Management and Artificial Intelligence

Behavioral economics tries to understand how humans act as economic agents in decision-making processes, not always in the most optimal way. One of the fields of study of behavioral economics is neuroeconomics, which focuses on cognitive behavior when making decisions, purchases and investments. AI works in management to automate these processes and make them more efficient.

AI and process optimization

Our expert Michael Schrage works on the application of behavioral economics in models, prototypes and the opportunity for innovation. In the case of AI, these recommendations can be used to optimize work and its management, reduce uncertainty, and increase sales. AI optimizes everything, and this forces us to revisit basic ideas. It not only improves our capabilities but makes us focus on what we want the outcome to look like. 

His hypothesis: the essential ingredient is the capacity to learn how to optimize. Optimization is the origin of differentiation with respect to what management and leadership mean in an AI setting. This is one of the key findings that Schrage has discovered together with his MIT colleagues after extensive analysis. This forces us to revisit basic ideas. It doesn’t have to do with how we improve, simplify or synthesize our capabilities nor how we make them more efficient, but with what we want the outcome to look like. According to Michael, the practical conclusion of this is to use KPIs (performance indicators) as the strategy, let them be the ones who define what we need to optimize.

Machine learning is driving a huge change in those KPIs; they’ve gone from being results that helped people make decisions to being used as any sort of input in the production of other goods for AI and machines. This is why KPIs are no longer considered metrics, but rather intelligent software agents that want to learn how to optimize.

Investing in virtuous cycles is essential and will force a review of the anthology of optimization in the short and long term. It’s the new language for strategic planning, he says, in a context where companies are switching to micro services and prioritizing KPIs. “The future of optimization is the future of machine learning and AI, and the future of AI and machine learning will be the future of optimization. That is the future of management.,” states Michael Schrage.

AI and machine learning

For David Weinberger -technology expert and researcher at the Harvard Berkman Klein Center for Internet & Society- the key is to handle uncertainty in the current setting of change, volatility and ambiguity. The philosopher and writer, a resident in the artificial intelligence Google PAIR  program, points out that we have adopted techniques and behaviors that only make sense in a connected, digital and open environment..In this context, AI and machine learning models help us put that chaos into perspective since they are not based on reducing complex situations to a bunch of well-known, general factors and principles; instead, they work through the connection of large volumes of data in statistically significant correlations. According to Weinberg, the results can be generated from a network of complex, delicate influences that human thinking cannot even reach. “The success of these models, many of which are incomprehensible to us, allows us to acknowledge and embrace the chaotic, contingent nature of our world”, he concludes.