The dead horse syndrome

Recently, in my habitual LinkedIn scrolling routine, I see posts referring to the dead horse syndrome. The analogy refers to projects or strategies that require a huge amount of effort and investment, but does not move anywhere. Cases where hours are dedicated to looking at the horse in different angles, the amount of resources and ways to pull it, but no meaningful change.

Based on the number of posts, the dead horse syndrome is happening frequently in business environments. I work in the digital sector for a while now. This is a particular area to observe this syndrome in endemic proportions, with projects failing in delivering the original enthusiasm.

As a Veterinary Doctor, I know that preventing death is easier than resuscitating horses. Project success or failure is not much about the precise selection of alternative solutions. Success begins with a clear, well-defined problem. In the analogy to the veterinary practice, a doctor first makes a thorough diagnosis before prescribing treatment. Likewise, often in business, failure is the result of applying a detailed solution to an ill-defined problem.

Two of my own illustrate the case. In one digital project for agriculture, the problem was badly defined. In the beginning, project managers confused who was their real user; they created a too sophisticated app. Farmers, the real users, were forced to collect data they misunderstood and could not immediately apply the output from the tool. As a technician translated the output in recommendations, farmers were reluctant to adopt it. After ten years of trial and error and dozens of millions of investment, the project was officially declared dead.

On the other extreme, I recently got involved in a project where the users themselves brought up a problem they had been dueling for a while. They knew precisely what they needed. The cause and effect were crystal clear in a simple diagnosis of the problem. Most importantly, the problem definition was a consensus among the vast majority of users-to-be. The first prototype with a very basic solution was designed in one week. On the launch date, it was far from perfect, but after 3 months, the adoption rate reached 90%, and financial benefits were expected to be multiples of the initial investment.