GenAI vendors’ self-destructive habit of overpromising

One of the ongoing issues in enterprise IT is the gap between perception and reality. But when it comes to generative AI (genAI), vendors are about to discover that there is a big price to pay for overpromising. 

Not only are corporate execs dealing with disappointment and a lack of meaningful ROI, but the same senior non-tech leaders (think CFOs, CEOs, COOs, and some board members) who pushed for the technology before it was ready are the ones who will quickly resist deployment efforts down the road. The irony is that those later rollouts will more likely deliver on long-promised benefits. A little “AI-sales-rep-who-cried-wolf” goes a long way.

There are plenty of enterprise examples of genAI ROI not happening, but perhaps the best illustration of the conundrum involves Apple’s upcoming iPhone rollout and consumers.

Apple will add AI (it’s branded Apple Intelligence) to some of its iPhone 16 line to function on-device. In theory, on-device access might accelerate AI responses (compared with the cloud), and it could allow Siri to grab information seamlessly from all installed apps. 

If you buy into the argument, this setup could eventually change the dynamics of apps. Why wait for a weather app to launch and tell you the hourly forecast if Siri can do it easier and faster? 

For example, I have an app solely to tell me the humidity level and no fewer than a half-dozen communications apps (WhatsApp, Webex, Signal, etc.), plus apps that can directly message me (LinkedIn, X, and Facebook) —  all in addition to text messages, emails, and transcribed voicemails. Why should I have to mess with all of that?

In theory, Apple Intelligence could consolidate all of those bits and bytes of information and deliver my communications and updates in a consistent format.

But this is where reality gets in the way of tech dreams. As friend and fellow tech Jason Perlow writes, Apple is delivering a slimmed-down version of genAI in a way that could fuel more disappointment.

“Unlike typical iOS or MacOS feature upgrades, Apple Intelligence loads a downsized version of Apple’s Foundation Models, a home-grown large language model (LLM) with approximately 3 billion parameters,” Perlow wrote. “While impressive, this is tiny compared to models like GPT-3.5 and GPT-4, which boast hundreds of billions of parameters. Even Meta’s open source Llama 3, which you can run on a desktop computer, has 8 billion parameters.”

On top of that, Apple Intelligence will grab as much as 2GB of RAM, which means users will either need more RAM than they want or deal with performance slowdowns in other iPhone functions. Then there’s the potential drag on battery performance which, again, threatens to undermine everything else on the device.

Bottom line: Not only will this initial rollout likely eat battery and RAM for breakfast, but it will be smaller and therefore less powerful than most other genAI deployments. That’s a recipe for buyer remorse.

Then there is the issue of app developers. First, it will take some time for them to work with the Apple API and deliver versions of their apps that play nicely with Apple Intelligence. Other developers may question whether it is even in their interest to embrace Apple Intelligence. Once they enable Apple to effortlessly grab their data and deliver it via Siri, doesn’t the value of their standalone app diminish? And doesn’t that undermine their monetization strategies? 

Why look at ads on a movie-ticket or concert venue app when Siri can deliver the needed info directly?

Research firm IDC looked recently at those Apple-promised capabilities and predicted they could initally boost phone sales. “Initially” is the key word. People often buy based on tech promises, then talk things over with others and decide whether to buy a future phone (or keep the one they just got) based on their actual experience.

This brings us back to enterprise IT and genAI. Business execs who pushed for genAI rollouts before the technology was ready are unlikely to be patient and realistic waiting for the solid results to surface.

And then, just when meaningful ROI is likely to arrive (roughly two or three years from now), they’ll have moved on, feeling burned by early deployments and unwilling to be fooled again. 

GenAI has great potential to push near-term sales with unrealistic promises — a self-destructive marketing approach, whether you’re OpenAI, Microsoft, Google, or Amazon selling to enterprise CIOs or Apple selling to consumers. 

Overpromising is a dangerous and foolhardy strategy. And yet, with enterprise genAI sales these days, overpromising isn’t a side course — it’s the main course. It’s unlikely to prove appetizing for anyone.