The productivity paradox of AI: Parkinson’s Law is eating the gains

In 1955, British historian Cyril Northcote Parkinson observed that "work expands to fill the time available for its completion". Originally a critique of British bureaucracy, Parkinson's Law is especially relevant today, in the AI age. AI promises to take productivity to the next level, automating repetitive tasks so we can do more with the time saved. But is this really what's happening? Are we truly saving time?

It's an interesting paradox. Instead of reducing human work or freeing up our cognitive bandwidth, the experience of many is that AI simply shifts the burden of labor. This phenomenon is now called task creep, and it means that the time once spent on manual work is now consumed by other tasks. Some are new responsibilities that are necessary to work effectively alongside AI agents, such as crafting effective prompts, overseeing a growing volume of AI-generated work or validating agents' outputs. But there's also a proliferation of low-value activities that dilute focus and erode the gains AI is meant to deliver. These are the tasks we do simply because "now we have the time for it". Instead of delivering in less time, people sometimes create more work for themselves. Work that adds little value. Therefore, Parkinson's Law proves true: delivery doesn't happen any faster (and sometimes, it isn't even better in quality).

In organizations where hours-based billing or tracking is the norm, this effect is amplified, as work is inherently time-bound and the system quietly incentivizes filling every available minute.

Why AI productivity depends on experience

AI is not a universal productivity booster. In fact, in inexperienced hands, AI can create more work than it removes. An amateur using AI often generates outputs that require extensive review, correction, or even rework, turning a supposed shortcut into a detour.

For example, in software engineering, code assistants act as force multipliers in a seasoned professional's hands. These tools drive productivity for senior engineers, much like two experienced professionals collaborating to solve a complex problem. But when less experienced individuals use coding agents, risk increases. Junior engineers may not know which tasks to delegate or how to give the correct feedback to the agent.

This principle applies across roles: experience matters. You wouldn't put an inexperienced person in charge of supervising another. The same logic applies to AI. Experienced people are better at overseeing AI's work and extracting its full value.

5 AI productivity strategies to beat Parkinson's Law

As business leaders, we must rethink how work is structured and measured to translate AI's potential into real business value. Here are five strategies to break the cycle of task creep and unlock the productivity gains AI promised us:

1. 1.Prioritize outcomes over hours. If AI can complete a task in a fraction of the time, success should be measured by milestones achieved, not hours logged. Redesign workflows so that time savings are reinvested in higher-value work.
2. 2.Redesign incentives to reward value. Incentives that reward visible busyness or hours worked instead of actual impact are a thing of the past. When teams are recognized for achieving strategic milestones, AI-driven efficiencies are more likely to translate into measurable business value.
3. 3.Monitor and eliminate low-value tasks. As new tasks appear, evaluate their impact, and proactively eliminate or automate those that do not contribute to strategic objectives. This frees teams to focus on high-impact work. Avoid confusing this with micromanagement: instead, empower teams to make decisions and prioritize value.
4. 4.Time-box with intent. Set realistic deadlines that reflect AI's capabilities. This prevents work from expanding to fill the void and keeps teams moving at the pace technology enables.
5. 5.Embrace hybrid teams of human experts + AI agents. This collaboration is where the greatest value emerges. Invest in upskilling and ensure your most seasoned talent is at the center of your AI strategy.

Productivity isn’t about doing more things just because we can. It’s about being deliberate with where time and attention go. In practice, I’ve seen teams adopt AI, work harder than before, and still deliver at the same pace. Because AI can increase throughput, but without clear prioritization it often just accelerates low-value work. Parkinson’s Law reminds us that efficiency alone doesn’t change outcomes. Discipline does. The real challenge for organizations is deciding what not to do, and having the courage to let go of tasks that exist only because time allows them to.