Being adaptive means that we respond to change, but it also means we may create change. When the wind blows, you can be cold, put a jacket on, or get out of the wind.
Adaptive intelligence—a subset of artificial intelligence (AI)—is the analytics layer for AI and machine learning applications, and it’s the intersection of human judgment and machine automation. It’s also the topic of a recent article penned by Rich Clayton, vice president of the Business Analytics Product Group at Oracle.
Clayton’s premise is that the next phase of business analytics will use data as its core and combine it with human knowledge and experiences programmed in. Current examples include personalized shopping, self-driving vehicles, online wealth management, and virtual assistants.
“The ability to understand and adjust analytic model inputs and training data, improve data imperfections, and apply ethics to our use and interpretation of data are a few examples of what machines can’t completely replace,” Clayton says.
Imagine: What could be possible in your organization if insights came to you when you need them most? What might happen if half the content for your next operations review were generated by machines? How might you automate your business if you could use voice commands to ask the system some complex questions?
From a technology perspective, adaptive intelligence covers three main areas: intelligent applications, an intelligent platform, and data itself.
Adaptive intelligence applications are a new category of continuously adapting, self-learning applications powered by enterprise data from transactional business apps, such as customer experience, enterprise resource planning, supply chain management, and human resources. These are micro-solutions that operate without human bias and deliver a very high degree of confidence and on a very large scale.
Adaptive intelligence platforms provide interactive data visualization capabilities to discover, explain, and predict outcomes. It does not compromise between the need for analytic speed by business users and the need to govern data access and preparation by administrators.
Data used to power adaptive intelligence apps and platforms comes from many sources. Adapting these types of data generate observations through self-learning algorithms available in the apps. The apps consolidate data, including social data and profile information, from data exchanges.
“Data is the fuel that drives organizations towards automation, but most organizations lack a comprehensive data strategy, one that seeks to acquire, curate, combine, and commercialize it,” Clayton says.
Adaptive intelligent systems are not in the distant future, he concludes. “Their impact will depend on how quickly and accurately we can take advantage of all this intelligence.”
But first, check out Rich Clayton’s full article at CIO Review.
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