It is becoming almost impossible to have a business conversation without AI showing up somewhere. Sometimes that is entirely justified. Sometimes I suspect somebody has simply decided that adding the letters AI to a presentation makes it sound more interesting. Either way, the subject is not going away.
Over the past couple of years, AI has moved from being something most businesses were experimenting with to something that is beginning to find its way into normal day-to-day work. It is being used for research, writing, analysis, coding, sales support, customer service and a growing list of other tasks.
Some of it is genuinely useful. Some of it is clever but not particularly valuable. And quite a lot of it still sits somewhere between the two.
For those of us working in technology, telecoms and digital infrastructure, there is another side to the story as well. AI may look like software when you are sitting in front of a laptop, but behind it sits a very large amount of physical infrastructure. Data centres, power, fibre, cooling, chips, land and an extraordinary amount of capital all have to come from somewhere.
That is part of what I want this series to explore. Not simply what AI can do, but where it is genuinely useful, where it can waste time and money, and what businesses should understand before making serious commitments.
That makes the AI story rather more interesting than simply asking which chatbot is best. So, over the next 12 weeks, we are going to look at AI from a practical business perspective.
Not a guide to 50 AI tools you supposedly cannot live without. Not predictions that every job will disappear within five years. And certainly not another article telling businesses they must “embrace AI” without explaining what that actually means.
Instead, I want to look at the questions I think business leaders are genuinely trying to answer. Where is AI actually useful? Where should a business start? Which applications are worth spending money on? Where can it improve research, sales, decision-making or productivity, and where does it simply create another layer of complexity?
We will also look at some of the things that receive rather less attention. What happens to company data when staff start using AI tools? How reliable is AI-generated research? How do you decide whether an AI project is actually saving money? And, perhaps most importantly, when is the sensible decision simply not to use it?
There is also the infrastructure question. The growth of AI is already having a significant effect on data centre demand, networks and electricity consumption. That will create opportunities, but it will also create constraints, and those constraints may ultimately prove just as important as the technology itself.
Some of the articles will be very practical. Others will take a broader look at what AI means for technology and infrastructure businesses. I also expect that some conclusions will change as the series progresses, because the technology is moving quickly and pretending otherwise would be slightly ridiculous.
The aim is simply to produce something useful: a straightforward look at what AI can do, what it still cannot do particularly well, and how senior people should think about it without getting carried away by the noise.
We start next week with what seems to me the obvious first question:
What Should AI Actually Be Doing in Your Business?
Because before spending a fortune developing an AI strategy, it might be worth working out what problem you are trying to solve.
