How to succeed with AI in your product – customer first

AI is the most disruptive technical advancement of the current decade. Understandably, many organizations are looking to harness the power of AI — or more specifically, to determine how they can embed AI within their products.

Because transformative technology advancements like AI don’t come around often, many organizations don’t have experience applying these transformative technologies and therefore embed AI in suboptimal ways. In the most extreme example, teams slap “AI” on the box and add some “cool AI features”  — that do not solve a real customer problem  — and are then surprised when prospects don’t buy.  There is a better way.

Astute product teams can lean on time-tested models — such as the chasm model — to understand how technologies like AI evolve, what customers expect and, by extension, how to apply them in the right way. 

The chasm model

The premise of the chasm model is that there are multiple stages of market adoption and different buyers, with different purchasing motives depending on the stage. 

According to Moore, early adopters are visionaries. Early adopters see beyond what the technology is “today” and ahead to what it can become. They see the future and believe investing in this technology will give them a competitive advantage. 

On the other side of the chasm is the early majority. The early majority is pragmatic, they want to buy proven technology. Unlike the visionary, early adopter, the early majority tends to be more risk averse. This group will buy when there are demonstrable results, preferably from referenceable peers in their own market.

The big idea, hence the name, is that there is a chasm between early adopters and early majority. Crossing this chasm is the most critical step to successfully scale your technology.

What does this mean to me? Know thy customer.

Now, you’re probably asking, “Ok, how can I apply this to my product?” A mistake teams make is to assume any customer will buy because the product includes AI — not true. 

Selling a vision of AI, or assuming that someone will buy because your product includes AI, appeals primarily to the early adopters but not the early or late majority.  The early and late majority will not buy your product based on the mere inclusion of AI.  The early and late majority are pragmatic buyers that are interested in solving real problems and obtaining real benefits.  

The better approach therefore is to tailor your product/AI implementation and go-to-market messages based on who your customer is. Below is a simple description of the prospective buyer based on the chasm model.

Early adopters: Buy the vision

In the cases where you are developing a new, innovative technology, you should appeal to the early adopter and focus on achieving the “promise of the vision.” You should also work to obtain lighthouse customers, prove the benefits with early technology, and communicate that to the broader market. 

With that said, most of the market, and most of your customers, are not early adopters and so this is where teams can make a misstep.  Many teams assume that any product with AI will sell to any customer in any market — again, not true.

The following are a few examples of AI products, only the marketing taglines, the product name is removed. These products are “search” tools, a well-established market with many options. The only difference in these products is that they appear to only offer AI search.  

• AI tool for finding exactly what you need
• AI bot for word documents

The examples may appeal to an early adopter because it uses AI; however, if the AI search is not meaningfully better than existing search tools, they will not appeal to the pragmatic, early or late majority buyer.  And, let’s be honest, the competition for these products is probably Google. 

If your customer is the pragmatic buyer, keep reading. If you are selling to the visionary, early adopter, then follow the chasm model to the early adopter: 

The OpenAI product is a great example of the above, particularly when you view the research page.  

The research describes a vision of the future — artificial general intelligence that solves human-level problems. It demonstrates product leadership with many (potential) example applications. Additionally, they further demonstrate product leadership with research papers describing their scientific breakthroughs.  OpenAI caps it off with customer stories including marquee brands like Morgan Stanley. 

Early and late majority: Buy proven results

Most markets (and software) are in this phase. In this case, you are dealing with a pragmatic buyer who is likely less interested in the “vision of AI,” rather the pragmatic buyer is hoping to obtain proven benefits. You should therefore look to meaningfully improve things they are already doing (buying), but do them better. In other words, integrate AI in ways that solve current customer problems and promote those (proven) benefits.

There are many possibilities from a customer perspective: 

• Improve quality of existing product or service
  • Ad-tech – Serving better/more-targeted ads
  • Email – Type ahead on Google mail
• Reduce repetitive or manual work
  • Developer tools – Microsoft Copilot checking code for errors
  • Digital marketing – Content creation
  • Customer service – Search/information retrieval in proprietary data sets
• Unlock insights from data
  • Business intelligence – Ability to analyze unstructured data (text, image, audio)
  • Financial markets – Unstructured data analysis/pattern matching in trading
  • Information technology – Network intrusion/Anomaly detection 
• And more…

Focus on solving real customer problems in unique and differentiated ways, again following the model: 

A good example of AI embedded and communicated well is the PlantIn app.  Below are screenshots of their marketing materials.

Make no mistake, PlantIn uses AI to build their product.  You can see in the ad, the app is using image recognition to identify the plant type, identify potential issues.  The app then helps the buyer, a “gardener” to identify solutions to provide better care for the plant.  The value prop, essentially, is to identify a plant-type and to provide personalized, more accurate, information and care for the gardener.

This is a great example of an AI-embedded product, targeted at a pragmatic buyer – not the visionary, early adopter – as it speaks to the problem, solution and value prop (personalized to your needs, versus generalized).  Perhaps even better, the ad doesn’t mention AI at all.  The pragmatic buyer ultimately cares about the “what” (healthy plants, personalized advice) not the “how” (AI).

Conclusion

The chasm model describes how technology evolves — and guides how technology companies can match their strategy based on that evolution. To achieve successful adoption of your products while embracing the latest trends like AI, it is imperative to understand the full range of customers, and their motivations. Remember, it’s about their needs and their challenges, in the context of the adoption curve — not just about latching on to the latest trend.