UPDATED 11:36 EDT / NOVEMBER 07 2017

BIG DATA

Experts get down and dirty on ‘AI washing’ and data-driven business

It seems businesses these days are pinning their hopes on artificial intelligence to finally make bank from big data — and this hasn’t escaped the notice of software vendors. They’re stamping AI on new products with such repetitive thuds, one can easily guess the puff inside the packaging. Knowing what AI can and can’t do could help companies shop wisely for technology that delivers on its promise.

“Cognitive organizations are not going to happen tomorrow morning,” said Tripp Braden (pictured, third from left), executive coach and growth strategist at Strategic Performance Partners. A cognitive business would be fully data and AI-driven across all departments; even companies on the cutting edge are several years out from becoming one, he added.

Braden broke down the state of business data analytics and AI during a panel discussion at the recent IBM Chief Data Officer Summit in Boston, Massachusetts. He spoke with Dave Vellante (@dvellante) and Rebecca Knight (@knightrm), co-hosts of theCUBE, SiliconANGLE Media’s mobile livestreaming studio. (* Disclosure below.) The panel also included Mike Tamir (pictured, second from left), chief data science officer at Takt Inc.; Bob Hayes (pictured, right), president of Business Over Broadway; and Christopher Penn (pictured, left), vice president of marketing technology at SHIFT Communications LLC.

Between now and the arrival of the bona fide cognitive business, there are kinks companies must massage out of their workplace cultures. Artificial intelligence will not make the brains in employees’ skulls unnecessary, according to the panel.

Digging up dirt on AI

While machine learning capabilities are getting sharper, AI in 2017 is not yet what sci-fi film director Steven Spielberg had in mind. “It’s not robots and Cylons and that sort of thing that are going to be able to think intelligently,” Tamir said.

Researchers at the Chinese Academy of Sciences in Beijing administered an intelligence test to Apple Inc.’s Siri, as well as AI assistants from Google LLC and Microsoft Corp. How did the state-of-the-art systems fare? The best of them — Google Assistant — scored lower than the average human six-year-old.

This doesn’t prove these systems are terribly deficient at doing what users expect them to, according to Tamir. It simply shows how liberal the use of AI has become in marketing products of dubious deserving. “These are not actually artificial intelligences. These are just tools that apply machine learning strategically,” he said.

The AI washing doesn’t end with consumer tech. “Nearly every technology provider is now claiming to be an AI company,” Gartner Inc. analyst Jim Hare wrote last July in a report titled “How Enterprise Software Providers Should (and Should Not) Exploit the AI Disruption.” More than 1,000 vendors are now selling AI in some form, according to Hare. “Most vendors are overselling the AI capabilities of their products with shiny, bold marketing when their technology provides strictly rule-based machine learning and analytics (rather than anything remotely self-learning),” he wrote.

Machine learning middle way

More advanced machine learning — the mechanics underneath most AI labels — delivers solid, profitable insights, according to Hayes. “You throw this data in the machine learning, you find the predictors of your outcome that interests you, and then using that information, you say, ‘Oh, maybe predictors a, b and c are the ones that actually drive loyalty behaviors,'” Hayes explained. Those insights can be funneled to decision makers in marketing who use it to segment customers and more effectively sell to them, he added.

Predictive analytics also takes periods like the busy first quarter and the slower summer and whittles them down. “Data and predictive analytics gives us specificity,” Penn said. “We know what week to send out email campaigns, what week to turn our ad budgets all the way to full, and so on and so forth.”

Machine learning and data are not silver bullets that take human discernment out of business strategy. And it appears many business people would not be interested in such ammunition if they could buy it. “One thing I hear from most of the executives I talk to when they talk about how data is being used in their organizations is the lack of trust,” Braden said. These executives want to call their customers people, not data points.

The human element may assert itself again in the interpretation of data. Data and divergent interpretations combined could make work more complicated but also more interesting and perhaps more profitable. “Now we have the ability to see it five different ways and share that with our executive team,” Braden said.

Why DIY?

Eighty-five percent of executives expect to invest in AI extensively in the next three years, according to research from Accenture PLC. There are a number of pitfalls they must avoid in order to see worthwhile returns. Shoddy AI-washed technology is one. Another is blowing money on in-house DIY AI development.

“With the likes of Amazon, Google, Microsoft, and a handful of other high-volume, global players investing in building machine learning engines at scale, anyone else who is trying to compete to do the same is really just throwing their money away,” wrote blogger and consultant Phil Wainewright in a Diginomica.com article. “Instead of attempting to build do-it-yourself machine learning capabilities, it makes much more sense to become expert instead at using the AI engines and tools the leading players are making available to use on-demand.”

Examples of companies successfully outsourcing include British retailer Ocado Group PLC. It’s using Google’s open-source TensorFlow machine learning framework to route algorithms from warehouse robots, sharpen demand forecasting, and recommend items to shoppers.

Fukoku Mutual Life Insurance, an insurance firm in Japan, is replacing 34 employees with IBM Corp.’s Watson Explorer AI to calculate payouts (not to stoke automation job loss fears).

How much AI is enough? Companies that increase data analytics’ and AI’s role in business by just five or 10 percent can gain a differentiating market advantage, according to Braden.

AI as a service from companies that have put extensive resources into development can give enterprises an edge, according to Penn. “If you’re a developer on Bluemix [IBM’s cloud platform], you can plug into the different components of Watson at literally pennies per usage,” he said.

Watch the complete video interview below, and be sure to check out more of SiliconANGLE’s and theCUBE’s coverage of the IBM Chief Data Officer Summit. (* Disclosure: TheCUBE is a paid media partner for the IBM Chief Data Officer Summit. Neither IBM, the event sponsor, nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

Photo: SiliconANGLE

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