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July 26, 2017

Now Trending: AI Washing

Alex Woodie

(I000s pixels/Shutterstock)

First there was “green washing,” where companies exaggerated the environmental benefits of their products in order to boost sales. Now technology experts are warning us about “AI washing,” an equally questionable tactic pursued by software and technology vendors to boost their artificial intelligence bona fides.

The AI washing phenomenon seemingly came out of the blue. Just 18 months ago, the phrase “artificial intelligence” wasn’t even in the top 100 search terms on the Gartner website, the company says. By May of this year, AI was the seventh most popular search term.

The rapid rise in interest around AI has spurred a good deal of confusion over the term and how it differs from machine learning and deep learning. In many ways, AI has become the go-to phrase to identify all manner of big data and advanced analytics technologies and techniques, much to the dismay of AI traditionalists, who believe the phrase should be reserved to refer to robots or programs that display human-like cognitive capabilities (what is now often called “true AI”).

Whatever your definition, there’s no denying that AI is hot. Few people dispute that the power to use predictive analytic techniques – either traditional machine learning or newer deep learning methods – will fundamentally alter how we utilize software, data, and devices. Gartner itself predicts that by 2020, AI technologies “will be virtually pervasive in almost every new software product and service.” AI will be a top five investment priority for more than 30 percent of CIOs by that time, the analyst group says.

While there is a huge potential in using AI and machine learning techniques to automate a range of tasks and boost the productivity of humans, these benefits are obscured by the large amounts of hype and confusion that have settled on the industry in a relatively short amount of time.

“All the rage is about AI and the predictions it can do,” Databricks CEO and co-founder Ali Ghodsi told Datanami recently for a story “Exposing AI’s 1% Problem.” “But there are only about five companies who are truly conducting AI today…There’s a huge gap between those companies and the rest, the haves and have nots.”

Don’t be fooled by the AI hype, says Jim Hare, research vice president at Gartner.

“As AI accelerates up the Hype Cycle, many software providers are looking to stake their claim in the biggest gold rush in recent years,” Hare says in a press release. “AI offers exciting possibilities, but unfortunately, most vendors are focused on the goal of simply building and marketing an AI-based product rather than first identifying needs, potential uses and the business value to customers.”

Hare says tech vendors should be aware of three issues surrounding to avoid falling into the AI washing trap.

The number of companies using the phrase “artificial intelligence” has risen rapidly, according to Bloomberg’s Michael McDonough

First, be careful how you use the term.

There are more than 1,000 vendors who describe themselves as AI vendor or claim to use AI in their products, Gartner says. “Many technology vendors are now ‘AI washing’ by applying the AI label a little too indiscriminately,” the analyst firm says. “Use the term ‘AI’ wisely in your sales and marketing materials,” Hare says. “Be clear what differentiates your AI offering and what problem it solves.”

Second, don’t be afraid to highlight other data analysis techniques, even those that can’t remotely be considered AI.

“Advancements in AI, such as deep learning, are getting a lot of buzz but are obfuscating the value of more straightforward, proven approaches,” the firm writes. “Gartner recommends that vendors use the simplest approach that can do the job over cutting-edge AI techniques.” (Perhaps there’s room for a phrase like “SQL Inside!” to move the marketing needle.)

Lastly, be aware that the lack of AI skills is a big concern among technology adopters.

More than half of respondents to a recent Gartner poll indicated the lack of AI skills is a major impediment to adopting AI. Most companies would rather buy AI than build it, Gartner says – as long as it’s focused on solving business problems and not just using technology for technology’s sake.

“Highlight how your AI solution helps address the skills shortage and how it can deliver value faster than trying to build a custom AI solution in-house,” Hare says.

AI is not a new term, but it’s quickly taken on new meaning over the past 18 months. As the hype surrounding AI fades – and the strange dustup between tech giants Elon Musk and Mark Zuckerburg over the potential for AI robots to take over the world recede into the memory banks – remember that we’ve barely scratched the potential for machine learning to provide a productivity.

When the big pieces for mass adoption of AI fall into place – which won’t be long now — the question then becomes: How will you use them?

Related Items:

Exposing AI’s 1% Problem

Taking the Data Scientist Out of Data Science

Machine Learning, Deep Learning, and AI: What’s the Difference?

 

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