- 3rd August 2018
- Posted by: Manolis
- Category: Blockchain
If ever a technology was the opposite of plug-and-play, artificial intelligence is it. Beyond the obvious table stakes – having sound information and a smart data strategy – getting AI right requires a broad array of closely interrelated technologies to work well together. And it demands that each organization be able to tailor its deployments to their own particular needs.
The data literacy part of the equation is hard enough to get right, for instance.
“Very few health systems that I have seen and talked to have figured out their data strategies,” said Nevenka Dimitrova, chief technology officer for oncology informatics and genomics at Philips Healthcare, speaking at the HIMSS Precision Medicine Summit this spring.
Perhaps that explains why many hospitals underestimate the legwork and elbow grease it takes to build out a technology infrastructure on which AI and machine learning algorithms can run.
And that means that IT decision-makers at hospitals and health systems have their work cut out for them as they try to make sense of a fast-evolving tech landscape with all sorts of moving parts.
“A technology race has started along the S-curve for artificial intelligence, a set of new technologies is now in the early stages of deployment,” writes Jacques Bughin, director of the McKinsey Global Institutes, and Nicolas van Zeebroeck, professor at the Solvay Brussels School of Economics and Management in a new McKinsey report.
They noted that organizations looking to take advantage of AI “can’t flourish without a solid base of core and advanced digital technologies. Companies that can assemble this bundle of capabilities are starting to pull away from the pack and will probably be AI’s ultimate winners.”
That base inevitably includes basics, such as cloud infrastructure and connectivity, mobile and web tools as well as more advanced technologies including big data and analytics.
Smart AI deployments make use, to varying degrees depending on the health systems needs and specific use cases, on several technologies that work together: virtual agents, natural-language generation, machine learning, image recognition, decision-making, robotic process automation, NLP, robotics, speech recognition and more, McKinsey wrote.
Organizations that had deeper experience and better fluency those core fundamentals “were statistically more likely to have adopted each of the AI tools — about 30 percent more likely when the two clusters of technologies are combined,” they said. “These companies presumably were better able to integrate AI with existing digital technologies, and that gave them a head start.”
More and more C-suite leaders have become convinced that advanced AI tools and shrewd analytics strategies have become imperative they added. McKinsey found that 45 percent of executives at organizations that haven’t yet invested in AI tools worry about being left at a disadvantage
“Our statistical analysis suggests that faced with AI-fueled competitive threats, companies are twice as likely to embrace AI as they were to adopt new technologies in past technology cycles,” the researchers write.
But doing so haphazardly, or without proper understanding of how the technologies work, and work well together, has its risks too, of course. Taking the time to lay a good foundation is critical to the construction of a solid building. Pouring the concrete too quickly and thoughtlessly could undermine the whole effort.
Even as they rush to invest in AI tools, Bughin and van Zeebroec note, a basic “digital substructure is still lacking” in many organizations.
They estimate that just one in three organizations has yet “fully diffused the underlying digital technologies and that the biggest gaps were in more recent tools, such as big data, analytics, and the cloud. This weak base, according to our estimates, has put AI out of reach for a fifth of the companies we studied.”
Still, for those with the patience and foresight to build the right IT infrastructure for their own specific needs, there are big opportunities ahead for AI-driven clinical and operational improvements.
The number of organizations deploying the “full range of AI technologies, of course, is still small, and many of the most advanced power users in our research, notably, were digital natives,” according to McKinsey. But those hospitals and health systems that have invested in and honed a “strong base in digital capabilities will benefit, since they can move more quickly to adopt AI.”