We have spent more than a century trying to make work more productive.
Sit with that for a moment. We’ve built entire industries, organizational structures, management philosophies, technologies, and careers around one fundamental pursuit: How can we produce more, faster, and with fewer employees?
And honestly, if you look back at recent human history, it worked. We have almost perfected the art of efficiency in many aspects of our lives. Today, you and I can accomplish more in a single day than most people throughout history could have imagined possible. At least, we accomplish more by the standards we have created for ourselves. We can communicate instantly across the world, access more information than any generation before us, produce goods at incredible speeds, and accomplish tasks in hours that once took days or even weeks. Much of this became possible because of the machines we built, and those machines became widely available because of the Industrial Revolution and the fundamental changes it brought to the way we get work done.
The impetus for how our modern world evolved was the Industrial Revolution. It changed the way the world worked. The idea of the assembly line was genius and advanced for its time. Breaking complicated work into smaller, repeatable tasks created many of the specialized fields of work we know today. Instead of asking one person to understand how to build an entire product, the work was divided into pieces, and those pieces were assigned to different people. One person became incredibly efficient at one task, another person became incredibly efficient at the next, and eventually all those individual tasks came together to create one finished product. This was an incredible innovation in productivity, and it has served us well for more than a century. But I think we are reaching a point where our relentless pursuit of productivity is creating a problem, and some of the efficiencies gained over 100 years ago are beginning to break down. We have become so good at breaking work apart that we may be losing our ability to see the bigger picture. And artificial intelligence has made that problem much harder to ignore.
I think this is one reason why so many organizations are struggling to truly extract value from AI and other newly developed technologies. The people they have tasked with using these technologies often know only one small part of the puzzle. On one hand, they are hyper-focused on the areas they know well because of their subject matter expertise. On the other, they can struggle to recognize or understand their blind spots when it comes to everything happening outside of their specific area of responsibility.
And this is where I think we need to change the way we are thinking about AI implementation. Personally, I don’t think the biggest question surrounding AI is whether it will make us more productive. It almost certainly will. We have essentially designed it for that. The technology is already capable of helping us write, analyze, code, research, summarize, design, automate, and execute work at speeds that would have seemed impossible just a few years ago.
The problem is that we are ignoring the fact that most of our work, regardless of industry, is bifurcated and siloed. Most companies depend on entire teams, departments, and sometimes even entirely different industries to produce the products and services they sell. So now we are seeing what happens when we give incredibly powerful tools to a workforce that has been designed around increasingly specialized pieces of work. This is why so many people are struggling to find value and figure out how to use AI to help themselves with their very specialized and relatively small piece of “the puzzle.”
We have built general-purpose machines that can increasingly work across an entire process, while most of the humans expected to use those machines have been trained to understand only a small part of that process. This gap, not the technology itself, has become one of the main challenges facing today’s workplace and workforce. How can people be expected to supervise and ensure the accuracy of a machine’s output if the technology has better visibility into the overall process than the person using it?
Because of this, the “human in the loop” is sometimes seen as the weak point, or even as the reason why peak productivity cannot be reached. While many are currently blaming the people operating these tools, the real problem is that the people being asked to take the lead have never been allowed or expected to learn the information needed to understand the new responsibility they’ve been given.
Picture how you would feel, or how you would perform, if someone asked you to inspect a car as it is leaving the assembly line and sign off on it, ensuring it is safe and functioning correctly, while your expertise is in installing windows on the care. Your job is simply doomed from the start. You can be the best window installer in the world, but that doesn’t mean you have the knowledge necessary to determine whether the entire car is safe and functioning. Those tasks were simply not part of your knowledge or experience.
This is why I feel strongly about ensuring that leaders see productivity and expertise across the process as being connected. You can no longer have one without the other. This requires a shift in how we determine success. We tend to think of productivity as something we should always strive for, an unquestionable good. But perhaps productivity is becoming a second-tier objective when compared to things like safety, problem solving, value generation, and understanding the broader system we are working within. If you want to succeed in the future being built today, my advice is to zoom out from what you do and know today and begin asking larger questions. How does what I do fit into the larger result or product?
The challenge we face today with AI may not simply be figuring out how to make individual people more productive. We need to start asking ourselves whether the way we have organized work is still the best way to work at all.
Read my previous post to understand how to think with AI.
