Why ‘Innovation for Efficiency’ Isn’t Enough: The Hidden Value of Slowing Down

By Mark House

In today’s world of rapid technological change, the dominant narrative is that innovation should serve efficiency. “Get more done for less” is the rallying cry, especially in the age of AI, where speed, automation, and optimisation are often seen as the ultimate goals. But what if this relentless pursuit of efficiency is not just limiting, but counterproductive?

Behavioural economist Rory Sutherland offers a compelling argument for why innovation should sometimes aim for the opposite: embracing friction, slowness, and the value found in inefficiency.

The Problem with Efficiency as a North Star

The obsession with efficiency assumes that faster, cheaper, and more streamlined is always better. This mindset drives projects like high-speed railways, instant AI responses, and business processes that strip away anything deemed “wasteful.” Yet, as Sutherland points out, this approach is fundamentally narrow. It reduces complex human experiences to numbers and overlooks the psychological, emotional, and creative dimensions that make innovations truly valuable

Rory Sutherland’s Case Studies: When Slower is Better

High-Speed Rail (HS2) and the Value of Time Spent

The UK’s HS2 project is a classic example. The government justifies its enormous cost by promising faster journeys and increased capacity. But Sutherland argues that passengers often don’t mind the time spent on a train; in fact, they may be more productive or relaxed there than elsewhere. The real pain point is uncertainty and inconvenience, not the journey itself. Cheaper, app-based solutions that reduce waiting times or improve the travel experience could deliver more value than simply shaving minutes off the timetable.

London Underground: “Time to Next Train” Displays

One of the most effective improvements to the London Underground wasn’t running trains more frequently, but installing dot-matrix displays that show the time until the next train. Passengers are happier waiting nine minutes for a train when they know exactly how long the wait will be, compared to waiting four minutes in uncertainty. The psychological benefit of certainty far outweighs the marginal gains of efficiency.

Speedometers and the Power of Framing

Sutherland also discusses how information is presented—using the example of speedometers. Some modern speedometers show not just speed, but how long it will take to reach a destination at the current speed. This reframing reveals that driving much faster rarely saves significant time, but greatly increases risk. The lesson: how we frame and experience time, not just how efficiently we use it, shapes our decisions and satisfaction.

The IKEA Effect and Deliberate Friction

Sutherland highlights the “IKEA effect”—we value furniture more because we assemble it ourselves. The effort and friction involved create emotional attachment. In recruitment, companies like Goldman Sachs deliberately introduce friction (multiple interviews, requiring follow-up) to filter for commitment. Sometimes, slowing things down or making them harder actually leads to better outcomes and deeper engagement.

Why Efficiency Alone Fails—Especially with AI

AI is often designed to deliver instant answers and optimise for speed. But Sutherland warns that this ignores how humans actually make decisions: iteratively, reflectively, and often valuing the journey as much as the destination. If AI only optimises for efficiency, it risks missing the nuances of human preference, creativity, and discovery.

He imagines a “slow AI” that helps users explore options over time, refining preferences through conversation, much like how we plan holidays or make major life decisions. The best outcomes often emerge from a process of mulling, discussing, and reconsidering—not from instant optimisation.

Why Outcomes Matter More Than Output

We have written before about the efficiency gains of RM Compare and how it could be used as an alternative to marking at scale. However, this is not the only benefit of the approach.

The distinction between outcomes and output is critical—especially in education. Output is about quantity: how many essays are marked, how quickly grades are assigned, how many boxes are ticked. Outcomes, on the other hand, are about quality: what students actually learn, how their understanding deepens, and how well they are prepared for the next stage of their education or life.

Focusing on output—more things marked, faster—can lead to superficial assessment, where the process becomes a numbers game. Teachers and students are pressured to rush, and the richness of feedback and reflection is lost. In contrast, focusing on outcomes means prioritising better learning, fairer assessment, and genuine professional growth.

  • Better learning happens when assessment is used not just to judge, but to inform, guide, and inspire students.
  • Better outcomes are achieved when teachers have the time and space to reflect, collaborate, and make nuanced judgements—rather than simply processing as much as possible, as quickly as possible.

Good Things Come to Those Who Wait: RM Compare and the Value of Collaborative Judgement

The old Guinness slogan—“Good things come to those who wait”—captures a truth that’s often overlooked in the relentless drive for efficiency. As we’ve seen, innovation focused solely on speed and optimisation risks missing the deeper value that comes from thoughtful collaboration and human connection. Nowhere is this more evident than in the way RM Compare reimagines assessment, not as a race to the quickest answer, but as a process that brings people together to enhance judgement and produce better outcomes.

RM Compare: Collaboration Over Speed

At its heart, RM Compare is a collaborative system designed to break down silos and encourage collective decision-making at scale. In a typical session, multiple judges—often teachers from different schools—work together toward a common cause, such as assessing student work or moderating standards across a network. The platform’s DataShare and Multi-Centre features make it easy to connect organisations, enabling broad participation and richer professional dialogue.

This approach is the antithesis of “innovation for efficiency.” Rather than automating judgement or reducing assessment to a set of metrics, RM Compare leverages the power of human expertise. Teachers compare pairs of anonymised work, using their professional judgement to decide which best meets the criteria. Through many such comparisons, a consensus emerges—not instantly, but through a process that values deliberation, discussion, and reflection.

Why Waiting—and Working Together—Matters

The process is intentionally collaborative and, yes, a little slower than simply letting an algorithm decide. But this “slowness” is where the magic happens:

  • Enhanced Judgement: By bringing together multiple perspectives, RM Compare reduces individual bias and taps into the collective wisdom of a diverse group of professionals.
  • Professional Growth: Teachers engage in meaningful discussions about what “good” looks like, leading to improved understanding and better outcomes for students.
  • Authentic Assessment: The system supports nuanced, contextual judgements that AI alone cannot replicate, ensuring that outcomes reflect real-world standards and values.
  • Community Building: Collaboration across schools fosters a sense of shared purpose and continuous improvement, transforming assessment from a solitary task into a communal achievement.

Conclusion: Rethinking the North Star

Making efficiency the sole North Star of innovation is deeply limiting. As Sutherland shows, the greatest value often lies in the inefficiencies, the friction, and the time spent. Whether designing AI, public transport, or educational assessment, innovators should ask not just “How can we make this faster?” but “How can we make this more meaningful?”

By focusing on outcomes—better learning, deeper understanding, and fairer assessment—rather than just output, we ensure that innovation serves real human needs. Sometimes, the best innovation is to slow down, bring people together, and trust that—as Guinness famously put it—good things really do come to those who wait. RM Compare is proof that when we prioritise collaboration and human judgement over mere efficiency, we don’t just get quicker results—we get better ones.