Your employees may already be experimenting with ChatGPT. The next question is where the business should invest: helping people use an assistant, connecting AI to internal information, or building it into an operational workflow.

Those choices involve different amounts of work. A useful starting point is one recurring task with a clear owner, a measurable baseline, and an output someone can reliably review. That gives you a way to test value before expanding the investment.

Understand what you are putting to work

ChatGPT is OpenAI’s AI assistant. It can help people work with language, such as drafting, explaining, and summarizing material. Its output still needs to be evaluated for the task at hand; a fluent answer can contain an unsupported statement or miss an important qualification.

Using an assistant directly and building a custom application are separate decisions. An employee may be able to improve a drafting task with clear instructions and approved source material. A workflow that reads business records, enforces permissions, or updates another system also needs application design and integration.

Start with the user and the task. The choice of model or product should follow the requirements that emerge.

Choose work with a reviewable result

A man reviewing paper documents beside a laptop
Choose a task whose source material and output a person can check without repeating all of the work.

Consider these as candidates for investigation, rather than promises of performance:

  • Support preparation: Draft a response from an approved knowledge base for an agent to check.
  • Document intake: Extract specified fields into a review queue and flag missing information.
  • Account handoffs: Turn approved notes into a draft summary that the account owner verifies.

For a first project, prefer work where the source material is available and a reviewer can identify errors without repeating the entire task. If checking the answer takes longer than producing it manually, the apparent productivity gain may disappear.

A poorly defined workflow can be a better candidate for process improvement than AI. Resolve unclear ownership and inconsistent source material before asking a model to work around them.

Measure the whole task

Suppose, as an illustrative example, a team wants help drafting answers to recurring customer questions. Time the current process from opening the request to sending the approved response. Then compare the assisted process, including source lookup, editing, verification, and exceptions.

Track whether the response is supported by the approved material, whether it answers the actual question, and how much correction it requires. Include difficult examples: conflicting policies, outdated documents, and questions the system should escalate.

OpenAI’s evaluation guidance recommends tests that reflect the actual task, combined with human judgment and continued evaluation as the application changes. Define the acceptance criteria before the trial so the team can make a clear decision about expansion.

Establish data and action boundaries

A person seated opposite a robotic arm at a chessboard
Define the task, its rules, and how a person will judge the result before introducing automation.

Decide which information is appropriate for the selected tool and configuration. Have the responsible owners review access, retention, and handling requirements before introducing business data. Avoid treating every AI product or account setting as interchangeable.

For a connected application, enforce permissions in the surrounding system. A request to the model to keep information private does not replace access control. Begin with a narrow set of allowed actions and an explicit approval step for changes that affect customers or business records.

Decide what happens after the trial

Compare the result with your baseline, including review effort, operating cost, and the consequences of errors. Choose whether to expand, adjust the scope, or stop. Keep the examples that exposed failures so later changes can be checked against them.

If the trial exposes inconsistent records or unclear document ownership, address those through data curation. If the task works but requires repeated manual transfers between systems, investigate application design and integration.

Talk with bixod about one workflow you want to improve. Describe who performs it, where the information comes from, and what a successful result would let them do differently.