Our new\u00a0Global CIO Point of View,\u00a0a survey\u00a0of 500 CIOs across\u00a011 countries and 25 industries, shows that\u00a0real enterprises are using\u00a0machine learning\u00a0to\u00a0accelerate digital\u00a0transformation,\u00a0increase speed,\u00a0and\u00a0optimize cost structures.\u00a0\u00a0\u00a0\nMachine learning\u00a0is software that promises to analyze and improve its own performance without direct human intervention, enabling the automation of various business tasks.\u00a0Most CIOs are eager to put the technology to use. However, our research found many are slower to make the organizational changes that allow machine learning to reach its full potential: hiring and training\u00a0talent;\u00a0organizing\u00a0data;\u00a0and\u00a0digitizing\u00a0business processes.\nAmong the key findings\u00a0in the report:\nCIOs see machine learning as an integral part of digital transformation.\n\n72% of CIOs are leading digitalization efforts, and 53% say machine learning is a focus.\n90% are\u00a0either planning or are\u00a0already using machine learning.\n63% of\u00a0CIOs intend to make some investment\u00a0in machine learning in the next three years, nearly double the number of CIOs today.\n\n ServiceNow\nCIOs are\u00a0making the needed\u00a0organizational\u00a0changes to\u00a0benefit from\u00a0machine learning.\n\nAlmost one-third\u00a0of CIOs have recruited employees with the skill sets needed to manage machine learning technology.\nAbout 40%\u00a0have redefined job descriptions to focus on work with machines.\nNearly two-thirds\u00a0of CIOs have made substantive changes to processes or leadership to accommodate digital labor.\n\nCIOs agree there are several challenges they need to solve.\n\n48% say outdated processes are holding back the use of machine learning.\n51% say\u00a0data quality\u00a0interfering with machine learning adoption.\n47% say lack of budget for new skills and technology.\n\nDoes any of this sound familiar? Every CIO who has led a cloud adoption\u00a0initiative\u00a0should be nodding. In the same way that migration to the cloud\u00a0was a journey, not\u00a0a task, the adoption of machine learning requires\u00a0a\u00a0steady evolution of processes, procedures, roles, skills, and organizational structures. And that requires careful\u00a0consideration\u00a0on multiple levels.\nFor example,\u00a0you\u00a0may understand the vision for machine learning, but\u00a0do your business counterparts?\u00a0Have you quantified the expected benefits?\nTake Action Now In order to address these issues, the following are areas\u00a0that we are focused on within ServiceNow\u00a0and recommend to other IT leaders:\n\n Build on a foundation of high data quality. Data is the lifeblood of machine learning, and your results will only be as good as your data.\u00a0CIOs\u00a0must ask:\u00a0Have you digitized your\u00a0processes\u00a0so\u00a0that\u00a0you can capture the right data\u00a0to\u00a0feed machine learning algorithms? Have you identified data outside your\u00a0enterprise\u00a0that\u00a0can enhance\u00a0the quality of\u00a0business\u00a0decisions?\u00a0CIOs must\u00a0also\u00a0ensure that a strong\u00a0data management\u00a0strategy is in place across silos of data.\n Attract new skills and double down on culture. It\u2019s vital to focus on\u00a0skillsets and\u00a0corporate culture as you\u00a0implement machine learning. Identify the roles of the future and anticipate how employees will need to engage with machines, but\u00a0also\u00a0build a culture that embraces this new working model. Talent will go to the enterprises that are innovative and clear on the relationship model between mind and machine.\n Prioritize based on value realization. CIOs must\u00a0quantify the expected results and\u00a0articulate the business value of\u00a0machine learning\u00a0goals.\u00a0Where are the most unstructured work patterns\u00a0today, and which\u00a0would benefit\u00a0most\u00a0from automation? What would be the productivity gains from increased automation?\u00a0Without this evaluation,\u00a0machine learning will remain a science project versus a viable offering that delivers competitive advantage.\nMeasure and report. It is critical to measure\u00a0outcomes to continuously reinforce the business case. But you can\u2019t use the same metrics you always have.\u202fNew measures to start\u00a0considering include\u00a0the\u00a0percentage of machine learning\u00a0recommendations\u00a0accepted and\u00a0the\u00a0percentage of decisions automated.\n\nTo learn more, visit ServiceNow\u2019s website dedicated to CIOs and education about the benefits of machine learning. You can also read the global study.