While many companies have hobbled together a big data analytics strategy, most have not quite reached the lofty level of artificial intelligence. For that they will need to scale, rung by rung, what IBM Corp. is calling the AI Ladder.

“What I mean by an AI ladder is, you can’t do AI without machine learning; you can’t do machine learning without analytics; you can’t do analytics without the right data architecture,” said Rob Thomas (pictured), general manager of analytics at IBM.

Many companies are understandably not equipped to launch into bleeding-edge use cases, Thomas pointed out. For the moment, though, they ought to be working with these building blocks. Data analytics and AI have absolutely become competitive differentiators for companies, and the contest will only intensify. The AI Ladder can be the bridge between where they are with AI and where they’ll need to be before long, he added.

Thomas spoke with Dave Vellante (@dvellante) and John Walls (@JohnWalls21), co-hosts of theCUBE, SiliconANGLE Media’s mobile livestreaming studio, during the IBM Signature Moment — Machine Learning Everywhere event in New York. They discussed the toils and triumphs of putting data analytics and machine learning into practice. (* Disclosure below.)

The wobbly waltz to AI sea legs

Data and AI might not seem relevant to companies huddled in a we’re-not-a-tech-company defense crouch. More likely than not, however, these very companies are technology companies in a greater sense than they grasp, according to Thomas. It’s important for organizations — in the the business of tractors, shoes, watches, you-name-it — to own that they are all tech companies in 2018.

“Because if you set that culture from the top, everything else flows from there,” Thomas said. Companies will be successful going forward in proportion to how completely they knead technology into their core business. Those who push it to the side of their plate, like so much parsley garnish, will eat dust.

“There was an MIT study that said data-driven cultures drive productivity gains of six to 10 percent better than their competition,” Thomas said. “So if your competitors are doing that and you’re not, not only do you fall behind in the short term, but you fall woefully behind in the medium term.”

Not everyone is lusting after that six to 10 percent advantage just yet. “If you go to a company that hasn’t really viewed themselves as a technology company, and you start talking about machine intelligence, AI … everybody runs away scared, like it’s not interesting,” Thomas stated. Data and AI newbies can get started with small bites in the form of rudimentary building blocks, he added.

So what — explicitly — are the building blocks? There are three crucial data platforms companies need, according to Thomas. They are: a platform for data management, a platform for unified governance and integration, and a platform for data science and business analytics. Where companies start is not important, just that they gather all three together in due time.

“And if you do that, you know, you’ll start to create a flywheel of momentum where you’ll get some small successes,” Thomas said. “Once you have those in place, you’ll be able to scale AI and ML in the future in your organization.”

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

Photo: SiliconANGLE