There are so many new AI tools on the market that are tangential to product. They can help with content creation and distribution as well as specific product features, such as summaries and narrated articles. And there are likely going to be a lot more to come.

But where do you start? How do you know what is worth building into the internal processes? How do you prioritise which to experiment with?

It’s hard to focus and to understand what is actually going to make an impact. In fact, most product leaders I speak to are trying to understand where they should make their bets. 

We can be pretty confident AI will provide cost savings and help us improve products, but there is an upfront cost — and it’s hard to put numbers against unknown outcomes. 

Something that struck home recently:

When asked about making bets on innovation during the recent INMA Silicon Valley Study Tour, Justin Ferrell at Stanford’s d.school replied that you can’t make decisions based on data from the past unless the future will look like the past. It’s simply not rational. And yet, in this time of exceptional technological change, that is what we are trying to do. 

Some media companies are buying enterprise licenses to ChatGPT or similar (the former of which is actually quite hard to do as they have only two sales people and currently no automated process). This is an excellent way to get people using it. Because once we have teams using it and see users interacting with the fruits on these trials, we can start to understand where the benefits are. 

Which brings me to another point that Justin made when talking about AI. He used an analogy of a bike: You can learn all about a bike, how the wheels and gears work, the best angles and height for handlebars, etc. But until you actually get on it, you don’t know how to ride it. And that is effectively what we are dealing with at this moment in time. 

For many leaders, this will be uncomfortable. Some control will need to be ceded. There will need to be AI guidelines drawn up that everyone adheres to, and you’ll have to assume that people will act as grown-ups.  Here is a good example of an approach from Ringier in Switzerland. 

It’s an uncomfortable moment in time. AI is rapidly changing the game, but we don’t yet know how. There will be a lot of experiments, which means some degree of failure and hopefully some successes. But things are changing — quickly — and doing nothing may mean that you can’t catch up. So now it’s all got to be about educated guesses, empowering people, and learning.

Just keep in mind the end goal: What benefits are you bringing your users?

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