Slow Down To Speed Up: Problem Identification Drives Transformation

Most managers are both excellent and flawed problem solvers, depending on the context. They have remarkable cognitive skills, but being human, they also suffer from a broad range of innate biases and limitations.

There’s an old story of a man searching for his keys one night under a streetlight. Eventually, a handful of good Samaritans join the search. After some time with no success, someone finally asks the man, “Are you sure this is where you lost them?” The man replies, “No, I lost them in the park.” The helper, confused, asks him why he is looking for them here. The man replies, “Because this is where the light is.”

Why Problem Solving Is Harder Than It Seems

This scenario is more common than many people realize. In business, the data we collect and the way we interpret it can sometimes be like the streetlight in the parable. People tend to look for answers in the most familiar or obvious places rather than digging deeper. In organizations, this type of problem-solving can lead to wasted resources, lost time, and mounting frustration.

Consider the case of a food manufacturing company we recently worked with. This company had a specialty line of allergen-free food products, which required rigorous testing to ensure that trace amounts of peanut residue were not present in their facilities. Despite the fact that their workers diligently cleaned and re-cleaned the lines, testing after each cycle, their production lines repeatedly failed quality control tests. Production was backlogged, costs were up, and workers were frustrated. How could even the slightest trace of residue remain?

Someone finally asked a key question: “Have you tested the testing room?” It turned out that the testing room—not the production line—was contaminated with peanut residue.

Overcoming Proximity Blindness

This type of situational tunnel vision is often heightened when problems arise in routine tasks or familiar environments. Thinking outside the box becomes increasingly challenging when you spend all your time inside it—whether that box is your role, company, or industry.

Nobel-Prize-winning psychologist Daniel Kahneman introduced the concept of two types of thinking in his book Thinking, Fast and Slow. His theory breaks down human thought processes into two systems. System 1 thinking is fast, intuitive, and handles routine tasks. It’s very efficient but prone to errors and biases because we all tend to jump to conclusions based on patterns we recognize. Within familiar settings, we often rely on mental shortcuts and routine assumptions. This is efficient for everyday tasks but can create blind spots when tackling unique or complex problems.

System 2 thinking is more deliberate and analytical. It requires effort, attention, and reasoning, and we use it for complex problems or unfamiliar situations. It might be more reliable, but it’s definitely more taxing and slower.

A simple example might be when you’re driving a familiar route home. You primarily use System 1. You basically operate on autopilot, checking your mirrors or changing lanes without much conscious input. But if something unexpected happens, you’re low on gas, or a detour sign forces you to find a new route, your slower and more conscious System 2 thinking kicks in.

Seasoned managers and experienced consultants can sometimes fall into the same trap. Familiarity with a topic is not always an advantage for solving complex problems, particularly if your experience leads you to think the problem is not complex.

With our own consulting teams, we try to stem this tendency to jump to conclusions by relying on a structured problem-solving methodology designed to reveal problems that are baked into routine operations. But the real secret to this is that we force System 2 thinking on bright people who might otherwise believe they can jump to a solution. This is particularly important at the front end of an engagement, to make sure we properly understand what the actual problems are that we, and the client, need to solve.

The “Solution First” Trap of AI Integration

System 1 and System 2 thinking is well worth considering for companies rushing to take advantage of the many benefits of artificial intelligence (AI). While AI offers enormous potential, it is often treated as a solution in search of a problem. What we currently observe with many organizations is a rush to apply the solution without carefully and thoughtfully understanding the underlying problems that need to be fixed.

One company invested heavily in an AI tool to speed up customer support response times. The system performed well, but customer satisfaction scores did not improve. It turned out that customers valued resolution accuracy more than speed. The company had wasted considerable resources applying an elegant fix to the wrong problem.

Another recent client believed their challenges stemmed from outdated technology. A deeper analysis revealed that the true cause was poor interdepartmental communication. Fixing the miscommunication saved significant time and money, while the assumed “solution” (investing in new technology) would not have addressed the underlying issues and may have baked the problems deeper into the routine process.

I asked one of our managers, Caleb Emerson, for his thoughts on AI integration. He had three points:

1. Like any business change, the approach to AI needs to be intentional and structured. Throwing multiple different AI tools at the wall is not a sustainable approach to scalable improvement.
2. Businesses need to understand their level of readiness for change. Like in any project, understanding the base state is key: Do the right pieces of information and data exist to enable change?
3. Integrating change, AI or otherwise, must occur across the organization and requires buy-in and leadership support. The most effective AI tool is only useful if the organization’s behavior changes to implement it.

Whether it’s AI or any other tool, solutions are only as effective as the value of the problems they address. A key constraint that hinders capturing AI’s value is the integrity of the underlying data. Unless that is addressed, automation and increasingly sophisticated algorithms will struggle to deliver meaningful results.

Slowing Down to Speed Up

Slowing down to define the problem may feel counterintuitive when urgency is high. Yet, it is the clearest path to long-term success. Proper problem identification saves time, money, and frustration by focusing resources on effective solutions instead of misguided assumptions. When you take the time to identify the real problems, you accelerate the pace of meaningful change. Instead of spinning your wheels, you are better equipped to drive progress where it counts most.