This article is based on Mike Kolman’s talk at the Amsterdam Product Marketing Summit. As a PMA member, you can enjoy the complete talk here.

Need to bring an AI-powered product to market but don't know where to start? You're in the right place. As AI transforms our industry at lightning speed, it's easy to feel left behind. But don't worry – I've got your back. 

Drawing from my experience at Salesforce, I'll share three essential learnings to help you navigate the AI landscape with confidence. In this article, we'll dive into:

  • The evolution of AI
  • The AI hype cycle and where we stand today
  • Why many AI projects fail and how to set yours up for success
  • Three key learnings from my experience of launching an AI product.

Let’s get into it.

The evolution of AI

It can’t have escaped your notice that we're in a bit of an AI revolution right now – but how did we get here? Let me set the stage.

For about 30 years, we were in wave one of artificial intelligence – predictive AI, which uses numbers data to generate very simple predictions.

Since 2022, when ChatGPT launched their 3.5 model, anyone working in B2B SaaS has been hearing the terms "generative AI," "Gen AI," or "artificial intelligence" about a thousand times a day. This marks the beginning of wave two, which involves using natural language – speaking or writing to a large language model (LLM) – to generate something that didn’t exist before.

We're already moving rapidly towards the third wave. This involves building autonomous agents that can automate tasks so you don't have to do them anymore. 

I imagine it won’t be long before we see wave four – artificial general intelligence. Think Terminator! 

The AI hype cycle

You may have heard of the Gartner Hype Cycle, but in case you haven’t, let me give you a quick overview. 

The cycle is triggered by the emergence of an exciting new technology.  Everybody gets super hyped up until the new tech reaches the Peak of Inflated Expectations. 

Want to know how you can tell something has reached that peak? It's when your mom calls you on a Friday afternoon to ask if you know about this “generative AI” thing that she's seen on TV. If even your mom cares about it, you can be sure that everybody everybody else does!

Graph showing the Gartner Hype Cycle. Stages: technology trigger, expectations peak, trough of disillusionment, slope of enlightenment, plateau of productivity
Image courtesy of Salesforcego

However, as its name suggests, the Peak of Inflated Expectations can’t last forever; it’s swiftly followed by the Trough of Disillusionment – and that’s where generative AI is currently heading. 

We're seeing a lot of failures related to Gen AI. Last year, by some estimates, about 80% of all AI projects failed. Now, the success rate is improving, but it’s still predicted that in 2025, at least 30% of generative AI projects will be abandoned after the proof-of-concept stage. 

In short, a huge amount of time, effort, and money are going into generative AI projects that are doomed to fail. 

What AI use cases should you invest in?

It’s easy to understand why businesses continue to pour resources into this technology. Just look at this huge array of potential use cases for generative AI:  

Chart showing dozens of general, domain-specific and industry-specific use cases for AI
Image courtesy of Salesforce

There are essential use cases like web page design and translation, which will be critical within the next three years – not just to gain a competitive edge over your competitors, but simply to do your work effectively. If you don't have these capabilities, your employees will probably go to competitors that do.

Then there are domain-specific and industry-specific use cases like product quality control, underwriting risk analysis, and even car design. If you’re looking to build an AI capability, your smartest choice is probably to focus on these use cases. Why? Because those more general productivity-related capabilities have likely already been built by the big SaaS vendors.