CUTTER BUSINESS TECHNOLOGY JOURNAL,  VOL. 29, NO. 12
  

Let’s take a look at a technology poised for a breakout year: cognitive computing. There is a considerable amount of innovation in the development and application of cognitive computing across almost every industry. Consequently, companies should start examining how they can benefit from applying cognitive computing, focusing on five key domains: (1) research and discovery; (2) decision support and advisory; (3) customer engagement/customer experience management; (4) Internet of Things (IoT); and (5) cybersecurity.

Research and Discovery

Cognitive research and discovery applications, due to their ability to ingest and analyze huge amounts of structured and unstructured data, are being applied in many industries and domains today. Good target areas for cognitive research and discovery applications include market analysis, marketing, and product R&D. In addition, those in highly regulated industries should examine how cognitive systems can benefit their risk management, compliance, and antifraud efforts, as these areas are experiencing significant commercial cognitive solutions development.

Communications, marketing, and advertising firms are using cognitive research and discovery to uncover consumer trends — including behavioral and personality patterns — from social media, surveys, and other data to facilitate richer customer profiling. As a general trend, the use of cognitive systems for automating and enhancing social media analysis will increase considerably over the next few years.

Decision Support and Advisory

This domain includes the use of cognitive systems for implementing decision support systems and advisory applications that promote automated, easy-to-use approaches to customer service, issue handling, and product inquiries.

Healthcare represents the leading area of usage in cognitive decision support and advisory systems. Providing researchers, physicians, clinicians, and other practitioners with advice on illnesses has become a wide-open field. As a result, healthcare organizations are implementing cognitive applications for advising on everything from cancer, heart disease, and kidney trouble to general health and wellness.

Banking and finance are also investing in this particular cognitive domain to assist human financial advisors, for example, to conduct highly targeted search and trends analysis into market history and current events in order to optimize investment decisions and recommendations for clients.

Although still experiencing limited use of cognitive computing today, insurance is another industry where we are starting to see the use of smart cognitive decision support and advisory applications. I expect such use (for both internal and customer-facing operations) to increase considerably over the next few years.

We are also witnessing usage in other industries, including education (to provide advice to students on enrollment and other activities) and government (where cognitive systems serve to advise both staff and citizens on such topics as city operations and planning and the availability and requirements for social services and other programs).

The oil and gas industry is also employing cognitive advisors, particularly to facilitate knowledge management and more productive group interaction as well as to help disseminate oil and gas exploration and engineering expertise across organizations.

As a general trend, we should expect advisory and decision support to be the leading area for applying cognitive technology for the foreseeable future. Ideally, companies should look to implement cognitive decision support and advisory systems that can serve both internal operations and customer-facing scenarios.

Customer Engagement/Customer Experience Management

Organizations should investigate how cognitive technologies — especially natural language processing and conversational interfaces — can make ­interacting with their company more intuitive, satisfying, and engaging for customers. This includes examining the use of cognitive-powered intelligent virtual agents, smart advisors, and chatbots for facilitating new (truly) real-time customer engagement on popular social media, text, and messaging platforms.

Today, companies are applying cognitive systems to customer engagement scenarios with two main goals: (1) to achieve a greater level of automation for optimizing customer service operations, including encouraging customers to take a self-service approach; and (2) to facilitate easier, more satisfying, and engaging dialogues between customers and the company across various digital channels.

In short, there is a great deal of innovation, with companies employing cognitive systems to support a range of customer-focused scenarios — everything from mobile apps designed to assist shoppers with choosing the correct product, to online wellness programs offering personalized recommendations pertaining to lifestyle choices, to intelligent banking apps designed to assist consumers. As a general trend, expect cognitive systems for customer engagement and customer experience management to become a leading area for applying the technology over the next two to three years.

Internet of Things

Although cognitive systems are currently experiencing limited usage in this capacity, their ability to ingest and analyze extremely large volumes of data (in varying formats) makes them well suited to automating IoT maintenance and analysis operations.

Real-world (i.e., production) cognitive IoT applications require the collection, management, and analysis of rapidly streaming, time-sensitive data. A good example is offered by elevator and escalator manufacturer and servicer KONE, which cut a multiyear deal to use IBM’s Watson IoT Cloud Platform to connect, remotely monitor, and manage millions of elevators, escalators, doors, and turnstiles in buildings and cities worldwide. Analyzing data from sensors embedded in equipment will enable KONE to identify and predict issues and minimize downtime.

As more companies ramp up their IoT initiatives over the next few years, the use of cognitive IoT applications will accelerate as companies begin to struggle with the difficulties involved in managing and analyzing data generated from millions of connected devices. Watch for IoT platform providers to incorporate cognitive capabilities in their offerings.

Cybersecurity

A recent use case seeing much commercial development is cognitive application for cybersecurity, where the technology can identify potential threats, predict the likelihood of an attack, and interdict attacks as they occur.

IBM and its partners have been busy in this area, working to apply Watson to cybersecurity for various scenarios ranging from general IT security to fraud and breach solutions targeting banking, finance, retail, and other industries. A potentially game-changing development is cognitive behavioral biometric analysis capabilities for IBM’s digital banking fraud-prevention solutions. Behavioral biometric capabilities apply machine learning (ML) to help understand how users interact with banking websites, creating gesture models based on patterns of mouse movements.

Such cognitive analysis can help determine fraudulent activities — such as when unauthorized users attempt to take over a bank account by using stolen credentials — by detecting anomalies from the real customer’s interaction with a banking website. The technology understands the context and meaning of subtle mouse movements/clicks and uses this information to continually generate increasingly more accurate gesture models (via ML) over time. Although the technology was initially developed for banking, with the proper data sets cognitive biometric models could also be trained to support antifraud and other cybersecurity scenarios for other industries.

Conclusion

Cognitive computing is proceeding rapidly, and we are seeing considerable application of the technology across many domains and industries. Organizations should investigate how the technology could benefit various areas of their operations, including internal and customer-facing ones. Finally, due to a lack of experienced cognitive application developers, many organizations would do well to utilize commercial domain-specific or industry-focused cognitive solutions, of which a growing number are entering the market.