The First Workflow a Leader Should Examine

A good first workflow happens often enough to measure.

It might be preparing the first draft of a customer proposal. Turning meeting notes into a structured follow-up. Classifying incoming requests. Summarizing a standard report. Comparing information from several documents.

The specific task matters less than its shape.

Look for work that is:

• Frequent
• Familiar
• Bounded
• Time-consuming
• Reviewed by a person already

That last characteristic is especially important.

If someone already checks the work before it reaches a customer, changes a record, or triggers a decision, you have a natural control point for an AI experiment.

The review does not need to be invented.

It already exists.

Before selecting a first workflow, ask four questions.

Does it happen often?

A monthly task may take too long to produce useful evidence. A task performed several times a week gives you enough repetition to compare results.

Can people explain how it works today?

If no one can describe the current process, adding AI will make the confusion harder to diagnose.

Will a weak result be visible?

Good first workflows produce output that a knowledgeable person can inspect.

Can a mistake be corrected before it causes damage?

An imperfect internal draft is recoverable. An incorrect payment, customer commitment, or employment decision may not be.

Boring, frequent, visible, and recoverable.

That is a stronger first target than “transformational.”

Start here.

Before beginning, you should be able to answer a few basic questions:

NIST’s AI Risk Management Framework begins from a similar position. It calls for organizations to define the intended purpose, business context, supported tasks, targeted scope, expected benefits, potential costs, knowledge limits, and human oversight associated with an AI system.^[1]

That is easier to do with a familiar workflow than with an ambitious transformation project.

The first workflow should also be one where mistakes are visible and recoverable.

Research involving 758 Boston Consulting Group professionals demonstrated why. Across tasks that fell within the tested AI capability frontier, access to GPT-4 increased speed by more than 25%, improved evaluated performance by more than 30%, and increased task completion.

On a task outside that frontier, however, participants using AI were 19 percentage points less likely to reach the correct answer.^[2]

Watch this.

AI can be remarkably capable at one task and unreliable at another that appears similar.

The polished writing does not tell you which is which.

That is why the first workflow should not depend on blind trust. It should make checking the output practical.

Do not begin by automating the entire process.

Choose one step.

Document what happens today. Measure the current time, quality, error rate, or turnaround time. Decide what a successful improvement would look like.

Then introduce AI into that step with a named human reviewer.

Compare the result against the baseline.

Did the work become faster?

Did quality remain acceptable?

What errors appeared?

How much review was required?

Did time saved in production simply move into correction?

Those answers tell you whether to expand, revise, or stop.

They are far more useful than announcing an AI pilot and counting how many employees tried it.

Your first AI project does not need to prove that AI can transform the company.

It needs to prove that your company can identify a useful problem, apply AI responsibly, measure the result, and learn from what happens.

Do not ask your first project to be revolutionary.

Ask it to be observable.

Choose something boring.

Choose something frequent.

Choose something people already understand and review.

Then make it better.