Good problems.
Almost every job description you will read, will have some degree of focus on problem-solving. Roll up your sleeves and tackle challenging scenarios to make the machine run more smoothly. As valuable (and by no means trivial) as that is, there seems to be not enough focus on problem-finding (or troubleshooting).
Problem-finding isn’t a skill that gets talked about too much because it is harder to make it feel practical or valuable (i.e. when people say “problem solved”, there is little cheer that can be expected when several steps before that someone says “problem found”). Unless you are a car mechanic or a systems support specialist of course. No one wants to be problematic.
With that said, the best experience designers are not the ones that have the shiniest portfolio images, the best product managers are not the ones that can get the most features out and the best executives are not the ones that just solve problems. All of the above are made far better when they FIND THE RIGHT PROBLEM and move towards a strategy built on the right foundation to tackle that problem.
A few business scenarios for problem finding:
In innovation management, problem finding is the foundation for uncovering meaningful, data-driven new initiatives in the portfolio.
In consultancy, problem finding is often the the component of value add that takes strategic advisory out of the realm of generic support into visionary support from a trusted advisor
In product management, there is often a grey area for where the value is found, but it isn’t so grey at all if the intersection of data, design, engineering and commercial targets is used to find the right challenges to solve for
In experience design, user research has long been the foundation of successful initiatives, particularly because when it is done well and without confirmation bias, it allows to uncover the real problem. Sometimes this could be as banal as users not understanding certain teminology - but it makes an impact and it focuses on the right problem.
A simple, practical example for problem finding can be pulled out of the current artificial intelligence frenzy. Whilst objectively integrating AI makes a meaningful impact on pace and cost (an assumption to be validated depending on the use case), without looking at the right problem there is zero guarantee that the solution will be more effective. For example, if a change initiative leads with “We need to integrate AI because the rest of the industry is doing it”, there is probably more digging to do based on customer and company insights, not just competitors or channels. Specifically, the question “Why?”, which needs to have significant purpose and substantiation behind it to create a “good problem”. A better premise to examine the situation would be “our contact centres are constantly overburdened, the self-service portal has 1 star reviews and the onboarding emails have a 5% open rate”. From here the thread can start to unravel, is AI-led automation really a valid solution? Or is the quality of the support function lacking overall? AI operates on a “crap in, crap out” basis, so jumping to a solution would actually be unlikely to help, it would just accelerate the problem, send users to another channel and ultimately back to the overburdened contact centre. Bad problem. Hasty conclusion. Solution infatuation.
Solving problems is reactionary, finding problems can also be reactionary but is also inherently pro-active. More importantly, it assumes that you are able to step back, put your assumptions or biases aside, assess the quality of the problem you are solving, create optionality and ultimately boost credibility because if you can’t sell the problem, the solution will likely not hold up either. With a focus on pace, optimisation and tooling; the focus on deliberating and analysing the right subject matter falls away, which is a dangerous way to end up running fast in the wrong direction.
