Are you ready for AI? Some firms are. To be counted among them requires preparation. Author, speaker, consultant, and The Agile Brand podcast host Greg Kihlstrom, (www.gregkihlstrom.com) Principal Chief Strategies for GK5A, joined us on a recent podcast to discuss what it takes to be prepared to implement AI and improve your customer experience. Since I thought many of you were facing the same projects, this newsletter issue will explore what he said.
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As you might recall, the next competitive battleground for the experience economy will be predictive experiences. These predictions will ostensibly come from AI. However, while some organizations have positioned themselves to ride this wave of anticipatory experiences leveraging their AI, others are not. We asked Kihlstrom about what he sees out there that firms should be aware of in this effort.
Before we get into that, it is essential to mention that AI has a lot to live up to, and it isn't the first-time business had high expectations for technology. Going back about 25 years to my corporate career, we were implementing a system that was so fantastic; it was going to make teas and coffees and sandwiches at lunchtime for everybody.
It didn't.
We are facing these same unrealistic expectations for AI today. We believe it will fix everything for us. The reality is it will fix some things, but how we implement it will determine its effectiveness.
AI Is Only as Good as Its Data
Excellent AI insight requires excellent data. Moreover, the data should be un-siloed, among many other adjectives. Therefore, getting your data house in order is essential to implement AI.
There are four different types of AI that many organizations are looking to implement, Kihlstrom says. These include:
- Generative AI: These programs create things like text or photos for you to edit. This section is where ChatGPT, Bard, and others are hanging out. These programs enhance a person's work rather than replace them. Kihlstrom says one concern here is intellectual property; they are still a bit loosey-goosey with that in these programs. Moreover, he doesn't think companies are using it at scale yet.
- Predictive Analytics: As the name implies, these can tell you what will happen based on what has already happened. These programs have been around for 30 years or so and are in widespread use for areas like propensity models or trend predictions.
- Task Automation: This type of AI is heavily used for software applications where tasks route from one party to another. Organizations have been using this type of AI for decades. However, the news in this area of AI is that these programs are mixing with predictive analytics and generative AI, which changes how organizations can automate workflows. Kihlstrom says this AI combines what humans do best (i.e., abstract thinking and finessing) and what AI does best (i.e., automating tasks and providing an option) to improve workflows.
- Personalized Customer Journeys: Kihlstrom says this AI doubles down on all the above categories. It automates what an organization shows a customer when and how it will show it. This AI category also automates internal processes and generates personalized content for that customer along their journey. While this area is in its early days, customer journey orchestration platforms use this technology, which Kihlstrom has worked with for years. However, few companies use them broadly and instead implement them for specific business areas. For example, the email follow-up to an abandoned cart online is an example of this type of AI implementation.
Several of these types of AI improvements are incremental. Some of them we have used for so long that we don't consider them AI anymore. By contrast, generative AI is new and exciting because we haven't had that before. However, the other categories are types of automation that we almost take for granted now.
Kihlstrom agrees, adding that there are intersecting trends here, too. The concept of self-service application creation by dragging and dropping things is an example of an incremental change. Some internal teams can make their own software, some of which can do complex things.
The Worry I Have About Using AI for Cost Savings
Many organizations view AI as a way to cut costs, which isn't bad in and of itself. However, my concern is that too many will leverage AI to cut costs and ignore the part about improving the customer experience.