While modern day A.I. or machine intelligence (MI) hype revolves around big ”eureka” moments and broad scale “disruption”, expecting these events to occur regularly is unrealistic. The reality is that working with AI’s on simple routine tasks will drive better decision making systemically as people become comfortable with the questions and strategies that are now possible. And hopefully, it will also build the foundation for more of those eureka(!) moments. Regardless, technological instruments allow workers to process information at a faster rate while increasing the precision of understanding. Enabling more complex and accurate strategies. In short, it’s s now possible to do in hours what it once took weeks to do. Below are a few things I’ve found helpful to think about when drive machine intelligence at a large organization, as well as what is possible.
- Algorithm Aversion — humans are more willing to accept the flawed human judgment. However, people are very judgmental if a machine makes a mistake – even within the lowest margin or error. Decisions generated by simple algorithms are often more accurate than those made by experts, even when the experts have access to more information than the formulas use. For further elaboration on making better predictions, the book Superforecasting is a must read.
- Silos! The value of keeping your data/information a secret as a competitive edge does not outrun the value of potential innovation or insights if data is liberated within the broader organization. If this is possible build what I call diplomatic back channels where teams or analysts can sure data with each other.
- Build a culture of capacity. Managers are willing to spend 42 percent more on the outside competitor’s ideas. “This is why consultants get hired,” Leigh Thompson, a professor of management and organizations at the Kellogg School says. “We bring in outside people to tell us something that we already know,” because it paradoxically means all the wannabe “winners” in the team can avoid losing face. It’s not a bad thing to seek external help, but if this is how most of your work is done and where you go to get new ideas you’ll have problems. As a residual, the organization will fail to build strategic and technological muscle. And it’s likely to create a culture which emphasizes generalists, not novel technical thinkers in leadership roles. In turn, you end up with an environment where technology is appropriated at legacy processes & thinking. Not the other way around as it needs to be if you want to stay relevant. Avoid the temptation to outsource everything because nothing seems to be going anywhere right away. That 100-page power point deck from your consultant is only going to help in the most superficial of ways if you don’t have the infrastructure to drive the suggested outputs.
- Organizations need to evolve their processes in step with technology. Not fit new technologies to old/outdated processes. If so “money” is left on the table that isn’t even considered or known due to lack of domain depth, which creates hierarchical bottlenecks that inhibit innovation and merit-based thinking. As a result, the talented people in your organization will defect, leaving mediocre skill sets to run general operations which standardize low common denominator outputs. As well as not maximize the prior investment in capabilities or assets which may have offered a competitive edge had they been seen through sufficiency.