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Competitive Intelligence

Kim Storin and Adel El-Hallak
Vice President, Marketing, IBM Cognitive Systems Adel El-Hallak, Director, Product Marketing, IBM Cognitive Systems-Photo by Matt Carr

For patients with critical diseases, participation in a clinical trial can be life changing, if not lifesaving. Before that can happen, however, the patient must be admitted to the trial. Traditionally, that entailed sifting through protocols for open trials to find relevant opportunities. Next, medical teams cross-referenced the frequently complex and arcane eligibility criteria with the mass of structured and unstructured data in patient medical records to find a match. The painstaking process resembled a treasure hunt rather than a scientific search. Meanwhile, for the patient, every minute counted.

Imagine a computing platform that can perform that same comprehensive, global search in a snap. Instead of waiting days or weeks for useful information, a doctor could call up a list of clinical trial options while the patient sits in the doctor’s office. At each appointment, the patient could be fully informed of their options, ranked by relevance and potential effectiveness.

While this sounds like a dream for the future, it’s reality today via cognitive computing ( It just requires the right infrastructure, the right software and the deep-learning heroes in the IT department capable of bringing it together.

Transforming Businesses

Healthcare is an obvious example of artificial intelligence (AI) and cognitive computing, but the business reach of these groundbreaking technologies extends to every industry sector. Able to apply cognitive techniques to vast amounts of data in real-time, AI promises solutions to new kinds of challenges in insurance, utilities, financial services and more. “AI is the topic of discussion in almost every conversation that we’re having now,” says Kimberly Storin, vice president, Marketing, IBM Cognitive Systems. “We’re seeing people apply machine learning and deep learning solutions to be able to extract real-time, actionable insights from all sorts of data, wherever that data might be housed and whatever kind of data it is. We’re starting to see use cases crop up across all industries.”

Consider supply-chain management: Multisource (differentiated) supply chains are designed to improve reliability by eliminating an organization’s dependence on a single vendor. AI can dramatically increase the effectiveness of this technique by synthesizing information from outside sources. It might access meteorological data on the public cloud to identify weather patterns that could potentially interrupt transportation, for example. Over time, the application would learn the early warning signs, allowing it to change routing or sourcing if a hurricane appears to be brewing, well before that information reaches the headlines.

“We’re seeing people apply machine learning and deep learning solutions to be able to extract real-time, actionable insights from all sorts of data, wherever that data might be housed and whatever kind of data it is.”
—Kimberly Storin, vice president, Marketing, IBM Cognitive Systems

For other enterprises, the biggest benefit may come from applying machine learning and deep learning techniques to data that sits on premises. A bank could apply AI to historical transaction data, training it to identify and screen out mortgage applicants likely to default. More importantly, the technique can be used to detect fraudulent transactions while they are in process.

Fraud detection is an important issue for the insurance industry. According to the FBI, insurance fraud in the U.S. alone amounts to more than $32 billion annually (, and costs the average family $400 to $700 per year in inflated premiums. Fraudulent claims also divert resources that would otherwise assist policyholders who are genuinely in need.

By analyzing an image library formed of historical data and comparing it to pictures submitted with new claims, an AI system can learn to identify anomalies such as new claims being filed for old damage. With this evidence in hand, human investigators can step in. Examples such as this illustrate how pre-existing data can be applied to solve an ongoing problem in a new way.

“Ultimately, cognitive computing is about solving problems that you couldn’t solve before,” says Storin. “It’s really opening the door to how quickly you can take data, synthesize it, process it through an algorithm to train the machine, and get actionable and predictive insights in real time.”

IT in the Driver’s Seat

For many years, IT has struggled with its reputation as a cost center: It provides the tools requested by internal clients but isn’t necessarily credited with driving revenue. With the advent of AI, the IT department has the potential to deliver the foundation for business transformation. This hinges on IT as the champion of deep learning and what it can do to bring an organization’s vision—predicting terrorist attacks, preempting fraud, preventing disease, personalizing client journeys—to reality.

Kristin Lewotsky is a freelance technology writer based in Amherst, N.H.



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