Intelligent CIO Africa Issue 11 | Page 18

TRENDING • Fewer than half (40%) of CIOs have redefined job descriptions to focus on work with intelligent machines, 41% cite a lack of skills to manage intelligent machines and about half (47%) say they lack budget for new skills development. • CIOs cite data quality (51%) and outdated processes (48%) as substantial barriers to adoption. • Fewer than half (45%) have developed methods for monitoring mistakes made by machines. “Machine learning allows enterprises to digitise in ways that were not possible before,” Bedi said. “To realise the full potential of machine learning technology, CIOs must elevate their role to transformational leaders who influence how our organisations design business processes, leverage data, and hire and train talent.” First mover CIO advantages – delivering results today A select group of CIOs surveyed (fewer than 10%) are running ahead of their peers in the use of machine learning. These ‘first movers’ provide a model for how CIOs can better utilise machine learning: • Almost 90% of first movers expect decision automation to support top- line growth vs 67% of others. • Roughly 80% have developed methods to monitor machine-made mistakes vs 41% of others. • More than three-quarters have redesigned job descriptions to focus on work with machines compared with 35% of others. • More than 70% have developed a road map for future business process changes compared with just 33% of others. “First mover CIOs who combine machine learning with new business processes and skill sets will better support their enterprise growth,” Bedi said. “They report higher levels of maturity in the use of leading platforms, which allows them to concentrate on innovation, such as automating complex decision- making, which immediately impacts the bottom line.” 18 INTELLIGENTCIO “To realise the full potential of machine learning technology, CIOs must elevate their role to transformational leaders.” 2. Financial services leads, healthcare industry lag The survey uncovered viewpoints from CIOs in the financial services and healthcare sectors. Of note: 3. • CIOs from financial services are more likely to say their company is moving from the automation of simple decisions to the automation of increasingly complex decisions (68% vs 52% of others). They are more likely to have made organisational changes to accommodate digital labour, including redefining job descriptions to focus on work with machines (62% vs 36%), developing a road map for future process changes (52% vs 35%), and recruiting employees with new skill sets (42% vs 25%). • CIOs in the healthcare industry remain cautious. They are less likely to use machine learning across the organisation and less likely to say the technology will have a positive impact on top-line growth, competitiveness, or reducing risk. They are less likely to expect value from decision automation in a number of functional areas, including security (70% vs 80%), operations (46% vs 58%), risk and compliance (36% vs 58%). Five steps to achieve value from machine learning ServiceNow recommends how CIOs can jump-start their journey to digital transformation with machine learning: 1. Build the foundation and improve data quality. One of the top barriers 4. 5. to machine learning adoption is the quality of data. If machines make decisions based on poor data, the results will not provide value and could increase risk. CIOs must utilise technologies that will simplify data maintenance and the transition to machine learning. Prioritise based on value realisation. When building a road map, focus on those services that are most commonly used, as automating these services will deliver the greatest business benefits. At a high level, where are the most unstructured work patterns that would benefit from automation? Commit to re-engineering services and processes as part of this transformation, and not simply lifting and shifting current processes into a new model. Build an exceptional customer experience. A core benefit of increasing the speed and accuracy of decision-making lies in creating an exceptional internal and external customer experience. When creating a road map to implement machine learning capabilities, imagine the ideal customer experience and prioritise investment against those goals. Attract new skills and double down on culture. CIOs must identify the roles of the future and anticipate how employees will engage with machines – and start hiring and training in advance. CIOs must build a culture that embraces a new working model and skills. That means establishing guidelines for executives, engineers, and front- line workers about their work with machines and the future of human- machine collaboration. Measure and report. The benefits of machine learning may be clear to CIOs, but other C-level executives and corporate boards often need to be educated on its value. CIOs must set expectations, develop success metrics prior to implementation, and build a sound business case in order to acquire and maintain the requisite funding. CIOs should also consider building automated benchmarks against peers in their industry and other companies that are of similar size. n www.intelligentcio.com