Contrary to traditional artificial intelligence (AI) systems, adaptive AI has the ability to revise its own learning methods to adjust for real-world changes that weren’t known when the system was first developed. Being adaptable and resilient in this way allows organizations to react more quickly and effectively to disruptions.
Flexibility and adaptability are now vital, as many businesses have learned during ongoing health, climate and economic crises. Adaptive AI systems aim to continuously retrain models or apply other mechanisms to adapt and learn within runtime and development environments — making them more 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%.
The Importance of Adaptive AI in 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.
For all enterprises, 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.
How to Implement Adaptive AI
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.
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.
To make it easier for business users to adopt AI and contribute toward managing adaptive AI systems, incorporate explicit and measurable business indicators through operationalized systems, as well as incorporate trust within the decisioning framework.
Erick Brethenoux is a Distinguished VP Analyst covering artificial intelligence at Gartner, Inc. where he guides organizations on the strategic, organizational and technology aspects of using artificial intelligence as a driving force of their growth.