Cognitive Computing and the Future of Decision-making

Stephen DeAngelis

January 19, 2021

In the book entitled The Book of Secrets: Unlocking the Hidden Dimensions of Your Life, Deepak Chopra, writes, “If you obsess over whether you are making the right decision, you are basically assuming that the universe will reward you for one thing and punish you for another. The universe has no fixed agenda. Once you make any decision, it works around that decision. There is no right or wrong, only a series of possibilities that shift with each thought, feeling, and action that you experience.” Although he is correct that the universe has no fixed agenda, business leaders know bad decisions can lead to punishing results. Hans-Paul Bürkner, Boston Consulting Group’s Global Chairman, and Arindam Bhattacharya, a managing director and senior partner, write, “We are living in a confusing, polarizing world of dizzying complexity and puzzling contradictions. For every one of us, these are unsettling times. But for leaders, who must plot the path forward for hundreds and thousands of people, these are exceptionally challenging times.”[1]


There are times when we would like to avoid having to make a decision, but indecision is also a choice that has consequences. I tend to agree with Bain analysts, Michael C. Mankins and Lori Sherer (), who assert, “The best way to understand any company’s operations is to view them as a series of decisions.”[2] Mankins and Sherer add, “We know from extensive research that decisions matter — a lot. Companies that make better decisions, make them faster and execute them more effectively than rivals nearly always turn in better financial performance. Not surprisingly, companies that employ advanced analytics to improve decision making and execution have the results to show for it.” Many business leaders are learning that cognitive computing platforms with embedded advanced analytics — like the Enterra Cognitive Core™, a system that can Sense, Think, Act, and Learn® — are helping them make better decisions — even when confronted with ambiguous data.


Cognitive computing and decision-making


Bürkner and Bhattacharya observe, “In normal circumstances, leaders struggle to make cool, calculated decisions in a measured, unbiased, unemotional way. They are human, after all. But there is nothing normal or predictable about today’s world.” They add, “It begs the question: How can business leaders make the right decisions for their company when they face unfamiliar and volatile situations and there are no obvious choices, when they face mounting pressure from multiple stakeholders with different expectations, when the second- or third-order effects of their decisions are unclear, and when the consequence of making the wrong decision could be a dramatically negative impact on their company’s brand, revenue, and valuation?” These are exactly the types of situations that can be helped by cognitive computing. The now defunct Cognitive Computing Consortium explained, “Cognitive computing makes a new class of problems computable. It addresses complex situations that are characterized by ambiguity and uncertainty; in other words, it handles human kinds of problems.” The Consortium added:


In these dynamic, information-rich, and shifting situations, data tends to change frequently, and it is often conflicting. The goals of users evolve as they learn more and redefine their objectives. To respond to the fluid nature of users’ understanding of their problems, the cognitive computing system offers a synthesis not just of information sources but of influences, contexts, and insights. To do this, systems often need to weigh conflicting evidence and suggest an answer that is ‘best’ rather than ‘right’. Cognitive computing systems make context computable. They identify and extract context features such as hour, location, task, history or profile to present an information set that is appropriate for an individual or for a dependent application engaged in a specific process at a specific time and place. They provide machine-aided serendipity by wading through massive collections of diverse information to find patterns and then apply those patterns to respond to the needs of the moment.”


Computer-augmented decision-making begins with data — lots of the right kind of data. However, data locked inside databases is no more useful than seeds lying fallow in a field. That’s where cognitive technologies enter the picture. Former IBM Executive Irving Wladawsky-Berger writes, “Increasingly powerful and inexpensive computers, advanced machine-learning algorithms, and the explosive growth of big data have enabled us to extract insights from all that data and turn them into valuable predictions.”[3] He adds, “Given the widespread role of predictions in business, government and everyday life, AI is already having a major impact on many human activities. As was previously the case with arithmetic, communications and access to information, we will be able to use predictions in all kinds of new applications. Over time, we’ll discover that lots of tasks can be reframed as prediction problems.” He also insists cognitive technologies are best when they augment, rather than replace, human decision-making.


Wladawsky-Berger quotes from Harvard University Professor David Parkes’ essay entitled “A Responsibility to Judge Carefully in the Era of Decision Machines.” Parkes writes, “Artificial intelligence is the pursuit of machines that are able to act purposefully to make decisions towards the pursuit of goals. Machines need to be able to predict to decide, but decision making requires much more. Decision making requires bringing together and reconciling multiple points of view. Decision making requires leadership in advocating and explaining a path forward. Decision making requires dialogue.” Cognitive technologies can add to, but not replace, that dialogue. Parkes continues, “[It’s] decisions, not predictions, that have consequences. If the narrative of the present is one of managers who are valued for showing judgment in decision making … then the narrative of the future will be one in which we are valued for our ability to judge and shape the decision-making capabilities of machines.”


Concluding thoughts


Dharmendra Modha (@DharmendraModha), an IBM fellow and Chief Scientist, believes the future may bring us a cognitive computing platform that can dialogue even more fully with human decision-makers. He states, “Cognitive computing goes well beyond artificial intelligence and human-computer interaction as we know it — it explores the concepts of perception, memory, attention, language, intelligence, and consciousness. Typically, in AI, one creates an algorithm to solve a particular problem. Cognitive computing seeks a universal algorithm for the brain. This algorithm would be able to solve a vast array of problems.”[4] Although I’m not sanguine that a “universal algorithm” will ever be created, I am convinced cognitive computing will continue to evolve in order to enhance human decision-making.


[1] Hans-Paul Bürkner and Arindam Bhattacharya, “Squaring the Circle,” Boston Consulting Group, 22 October 2020.
[2] Michael C. Mankins and Lori Sherer, “Creating value through advanced analytics,” Bain Brief, 11 February 2015.
[3] Irving Wladawsky-Berger, “The Coming Era of Decision Machines,” The Wall Street Journal, 27 March 2020.
[4] Thomas Caldwell, “Cognitive Computing In The Next Decade Of AI,” Forbes, 28 January 2020.