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 or a scalable manner is unrealistic. The reality is that working with AI’s on simple or routine tasks will drive better decision making systemically, and as people become comfortable with the questions and strategies that are now possible to ask. And hopefully, it will also build the foundation for those eureka(!) moments downstream. 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 as one tries to drive machine intelligence in a large organization.
Facts and Thoughts:
- Only 21% of managers who implement strategy actually test how KPI’s actually link back to organizational performance, and many of those tested found their early assumptions flawed.
- Companies in the top third of data-driven decision making are 5% more productive and 6% more profitable.
- 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.
- Algorithm aversion— humans are more willing to accept flawed human judgment. However, people are very judgmental if a machine makes a mistake – even within the lowest margin or error.
- Silos — The value of keeping 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.
- Understand the solutions most problems and opportunities in the modern business climate are centered around a combination of creativity, data or technology. To find opportunity or address problems the organization will need to quickly move from linear analytics or business intelligence to weighting/graphing of disparate data sets with artificial intelligence. Machines are far less biased than people and better at weighting the relationships between desperate events, objects, and data sets. Even simple statistics have been shown to outperform even the most experienced analyst.
- Develop a culture of skill and capacity. The majority of abstract, complex and pressing work is typically outsourced, not built internally. 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 so much that it is a bad thing to seek external help, but in reality, this translates into a lack of strategic capacity internally, and as a residual, the organization will loose the opportunity to build those thinking and technological muscles. In short, avoid the temptation to outsource everything because nothing seems to be going anywhere right away. That 100 page power point deck isn’t going to help. Also note that those that generally push for everything to be outsourced are probably doing so to limit exposure of their lacking skills sets or vision in the space, so be skeptical of managers who always want to take this approach opposed to building a solid foundation in MI. This is the derivative of a strong management culture which emphasizes generalists, not novel, unorthodox or technical thinkers in leadership roles. Research not only shows general managerial styles are less successful in technical or data-driven tasks contrast to technical competence, (and most work is data or technology related these days) but they also foster a culture where exercising intellectual integrity or novelty on subject matters are liability for one’s career. Further, it leads to an environment where technology is appropriated at legacy processes & thinking i.e. not as efficient nor effective. Organizations need to evolve their processes in step with technology. Not the other way around. In return there is “money” 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 standardizes 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.
- Be more introspective and strategic with assets and cash. By integrating sensors (Internet of things), more sophisticated tracking and the understanding and integration of physical and cash assets, organizations have the opportunity to maximize the utility of its assets. In turn, this would foster a “trading desk mentality” where companies would operate more like a hedge fund on its own cash flow and purchases, as well as being mindful of foreign exchange rates & commodities markets. This approach could also take advantage of assets such as shelf space and storage – much like AirBNB did with extra home space. Generally, over the last 10 years, companies with multiple revenue streams and consumer offerings such as Apple and Amazon have weathered the storm better than companies whose focus remains narrow.
- Create a data markets or indices that your suppliers/vendors can opt in. In doing so it helps compress prices by sharing the burden of costs associated with market research and or supply chains. Furthermore, how to create value from data quickly will perhaps be the more pressing issue for large organizations. This will inherently drive the need for all of these organizations to be more strategic with their data and technology, not like a commodity to be hoarded. Remember data is only valuable if acted on.
- Create a social graph of your organization to understand how information becomes action. By leveraging machines to analyze internal information and asset flows & allocation within a graph-based approach, are able to systemize and automate many routine processes such as meetings or approval processes that humans currently do. Furthermore intelligence agents analyzing the graph can bring together people working on disparate projects.