Understanding the work before automating it: A survival skill for the AI age

I graduated from university in May 1995 with a degree in mass communication. This is the same year that Netscape came out and radically transformed all forms of mass communication. While not everything I learned in university became obsolete overnight, a lot of it did. There was one class I particularly enjoyed during my senior year – audio editing. Interestingly (perhaps only to me and other audio nerds), we were the last class at my university to learn how to edit physical audio tape with a chalk pencil, razor blade and sticky tape. Now, I know 1995 sounds like ancient times but I assure you that digital audio editing existed then. Why, then, did our professor insist we learn how to do it the analog way? 

Understanding how the sausage is made

There is tremendous value in understanding what is actually happening inside the systems we build. Doing the, sometimes analog, work of developing those systems ensures that when we start using automated tools for the same work we can critically reflect on the output of those tools. I believe this is why my professor back in 1995 made us splice analog audio tape. He wanted us to understand the purpose of the work. He also wanted us to understand the impact of the work we were doing and most importantly, what good looked and sounded like. 

When we switched to digital audio editing tools our work was deliberate, precise and simply better. We took care to pay attention to the details and critically assess the output. We learned to do that in a world with no “undos” so that even in a world with infinite “undos” we didn’t simply outsource the work to the tool itself. 

The AI tools we use today for product management, development, design, testing and research take a lot of the “splicing” out of the workflow. The efficiencies are undeniable. However, if you’ve never done the “analog” work prior to using these tools you have less of a sense of what “good” looks like. I’ve seen teams not only outsource the work to their AI tools, but also lose their critical thinking skills to assess the output generated by these same tools. They accept them as “done.” And that’s a big risk. We’re letting the machines decide what’s best for humans. 

AI tools exponentially drive progress and productivity. But at what cost?

I am a huge fan of tools that make me more efficient. I am equally grateful for tools that make me more creative and innovative and that challenge me to be better at what I do. I believe the broad implementation of AI in our daily lives is doing exactly that. I also think that the folks who haven’t gone through the trial and error of doing the same work in a pre-AI world are at risk of trusting the tools too much. The time savings are too tempting. “Why should I go back and check the work when the system says this is the best approach?” 

AI and the human brain are an unstoppable force for creativity

There will never be a more creative, innovative force than the human brain. The tools we build should enhance that rather than discourage the use of that creativity and innovation. I’m not suggesting we all need to slog through hours of manual document, design and deliverable creation only to then start using AI-powered tools properly. And none of us should ever have to go back to splicing audio tape with a razor blade. 

What I am suggesting is that, as we bring newly minted product managers, designers and developers into this new and evolving environment, we ensure that we also teach them to question the generated output they’re creating and apply their own thinking to the work. Help them see how the sausage is made so that they can critique, edit and advance their work with a human lens. After all, it’s not the AI tools that need to use these products and services. It’s people.