When it comes to artificial intelligence, IBM Corp. is increasingly finding itself having to fill gaps.

There are skills gaps for data scientists who build training models, organizational gaps where companies struggle over how AI should be built and implemented in the enterprise, and credibility gaps as adoption has grown, but many people are still uncertain about its potential or nervous about it taking away jobs.

Yet perhaps the most-significant gap that exists today is the knowledge fundamental to the technology’s future success: What exactly is AI?

This has led the head of IBM’s Watson AI technology group to take the unusual step of unpacking the “black box,” a system or device that performs a function without widespread knowledge around how it actually works inside.

“People overreact to hype on topics like AI; this is not black magic, and this is not some far off thing,” said Rob Thomas (pictured), general manager of IBM Data and Watson AI and IBM. “How do you optimize process to drive greater productivity? We’re talking about the basics: better predictions, better automation, better optimization.”

Thomas spoke with Dave Vellante, host of theCUBE, SiliconANGLE Media’s mobile livestreaming studio, during the IBM Data and AI Forum in Miami, Florida. They discussed efforts to demystify the technology, a step-by-step approach to implement effective AI solutions, the role of data virtualization, and how Watson tools have been integrated into the Red Hat platform (see the full interview with transcript here). (* Disclosure below.)

This week, theCUBE features Rob Thomas as its Guest of the Week.

Data’s critical role

At the IBM gathering in Miami in October, Thomas and a number of company executives devoted time to demystifying AI technology. A clue to how the subject should be approached can be found in Thomas’ own job title, because data sits at the heart of the AI conversation. AI models train on data, and if a user has poor data or starts out with good data but doesn’t recognize information shifts or “drifts” over time, critical analytic mistakes can be easily made.

“Your AI is only as good as your data,” Thomas said. “That’s the fundamental problem. In organizations we work with, 80% of the projects get stopped or slowed down because the company has a data problem.”

To help resolve this issue, IBM recently announced that it would add drift-detection software to Watson OpenScale in an effort to help users more easily detect how far an AI model may have shifted from its original parameters. This tool is designed to assist DevOps teams and data scientists in collaborating more closely so that AI models actually make it into production applications.

This important collaborative step is just one of what Thomas has identified as part of the AI Ladder, the process of gathering, organizing, analyzing and implementing AI throughout an organization. The message here is while a ladder may enable a user to climb higher, it’s still done one step at a time  — and that starts with a data strategy.

“We use the AI Ladder as a tool to encourage companies to think about a data strategy,” Thomas said. “I ask every company I visit: Do you have a data strategy? You wouldn’t believe the looks you get when you ask that question.”

Virtualization gains significance

IBM has also been devoting its resources to a couple of intriguing initiatives, one of which deserves more attention, according to Thomas.

This involves the area of data virtualization, the integration of information sources across multiple types and locations while creating one logical data view. Users can query data across many systems without having to copy or replicate it, thus saving time and money.

“One of the greatest inventions out of IBM Research in the last 10 years that hasn’t gotten a lot of attention is data virtualization,” Thomas said. “We don’t have to move the data; we just virtualize data sets into Cloud Pak for Data, and then we can train the model in one place. This is actually breaking down data silos that exist in every organization, and it’s really unique.”

Integration with OpenShift

In addition to bringing Watson AI to the data, the other significant solution being pursued by IBM is the integration of its software tools on Red Hat OpenShift, the container application platform acquired in a mega-billion-dollar purchase one year ago. IBM’s integrated cloud-native applications or Cloud Paks now include AI tools, such as Watson Studio, OpenScale, and support for important open-source development tools like R and Python.

“What Red Hat OpenShift is, it’s a liberator,” Thomas explained. “What that means is you can have the best data platform, the best AI, and you can run it on Google, Amazon Web Services, Azure, your own private cloud. You can get the best AI with Watson from IBM and run it in any of those places.”

IBM’s integration with Red Hat represents an important step if AI is going to progress forward in wider enterprise adoption. By recognizing the influence of open-source tools in the all-powerful developer community and integrating its AI solutions into platforms like OpenShift, IBM hopes to make the entire technology infinitely easier to use.

One example of this can be found in AutoAI features that automate data preparation and preprocessing steps in enterprise environments. AutoAI has been integrated with Watson Studio and is now readily available on OpenShift, according to Thomas.

“If you’re building models in Python, you can use AutoAI to make things like feature engineering, algorithm selection, the kinds of things that are hard for a lot of data scientists,” Thomas said. “We’re not trying to create our own language. We’re using open source, but then we make that better so that a data scientist can do their job better.”

Better data and better data scientists equal better AI. That’s the playbook IBM is clearly following as it pursues its AI initiatives, one step up the ladder at a time.

“We are trying to inspire clients to give AI a shot,” Thomas said. “Go try things. Everybody will be successful with AI if they have this iterative mindset.”

Here’s the complete video interview, part of SiliconANGLE’s and theCUBE’s coverage of the IBM Data and AI Forum. (* Disclosure: TheCUBE is a paid media partner for the IBM Data and AI Forum. Neither IBM, the sponsor for theCUBE’s event coverage, nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

Photo: SiliconANGLE