Artificial Intelligence and Business Strategy
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Many of our current experiences with AI applications so far are contradictory, largely because AI is still a young, immature field. Consider the following:
- Algorithms are competing with, and often winning against, expert humans in complex games such as chess and Go. But at the same time, customer service chatbots often are easily confused and can be more annoying than helpful. These interactions are more reminiscent of the transparent ELIZA than the almost human Nexus-6.
- Hardly a day goes by without more breathless reporting about improvements in self-driving cars. Yet, my own attempts to use AI for far simpler tasks, like scheduling meetings, have been frustrating for all involved — and I don’t yet see a glimmer of hope that my fixed-cost investments in setup will ever be offset in improved processes. I currently can’t convince the AI-based scheduler that Florida is not the best place for a meeting with a colleague located next door in Boston, though perhaps the AI has deeper predictive insights into the weather that it thinks I should factor in.
- AI has the potential to apply data and algorithms to offer quick decision making based on open rubrics, exposing the processes behind decisions that are now made in the shadows behind closed doors. However, attempts so far in contexts such as crime investigation or contest judging might simply be improving speed and reinforcing bias, which is not exactly an area our society needs help increasing.
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It can be difficult for managers to reconcile the vision of AI with the reality of its current state. Rather than getting clean, unequivocal answers about the usefulness of AI, managers may need to deal with a somewhat messy fact: Even as it continues to fall short on its promise, AI has tremendous potential.
Facing this contradiction, the temptation may be strong to sit on the sidelines and wait to see how things settle. But I don’t think things will settle, at least not any time soon.
In the MIT SMR article, “Minding the Analytics Gap”, my coauthors and I discussed how analytics advances are outpacing organizational abilities to use those advances. Organizations are able to add complexity to their analytical production capabilities faster than they can consume this increased complexity.