If anyone was ever forced to pick the tritest phrase in the world, it just might be this one: “The only thing constant is change.” But like so many other overworn clichés, the surface hides a much deeper truth that we simply can’t afford to ignore. Yet, more often than not, we do ignore the underlying challenges of change, and the reason is anything but pretty. Or intelligent.
Consider a 50-year-old argument against natural language programming
About half-a-century ago, the renowned Dutch computer scientist, programmer, and mathematician, Edsger Dijkstra, made a compelling argument about the limitations of natural language in computer programming.
While so many programmers were touting how much easier it would be to use natural languages instead of rigid machine codes to create computer programs, Dijkstra argued that the inherent ambiguities and slow evolution of natural languages were conclusive reasons to abandon any real idea of programming in human languages.
And he was right.
Although it might be simpler to program in your native tongue at first, doing so would be harder in the long run because the built-in inconsistencies of natural language would lead to undetected inaccuracies and misinformation in the final computer program. In other words, it would undermine the very goal of the project itself.
Whether it’s harder and more time-consuming or not, the precision that comes with machine coding is an absolute requirement for accuracy and improved business performance.
You can’t just switch from Dutch, to English, to French—or any other tongue—to replace a computer language, no matter how basic or advanced the computer language in question may be. Or how equally frustrating it is to receive a “Machine Error” message.
It might be annoying to get a machine-generated error message of any kind after you have put hours of work into trying to get it perfect. But no one would ever argue that you would be better off to ignore the error notice and proceed with what appears to be an easier solution.
What does any of this have to do with where we are today? Especially when we’ve possibly had the largest disruption in the tech industry since its very inception?
The answer might surprise you. And not necessarily in a good way.
Changing the way we manage change
Change management as a whole follows two distinct trajectories: people-driven change and machine-driven change.
People-driven change is driven by human intuition, culture, and adaptability, whereas machine-driven change is defined by data-driven, consistent, and scalable processes. While people-driven change excels in innovation, relationship building, and navigating unpredictable situations, machine-driven (or process-driven) change is superior for speed, efficiency, and reducing reliance on specific individuals.
In this view, both people and machines play essential roles in achieving holistic and strategic change.
But fast-forward to where we are today. With the skyrocketing presence of AI, far too many people are jumping on the machine bandwagon with no real concern for the people-driven component. That means they are underestimating the need for innovation, relationship building, and the ability to navigate unpredictable solutions.
Which is just what so many people are doing today. By blindly accepting a seemingly easier fix today, too many companies are likely to find it difficult—and in some cases impossible —to correct mistakes, embrace innovations, and manage their IT systems tomorrow.
Getting there is easy, but getting back home is no longer a familiar road
Someone, I don’t remember who, did a survey a while ago on what the most overused word in high-tech was.
The word (you might have guessed it) was “disruptive”.
It’s easy to understand why. Disruptive has come to mean something truly paradigm-changing, and if the disruption catches hold, in the long term, it’s also likely to simplify the lives of the people who adopt it.
But not necessarily this time. Change is still going to happen. AI is going to expand and continue to improve, and other machine-driven solutions will get more and more accurate. All of this is definitely great. Until it isn’t.
When companies start eliminating their best and their brightest for machines that require little more than pushing a button, much more is lost than is gained. Companies thrive and grow because they understand how their products work, what it takes to maintain quality, how to get the best people to want to work there, and most importantly, how to fix critical problems when they need to be handled.
They must continue to do so no matter how fast and how much the onslaught of change continues to disrupt their business models going forward.
So, while we’re in the midst of such monumental change, maybe the best thing we can do right now is to take another lesson from Edsger Dykstra’s argument on the limitations of natural language. He said:
“When all is said and done, the “naturalness” with which we use our native tongues boils down to the ease with which we can use them for making statements the nonsense of which is not obvious.”
Let’s just hope that we don’t allow machine-driven tools to succumb to the same nonsense.
This article was submitted by Klarrio.