What Does Succes Look Like?

Was yesterday a success for you? Most people can’t answer that question because they have not thought about what a successful day looks like.

Setting a goal and achieving it has a major impact on your happiness. Interestingly, it does not have to be a big, audacious goal. Setting a simple goal like “today I will answer that email that I’ve been avoiding” is enough.

Take a moment to define success for tomorrow. You’ll find that it becomes very likely that you do the task you envision. And you’ll be happier afterwards.

The Usability War

When your users don’t like what you offer, they will use something else. If your secure communication app is cumbersome and slow, people will take to insecure but user-friendly commercial software.

The U.S. Secretary of War couldn’t be bothered to use the Pentagon-supplied app and leaked classified information on WhatsApp. Now, Russia is restricting Telegram only to figure out that its soldiers in Ukraine depend on it.

Have you compared usage data with expected usage? If those numbers are far apart, it means your users are finding other ways.

Invisible Problems

“We don’t see a problem.” That is the typical response when a company is investigated for unsafe products or features. Latest car in point: Ford’s BlueCruise self-driving feature.

They might be right in claiming that customers have not reported problems to them. But when the National Highway Traffic Safety Administration (NHTSA) started to take an interest, they could easily compile 2,000 claims of malfunctioning self-driving features.

How are you gathering customer feedback? The fact that you don’t receive any problem reports doesn’t mean there are no problems.

The 996 Fallacy

996 working is back in fashion. That means working from 9am to 9pm, 6 days a week. The concept originated in China, but AI startups in The U.S. had taken it up with a vengeance.

It is a stupid idea, conflating effort with results. You need a certain amount of effort to produce a result, but more effort does not translate linearly into more results. The science shows that productivity tapers off after 40 hours a week, and workers doing 70 hours so not produce more than workers doing 50 hours.

If you are working on your own startup or have another good reason, by all means put in some extra effort. But don’t sacrifice your health for someone else’s agenda.

The Missing AI Business Model

OpenAI has started running ads in ChatGPT. For now, they say they are “testing” the feature in the U.S., but there is little doubt it will eventually be rolled out globally.

Just like all the other AI companies, OpenAI is burning cash at an unsustainable rate – using more than 10x what they make on the compute they consume. Some of them will fail.

If you are using external AI tools in any of your systems, make sure your developers are plugging in the AI in a way that makes it easily replaceable. As the shakedown starts, you’ll be seeing price hikes on the paid plans as well, and you need to be able to quickly and easily change to another provider. Or run an Open Source model in-house.

Keeping Up With AI

How are you keeping up with developments in AI? There are several major AI players releasing new versions with new capabilities every few months. They have different strengths and weaknesses, and we are all inundated with news about how AI will take away our jobs. So how can we keep up when we have a day job?

If your organization doesn’t have an AI knowledge-sharing program, establish one with colleagues or friends. Meet over beer and pizza, share your current knowledge, and assign responsibilities. Someone might have the task of keeping up with Claude Code. Someone else might be responsible for investigating Gemini CLI. Meet up regularly and informally share what you’ve found.

The AI field is too big and fast-moving for you to keep up with it on your own.

Unknown AI Policies

Does everyone in your IT organization know what your rules are regarding AI use? 60% of employees report using AI tools, while fewer than 20% say they know the company’s AI policy.

That is not because the policies don’t exist. More than 80% of IT leaders report that their organizations have formulated polices for AI use.

How are you going to close that gap?

Detecting Bias in Yourself

Can you see your own biases? Most people can’t.

I recently posted here and on social media about product design trade-offs and being able to see the downside of a design decision. I used Tesla as an example. Bad choice.

The comment track was immediately swamped with Elon-haters and Tesla fanboys (in about equal measure). Not many people wanted to participate in the discussion about product design decisions and blind spots.

Interestingly, this proved my point exactly: We all have blind spots. Once we have made a point publicly, it becomes part of our identity. And society appreciates people who stand their ground, while people changing their minds are written off as flip-floppers. But if we want to make good decisions, we have to overcome our biases.

Ideally, you have a group of trusted friends you can discuss important issues with before you make a decision. Failing that, you can borrow someone else’s viewpoint: Ask yourself what that other person would say.

Only Outsiders Can See the Faults

Would you design a product with a sleek but potentially deadly feature? Most people wouldn’t, but aerodynamics and design have led to at least 15 people dying in burning Teslas when the electric doors wouldn’t open. The Chinese are no longer having it, and are ordering all cars to have a mechanical door release both inside and outside the vehicle from January 1st next year.

Thousands of trade-offs are made when designing products. But some outcomes are so bad that they ought to disqualify a feature. Product owners want a great product with awesome features, and are not capable of imagining all the bad things that could happen. Even if you don’t have a dedicated Red Team, you need someone outside the product team to probe your products for weaknesses. The people building it can’t see them.