Why faster AI tasks don’t always produce better business results

An AI tool can summarize a proposal in seconds while the evaluation still takes a week. Someone has to check the summary, find the supporting evidence, apply the right criteria, resolve gaps and prepare the recommendation. Faster reading helps. The work around it can still determine when a decision gets made.

That distinction matters when a team builds its business case. A task that gets faster is a useful improvement. An operating process that handles more work, reduces repeated effort and gives reviewers a stronger basis for judgment is a different kind of result.

Start with the completed review

Consider a procurement team assessing proposals across several service categories. A useful summary might describe each supplier clearly. The team still needs comparable evidence against each category’s scorecard. Pricing assumptions, exclusions and delivery commitments may appear in different sections or attachments.

The useful unit of improvement is the review-ready case: relevant facts brought together, criteria applied consistently, missing information identified and source references available for inspection. The reviewer can then spend time on differences that affect the choice, rather than reconstructing the file.

Research points toward the whole workflow

McKinsey’s October 2026 analysis examines the gap between individual AI productivity and enterprise value. It connects stronger performance with changes to workflows, decision authority and the way organizations use recovered capacity. Its argument extends beyond adopting more tools. [1]

A practical response is to map the work from the first submission to the recorded outcome. Where does information get re-entered? Which checks are repeated? Who requests missing evidence? What makes a case ready for review? These questions expose the handoffs that a faster standalone task may leave untouched.

Measure what the team can now complete

Track active review effort alongside completed cases, unresolved exceptions and the time spent correcting or reopening work. Keep waiting for an outside response separate from time spent working. These measures answer different questions and should stay visible together.

Then decide how to use recovered capacity. A team might absorb a backlog, examine more alternatives or devote more attention to difficult cases. Those are operational choices, not automatic savings. The business case becomes more credible when it explains both what changed in the workflow and what the organization did with the improvement.