Every Solution Creates a Larger World

I’m sure many leaders would’ve felt that moment when some long-awaited improvement finally worked. An improved process flow, work redesigned, or some new tool that made the actual work faster or cheaper removing hours of manual effort and expected things to settle down once the project got over.

But that’s not what happens. With increased efficiency, the demand also increases thereby actually increasing the workload. Work that once seemed impractical becomes baseline expectation. The original problem has been solved, yet the work feels just as demanding. This recurring pattern is generally how the dynamic plays out in organizations as they evolve.

Finally after several weeks, I managed to finish David Deutsch’s The Beginning of Infinity. It is a hard read, nonetheless an ambitious work that moves between physics, evolutionary biology and philosophy, all in any given page. Deutsch’s emphasis on good explanations, criticism, error correction and the continuing growth of knowledge prompted me to think about the problem-solving cycle within organizations.

We solve a problem. The solution makes an activity easier, faster or cheaper, or makes something entirely new possible. Our ambitions expand. That expansion reveals further problems, requiring new knowledge.

Solving a problem doesn't end the story, but simply clears the way for better, more interesting problems to solve.

Consider an automated workflow that reduces a task from forty minutes to ten. At the existing volume, that is a substantial capacity gain. But once the organization knows what is possible, expectations change. How can we expand the scope? Capture more markets? Add more detail? Respond to customers even quicker than we already do?

Together, they can absorb the entire savings. For example, after automating basic customer support inquiries to save hundreds of hours, the new challenge isn't handling high volume anymore. The new challenge will be using that freed-up capacity to potentially design highly personalized, high-touch consultation experiences for complex client needs.

The efficiency improvements cause the rebound effect. Jevons’ paradox describes that increased use more than offsets the resources saved through greater efficiency. That may not always be the case, but a strong possibility.

The implication for managers is pretty straightforward: capacity needs to be allocated and we might use the gain to reduce cost, improve quality, increase scope, give people time to learn, or create some breathing space (which is a luxury). We cannot promise full savings to all of these at once.

Perhaps before implementing a tool or a new workflow etc., it would make sense that leaders agree what its gains are intended to achieve and how additional demand will be prioritized.

And when managed intentionally, this cycle of solving baseline issues to open up higher-order ambitions is how an organization continually adapts, improves, and expands its capability over time.

An improving organization can appear to have more problems. Why is that? Because it becomes better at seeing them, which, previously, were either not visible or people simply didn’t have the knowledge about what to see. The metrics can also initially look worse due to increased visibility of the problems.

And so the job of the leader becomes critical here. They would do well to encourage their teams to speak openly about those issues that never appeared earlier. If the person who exposes a problem is treated as the person who caused it, we teach everyone else to guard the appearance of stability.

Obviously, it’s concerning if a team never reports mistakes. It may appear to be performing well but mainly because it has learned which information to share and which to withhold.

Deutsch argues that without the process of criticism, error correction and continuous improvement, we cannot arrive at good explanations or good solutions.

In organizations, any good explanation must be open to challenge. For example, a manager may say that his team needs more training which may sound plausible, but it remains a hypothesis without facts. Are errors concentrated among new starters? Do experienced people make the same mistakes? Are the instructions ambiguous? Do the SLAs encourage speed at the expense of accuracy?

Until we answer those questions, training may simply be the most convenient response.

A good explanation helps us decide what to change and what evidence would make us reconsider. It also makes learning possible when an intervention fails. We can identify which assumption was wrong, rather than introducing another initiative on top of the first.

However, there is a danger in taking the idea of endless problem-solving too far. We could begin treating every new difficulty as proof that we are progressing.

Some problems emerge because we can now attempt more. Others arise because we designed the improvement badly.

Suppose automation removes routine processing and leaves people handling exceptions all day. The remaining work may require greater judgment, but it may also be more mentally taxing. If recruitment, training and performance goals still assume the old mix of tasks, the organization has changed the work without adapting the conditions around it.

Similarly, faster turnaround can overwhelm a slower review process. A team may process tasks in minutes while managers spend hours checking them. Local productivity rises, but the overall process barely improves.

It is not just about if a particular task became faster. We also need to understand what happened to the whole flow of work, including the effort transferred to colleagues and customers.

This is also where the chapter on Optimism in the book is useful for managers. The belief that problems can be solved is worthwhile. And that belief must improve our confidence in future problem-solving abilities. But it should not become permission to create avoidable harm today as it may not tell us how quickly a solution will arrive, who will bear the cost in the meantime, or whether a particular problem is even worth solving.

Nor do all persistent organizational problems come from a shortage of understanding. Sometimes everyone understands the issue, but the incentives keep it in place.

A sales team may be rewarded for making commitments that operations struggle to deliver. A department may protect its own budget by transferring work elsewhere. Employees may know that challenging an unrealistic deadline will damage their reputation more than missing it later. Another reporting mechanism will not resolve those arrangements on its own. Leaders have to change targets, responsibilities and consequences.

Teams need stable processes and time to become proficient. Continuous learning does not require continuous restructuring. It requires a way to notice and test an alternative when an established process no longer fits.

Eventually, it’s all about whether value gets created or not. Are customers better served? Has rework fallen? Can people exercise better judgment? Have we made the workload more sustainable? Are we solving a more valuable problems, or simply producing more activity?

This is an ongoing cycle. True progress isn't about reaching a state where no problems exist, but about ensuring your team is constantly working on the next higher-value and more meaningful challenges.

Every solution creates a larger world of possibilities. A leader's responsibility is to decide which of those possibilities deserve the team’s time and ensure that solving the next problem does not undo the value of solving the last.

Deutsch argues that problem-solving and thus, progress never stops. There is no final, perfect state of the world, and so is true for organizations. Or, in other words, organizations also, are always at the beginning of infinity.