The emerging Principal-Agent-Machine problem in the enterprise

Ever since the owners of organizations put agents in charge of operations, because of growing scale and perceived need for management specialization, shareholder value maximization has not been Pareto optimal for decision-makers. Much has been written and studied in this area with little effect on organizational structure, systems, and strategies (1). From the advent of computers, agents have been effective in claiming superiority over machines because of a lack of transparency for the owners. Although it is difficult to prove that machines possess superior decision-making capabilities in real companies and markets because of the lack of data from long and repeated experiments, it is clearly the case in financial markets.

With clear and consistent data in the financial markets, it has long been clear that financial intermediaries and traders have been destroying alpha, forever. With misguided and a confusing focus on "absolute returns," these agents have been successful in siphoning out wealth from owners in fees and expenses. An illustrious investment bank seems to have recently recognized that "trading," done by humans creates no value for its clients. Machines are infinitely better as they can act based on complete information without bias. Decision-making, thus, is better delegated to machines.

In real markets, this is equally applicable. Because of high diversity in types of decisions and long durations to outcomes, agents have long claimed superior capabilities compared to machines. This is true at all levels of organizations (1) and in every function. Since distributed owners are unable to understand the inner workings of complex organizations, agents simply claimed they are better without any contention. This has significant negative effects on the economy and its potential to grow. A structural change that culminates in the reassignment of human responsibilities in the enterprise may be afoot.

The emerging principal-agent-machine problem is real for modern organizations. Institutionalization of agents since the industrial revolution has run its course. Owners may finally have an opportunity to break this stalemate.

(1)    https://www.crcpress.com/Flexibility-Flexible-Companies-for-the-Uncertain-World/Eapen/p/book/9781138112391

Decision-making is different from finding cats and dogs

As AI moves toward the peak of the hype cycle, it is important to recognize that decision-making in an enterprise is distinctly different from training machines to differentiate between cats and dogs. Most of the field is focused on deep neural networks, convoluted and otherwise, to recognize text, pictures, audio clips and patterns. This is certainly interesting but an extrapolation from these techniques to improving decision-making in the enterprise is fraught with danger. As companies find that enterprise productivity is inversely proportional to the number of data scientists they have on staff, reality is beginning to sink in.

As technology and consulting companies try to mop up the last remaining "data scientist," on Earth, it may be interesting to take a measurement of how enterprise productivity is related to them. Data science, an ill-defined field, has been the latest hype that led many companies down rabbit holes with very little to show. Although there are interesting developments in Artificial Intelligence - in robotics and autonomous equipment, much of these are better called expert systems as they do not learn from data but work on coded heuristics. The stars in the field do not prefer the old terms such as "expert systems," and "neural networks," as they believe they have reinvented mathematics. This is symptomatic of a field beginning to go off the rails as the investors have unrealistic expectations of the "second coming," of AI.

Let's not throw out the baby with the bath water. It is just that the baby has a lot of growing up to do. Decision-making takes a lot more than supervised or unsupervised machine learning. Educational institutions do a disservice to the next generation by blindly following the latest trends and spawning "analytics courses," for everybody. The question educators should ask is whether such programs are leading to people who can take advantage of the technology to enhance enterprise value. To do this, they have to first understand how value is created and that is a lot harder than cranking the supercomputer in the cloud.

Artificial Intelligence has a lot of potential, but not in the hands of those who believe it is about games, computers, deep mind and deeper mathematical techniques. The beauty of mathematics is that it is fully democratized. However, to add value to an organization, it has to be combined with many other attributes.