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CIOs looking for a fresh approach to intractable business problems may find a solution in cognitive computing.

In 2011, I was given the opportunity to lead IBM’s Watson project and to build a business around it. As a serial entrepreneur, I am passionate about the journey from “presentations to products to profits,” so this endeavor really excited me.

We first needed to decide which markets and industries to enter with these new class of computers that interpreted our language, processed unstructured data, and understood context or intent. We decided to focus on information-intensive industries where multistructured data is important to driving better decisions. We discussed all the obvious choices—insurance, health care, telecom, and banking—before deciding to start with health care: a multitrillion-dollar industry in which our technology could help improve the quality of care delivered, drive toward significant cost reduction, and have a positive impact on society.

Company results over the past three years from the application of cognitive systems in various industries have given us a glimpse of the disruptive and game-changing potential of this technology. For example, Watson has demonstrated dramatic reduction in the time required to create new cancer drug treatments and is now helping knowledge workers in insurance and retail get access to expert knowledge to improve customer engagement and loyalty. Unlike traditional IT systems, cognitive systems like Watson get better over time. The more Watson learns about an industry and domain, the better its confidence in responding to user questions or system queries and the quicker it can be deployed against new problems.

Cognitive computing is the most significant disruption in the evolution of computing since the advent of the Internet. It helps extract patterns and insights from data sources that are almost totally opaque today, what is sometimes known as “dark data.” Examples include extracting disease insights from health care records and social feeds, or finding financial fraud or opportunities from discrete geopolitical, social, and business events.

To be successful with cognitive computing, companies should be able to articulate how they will make better decisions and drive better outcomes. Companies will struggle if they approach it from the “technology out” angle rather than “business in.” The technology is no doubt fundamental but should be coupled with business goals and domain knowledge of the industry, the theoretical and practical experience of the field, and the nuances around a given problem set.

In addition, companies will require a new set of cognitive application development platforms and skills to design, build, and implement cognitive applications. In my new role as a venture capitalist, I have now invested in six such startups that are building both horizontal and vertical technology platforms to accelerate design, delivery, and adoption of cognitive systems in healthcare, travel, retail, and financial services.

With respect to the talent needed to support cognitive solutions, I liken this to the early stages of the Internet, when companies worried about a shortage of HTML developers. Ultimately, new products and systems arose to streamline the process and reduce the need for specialized skills. We have already substantially reduced the complexity inherent in cognitive computing since the earliest days with Watson, and newer startups entering the ecosystem are continuing to drive down the learning curve. Ph.D.'s and data scientists won’t be the only ones capable of implementing cognitive computing. Over time, less highly specialized people will be able to complete more complex tasks.

Additionally, companies considering cognitive computing should have: C-suite buy-in and a commitment to experimental pilots that can scale rapidly to deliver increasing value to the organization; a commitment to transform the business over three-to-five years; compelling use cases backed by clear success metrics; and a set of trusted business partners that bring requisite technology, data, and skills to the table.

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It is my firm belief that, over time, cognitive computing will transform all types of descriptive, prescriptive, and predictive analytics. By fusing traditional structured data analysis with unstructured information (e.g., tweets, blogs, call center notes), cognitive analytics will deliver insights and advice at a depth and scale that will mimic the human brain. This is not a futuristic statement but something that is already happening across various industries.

—Manoj Saxena, founding general partner, The Entrepreneurs' Fund, chairman, Cognitive Scale, and former general manager for IBM Watson software division.