Humans Work Side-by-Side with Smart Machines for Better Accuracy, Speed and Growth of Business
The survey finds a growing sense of confidence among senior executives in the UK that machine learning will lead to faster and more accurate decisions. Machine learning software possesses the ability to analyse and improve upon its own performance without direct human intervention, allowing them to make increasingly complex decisions over time:
- More than half (52%) of UK respondents say they are advancing beyond the automation of routine tasks, such as security alerts, toward the automation of complex decisions, such as how to respond to security alerts.
- 89% of UK CIOs said that they would get substantial value or transformative value to the accuracy of decisions. In fact, 69% of UK respondents said decisions made by machine learning will be more accurate than those made by humans.
- 57% of UK CIOs said that routine decision making takes up a meaningful amount of employee and executive time, so the potential value of automation is high. CIOs in the UK expect this decision automation to contribute to their organisation’s top-line growth (72%).
“We see three kinds of decisions as targets for automation—anything requiring rating, ranking or forecasting,” said Paul Hardy of the Chief Strategy Office, ServiceNow . “Everyday work such as the assignment of IT tickets and prioritising sales leads are already delivering results. Machine learning has moved from hype to reality .”
Machine Learning Specialists Alone Won’t Help CIOs Succeed in Digital Transformation
Around three-quarters (74%) of UK CIOs said they’re leading their company’s digitalisation efforts, and 57% agree that machine learning plays a critical role. Over half (54%) of the UK CIOs surveyed say their companies are using machine learning and 33% are planning to adopt the technology.
But there are key talent, organisation and process areas that must be addressed in order for companies to take full advantage of machine learning technology:
- Only 35% of UK CIOs have hired employees with new skill sets to work with intelligent machines.
- Fewer than half (39%) of UK CIOs have redefined job descriptions to focus on work with intelligent machines, 41% cite a lack of skills to manage smart machines and less than half (35%) say they lack budget for new skills development.
- UK CIOs cite data quality (54%) and outdated processes (46%) as substantial barriers to adoption
- Less than half (43%) have developed methods for monitoring mistakes made by machines.
“Machine learning allows enterprises to digitise in ways that were not possible before,” said Hardy. “To realise the full potential of machine learning technology, CIOs must elevate their role to transformational leader who influences 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 roadmap for future business process changes compared with just 33% of others. ?
“First-mover CIOs who combine machine learning with new business processes and skillsets will better able to support their enterprise growth ,” said Hardy . “ 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 bottomline.”
Five Steps to Achieve Value from Machine Learning
ServiceNow recommends on 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 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.
2) Prioritise based on value realisation. When building a roadmap, 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.
3) 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 roadmap to implement machine learning capabilities, imagine the ideal customer experience and prioritise investment against those goals.
4) 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.
5) 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.
