Written with Michael Neece
In my book, Smart Work: Why Organizations Full of Intelligent People Do So Many Dumb Things and What You Can Do About It, I discuss how leaders achieve quantum increases in productivity and avoid doing dumb things.
Recent research reports have revealed a major paradox. Despite decades of massive investment in information technology, U.S. knowledge-worker productivity has not improved as expected. In 2015, IDC Research estimated that $727B was spent on IT in the US, and the major productivity bump that justified each investment has not been seen by economists.
So what the heck is going on?
Our hypothesis is that productivity shortfalls are not directly related to technology itself but to corporate timidity and “maintain the status quo” decisions that masquerade as the safest path to improvement. When this occurs, new technologies are attached to old ways of working and deployed in support of existing power structures. For example, using Artificial Intelligence (AI) to automate a twenty-year old order entry process is like attaching a motor to a horse and expecting the performance of an automobile.
Shared folders, instant messaging, specialized apps, and monolithic systems continue to create data silos and fragmented teams who are already overwhelmed with large data sets, spreadsheet reports, and too many e-mails. And still, most organizations try the same status-quo thinking to solve today’s rapidly changing challenges.
In the technology world, we’ve repeatedly seen the “Less” phenomena. Radio was wireless, cars were horseless carriages, and early ATMs were teller-less windows. Even the “W” in Wi-Fi stands for Wireless. We tend to see the new through the lens of the old.
That’s what most organizations have been doing with all of today’s spectacular technologies. We’ve been making bigger candles rather than inventing the lightbulb. In organization after organization great technology has been either ignored or used for mundane and incremental uses.
It’s the triumph of TTWWADI (That’s The Way We’ve Always Done It). The past shapes the future as organizational timidity constrains the potential of technology. We’re limiting ourselves by looking to the past for solutions to today’s problems.
That’s not true at John Deere. Instead of making slightly better tractors and farm equipment, they’re redefining their product. Today’s Deere tractor can apply pesticide, herbicide, seed, or fertilizer in one-millimeter increments. Using that millimeter’s performance history, the tractor can vary the application for every square inch in the field. Instead of average application, they use actual data. With historical yield data they can vary the application of fertilizer, pesticides and seeds with extraordinary precision. It’s no longer smooth peanut butter but data-driven chunky. It’s a combination of software, AI, and GPS working together that creates this revolutionary agriculture capability. It’s not your grandfather’s farming.
That’s revolutionary! That’s what transformation looks like.
Continuous Improvement and Operational Excellence cannot fully exploit new technologies. They’re too rooted in today’s work. Taking waste out is very different from reinvention. Leaning is not innovating. Incremental improvements lead to diminishing returns. Incrementally improving the same way of doing things, does not lead to major leaps in productivity (output, costs, speed, quality).
The key to explosive productivity growth lives through transformation. Uber didn’t incrementally revolutionize the taxi industry, drones will not slightly improve home delivery, and personalized medicine dosages are not just a little bit better than traditional pharmaceuticals. Small change will always lead to small improvement.
Big productivity gains can only come from big process improvement change. But when organizations lack the courage and patience to invest in big change, and continue to fund better sameness, the result is inevitably incremental decay. Organizational inertia and status-quo thinking are the real barriers to quantum leaps in productivity.