How We Solve Problems Better Than Machines

In the winter of 2007, I waited at a bus stop with a backpack and my hands balled into fists and pressed firmly into my jacket. The harsh wind whipped as the free university bus approached. I exhaled a cloud into the air, pushing away the scent of diesel, as I climbed onboard.

The bus took me downtown, where I was an unpaid college intern at Action Greensboro, a nonprofit focused on building a stronger community through several projects dependent on public-private partnerships. I disembarked from the bus and walked two blocks to the small storefront building on Elm Street. The Greenway Project committee was starting soon and I was intent on being early and making a good impression.

fAs the meeting started, half a dozen leaders from the community, including a city planner, a former mayor, a lawyer, a sociology professor, and a few other civic-minded individuals, grabbed a pastry and a cup of coffee from a table in the room. They were all here to discuss plans to construct a 20+ mile greenway that would circle the entire downtown district and connect to other bike and pedestrian paths around the city.

On the wall hung a 6-foot wide map of the downtown area. Someone had used a highlighter to mark the proposed route. Sticky notes were pinned to certain sections with notes or questions. The committee had a finite agenda for the 90-minute meeting that never made it past the first few items. A blocker that repeatedly derailed the conversation was the group’s inability to remember what particular crossroads or sections of the proposed route actually looked like. Google Maps did not exist yet. We had to rely on our faulty memories.

I sighed as two members started a third of fourth debate about the details of an intersection where the greenway would cross. Though I wondered if an unpaid intern should say anything, I chimed in. “Maybe I can go photograph all these areas we’re debating, print out the photos, and plot them on the map on the wall.” After a few seconds of silence, the group agreed it was a good idea.

Fortunately, I had just purchased a new digital camera from Best Buy. This was before we all had amazing cameras embedded into our phones, so good photography was limited to people with real cameras. It was, frankly, more than I could afford, but as a budding college senior, I felt like the skill would differentiate me among my peers. (It did not.)

For weeks, I carried my camera, a notebook, and a small-sized map of the proposed greenway around Greensboro. I rode the bus, my bike, and walked (a lot). I took a few photos at every key intersection or area of proposed development. Every few days, I’d go to the university library and use part of my student per diem to print the photos, mostly in black and white (because it was cheaper). Then, I’d ride the bus back downtown, walk to the office, and pin my photos up on the wall next to the giant map.

Most times, the nonprofit director would thank me. Sometimes, she would ask questions or say she didn’t feel my photo captured the exact area correctly. I would make a note, return to the spot as soon as I could, and reprint the photos. This went on throughout the remainder of the spring semester. In the weekly committee meetings, I gave updates on which areas I photographed and which areas were up next. I answered questions and helped solve debates. It became natural for the group to turn to me when some members couldn’t agree about whether or not a building was still on that corner or if the sidewalk truly stopped at a certain point.

In the moment, it didn’t feel like I was doing anything valuable. It was fun in the beginning, but a few weeks in, I felt like I was doing busy work. An unpaid intern, spending his own money and time, for a project with a decades-long timeframe and low probability of getting fully-funded. But, I stuck with it for the college credit, the addition to my resume, and to meet interesting people.

While I learned quite a bit about Greensboro that semester, one of the biggest lessons I learned was the value of showing progress and releasing work quickly. Rather than waiting until I had all the photos taken, I brought the photos I did have each week. If I had waited, no one would have seen my photos for months. Frankly, they may have forgotten I was even doing it. I wouldn’t have been able to participate and contribute to the weekly committee meetings. I wouldn’t have received feedback from the director. The final outcome would have been less valuable.

In Kevin Roose’s book, Futureproof: 9 Rules of Surviving in the Age of AI, he talks about how we differentiate ourselves from machines (robots, AI, automation) as these technologies continue to change the nature of work. One of his recommendations is to lean into your humanness. Products and services imbued with humanism are inherently more valuable, he argues. Compare the prices of a used DVD player on eBay or a 30-year old handmade piece of pottery. The handmade item usually holds more value.

One way we show our humanness through knowledge work is being open about our progress (or lack thereof). At Method, we call this guiding principle, “Work in the open.” This means being transparent with your teammates and clients about what you’re working on, where you’ve made progress, what’s holding you back, and where you need help. It means we share what we’re learning while we’re in discovery or research mode. The result is having no surprises, getting feedback quickly, and course-correcting in smaller increments.

The “Work in the Open” principle fits nicely with Roose’s recommendation. When our teams show their work, their progress, and bring their perspectives and insights explicitly to our clients, we show them that we’re solving problems in a way that a machine can’t. This reinforces the value of work done by humans.

Improvements in technology make it so that the problems of today are not the problems of yesterday. My experience photographing streets and intersections is proof. Yet, human-sized problems will never go away. There will always be complex, multi-dimensional challenges that machines devoid of intuition and empathy can’t adequately solve. The way we prove this is by bringing our humanness to those problems and working in the open with others to co-create solutions.