Benefits realization is a hot topic these days, especially for intelligent automation programs. It is widely held that automating as many activities as possible that do not warrant human involvement is the right way to go, but the selection, prioritization, and measurement of these processes to get the most beneficial outcomes seems to be a bit haphazard for many organizations.
While it is highly common to use “hours returned to the business” or “manual hours of work automated” as a key measure of success in automation initiatives, this approach does not really move the dial in terms of showing real business value and solidifying support at the C-level. This measure is good at demonstrating progress and showing order of magnitude, but it is missing business context and is ambiguous in terms of the actual value delivered.
You can find examples of hundreds of different metrics for measuring automation impact from the perspective of the Center of Excellence, the business and IT. However, only a subset of them directly relate to the most important strategic objectives of the company (which matter the most).
If the metrics behind a project automation or otherwise cannot be connected to one or more outcomes that the business has outlined as top priorities, then the rationale behind executing the project at all should be in question. Just because you can automate nearly any process today does not mean you should.
Intelligent automation is helping companies achieve their top priorities today, such as:
- Accelerating growth
- Increasing profitability
- Minimizing risk
- Elevating customer experience
- Boosting employee experience
Each of the above can be measured quantitatively through a variety of metrics that demonstrate benefits realization. For example, % improvement in Net Promoter Score can be a gauge for customer experience as % improvement in retention can be a gauge for employee experience.
This topic brings to mind Moneyball, one of my favorite movies that is based on Michael Lewis’ best-selling book of the same name. It tells the real-life story of how the Oakland A’s revolutionized the game of baseball by radically changing the way players were analyzed.
Instead of focusing on the same metrics as everyone else, such as batting average, runs batted in, runs scored, etc., the A’s focused on metrics such as on base percentage, which statistically proved to have a higher impact on win percentage, their primary goal. Albeit highly unconventional, this approach enabled them to find undervalued players they could afford (which was important due to the A’s paltry budget) who could also help the team be more successful on the field.
While the A’s innovative usage of analytics did bring them positive results, it has not (to date) delivered a World Series championship. Regardless, their player analysis methods got the attention of Major League Baseball and forever transformed most team’s scouting programs.
Just like Major League Baseball did, it’s time for automation programs to identify the critical few metrics that matter the most and the ones that tie directly to strategy execution. Oh, and let’s retire the metric “Hours Returned to the Business” once and for all. It's good....but not great.
