Five Steps for Re-tooling Your Organization with Machine Learning
By: Allan Leinwand, Chief Technology Officer, ServiceNow
Let's say you decide to build a new house. Not only do you have to buy the materials, but you also have to hire the skilled talent who can get the job done. That is a lesson many CIOs in the Middle East are learning about their plans to implement machine learning technologies that are able to analyze and improve performance without direct human intervention. Despite investing in machine learning, a new survey from ServiceNow indicates that most CIOs do not have the talent, data quality and budgets to fully leverage the technology. If your organization is embarking on the machine learning journey (and it should be), there are five steps CIOs must take to maximize the value of their investment.
Take these steps today, because the long-awaited Age of Machine Learning may be upon us soon. Computer science has caught up to the hype around machines that emulate human intelligence. Now, the technology occupies the peak position of Gartner’s Hype Cycle for Emerging Technologies, indicating that it has matured enough to spur wide interest. In other words, your competitors are also investing in machine learning.
Five hundred CIOs were recently polled for the annual Global CIO Point of View Survey, and the findings reveal that businesses are preparing for the widespread adoption of this transformational technology to automate decision making. Nearly 90% are using machine learning in some capacity, and most are still developing strategies or piloting the technology. However, the full potential of machine learning remains largely unrealized. For most organizations, 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.
Designing an organizational structure to support data and analytics activities, an effective technology infrastructure and ensuring senior management is involved are the three most significant challenges to attaining data and analytics objectives related to machine learning, according to a McKinsey study. It goes on to claim that organizations that can harness these capabilities effectively will be able to create significant value and differentiate themselves, while others will find themselves increasingly at a disadvantage.
Capturing greater value requires regional organizations to invest in more than just technology. It is also necessary to make significant organizational and process changes, including approaches to talent, IT management and risk management. Making progress requires following five steps:
Improve Data Quality
Ensuring the quality of data is a common obstacle to machine learning adoption. Poor data leads to machines making poor decisions, which can lead to increased risk. CIOs need to consider implementing solutions that simplify data maintenance in order to accelerate the transition to machine learning. The first step should be to consolidate redundant legacy and on-premise IT tools into a single data model.
Establish Value Realization
Articulate the business value of all technology goals, then determine how best to reach those goals. This includes examining existing processes to identify which unstructured work patterns will benefit most from automation. Determining where fragmented data “lives” will enable you to identify how automation will lead to gains in productivity.
Create the Best Possible Customer Experience
Using machine learning for automation will boost operational efficiency, but do not overlook the ROI of accelerating decision making (without sacrificing accuracy) to improve the customer experience. Start by envisioning the customer experience you want to create, then prioritize investment against those elements of business processes that could most improve the customer experience. Machine learning allows organizations to personalize advertisements, call-center interactions and even products and services for individual customers—and to predict what they want next.
Set and Measure Metrics
CIOs understand the value that machine learning offers, but the other members of the senior executive team and board may not. CIOs must therefore set expectations, 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.
Understand the Effect on Corporate Culture
How employees’ roles will change as the organization introduces machine learning requires CIOs to adjust their hiring and training processes. This should not be too difficult, as it requires the same skill sets needed for the cloud era, such as data science, engineering, math and critical thinking. This transformation will likely be uncomfortable for some employees, so be sure to communicate the value machine learning will bring to their day-to-day work. The machines are not taking over the enterprise—they will alleviate employees of tedious manual processes and free them up to focus on more strategic projects.
It’s also important to understand that CIOs are not immune to that uncomfortable feeling. Their roles must evolve as well from being responsible for keeping the lights on when it comes to operational matters to an executive who has a much broader engagement across the business and, therefore, a new level of strategic importance.
Realizing a return on machine learning investments requires planning and disciplined follow-through—all while adjusting employees to how rapid and ongoing technology changes will affect their day-to-day work. Following the five steps described above will ease that transition for Middle East organizations.