The Sisyphean Slope of AI Productivity

There is a quiet pattern emerging in how AI is reshaping technical work, and it does not look like the story most leadership decks tell. AI is, without question, a tremendous multiplier of productivity. But that multiplier does not eliminate work. It relocates it. The boulder gets to the top of the hill, and there, waiting, is another slope. I have started calling this the Sisyphean slope of AI — the predictable second curve that appears the moment an automation effort succeeds.

The first slope: packaging

Consider enterprise application packaging — taking installers (EXEs, MSIs, and the rest), fixing them, updating them, testing them, putting them through QC, and getting them into a deployment platform such as Microsoft Intune. For years this was slow, manual, and unglamorous. In my own experience, a small but dedicated team could spend the better part of a decade refining the craft and still only move a modest volume of packages each month.

Recently, that has changed. With AI-assisted tooling, the intake, repackaging, testing, and publishing steps of the readiness process can now be automated end-to-end. What used to require a specialist’s full attention now runs as a pipeline. That is a genuine and significant win for most organizations.

And then the second slope appears.

The second slope: testing as the new bottleneck

In my own experience, application change volume used to sit at roughly five percent of the portfolio per month — three or four apps, sometimes five. With automated packaging in place, and with security-driven patch cadence accelerating in parallel, we are now seeing twenty to thirty application changes per week land in front of the testing function. That is an order-of-magnitude shift, and it was not what the original process was designed for.

The result is a workflow problem dressed up as a packaging problem. Packaging is no longer the constraint. Testing is. And because the security model is now what drives the packaging cadence, testing delays do not just slow down releases — they create a security concern of their own. Every patch sitting in a test queue is a known vulnerability with a known fix waiting on a manual sign-off.

It is also worth being honest about what testing actually has to cover. It is not just confirming that the installer runs. It is verifying the update path, the uninstall path, file associations, registry state, shortcuts, services, and — most importantly — that the application still delivers the business value it was deployed for in the first place. That is real work, and it does not compress just because the upstream pipeline got faster.

Why the Sisyphean dynamic matters to security and IT leadership

The Sisyphean dynamic has a few implications worth sitting with.

First, productivity gains are rarely retained where they appear. Automating packaging does not give the packaging team ten times their old capacity. It transfers ten times the load to whoever sits downstream — in this case, the QC and test function. If that team’s capacity has not been planned for, the gain evaporates into a new queue.

Second, the security argument cuts both ways. Faster packaging exists because security demands faster patching. But if testing cannot keep up, the same security posture that justified the automation is now being undermined by the bottleneck the automation created. The risk has not gone away. It has moved.

Third, the org chart has not caught up. Application packaging has historically been treated as a minor, somewhat isolated technical silo. With automation, the work itself has changed character — it is now closer to a release engineering discipline, with testing and verification as first-class concerns. The reporting lines, headcount, and tooling budgets often have not been redrawn to match.

The next boulder: automating the test loop

The encouraging part is that the same automation logic applies one slope up. Readiness platforms are now beginning to automate the comparison testing process — installing the previous version, installing the new version, exercising install, update, runtime, and uninstall, and surfacing only the differences for human review.

That is the right shape of solution. It does not try to replace human judgment about whether an application still delivers business value. It removes the mechanical, repetitive parts of the comparison so that human attention can be spent on the parts that actually require it.

If it works at scale — and the early indications are good — it should compress test cycles dramatically, raise release cadence further, and produce a more stable target platform overall. Packaging stops being a silo and starts being part of a continuous release process.

What to expect from the slope after that

If the pattern holds, the next bottleneck will appear somewhere we are not currently looking. It might be deployment ring management. It might be rollback and recovery. It might be the human capacity to interpret the diffs that automated testing surfaces. The specific location is hard to predict. The existence of a next slope is not.

The lesson I take from this is not that AI automation is overhyped — it genuinely is delivering ten-times outcomes in places that mattered. The lesson is that the work of leadership is to keep looking up the hill. Every successful automation reveals the next constraint. Pretending otherwise is how organizations end up celebrating a productivity win while quietly accumulating risk a layer downstream.

Sisyphus, in the original myth, was condemned. The version we are living through is more hopeful: each time we push the boulder up, the hill itself gets a little shorter. But the boulder is still there in the morning. Plan for it.