AI programs need an end-to-end throughput exercise

Make the conversion funnel the learning object

The applied exercise should select one bounded software flow and count its stages: work requested, coding activity started, change proposed, review entered, review accepted, integration completed, release candidate created, production release completed, intended user reached, meaningful use observed, and value or problem resolved. For each transition, define the eligible population, accepted output, queue owner, rejection and rework treatment, elapsed time, human effort, cost, and evidence source. Generated activity is the funnel's input, not its outcome.

Freeze a pre-intervention period and show volume, conversion, queue age, touch time, defect escape, reversal, support demand, adoption, and user result at every stage. Keep projects, releases, applications, and active usage distinct. The Wharton article's reported pattern—larger growth in activity than releases and rising application supply without comparable overall usage—should be treated as a prompt to inspect constraints, not as a benchmark promised to participants.

Introduce one intervention and locate the shifted constraint

Apply one controlled AI workflow to a declared cohort while keeping a comparable baseline. Retain tools and versions, eligible work, assignment, prompts or specifications, generated output, human review, rejected work, corrections, security and compliance checks, integration results, release evidence, user exposure, observed use, incidents, and costs. Measure whether the intervention changes conversion or time at the generation stage and whether another queue grows downstream.

The required diagnosis should identify the binding constraint before and after the intervention. It might move from coding capacity to specification quality, review, testing, integration, release governance, distribution, onboarding, reliability, or user demand. Quantify added capacity and added load. Do not call more artifacts throughput when accepted releases or use stay flat, and do not call lower activity a failure if the flow removes rework while increasing accepted outcomes.

Require a reproducible constraint-shift packet

The deliverable should contain the funnel definitions, frozen datasets, event queries, stage counts, conversion rates, queue distributions, exclusions, rejected and abandoned work, quality findings, cost, intervention record, uncertainty, before-and-after constraint, and links from aggregate results to representative artifacts. It should also state which observation would falsify the conclusion. A prompt library, demonstration, self-reported time saving, or same-model quality score is not sufficient.

Where the underlying research or article changes, retain the cited version and retrieval evidence. Separate the study's associations from the participant's result, and the participant's result from a general enterprise claim. Independent reviewers should be able to reproduce the conversion arithmetic and identify whether a change reflects more input, better flow, weaker acceptance criteria, shifted work, changed instrumentation, or real user adoption.

Decide which bottleneck to change next

At the end of the exercise, require one bounded operating decision: keep or stop the intervention, change work intake, add or redesign review capacity, improve tests or integration, reduce release batch size, strengthen user distribution and onboarding, retire unused output, or run another controlled cycle. Name the constraint, evidence, owner, expected stage conversion, capacity tradeoff, risk limit, review date, and rollback. The decision should target the observed bottleneck rather than automatically buying more generation capacity.

Knowledge at Wharton's article supports the cited study population and reported associations among AI tooling, coding activity, projects, releases, application creation, and usage, along with the review and adoption bottleneck framing. It does not establish causality in every setting, the quality of a particular program, a participant's throughput, business value, or outcome. Executive, engineering, product, operations, security, finance, learning, measurement, and legal owners retain those judgments.

Turn this source into a reviewable decision

For AI Leadership Programs for Executives, use this briefing as a dated decision record rather than a substitute for the source. Preserve AI Is Producing More Software. Why Isn't It Being Used?, the exact URL, the September 9, 2026 review date, the supported facts above, the editorial interpretation, the limitations, and any buyer-specific evidence. Link that record to the decisions most directly affected: Learning job and level; Curriculum depth and recency; Applied work and feedback; Transfer to organizational work. State whether the source changes the scope, evidence requirement, control, sequence, or only the language used to describe the decision.

Before action, name the accountable owner, affected population and workflow, exact offering or configuration, source data and rights, human decision point, exception and appeal path, complete cost, expected benefit, failure and stop conditions, retained evidence, and next review date. Keep official facts, provider statements, buyer observations, representative tests, measured outcomes, editorial inferences, and unknowns visibly separate. Reopen the record when the source, offer, model, integration, data, policy, population, responsible person, or measured result changes.

Limitations and unknowns

This briefing uses a Knowledge at Wharton article published after the prior successful-run cutoff and treats it as a verified post-cutoff source. The page summarizes research and notes that the paper was updated with new data after the article was written. Reported percentages are study results, not audited buyer outcomes or program claims. Current research, the exact course syllabus and faculty, secure applied-work rules, participant and sponsor evidence, and qualified learning, technical, security, workforce, measurement, procurement, and legal review control.