What’s with all these speed bumps en route to big data monetization and artificial intelligence in the business world? Enterprises are hungry for it, and the cookware’s on the shelf, so where’s the steak? We asked a data and analytics adviser in the thick of the struggle to fill in the blanks.

Where to start? That isn’t just rhetoric — it’s a primary question clients have regarding analytics and AI, according to Traci Gusher (pictured), principal of data, analytics and artificial intelligence at KPMG LLP. The advisory firm helps businesses identify data’s low-hanging fruit to score some immediate value.

One obstacle that comes up often is “failure to launch,” she said.  “What I mean by that is there’s a lot of great modeling, a lot of great prototyping and experimentation happening in the lab as it relates to applying AI to different problems and opportunities. But they’re staying in the lab. They’re not making it into production; they’re not making it into BAU business-as-usual processes inside organizations.”

KPMG is coaching clients to mind the gaps between planning and launching, as well as other adjacent steps to analytics success.

Gusher spoke with John Furrier (@furrier) and Dave Vellante (@dvellante), co-hosts of theCUBE, SiliconANGLE Media’s mobile livestreaming studio, during the Google Cloud Next event in San Francisco. They discussed the hiccups in keeping organizations from extracting value from their data and what it takes to implement AI in the real world. (* Disclosure below.)

Getting from here to there with training, abstractions

Other ponds business must jump involve people and skills, according to Gusher. For example, there may be computer programming gaps; often, new data processing models are constructed with the Python language, while many traditional information technology organizations are Java shops.

“And they’re saying what do I do now? Do I convert? Do I learn? Do I use different talent?” Gusher said.

Businesses have to make decisions about training their workforce and transitioning them to technologies and processes that will yield insights and value, Gusher furthered.

KPMG is looking out for anything that smacks of “insights as-a-service” for organizations that don’t have a lot of data-science talent. The type of abstraction layers that Google Cloud Platform is touting are at least drifting in that direction, Furrier noted.

Watch the complete video interview below, and be sure to check out more of SiliconANGLE’s and theCUBE’s coverage of the Google Cloud Next event. (* Disclosure: KPMG LLP sponsored this segment of theCUBE. Neither KPMG nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

Photo: SiliconANGLE