Improving Business Decisions Using Cognitive Computing

Stephen DeAngelis

September 2, 2015

“Computers cannot think,” Deloitte analysts David Schatsky (@dschatsky), Craig Muraskin, and Ragu Gurumurthy write. “But increasingly, they can do things only humans were able to do.”[1] What Schatsky and his colleagues really mean is that computers are not yet sentient (i.e., self-aware) although that day may be drawing closer. They draw a fine line between “thinking” and utilizing “cognitive skills.” They write, “It is now possible to automate tasks that require human perceptual skills, such as recognizing handwriting or identifying faces, and those that require cognitive skills, such as planning, reasoning from partial or uncertain information, and learning. Technologies able to perform tasks such as these, traditionally assumed to require human intelligence, are known as cognitive technologies.” Cognition is formally defined as “the action or process of acquiring knowledge and understanding through thought, experience, and the senses.” Of course, that definition has to be modified slightly when applied to a machine. At Enterra Solutions®, we believe a cognitive system is one that discovers insights and relationships through analysis, machine learning, and sensing. So in that limited sense, we believe that a cognitive computing, like the Enterra Enterprise Cognitive System™ (ECS), can Sense, Think, Act, and Learn®. James Kobielus (@jameskobielus), a senior program director at IBM, believes that arguing over such points is a waste of time.[2] He explains:

“I think the world ‘artificial’ in ‘AI’ has become distractingly obsolete, akin to referring to automobiles as ‘horseless’ carriages. The larger paradigm is that of intelligent systems, of which cognitive computing systems are just one manifestation. Yes, of course, everything is ‘artificial’ in the core sense that it’s a human invention, but so what? Automobiles, for example, are ‘artificial transportation’ (as opposed to walking on two legs), but it would be a pointless distinction to make.”

Splitting hairs with Schatsky and his colleagues over the metaphysical properties of cognitive computing is not what this article is about. All of us believe that implementing cognitive computing system solutions can significantly benefit most businesses. Schatsky and his colleagues explain, “Because cognitive technologies extend the power of information technology to tasks traditionally performed by humans, they have the potential to enable organizations to break prevailing tradeoffs between speed, cost, and quality.” One area where cognitive computing can be particularly helpful is improving decision making processes. Bain analysts, Michael C. Mankins and Lori Sherer (), note that decision making is one of the most important aspects of any business. “The best way to understand any company’s operations,” they write, “is to view them as a series of decisions.”[3] They explain:

“People in organizations make thousands of decisions every day. The decisions range from big, one-off strategic choices (such as where to locate the next multibillion-dollar plant) to everyday frontline decisions that add up to a lot of value over time (such as whether to suggest another purchase to a customer). In between those extremes are all the decisions that marketers, finance people, operations specialists and so on must make as they carry out their jobs week in and week out. 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.”

Allowing AI to make routine decisions frees human decision makers to concentrate on more important decisions. Mary Ann Richardson notes that computers have been used to make some routine decisions in the past.[4] Mostly, these decisions involved activities that have clear, predefined rules. Cognitive computing systems, however, can expand the number of areas in which computer-assisted decision making can be used. She explains:

“Knowledge-intensive enterprise processes, such as those involved in sales management, typically have no predefined workflow management system to support them. Rather, the inbox is used as the work management system, along with phone, chat and records in databases. Changes to process guidelines and templates are usually communicated through email. Unlike processes which automatically follow predefined rules and structures, these human-centric processes need a real-time, business-aware automation solution. Cognitive analytics can meet that need. In its Fifth Annual Technology Trends report, Deloitte Consulting LLP says that ‘For organizations that want to improve their ability to sense and respond, cognitive analytics can be a powerful way to bridge the gap between the intent of big data and the reality of practical decision making.’ Cognitive analytics is an extension of cognitive computing, which consists of machine learning, natural language processing, and its accompanying enabling infrastructure. Cognitive analytics applies cognitive computing’s massive data-processing power and ability to add channels for data collection (such as sensing applications) and environmental context to delivering practical business insights. What’s more, as a machine learning system, cognitive analytics gets ‘smarter’ as it obtains new information, and learns from users’ prior interactions and responses.”

Michael Fauscette (@mfauscette), an enterprise software analyst at IDC, implies that some business leaders may be deliberately avoiding implementing cognitive computing solutions because such solutions could dilute their power base.[5] He explains:

“Throughout the day, every day, employees make decisions that have an impact on businesses. There are all sorts of decision and organizational models that are common, many that still cling to the old industrial (hierarchical) model of structuring and managing a business. … In that model controlling information created organizational power. Today, with all the communication channels and ways to share content and information available to employees it is naive to think that anyone can ‘control’ information. Information simply flows around any attempts to block/control it. Some companies are starting to realize that sharing information and an increased level of transparency is not only the new organizational model, it is also inevitable, or at least it will be huge advantage to recruiting and keeping the best talent. Without that employee advantage it will at the least be a competitive disadvantage and at worst a contributor to the ultimate failure of a business. … There’s an additional element in modern decision processes, the growing availability of assistive technology in the form of artificial intelligence (AI) and cognitive systems. … Cognitive computers can interface in normal language and learns from interactions and from unstructured data. These capabilities are far beyond the old ‘expert systems’ that could only interact inside a strict rules based structure. This additional capability opens up a variety of use cases.”

The point that all of the pundits cited above are making is that cognitive computing technologies can make almost any business better by improving the decisions that a company makes. Schatsky and his colleagues conclude, “We think the greatest potential for cognitive technologies is to create value rather than to reduce cost. And we believe that for most organizations and most applications, cognitive technologies will restructure work and make it more efficient, perhaps restraining the growth of jobs in certain areas, but not leading to large-scale reductions in workforce.”

 

Footnotes
[1] David Schatsky, Craig Muraskin, and Ragu Gurumurthy, “Cognitive technologies: The real opportunities for business,” Deloitte University Press, 16 February 2015.
[2] James Kobielus, “Cognitive computing? Dump the word ‘artificial’ from ‘“artificial intelligence’ in discussing truly intelligent systems (part 1),” LinkedIn, 2 June 2015.
[3] Michael C. Mankins and Lori Sherer, “Creating value through advanced analytics,” Bain Brief, 11 February 2015.
[4] Mary Ann Richardson, “How cognitive analytics can improve decision making in business process applications,” Toolbox.com, 1 August 2015.
[5] Michael Fauscette, “Better Business Decisions: Assistive Technology,” Enterprise Irregulars, 20 July 2015.