Blocking AI is an Unwinnable Battle

Using AI is not cheating. It is a way to become more productive. You pay your employees because they perform tasks that create value for the organization. So it makes sense to let them use the best tools available to do their jobs.

Just like some schools are trying to prevent students from using AI, some companies are trying to outlaw AI. It won’t work. Research shows that 47% of people who used AI tools experienced increased job satisfaction, and 78% were more productive. You can’t fight such dramatic numbers with a blanket prohibition. If you try, your employees will use AI on their phones or in an incognito browser session while working from home.

By all means create rules about how and where employees can use AI, and explain them thoroughly. But trying to ban AI is futile.

Business Knowledge Beats Technical Skill

Most of the value of an IT developer comes from his knowledge of the business. His knowledge of specific programming languages or tools comes a distant second. With AI-supported development tools like Copilot, this value balance becomes even more skewed towards business skills.

That’s why I’m appalled every time I see yet another company replacing hundreds of skilled IT professionals. I’ll grant you that some organizations have too many people and might need to trim their headcount. But often, organizations are trying to kickstart a digital transformation by replacing old hands with a crowd of bright-eyed young things with the latest buzzwords on their CV.

Getting a new crew with better tools and techniques means you can build software faster. But by getting rid of the experienced people, you lose your ability to build the right software. Moving slowly in the right direction beats running fast in the wrong direction.

You Don’t Want a Sam Altman

You don’t want a Sam Altman in your organization. If you have, you’re not running an IT organization. You are just administering a cult.

I’m all for having brilliant and charismatic performers in the organization. However, having individuals perceived internally and externally as indispensable is not good. Mr. Altman admitted as much back in June when he said, “No one person should be trusted here. The board can fire me, I think that’s important.”

It turns out that the board couldn’t fire him. He had carefully maneuvered himself into a position where investors and almost everyone on the team believed that OpenAI would come crashing down around their ears if he left, costing them the billions of dollars of profit and stock options they were looking forward to.

Make a short list of your organization’s 3-5 star performers. For each of them, ask yourself what would happen if they were let go or decided to leave. If any of them are in a Sam Altman-like position, you have a serious risk to mitigate.

On-premise culture

The boss wants you back in the office. He has a point.

The point is that unless your organization was born fully remote, it is stuck with an on-premise culture. You can try to fight it. But remember what happened the last time a new strategy initiative was launched? Your organizational culture completely dominated the new ideas until you did things the way you had always done them. That is what management guru Peter Drucker meant when he said that “culture eats strategy for breakfast.”

In an on-premise culture, relationships are built through in-person interactions. The exciting projects, the conference trips, and the promotions go to the people seen in the organization. You can argue that’s not fair, but all the leaders in your organization grew up in an on-premise culture.

In an on-premise culture, new ideas germinate from chance encounters. The two Nobel Prize winners in medicine this year met at the copy machine. Both were frustrated that nobody took their ideas about mRNA seriously. They started working together, and their work enabled the coronavirus vaccine.

The fully remote organization is a technologically enabled deviation from how humans have organized themselves for thousands of years. Building the culture that makes such an organization work takes precise and conscious decisions. That goes into its DNA from the founding. You cannot retrofit fully remote onto an on-premise culture.

The ROI on AI Projects is Still Negative

Unless you are Microsoft, your IT solutions are expected to provide a positive return on the investment. You might have heard that Microsoft loses $20 a month for every GitHub Copilot customer. That’s after the customer pays $10 for the product. If you are a heavy user of Copilot, you might be causing Microsoft a loss of up to $80 every month.

Some organizations are rich enough to be able to afford unprofitable products like this. They typically have to spend their own money. VCs seem to have soured on the idea that “we lose money on every customer, but we make up for it in volume.”

If you are running an AI project right now, you should be clear that it will not pay for itself. Outside a very narrow range of applications, typically image recognition, AI is still experimental. If you have approved an AI project based on a business case showing a positive ROI, question the assumptions behind it. The AI failures are piling up, and even the largest, best-run, and most experienced organizations in the world cannot make money implementing AI yet. You probably can’t, either. Unless you have money to burn, let someone else figure out how to get AI to pay for itself.

The Guard Rail Pattern

There is a simple way to prevent many IT disasters, and it is sadly underused. It’s not on the standard lists of design patterns, but I call it the “Guard Rail” pattern.

It would have prevented the IT disaster that dominates the news cycle in Denmark these days. Techno-optimists have forced a new digital building valuation on the long-suffering Danes, and it is an unmitigated catastrophe. The point is to replace the professional appraisers who determine the value of a property for tax purposes with a computer system. And many of the results from the computer are way off. Implementing a Guard Rail pattern would mean that the output from the new system would be compared to the old one, and those valuations that are, for example, 3x higher would be stopped and manually processed.

A colleague just shared a video of the latest iteration of the Tesla Full Self Driving mode. This version seems to be fully based on Machine Learning. Previous versions used ML to detect objects and traditional algorithmic programming to determine how to drive. As always infatuated with his own cleverness, Elon Musk does not seem to think that guard rails are necessary. Never mind that the FSD Tesla would have run a red light had the driver not stopped it. Implementing the Guard Rail pattern would mean that a completely separate system gets to evaluate the output from the ML driver before it gets passed to the steering, accelerator, and brakes.

When I attach a computer to my (traditional) car to read the log, I can see many “unreasonable value from sensor” warnings. This indicates that traditional car manufacturers are implementing the Guard Rail pattern, doing a reasonableness check on inputs before it passes the values to the adaptive cruise control, lane assist, and other systems. But the Boeing 737 MAX8 flight control software was missing a crucial Guard Rail, allowing the computer to override the pilot and fly two aircraft into the ground.

In your IT organization, discuss where it makes sense to implement the Guard Rail pattern. Your experienced developers can probably remember several examples where Guard Rails would have saved you from embarrassing failures. There is no need to keep making these mistakes when there is an easy fix.

Would You Notice the Quality of Your AI Dropping?

You know that ChatGPT is getting more politically correct. But did you know that it is also getting dumber? Researchers have repeatedly been asking it to do tasks like generating code to solve math problems. In March, ChatGPT 4 could generate functioning code 50% of the time. By June, that ability had dropped to 10%. If you’re not paying, you are stuck with ChatGPT 3.5. This version managed 20% correct code in March but was down to approximately zero ability in June 2023.

This phenomenon is known to AI researchers as “drift.” It happens when you don’t like the answers the machine gives, and take the shortcut of tweaking the parameters instead of expensively re-training your model on a more appropriate data set. Twisting the arm of an AI to generate more socially acceptable answers has been proven to have unpredictable and sometimes negative consequences.

If you are using any AI-based services, do you know what the engine behind the solution is? If you ask, and your vendor is willing to tell you, you will find that most SaaS AI solutions today are running ChatGPT with a thin veneer of fine-tuning. Unless you continually test your AI solution with a suite of standard tests, you will never notice that the quality of your AI solution has gone down the drain because OpenAI engineers are pursuing the goal of not offending anyone.

Do Your Employees Follow your AI Guidelines?

Unless you override it, your organization’s policy for AI-driven tools is “anything goes.” That’s because your developers want to get their job done as quickly as possible. If that involves having Github Copilot write part of the code or copying a code block into ChatGPT for debugging help, so be it.

If you don’t have secrets, maybe that’s fine with you. But even though OpenAI is not training ChatGPT on user prompts, they have not been very diligent about keeping them safe. You should assume that everything your developers paste into ChatGPT will eventually leak.

That includes your data. AI tools are very good at data cleaning and visualization. Your Data Scientists are surely pasting data into ChatGPT and getting back fully functional Python code to run in a Jupyter Notebook. Unless you tell them not to.

If I asked one of your developers or Data Scientists about your policy on AI tools, would they know it? And would they follow the rules or would they take the 10x or 100x productivity boost?

How Do We Make IT Projects More Successful?

At least nuclear waste storage is worse. In his book “How Big Things Get Done,” professor Bent Flyvbjerg ranks 25 categories of projects by their average cost overrun. IT projects are the fifth worst offender, better than nuclear but worse than buildings, rail, airports, tunnels, and many others. We all know many public IT failures (Denmark has its fair share), and the private sector has suffered many more, even if less publicized.

What can we do about it? One chapter in the book is dedicated to creating better estimates. The problem with our estimating today is that we treat every project as unique. We then estimate each bit, and our usual how-hard-can-it-be optimism leads to the underestimation so common in IT. Flyvbjerg argues that we should start by identifying the class of projects this new project belongs to. The average for this class of projects is then the starting point for our estimate, adjusted up or down.

For example, you estimate an ERP project by looking at other ERP projects. If the cost in your industry is $20 million on average, that is your initial value. Then adjust up or down depending on whether your project is smaller or larger – or more straightforward or more complex – than the members of the reference class.

Bring this book with you to the beach this summer so that you can help our industry move forward when you return from vacation. IT projects exceed their budgets by an average of 73%. We can do better.

Did You Hear the One About the Gullible Lawyer?

You need the best arguments to win a discussion, get a project approved, or win a court case. But, if you are short of preparation time, you might take a shortcut like the New York Lawyer who asked ChatGPT for help.

Ever willing to help, ChatGPT offered six cases supporting the lawyer’s argument. Unfortunately, they were entirely made up. That might work if you write a marketing blog post, but it does not hold up in court. The gullible lawyer claims he did not know that ChatGPT might be hallucinating but is, of course, facing sanctions for lying to the court.

IT professionals know that ChatGPT cannot be trusted to answer truthfully. It is not much of a problem for a programmer because the compiler or the unit tests will catch defective answers. But the rest of the world doesn’t know.

Now is the time to remind everyone in the organization of your company policy on using ChatGPT and its ilk (you do have such a policy, right?). Tell the story of the gullible New York lawyer to make the point clear.