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Before approving another AI tool, I ask one question: what work stops if this succeeds?
If nothing stops, you are buying a faster version of the process you already have. Tasks finish sooner, but the approvals, handoffs, and end-stage reviews survive. Total work can rise because someone now has to check the AI.
AI workflow redesign means changing the sequence itself. The tool is the smallest part.
Faster Tasks Do Not Make a Faster Business
Most organizations introduce AI at the task level: drafting documents, summarizing meetings, generating code. Each activity speeds up, and the finished work still enters a workflow built around constraints that may no longer apply. Our earlier article on why the AI stack cannot fix a broken process addresses that foundation. The next question is which steps no longer need to exist.
McKinsey’s State of AI in 2026 found that nearly three-quarters of AI high performers reported fundamentally redesigning workflows because of AI, up from 55% a year earlier. Only about one-quarter of other respondents reported doing so. The association does not prove causation, but the gap is substantial.
That makes business value a test to define before rollout. In June 2025, Gartner predicted that more than 40% of agentic AI projects would be canceled by the end of 2027, citing escalating costs, unclear business value, or inadequate risk controls.
AI Workflow Redesign Requires Something to Leave
A real redesign removes or changes at least one of these:
- A duplicate data-entry step.
- An approval that exists only because information used to arrive incomplete.
- A handoff where the receiver has to rebuild context.
- A meeting held to reconcile inconsistent outputs.
- An end-stage review that earlier controls can replace.
Removing a step is not removing oversight. Often the better move is to shift human review earlier, set acceptance criteria, and define which exceptions need escalation.
This is where the project turns political. A workflow encodes authority. Change who prepares information, who signs off, or when a decision becomes final, and you have changed someone’s job. No technology team settles that alone. It explains why redesign can stall even when the software works.
- Pro tip: Name the step you intend to delete before you shortlist vendors. If nobody will commit to a deletion, you do not yet have a redesign plan.
Follow One Software Request From Start to Finish
Say a software team receives a request with a clear feature name but an unclear definition of “done.” AI generates code quickly. A reviewer then asks what the feature should do in an edge case. The work goes back to the requester, the team meets to resolve it, and the code is revised.
Now change the sequence. The requester and team settle the edge case and acceptance criteria before coding. AI helps produce the first implementation. A human reviews it against those criteria before it moves forward. The late clarification meeting is the step they are trying to remove; the review stays.
The late clarification meeting can return if the team stops maintaining its acceptance criteria. A deleted step stays deleted only while the thing that replaced it is still being done properly. Our delivery approach treats scope, roles, design review, and customer feedback as distinct stages: name what each stage has to resolve, then ask whether AI lets the next stage begin without rebuilding the same context.
Run the 90-Day AI Workflow Redesign Test
Before the pilot, choose one type of work and record its path from request to completion. Measure cycle time, human touches, handoffs, and how often work returns for correction. Count the step you propose to remove. Assign an owner who can authorize that change.
At 30 days, check whether people are using the new sequence or quietly keeping the old one alongside it. At 60 days, review exceptions: did the removed step catch problems that now appear later? Adjust the criteria or escalation point if it did.
At 90 days, compare the same measures with the baseline. Did the step actually stop? Did cycle time fall without a rise in rework or review effort? If the tool saved drafting time but added another approval, count the added work too.
Map the process, name the step that should end, assign decision rights, and record the baseline before the pilot rather than after. Then pick the technology. In that order, AI workflow redesign produces a number. Backwards, it produces a renewal.
If your next AI initiative needs a delivery plan more than another license, talk with our team.
