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 \u2014 more so than cloud, IoT, and analytics \u2014 have the potential to significantly alter the way businesses and entire industries operate.\nBut where is machine learning having the most impact? That\u2019s 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.\nCalling all industries\nPerhaps in a testament to its applicability, the IDG Influencers listed machine learning (ML) use cases in nearly every industry.\n\u201cMachine learning is becoming one of today\u2019s hot buttons,\u201d says Jeff Kagan (@jeffkagan), wireless industry analyst. \u201cAn increasing number of companies see this as a necessity to either gain a competitive advantage or ultimately just to keep up.\u201d\nThat\u2019s because \u201cany repetitive or learned task can be automated to shift the physical burden from person to machine,\u201d says John Nosta (@JohnNosta), WHO health tech expert.\nDiagnostic and predictive capabilities drive adoption\nHealthcare is one industry where \u201cmachine learning is being piloted heavily,\u201d says Sarbjeet Johal (@sarbjeetjohal), cloud leadership consultant. \u201cUse cases range from getting accurate results on medical tests to fast-tracking drug discoveries.\u201d\nOther Influencers offered more healthcare examples:\n\u201cMachine 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.\u201d \u2014 Gene De Libero (@GeneDeLibero), chief strategy officer and head of consulting at GeekHive\n\u201cIn healthcare, the advances in ML have enabled clinicians to increase the accuracy of coding for insurance reimbursements.\u201d \u2014 Frank Cutitta (@fcutitta), CEO and founder of HealthTech Decisions Lab\u00a0\n\u201cML is being used to review X-rays and CT scans.\u201d \u2014 Arsalan Khan (@ArsalanAKhan), blogger on business and digital transformation\nML\u2019s 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:\n\u201cMachine learning has been an important industrial tool for a long time now, especially around providing a framework for predictive maintenance. That\u2019s a trend that shows no sign of slowing, and if anything it\u2019s accelerating and covering a wider range of equipment.\u201d \u2014 Simon Bisson (@sbisson), tech journalist\n\u201cA 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.\u201d \u2014 Martin Davis (@mcdavis10), CIO\n\u201cMachine 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.\u201d \u2014 Will Kelly (@willkelly ), technical marketing manager for a container security startup\n\u201cThe 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\u2019s dynamically creating better pricing strategies or selecting better products to cross-sell and up-sell.\u201d \u2014 Noelle Silver (@NoelleSilver_), founder of AILI\n\u201cWe\u2019re 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.\u201d \u2014 Deb Gildersleeve (@DebGildersleeve), CIO at QuickBase\nEfficiency is a key driver\nMachines can process information more quickly, saving time and driving other efficiencies, say the Influencers:\n\u201cThe 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\u2019s 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.\u201d \u2014 Kayne McGladrey (@kaynemcgladrey), cybersecurity strategist at Ascent Solutions\n\u201cML does a great job of performing quick tasks that eliminate false positives and remove the noise so analysts can assess the signal. It\u2019s 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.\u201d \u2014 Mark Sangster (@mbsangster), author of No Safe Harbor\nWhat\u2019s next for ML\nAs ML technology evolves and improves, more benefits will emerge. The migration of digital assistants such as Amazon\u2019s 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. \u201cThe 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,\u201d he says. \u201cOnce this workflow cooperation between human and digital assistant is maximized, entire industries can benefit.\u201d\nInfluencers see other promising developments:\n\u201cAs 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.\u201d \u2014 Peggy Smedley (@ConnectedWMag), IoT Influencer\n\u201cBeing 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.\u201d \u2014 Mark Sangster\nThe possibilities seem limitless, says Kagan, who advises: \u201cThere 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.\u201d\nLearn more about ways to reinvent your business with data.