On 26 September 2026, Microsoft’s Bryan Goode argued in Fortune that people still bridge software gaps. This was commentary, not a product launch.

An AI assistant can finish a piece of work quickly while the customer still waits. The delay may sit between the systems, teams and decisions that surround the task. That is where a productivity experiment needs to prove its business value.

What happened

The question for a business is concrete: after an assistant produces a useful answer, what has to happen before anyone can use it? The answer may involve another application, a permission check, a missing identifier or a colleague who knows which record is current. These are different problems, and buying a faster model does not automatically solve them.

Why it matters

Take a hypothetical service company preparing a customer quotation. AI helps a salesperson draft the proposal. Someone then copies the requirements into a project tool, asks operations about capacity, checks the customer’s terms in a separate record and waits for a price exception to be approved. The draft arrives sooner, but the customer may receive the final offer at the same time as before.

Simply automating the copying could help, provided the data is reliable and the permitted actions are clear. Yet some of the waiting may represent a real decision. Capacity might be uncertain. An unusual service promise might need negotiation. Treating every pause as waste would remove useful judgement alongside unnecessary administration.

Start by following a single case through the process. Record where it waits, why it waits and who can release it. Ask the people doing the handovers what they check that the formal process does not describe. Their informal work may reveal either avoidable friction or a control the proposed automation must preserve.

The bigger shift

The useful unit of AI improvement is often a completed business transaction rather than an isolated task. That changes the evidence a pilot should collect. Time saved drafting is relevant, but so are the time to a usable quotation, the amount of rework and whether the eventual promise can be delivered.

It also changes ownership. If sales receives the benefit while operations absorbs the corrections, the project needs a shared decision about success. A process owner should be able to resolve that disagreement and change the workflow. Without that authority, integration can become another technical project waiting for a business decision.

This is the practical point behind putting AI strategy before tools. First specify the outcome and the responsibilities around it. Then decide which software connections and agent permissions are justified. An integration should serve a known process, not conceal uncertainty about how the process should work.

My take

Choose one handover that regularly delays useful work. Improve that boundary before expanding the assistant across every department. The first release might only prepare a validated record for a person to approve; it need not control the whole transaction.

Give the receiving team the power to reject an incomplete handover with a clear reason. Measure how often that happens and whether the same missing information keeps returning. This makes quality visible at the point where the work changes hands.

After a short pilot, compare complete cases with the previous process. Did the customer receive a usable result sooner? Did staff spend less time chasing context? Did an important check survive? Those answers make a stronger case for investment than the speed of the first draft. The aim is to remove unnecessary coordination while preserving the decisions people are there to make.