Machine learning technology is able to analyse and improve performance without direct human intervention, so it’s no wonder that it’s high on the investment roadmap for many CIOs
In fact five hundred CIOs were recently polled for the annual Global CIO Point of View Survey and nearly 90% are using machine learning in some capacity, with most developing strategies or piloting the technology.
But, despite investing in machine learning, the new survey indicates that most CIOs do not have the skilled talent, data quality and budgets to fully leverage the technology. For most CIOs, many decisions still require human input. Only 8% of respondents say their use of machine learning is substantially or highly developed, as opposed to 35% for the Internet of things or 65% for analytics.
According to a McKinsey study, the three main challenges companies have related to machine learning are designing an organisational structure to support data and analytics, having an effective technology infrastructure, and ensuring senior management are involved. The study then goes on to state that organisations that can harness these capabilities effectively will be able to create significant value and differentiate themselves, while those that fail will find themselves increasingly at a disadvantage.
Achieving great value from machine learning doesn’t come from just investing in new technologies, it is also necessary to make significant organisational and process changes, including approaches to talent, IT management and risk management.
The following five steps can help you to make progress when investing in machine learning:
1. Ensure you have quality data
Often a common hurdle when adopting machine learning is ensuring the quality of data. Poor data leads to machines making poor decisions, which can lead to increased risk. In order to accelerate the transition to machine learning CIOs need to consider implementing solutions that simplify data maintenance. Consolidating redundant legacy and on-premises IT tools into a single data model should be the first step.
2. Establish your goalposts
Review the business value of all technology goals and then decide on the best strategy to help reach them. This includes examining processes that already exist to identify which unstructured work patterns will benefit most from automation. Determining where fragmented data “lives” will enable you to identify how automation then leads to gains in productivity down the line.
3. Improve the customer experience
Implementing machine learning for automation will boost operational efficiency, but that’s not the only reason for using it. Machine learning can also speed up decision making time, which will help improve the customer experience. Start by envisioning the environment you want to create, then prioritise investment against business processes that could improve customer experience the most. In turn, it allows for that personal touch in an organisation’s advertisements, call-center interactions, and products and services for individual customers – even predicting what they want next.
4. Set and measure metrics
CIOs need to spread the value of machine learning to other members of the senior executive team, just because they understand its value doesn’t mean others do. Its therefore imperative to set expectations and develop metrics of success before beginning the implementation process and prepare a solid business case to present to the leadership team when requesting the necessary funding. Metrics will need to change as you adopt machine learning capabilities and reap the benefits of intelligent automation and reported back to show its importance.
5. Understand the effect on employees
This transformation will likely be uncomfortable for some employees, so CIOs need to ensure they communicate the value machine learning will bring to their day-to-day work. The machines will not take over the enterprise – instead they will free the employees of tedious manual processes, enabling them to focus on more strategic projects.
While CIOs themselves must evolve their roles too, from overseeing operational matters to an executive who has a broader engagement across the business and, therefore, a new level of strategic importance.
Realising a return on machine learning investments requires planning and disciplined follow-through, as well as equipping employees with the skills to adjust to rapid and ongoing technology changes. But following the above five steps will ease the adoption of machine learning and set organisations on the path to harnessing its full potential.
About the Author
Paul Hardy is Chief Innovation Office at ServiceNow. With the ServiceNow System of Action you can replace these unstructured work patterns of the past with intelligent workflows of the future. Now every employee, customer and machine can make requests on a single cloud platform. Every department working on these requests can assign and prioritize, collaborate, get down to root cause issues, gain real‑time insights and drive to action. Your employees are energized, your service levels improve and you realize game‑changing economics. Work at Lightspeed. To find out how, visit www.servicenow.com.
