In 30 seconds

An AI workflow succeeds or fails partly on how it is introduced. Involve the people who do the work early, start with the AI assisting rather than replacing, and make it easy to give feedback and to opt out when it is wrong.

Why this comes up

A technically sound workflow can still fail if the team does not trust it or feels it was imposed on them. The people who do the work know the exceptions, and they are the ones who will catch the AI's mistakes. Bringing them in is not just courteous; it is how the workflow stays safe and improves.

Introduce it as help, not replacement

People rightly worry when a system arrives to "handle" their work. Frame and design the workflow as support: it drafts, suggests or handles the routine, and the person stays in control of judgment and exceptions. This is both more accurate and more likely to succeed. A read-only assistant or a draft-and-approve step is a gentle first step.

Involve the experts from the start

The people who handle the work, especially the exceptions, should be involved before launch, not surprised by it. They will tell you where it will go wrong, and their early buy-in matters when the first mistakes happen. See how to find the person who actually knows.

Make feedback and opt-out easy

  • Give people a simple way to flag a wrong or unhelpful result, and make sure that feedback reaches the workflow's owner.
  • Let people override or ignore the AI when their judgment says so, especially early. An AI that cannot be overruled will be resented and worked around.
  • Show that feedback leads to changes. Nothing builds trust faster than a reported problem actually getting fixed.

Respect the existing expertise

The current process and the people running it are not obstacles. They kept the work running, exceptions and all. Introduce the AI as a way to remove drudgery and free their judgment for the hard cases, not as a verdict on their work.

A small example

A team introduced an AI that drafted responses. Rather than announcing it as a replacement, they had the two most experienced agents trial it first, shape it, and present it to colleagues. Because the experts vouched for it and every reported issue got fixed quickly, the rest of the team adopted it willingly.

What this does not solve

A good rollout builds trust and adoption; it does not fix a workflow that is not ready. If the AI is unreliable, careful introduction only delays the disappointment. Get the evaluation and safety right first, then introduce it well.

Good enough to move on when

  • The people who do the work were involved before launch.
  • The AI is introduced as assistance, with a person in control.
  • There is an easy way to give feedback and to override.
  • Reported problems visibly lead to changes.