Want to get smart about your digital technology investments? A recent HBR piece outlined five key areas to focus on, including: 


If machine intelligence is a strategic priority for your organization, you need to place equal prioritization on your governance process. Many leading companies have built dedicated centers of excellence to support implementations, ensure standards, and accelerate deployment. Another benefit of this centralized support approach is that it’s easier to keep digital programs on track and document how the organization’s portfolio is progressing. At the same time, however, it’s important that governance programs recognize that change is inevitable—particularly when it comes to machine intelligence. In order to scale successfully, it’s important that processes be continually refined and improved. 


According to HBR, leaders in machine intelligence apply the technology more widely and use more sophisticated approaches. The authors reference research they conducted in which every leader implemented machine intelligence in forecasting, maintenance operations, and logistics and transportation. 


Another best practice is to partner with more external groups—for example, technology vendors, start-ups, academic institutions, or consultants. These partnerships can be instrumental in exposing companies to new applications of machine intelligence and accelerating their learning and time to impact. 


To be successful, companies should ensure that as many people as possible have the necessary skills and resources to use advanced digital approaches as part of their daily operations. More than half of the leaders identified in the HBR article train their front-line employees on machine intelligence fundamentals, compared to just 4 percent of other organizations. 

Data Availability 

To arm more stakeholders with machine intelligence capabilities, companies must first make data accessible. According to HBR, leading companies are nearly twice as likely to enable remote access to data and store a significant portion of their data in the cloud compared to other organizations.

For more on the above and other considerations for ensuring successful usages of machine intelligence, check out the HBR piece in its entirety.