I’m David Knott. I’ve been working in enterprise technology for over forty years and I’m still learning. This blog is based on mistakes, failures, lessons and some things I find interesting:
It’s more complicated on the inside than it is on the outside
We don’t need time machines to create paradoxes in technology: they are built into the way we work. One of these paradoxes is that the simpler technology appears on the outside, the more complicated it is on the inside.
I was reminded of this recently when talking to someone who confidently told me that the more sophisticated AI models get, the easier they will be to use, for technologists as well as end users. AI would solve its own skills problem. I was surprised by this because, to me (and, I expect to most other technologists), while we understand how natural language interfaces can radically simplify the experience for end users, the introduction of the current wave of AI into our architecture makes it more complicated.
Learn to fail fast? Technologists fail all the time
From time to time, organisations attempt to learn new ways of working. They attempt to become digital or agile or data-driven or innovative. These attempts come with some familiar ideas: that we should execute through cross-functional teams who are empowered to experiment. One of these ideas is that we should not be scared of failure, and that we should learn to fail fast.
These attempts sometimes elicit eye rolls from the technology teams, especially the idea that we should embrace failure. This is not because these ideas are invalid: in fact, they are welcome to technology teams, and reflect their preferred ways of working. However, technologists have a different relationship with failure than non-technologists.
Are LLMs the air fryers of AI?
Do you know someone who got an air fryer for Christmas? Or did you get one yourself?
If you know someone who got an air fryer, then there’s a high chance that you have heard all about it, and how it has been a complete game changer. They can cook things in a fraction of the time it used to take! And it’s not a fryer at all - it’s really a mini-oven! If you got an air fryer yourself, then there’s a chance that you’ve used it for everything, and that, even now, you are thinking about what you could use it to cook next.
I don’t have an air fryer myself, but am old enough to remember when my family first got a microwave. We lived off jacket potatoes for at least a week, and tried microwaving many things that should not be microwaved (there’s a reason that roasts are called roasts). Eventually, we found, just as my friends with air fryers seem to be finding in the weeks after Christmas, that, while the microwave is a useful tool to have in the kitchen, it’s not the only answer, and certainly not the best answer for everything.
On the 2025 to-do list: figure out AI agents
Recent years have seen waves of AI innovation breaking faster than we can figure out good practice. Organisations around the world are working hard, not only to find ways to put AI to work, but to do so safely and responsibly. The AI to-do list often seems to be growing longer faster than we can strike items off it - but the only route to good practice is practice.
The advent of AI agents promises to add more items to the to-do list. The AI agent wave started cresting in 2024, and will break in 2025. Several major technology vendors and platforms already offer their customers the ability to build, configure and operate AI agents in an enterprise context, and the ability for consumers to build agents or to subscribe to existing agents, cannot be far behind (indeed, it is likely that, by the time this article is published, it will already be happening).