Adaptive AI absorbs learnings even as it’s being built. Think about that for a second.
Adaptive artificial intelligence (AI), unlike traditional AI systems, can revise its own code to adjust for real-world changes that weren't known or foreseen when the code was first written. Organizations that build adaptability and resilience into design in this way can react more quickly and effectively to disruptions.
“Flexibility and adaptability are now vital, as many businesses have learned during recent health and climate crises,” says Gartner Distinguished VP Analyst, Erick Brethenoux. “Adaptive AI systems aim to continuously retrain models or apply other mechanisms to adapt and learn within runtime and development environments — making them more adaptive and resilient to change.”
Gartner expects that by 2026, enterprises that have adopted AI engineering practices to build and manage adaptive AI systems will outperform their peers in the number and time it takes to operationalize artificial intelligence models by at least 25%.
Why adaptive AI matters to business
Adaptive AI brings together a set of methods (i.e., agent-based design) and AI techniques (i.e., reinforcement learning) to enable systems to adjust their learning practices and behaviors so they can adapt to changing real-world circumstances while in production.
By learning behavioral patterns from past human and machine experience, and within runtime environments, adaptive AI delivers faster, better outcomes. The U.S. Army and U.S. Air Force, for example, have built a learning system that adapts its lessons to the learner using their individual strengths. It knows what to teach, when to test and how to measure progress. The program acts like an individual tutor, tailoring the learning to the student.
And for any enterprise, decision making is a critical but increasingly complex activity that will require decision intelligence systems to exercise more autonomy. But decision-making processes will need to be reengineered to use adaptive AI. This can have major implications for existing process architectures — and requires business stakeholders to ensure the ethical use of AI for compliance and regulations.
Bring together representatives from business, IT and support functions to implement adaptive AI systems. Identify the use cases, provide insight into technologies and identify sourcing and resourcing impact. At a minimum, business stakeholders must collaborate with data and analytics, AI and software engineering practices to build adaptive AI systems. AI engineering will play a critical role in building and operationalizing the adaptive AI architectures.
Ultimately, though, adaptive systems will enable new ways of doing business, opening the door to new business models or products, services and channels that will break decision silos.
AI engineering provides the foundational components of implementation, operationalization and change management at the process level that enable adaptive AI systems. But adaptive AI requires significantly strengthening the change management aspect of AI engineering efforts. It will defeat the purpose if only a few functions around this principle are altered.
Reengineering systems for adaptive AI will significantly impact employees, businesses and technology partners and won’t happen overnight.
First, create the foundations of adaptive AI systems by complementing current AI implementations with continuous intelligence design patterns and event-stream capabilities — eventually moving toward agent-based methods to give more autonomy to systems components.
Also, make it easier for business users to adopt AI and contribute toward managing adaptive AI systems by incorporating explicit and measurable business indicators through operationalized systems, as well as incorporating trust within the decisioning framework.
Adaptive AI creates a superior and faster user experience by adapting to changing real-world circumstances.
Broadening decision making capabilities and flexibility happen while implementing decision intelligence capabilities.
IT leaders need to reengineer various processes to build adaptive AI systems that can learn and change their behaviors based on circumstances.
Erick Brethenoux is a Distinguished VP Analyst in Gartner Research. He specializes in machine learning, artificial intelligence and applied cognitive computing. Mr. Brethenoux guides organizations on the strategic, organizational and technology aspects of using advanced analytics as a driving force of their growth.
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