Artificial intelligence and machine learning are the most disruptive technologies, according to IT professionals in the 2020 CIO Tech Priorities Poll. Respondents say these solutions — more so than cloud, IoT, and analytics — have the potential to significantly alter the way businesses and entire industries operate.
But where is machine learning having the most impact? That’s the question we posed to the IDG Influencer Network, a community of industry analysts, IT professionals, and journalists who contribute their knowledge and expertise to the broader IDG community. Here are some key takeaways from their responses.
Calling all industries
Perhaps in a testament to its applicability, the IDG Influencers listed machine learning (ML) use cases in nearly every industry.
“Machine learning is becoming one of today’s hot buttons,” says Jeff Kagan (@jeffkagan), wireless industry analyst. “An increasing number of companies see this as a necessity to either gain a competitive advantage or ultimately just to keep up.”
That’s because “any repetitive or learned task can be automated to shift the physical burden from person to machine,” says John Nosta (@JohnNosta), WHO health tech expert.
Diagnostic and predictive capabilities drive adoption
Healthcare is one industry where “machine learning is being piloted heavily,” says Sarbjeet Johal (@sarbjeetjohal), cloud leadership consultant. “Use cases range from getting accurate results on medical tests to fast-tracking drug discoveries.”
Other Influencers offered more healthcare examples:
“Machine learning is helping organizations detect and treat disease more effectively and efficiently while improving patient outcomes. Applying machine learning to structured and unstructured patient medical data helps identify insights for treatment, studies, and clinical trials.” — Gene De Libero (@GeneDeLibero), chief strategy officer and head of consulting at GeekHive
“In healthcare, the advances in ML have enabled clinicians to increase the accuracy of coding for insurance reimbursements.” — Frank Cutitta (@fcutitta), CEO and founder of HealthTech Decisions Lab
“ML is being used to review X-rays and CT scans.” — Arsalan Khan (@ArsalanAKhan), blogger on business and digital transformation
ML’s predictive capabilities are also proving to be valuable in other industries, such as manufacturing, and in IT and business functions such as cybersecurity, DevOps, customer service, and sales:
“Machine learning has been an important industrial tool for a long time now, especially around providing a framework for predictive maintenance. That’s a trend that shows no sign of slowing, and if anything it’s accelerating and covering a wider range of equipment.” — Simon Bisson (@sbisson), tech journalist
“A key [factor] is identifying patterns in data, be it unusual activity for security purposes or predicting machine failure. This has the ability to remove hundreds of hours of manual work and make companies more secure or productive.” — Martin Davis (@mcdavis10), CIO
“Machine learning is having a growing impact in DevOps and DevSecOps because it gives stakeholders and teams the tools to interact with back-end data from their toolchains and cloud environments. Teams can look at trends, not faults. Better yet, teams can use ML to correlate data across monitoring tools with no more context switching between tools.” — Will Kelly (@willkelly ), technical marketing manager for a container security startup
“The biggest impact comes from taking data that we know about our customers and how they interact with our business and making predictions on how to better engage, whether it’s dynamically creating better pricing strategies or selecting better products to cross-sell and up-sell.” — Noelle Silver (@NoelleSilver_), founder of AILI
“We’re seeing impact in sales systems and in determining which leads would most likely become clients, or looking at signals that show the state of a customer relationship. Ultimately, ML is having the most impact on keeping up with consumer demand and customer experience, all of which supports the bottom line.” — Deb Gildersleeve (@DebGildersleeve), CIO at QuickBase
Efficiency is a key driver
Machines can process information more quickly, saving time and driving other efficiencies, say the Influencers:
“The modern business has far more potential cybersecurity events to investigate than can be reasonably reviewed by people, and machine learning has the benefit of quickly focusing people’s attention on the signal, not the noise, so that organizations can rapidly respond to potential incidents before threat actors can establish persistence in an environment.” — Kayne McGladrey (@kaynemcgladrey), cybersecurity strategist at Ascent Solutions
“ML does a great job of performing quick tasks that eliminate false positives and remove the noise so analysts can assess the signal. It’s also a great incubator to simulate multi-step threat analysis and determine the most effective and efficient steps that streamline attack response and reduce dwell times.” — Mark Sangster (@mbsangster), author of No Safe Harbor
What’s next for ML
As ML technology evolves and improves, more benefits will emerge. The migration of digital assistants such as Amazon’s Alexa into the business world uncovered some limitations in current machine learning and neural networks, says Scott Schober (@ScottBVS), president and CEO of Berkeley Varitronics Systems Inc. “The most effective way to utilize machine learning is the handoff from human to digital and back again to human, and every single industry requires a slightly different approach,” he says. “Once this workflow cooperation between human and digital assistant is maximized, entire industries can benefit.”
Influencers see other promising developments:
“As companies witness data moving closer from the edge to cloud architectures, enterprises will seek to build flexible, user-friendly machine learning applications that provide the data intelligence that connect a host of transparent actions. With actionable insights moving even faster, this data-driven world will create more agile and flexible organizations that will transform them in a cloud world. If done well, we will witness a lot of analytics performed in a no-code cloud marketplace.” — Peggy Smedley (@ConnectedWMag), IoT Influencer
“Being able to predict an outcome achieves only about half of the potential value of ML, whereas knowing what to do in order to optimize an outcome delivers full value.” — Mark Sangster
The possibilities seem limitless, says Kagan, who advises: “There is a growing difference between leaders of yesterday and tomorrow. Companies who not only embrace new technology, but also use it correctly and protect their customers, will be the big long-term winners.”
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