audited productivity measurements. They concern recruiting preparation, not month-end close or the newly announced Workspace.

Still, the shape of the work matters. The recruiter arrives at the hiring-manager conversation with research already assembled. My interpretation is that the more interesting opportunity is a better-informed discussion about the role, rather than simply a shorter wait for a document. The published account does not establish better hiring outcomes.

That distinction should shape your own trial. Count preparation time, but also inspect what the preparation allows someone to do.

Collaboration has evidence beyond a product launch

A Harvard Business School account of the Procter & Gamble field experiment, published October 17, 2025, offers supporting evidence from a different setting. Participants worked on product-development problems. Individuals using AI produced ideas comparable in quality to two-person teams without it, and AI-assisted work mixed technical and commercial perspectives more evenly.

HBS reports time reductions of 16% for individuals and 13% for teams. Those results concern the experiment’s tasks, not a forecast for your finance department.

The connection I draw is about access to another perspective. An assistant can be worth having alongside someone when it helps them explore explanations outside their immediate specialty. That is a stronger collaboration proposition than asking it to polish conclusions already reached.

Ask for the investigation, then change one fact

For a safe trial, I would give a finance employee a closed historical period and approved, read-only records. Choose a spending change whose explanation the reviewer already knows. Ask the employee to investigate it with the assistant, without supplying that explanation upfront.

Require the working note to distinguish documented facts from possible explanations. An invoice supports an amount. It may not establish why the purchase was necessary. Have the employee follow the references and decide which unresolved question belongs with purchasing or the department manager.

Then introduce a correction: one invoice covers a different period than originally assumed. Watch how the pair revises the explanation. Does the assistant reconsider its conclusion, or simply append the correction to a story it keeps defending?

Record the employee’s review time and whether the final explanation is sound. Ask whether they understand the issue better afterward. This is a proposed test, not a result from Oracle or the P&G study.

It belongs in human-AI collaboration in practice: design work that benefits from an active exchange, then examine what each participant contributes.

The useful outcome is a person who can explain the business more clearly because they investigated it with AI. Buy toward that, not the fluency of the first answer.

Research and draft: Mai, from John Lipe’s standing editorial direction. Prepared for editorial review. Last updated: October 28, 2026.