Once you have a range of solutions, you can cluster them according to themes and then sort them by impact and doability.
Ideally, you’ll find there are solutions you can implement that are high doability and also high impact— so-called “low hanging fruit.”
Next consider high doability/low impact solutions. There may be ways to create greater impact by including a broader range of stakeholders or bringing a number of smaller ones together. Even then, don’t be afraid to implement small, mundane changes. They can sometimes yield big results that aren’t foreseen.
In a conversation with us on Artificiality, Marina Nitze tells a story that reveals an important lesson: as you go about solving a complex problem, look for mundane and simple opportunities to make change at the lowest level. Small changes can have profound consequences.
While trying to solve the complex problem of improving care in child welfare, people discovered that a number of children who only spoke Spanish were being placed in homes where foster parents didn’t speak Spanish. This fundamental mismatch is a safety issue because children are placed in the home where people can’t communicate with them.
It turns out that when you give people a form that says: “Select the languages that you speak,” people who took highschool Spanish, or went abroad for a semester, have a default behavior of checking the box that says “I speak Spanish” but, of course, they don't actually speak Spanish. The team saw the opportunity for a small change, at the lowest level—alter the language on the form.
When the question was rephrased to say: “In which languages can you fluently communicate with a child?” the number of Spanish speaking families “went through the floor,” says Marina. But this was a good outcome because now only actual Spanish speaking families were captured in the data.
This is a brilliant example of how something that seemed trivial was a key node in the network of the problem. If you’re too anxious for big bang changes you can overlook meaningful mundane solutions that have outsized impact. Marina’s example highlights how something as mundane as default form-filling behavior can drive an undesirable yet unintentional outcome.
Finally, consider the quadrant high impact but low doability. These are the hard things that matter. Ask, why are they so hard? Is it because the technology to implement is too early or too expensive? Is it because of culture or people factors? Is it because of the sheer complexity of the problem? For complex problems, you likely need a different approach.