Automated decisions are making the news. This year, Tesla cars in autopilot mode have experienced crashes in Florida, Montana and China. (The company contends drivers that were not using the autopilot mode properly). Meanwhile on another planet, the Mars Rover can now make its own decisions about what rocks to investigate. Keep your eyes open, because I believe these stories will become more prevalent quickly.
When analytics drives automated decisions, people are faced with another set of decisions about how that capability fits into the work, culture and mission of the organization. It’s akin to when you hire a new employee. What role does the new capability fill? How much autonomy is granted to the new capability? What review and verification processes are in place to ensure safe, productive and profitable work?
As stated in an earlier blog, it’s unlikely that automation will replace entire jobs. New job descriptions will be written, and existing ones likely rewritten, to specify how humans and automation will interact to fulfill a needed role.
The analytics entering organizational roles for the foreseeable future will be focused on specific tasks, if not built for specific purposes. Finance and investment companies are using analytics extensively for trading and portfolio composition, but those same analytics aren’t likely to make employee benefits decisions without modification.
Because they are purpose built, analytics need to specialize in predictable decisions that they perform repeatedly. This is exactly the sort of dull work that’s best left to automation, since people have a tendency to get tired, bored and distracted doing repetitive work.
At least initially, organizations will want to assign to analytics decisions that carry known and usually low-level consequences. Consequences can be measured by the amount of money at stake, the number of employees or customers affected, or the ease with which an automated decision can be reversed if need be. Analytics can help accurately define the type, scope and severity of consequences associated with decisions.
The metrics of predictability and consequence come together nicely in a video from The Harvard Business Review describing how to decide what decisions you can entrust to automation.
What about frequency of decisions? Some decisions, like short-term financial trading, happen so rapidly that humans can’t make every single call. In these situations, humans move from doing the work to maintaining, tuning and improving the automated systems that do the work. Other decisions, such as whether to acquire another company, happen so infrequently that automating the decision probably isn’t worth the effort. Between these two points lies a spectrum of decision frequency that organizations must also weigh in identifying decisions to automate.
The framework of predictability, consequence and frequency gives organizations the model they need to determine what role automated decisions will play. What decisions would you like to automated in your organization, and how would you score them for predictability, consequence and frequency?
(Image courtesy of forplayday / 123RF Stock Photo)