How enterprises can put cognitive AI to work in their organizations

How enterprises can put cognitive AI to work in their organizations

Businesses across all sectors are taking a keen interest in the potential of artificial intelligence (AI) to address its most pressing challenges. AI is already known for its ability to speed up processes, streamline operations, and of course to crunch vast quantities of data faster than a human ever could. But when it comes to systems that can think for themselves? This reality is closer than you might think.

Cognitive AI assimilates data from multiple sources, in different formats, and is able to weigh up these data to form insights. This type of AI differs from others in its ability to mimic the way the human brain works. Cognitive AI systems are interactive, contextual, and, crucially, adaptive, in that they learn and evolve dynamically as new information comes to light. Far from replacing humans, AI is being taught to work alongside humans, helping to enhance the work we do or fulfill needs in other ways. Early adopters of cognitive technologies view them as critical to the future success of their organization and the ability to digitally evolve.

To understand the business implications of cognitive AI, look no further than the healthcare industry. Researchers are using cognitive AI to analyze blood samples, metabolism, speech and language patterns, and handwriting to understand risk factors associated with Alzheimer’s disease, resulting in programs that can diagnose the disease six years earlier than previously possible. A federation of 30 business, healthcare, and research institutions are developing cognitive AI models that identify brain tumors. And startups like MyndYou are using the brain as an AI sensor to help care providers assess and monitor elderly patients with a platform that monitors speech, walking, and driving over time to identify changes that indicate deteriorating physical or cognitive abilities. With these science-fiction like use cases, it’s little wonder that IDC predicts worldwide spending on cognitive and AI systems will reach $77.6B in 2022.

It is of course early days for cognitive AI in healthcare and, in time, history will judge its long-term success. Nonetheless, the early indications are hopeful and positive, to the degree that we can already extract some early lessons that other sectors can learn from. By examining the drivers and enablers for cognitive AI in healthcare, particularly at a research level, enterprises in other markets can identify ways to grow and improve their own business processes.

Without massive data sets, there is no place for AI, never mind cognitive AI – but readily available databases or spreadsheets are enough to get started. In the healthcare world, medical data is already vast and is growing at 36 percent CAGR through 2025, owing to advances in wearables and other IoT-enabled devices, medical imaging and real-time data production. One of the biggest enablers for cognitive AI in healthcare has simply been the sheer volume of data that are generated. Enabling connected systems to have access to this aggregated, anonymized data that already exists about patients allows cognitive AI to spot health trends and patterns, especially when combined with real-time health monitoring information (such as from wearables) and environmental data.

Assimilating disparate data, extracting insights, and turning them into actionable intelligence is the common digital challenge affecting all sectors. In the insurance industry, for instance, we are already seeing cognitive AI used to gather vast amounts of structured and unstructured data to improve the accuracy of underwriting, remotely process claims, streamline operations, and drive down costs. In the future, thanks to cognitive AI, the data streams we generate could be shared with insurance companies to automatically adapt premiums according to the choices we make and handle insurance claims in real-time based on events that take place.

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