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By Chris Bedi
Our new Global CIO Point of View, a survey of 500 CIOs across 11 countries and 25 industries, shows that real enterprises are using machine learning to accelerate digital transformation, increase speed, and optimize cost structures.
Machine learning is software that promises to analyze and improve its own performance without direct human intervention, enabling the automation of various business tasks. Most 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 talent; organizing data; and digitizing business processes.
Among the key findings in the report:
CIOs see machine learning as an integral part of digital transformation.
72% of CIOs are leading digitalization efforts, and 53% say machine learning is a focus.
90% are either planning or are already using machine learning.
63% of CIOs intend to make some investment in machine learning in the next three years, nearly double the number of CIOs today.
CIOs are making the needed organizational changes to benefit from machine learning.
Almost one-third of CIOs have recruited employees with the skill sets needed to manage machine learning technology.
About 40% have redefined job descriptions to focus on work with machines.
Nearly two-thirds of CIOs have made substantive changes to processes or leadership to accommodate digital labor.
CIOs agree there are several challenges they need to solve.
48% say outdated processes are holding back the use of machine learning.
51% say data quality interfering with machine learning adoption.
47% say lack of budget for new skills and technology.
Does any of this sound familiar? Every CIO who has led a cloud adoption initiative should be nodding. In the same way that migration to the cloud was a journey, not a task, the adoption of machine learning requires a steady evolution of processes, procedures, roles, skills, and organizational structures. And that requires careful consideration on multiple levels.
For example, you may understand the vision for machine learning, but do your business counterparts? Have you quantified the expected benefits?
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. CIOs must ask: Have you digitized your processes so that you can capture the right data to feed machine learning algorithms? Have you identified data outside your enterprise that can enhance the quality of business decisions? CIOs must also ensure that a strong data management strategy is in place across silos of data.
Attract new skills and double down on culture.
It’s vital to focus on skillsets and corporate culture as you implement machine learning. Identify the roles of the future and anticipate how employees will need to engage with machines, but also build 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.
Prioritize based on value realization.
CIOs must quantify the expected results and articulate the business value of machine learning goals. Where are the most unstructured work patterns today, and which would benefit most from automation? What would be the productivity gains from increased automation? Without this evaluation, machine learning will remain a science project versus a viable offering that delivers competitive advantage.
Measure and report.
It is critical to measure outcomes to continuously reinforce the business case. But you can’t use the same metrics you always have. New measures to start considering include the percentage of machine learning recommendations accepted and the percentage of decisions automated.