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What do Einstein, Watson, and Sensei have in common? All three brands are supposed to encompass a major tech company’s AI features, and they all need to go away.
It’s easy to see why a company would want a megabrand to represent its AI efforts — such a brand makes it possible to consolidate all of a company’s innovation under a single umbrella and promote all of it at once.
That’s obviously much better for marketing than introducing a bunch of disparate AI features for different products that may not influence one another at all. There’s also a nefarious reason: Companies can use these brands to hide capabilities that seem AI-powered but aren’t.
Take the Conversational Queries feature in Salesforce’s Einstein Analytics product, which the company announced earlier this month. When I first spoke with them about it, I thought that it was tied to other Einstein AI efforts, especially since Salesforce Research worked on a system to translate natural language into SQL queries.
But that’s not the case. The feature just matches patterns from what customers type with metadata that’s fed into the analytics system. Until I brought up the topic of Salesforce Research’s paper, the company representative I spoke to didn’t mention anything about the feature’s disconnection from the AI capabilities that Salesforce has been rolling out over the past couple years.
It’s a feature of Einstein Analytics, not Einstein, and therefore isn’t necessarily connected to the company’s AI efforts. That’s a fine distinction, but apparently one that Salesforce is okay with.
IBM pulls the same tricks with its Watson brand. Although they share a name with the system that wowed people by winning Jeopardy, the features IBM sells aren’t going to win a game show. And while company representatives talk about Watson as a single entity, it’s just an overarching brand with sets of capabilities that don’t necessarily benefit one another. If the company starts parsing social media posts better, that doesn’t mean it’ll suddenly become more adept at spotting cancer.
I’ve seen these megabrands undermine potential customers’ trust in the actual intelligent capabilities that companies provide. Technical decision makers ask me all the time whether one of these companies is really doing AI, and I think part of that mistrust can be traced back to the marketing behind their brands.
In each case, these companies are doing actual work with AI and have at their disposal large datasets that can be used to train unique models, but the smokescreen provided by their brands confuses the very people they’re trying to reach and makes the company seem less serious.
Rather than tack a brand name onto an entire suite of AI capabilities, companies would be better off applying the term judiciously. One complaint I frequently hear from people both inside and outside the tech industry is that it feels like AI is being applied too broadly and too often. Companies large and small add to this by sprinkling a little extra pixie dust on everything they do, like all the startups suddenly pitching themselves as AI companies. Large businesses like Salesforce, IBM, and Adobe are important contributors to the way people perceive AI, and it’s incumbent upon them to act responsibly or face a serious backlash.
Companies like Google, Microsoft, and Amazon tend to have the right idea. Each one wraps AI capabilities in fairly narrow features and individual brands (though Microsoft used to bundle some of its AI capabilities into the Cortana Intelligence Suite, which had basically nothing to do with its virtual assistant of the same name), in keeping with the current state of the art in AI. We’re a long way away from generalized artificial intelligence, and the way we represent these capabilities in public should reflect that.
For AI coverage, send news tips to Blair Hanley Frank and Khari Johnson and guest post submissions to Cosette Jarrett — and be sure to bookmark our AI Channel.
P.S. Please enjoy this video showing how Google’s TensorFlow AI framework can help with the cultivation of Cassava:
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