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IBM Think 2024: Could AI Help Figure Out What Customers Will Want?

IBM Think 2024: Could AI Help Figure Out What Customers Will Want?

By Rob Enderle for Techspective

Every industry has the same problem: figuring out what customers will want in a three-, five-, or 10-year timeframe. The reason we need to know what customers will want is that it takes from three to 10 years to create a new product, evaluate it, assure it, then bring it to market. Apple’s late CEO Steve Jobs figured that out early on.

At IBM’s Think 2024, the Q&A started with IBM’s CEO, Dr. Arvind Krishna, who said that whether it is hardware, software, or some mix of services, IBM will package what the customer wants. I’m ex-IBM, and my final job there was in marketing. One of the products that crossed my desk that I refused was a product IBM had spent several years and $20M developing, but I couldn’t figure out why anyone would want it. After doing a $20K study, it was determined I was right. No one wanted the product. It was an answer that would have been far more useful before the $20M was spent.

Steve Jobs argued that customers don’t know what they want and successfully showcased that if you adequately fund marketing, you could convince customers they want something and thus successfully anticipate a need because you created it. But even Apple seems to have forgotten that now. You can predict an outcome if you work to create that outcome, but it seems like most vendors thought the movie Field of Dreams was a manual on how to do things because they don’t adequately fund marketing for new products.

I think IBM’s watsonx, specifically, and AI generally, could fix this problem by helping to define a process and necessary marketing budget to ensure success.

Creating a Better Predictive Engine

We often focus AI on tactical projects like telephone sales, autonomous vehicles, robots, medical diagnosis, creating pictures or documents, and answering tough questions. But the big efforts, like Earth 2, are attempting to become more predictive. In fact, the industry is starting to focus AI on predictive securities trading.

The reason so many products fail in the market isn’t always that they were badly matched to the customers, but more often the fact that they are poorly marketed. The reason they are poorly marketed is that marketing isn’t treated like a strategic part of success. Ideally, when the initial product plan is set, what it will cost to market it and what the campaign should look like should be built into the budget.

The reason this isn’t done early is that those promoting the potential new product want to get a ‘yes’, so they leave or under-budget some costs, and marketing is a major part of that effort. But that means the product isn’t adequately funded, and much like a race car that isn’t given enough gas, the result is often a failed launch.

Granted this budget will reduce the number of products the company could launch in a given year, but it would better ensure those that are launched are successful. You don’t get credit for the most failed or marginally performing products. You get credit for successes.

Examples of this were Windows 95 and the Xbox, which were adequately marketed, while Zune, Cortana, and especially the Windows Phone were undermarketed and failed.

Read the rest of the article to find out more about the watsonx decision engine: IBM Think 2024: Could AI Help Figure Out What Customers Will Want?


Rob Enderle, The Enderle Group
An Internet search of media quotes validates Rob Enderle as one of the most influential technology pundits in the world. Leveraging world-class IT industry analysis skills honed at DataQuest, Giga Information Group, and Forrester Research, Rob seized upon the power of the information channel as a conduit to reach business strategists and deliver valuable, experienced-based insight on how to leverage industry advances for maximum business advantage.

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