When we talk about AI, we usually focus on a single metric: productivity. This metric has been used in every technology announcement since the beginning of the current technology era.
Going back to when I became an external technical analyst and in the lead up to the launch of Windows 95, the argument was that it improved productivity so much that it would provide a return on investment (ROI) within a year of its purchase. . It turned out that during the first year the product broke so much that it had an initial negative rather than positive effect on productivity.
The ROI for AI is potentially much worse and, ironically, a large part of our problem this century is not a lack of productivity or performance, but poor decision support.
Last week I attended a Komputex preparatory event. As I watched the presentations, I noticed a familiar undercurrent of productivity. I continue to worry that if we dramatically improve speeds but don’t also improve the quality of the associated decisions, we will make mistakes at machine speeds that may not be survivable.
Let’s talk about it this week, and we’ll end with my product of the week, which is the airline I just took to Taiwan. It was so much better than United, which I usually use for international travel, that I thought I’d explain why so many non-US airlines are significantly better than US airlines.
Productivity versus quality
I am a former IBM member. During my tenure there, I was part of a small group that completed IBM’s executive education program. One of the principles adopted by all employees was that quality is important.
The most memorable course I took in this regard was not from IBM but from the Society of Competitive Intelligence Professionals (SCIP). His goal was speed versus direction. The teacher argued that most companies focus first on speed when it comes to new processes and technologies.
He argued that if you don’t focus on direction first, you’ll end up going in the wrong direction at an ever faster rate. If you don’t focus on setting the goal first, speed won’t help you. This will only make matters worse.
When I was at IBM and Siemens as a competitive analyst, I had the unfortunate experience of providing decision support and having our recommendations not only ignored, but actively fought against, and then not followed up on. This led to catastrophic losses and the bankruptcy of several groups I worked for.
The reason was that leaders preferred to appear right rather than actually being right. After a while, my unit was disbanded (a trend that spread throughout the industry) because management didn’t like the embarrassment of being called out after a catastrophic failure for ignoring founded advice because their “instinct” told them what they had. predetermined. the steering should be better, but it wasn’t several times.
After I stopped working inside companies and became an external analyst, I was surprised to find that my advice was more likely to be followed because executives didn’t think I was right and created a threat to their career.
Within the company, they considered me a risk. From the outside looking in, that wasn’t the case, so they were more willing to listen and follow a different strategy because they didn’t feel like they were competing with me.
Leaders have access to massive amounts of data that should enable them to make better decisions. However, I still see too many making ill-informed decisions that lead to disastrous consequences.
That’s why AI should aim to help businesses make better decisions, and only then should it focus on productivity and performance. If you focus on speed without making sure that the decision behind the direction is the right one, you are more likely to go in the wrong direction much faster, leading to mistakes that are both more frequent and more expensive.
Decision Challenges
From our personal lives to our professional lives, we can make decisions faster thanks to AI, but the quality of those decisions is deteriorating. If you look at Microsoft and Intel, two of the main backers of the current AI technology wave, you will see that for much of their existence, especially this century, companies have made bad decisions that have cost them one or more CEOs. .
My old friend Steve Ballmer was cursed with bad decision after bad decision, which I always think was as much a result of the people or person supporting him as it was something inherent in the man himself.
This guy was top of his class at Harvard and probably the smartest person I’ve ever met. He is credited with the success of the Xbox. However, after that, despite managing well the financial performance of Microsoft, he failed with the Zune, Microsoft Phone and Yahoo, crippling the evaluation of Microsoft and causing his dismissal.
Along with a few other analysts, I was initially tasked with helping him make better decisions. However, we were sidelined almost immediately, even though I wrote email after email, arguing that if he didn’t improve the quality of his decisions, he would be fired. Unfortunately, he got angry at my attempts. I still regard his failure as my own, and it will haunt me for the rest of my life.
This problem mirrors what happened to John Akers at IBM, who was surrounded by people who wouldn’t let in information from those of us who were closer to the problems. Although my efforts at IBM to eliminate that company’s problems were rewarded, the impact of people like me, and there were many, was so undervalued that Akers lost his job. It wasn’t because he was stupid or didn’t listen. It is because we were blocked by executives who had his ear and who did not want to lose the status linked to this approach.
So the information that the CEOs of both companies needed to succeed was being denied by people they trusted. They focused more on status and access than the success of the companies they worked for.
The AI decision problem is twofold
First, we know that the results of AI efforts, while impressive in their capabilities, are also incredibly inaccurate or incomplete. The Wall Street Journal recently evaluated the best AI products and found that Google’s Gemini and Microsoft’s Copilot were, with a few exceptions, of the lowest quality, even though they should be the most widely used.
Additionally, as I indicated above, even if they were much more accurate, given their past behavior, leaders may not use them, preferring their instincts to whatever the system is telling them. While this may reduce the incidence of quality problems with these products, the result is a system that cannot or will not be reliable.
Today’s quality issues help support and reinforce bad behaviors that existed before the current generation of AI. So even if AI’s quality issues are resolved, it will still fall short in its potential for business and government success.
wrap up
Right now, our need for speed (productivity, performance) is much less than our need for technology that provides this advantage, and is both reliable and trustworthy. But even if we could solve this problem, argument theory suggests that technology will not be used for better decision support because of our general inability to view insider advice as anything other than a threat to our jobs, our status, and our image.
There is some truth in this position, because if people know that your decisions are based on AI advice, could they conclude that you are fired?
We need to stop focusing on AI with productivity as a primary goal and instead focus on much higher quality and better decision support so that we are not overwhelmed by bad decisions and advice at machine speed.
Next, we need to actively train people to accept sound advice that will more effectively allow us to move forward at machine speed instead of being buried by bad decisions at the same speed. We also need to reward people for using AI effectively, not make them feel that this use will put their jobs and careers at risk.
AI can help create a better world, but only if it provides quality results and we use those results to make our decisions.
Starlux Airlines
I almost stopped flying with United Airlines due to bad experiences, from being stuck in remote airports due to canceled flights to paying for first class tickets to finally ending up in coach due to a combination of poor operations and reluctance to do so. to ensure passengers delayed by operational errors that they arrive at their destination on time.
My experience with non-US carriers has been much better. On my trip to Computex last week, I took Starlux, a Taiwanese carrier. The experience on this airline was far superior.
In business class on United, I often feel less of a customer and more of a nuisance. On Starlux, people went out of their way to make sure my trip was comfortable; they took my personal care as a priority. When I asked for a special meal, they went out of their way to provide it. When I had difficulties with Wi-Fi, they were helpful until the problem was resolved and seemed eager to make sure my experience was exemplary.
I travel a lot in my career and I dread it, which is sad because when I was a kid I looked forward to it every time I got on a plane. When I flew Starlux, I rediscovered some of that love of flying and found myself looking forward to the flight home rather than dreading getting on the plane.
Starlux made my 13-hour flight fun, and I have to say that I have noticed it with other foreign carriers, such as Singapore Airlines, Emirates Airlines and others on this list. Starlux Airlines is therefore my product of the week.