The case for AI
The KIN Autumn Workshop on 6th December is on 'Data-driven decision making'. As Steve Dale, the workshop facilitator says...
"The world of data-driven intelligence is evolving. Big data is now moving from the sole care of data scientists and becoming accessible to employees throughout organisations. The mystique surrounding data analytics is falling away, with sophisticated data visualisation tools designed to let non-technically-minded people understand metrics. Information that supports “good” business or policy decisions is just a click away".
The implication is that decisions that are based upon empirical data through data are evidentially better. In fairness, Steve does point out that reliance on back-box algorithmic data must always be used with caution (although in many cases, it is impossible to validate or even understand how algorithms come up with results). As we have seen in a previous KIN workshop on 'Evidence-based decision making', AI algorithms and analytics may simply reflect innate biases, making them appear superficially 'trustworthy'.
If these caveats can be taken in to account, AI and machine learning is certainly going to make us smarter and our decisions more evidence-based. That's a good thing, no?
The case for Intuition
At KIN we are always looking for alternative perspectives that sometimes challenge orthodoxy or 'trends'. Julian Birkinshaw, our keynote speaker for the KIN Spring 2018 workshop 'Reimagining the Innovative Organisation' will certainly do that. In his new book 'Fast Forward', Julian proposes that over-reliance on IT, big-data and advanced analytics actually reduces competitive advantage.
"At corporate level, we end up with analysis paralysis, endless debate, and a bias toward rational, scientific evidence at the expense of intuition or gut feel."
There is a need for current management models to take into account the agility provided by ubiquitous data and upstart disintermediators. Similarly, managers' skills honed to support meritocracy will need to change to reflect adhocracy. These must draw on intuition as much as evidence if organisations are to constantly innovate.
On balance of course, rapid decisions, based on sound data-driven evidence that influences experiential judgment are an ideal. The reality is that time-critical decisions, an inability to understand the source of algorithmic outputs, engrained biases and shouty bosses all conspire to make decisions less perfect.
It will be fascinating to explore the balance of Intuition and Data-driven decisions over the course of the KIN Winter and KIN Spring workshops. In the meantime, if you are want to balance an AI dominated view, I recommend reading 'Fast/Forward'.