Traditional AI systems struggle to adapt to dynamic environments, posing a significant challenge for industries seeking real-time decision-making capabilities. Dynamic Decisioning System powered by Adaptive AI and leveraging reinforcement learning emerges as a transformative solution to this critical need. Unlike traditional AI, this system automatically adjusts to new or changing data by continually learning from its environment and incrementally updating its knowledge. This results in accurate and efficient outcomes, making it indispensable and revolutionizing decision-making capabilities in dynamic environments. At the core lies reinforcement learning techniques such as Q-learning and SARSA, as an enabler for self-learning.
According to Research and Markets, Adaptive AI market is estimated to grow at a CAGR of 44.71% over next several years (Reference source: Adaptive AI Market - Forecasts from 2024 to 2029, ResearchAndMarkets.com). Hence, its high time to embace this game-changing technology.
It’s capability can be applied across sectors:
- Skin care recommendation systems for recommending personalized skin care products based on environmental and lifestyle impacts on human skin.
- Wifi and Mobile network-based intelligence systems to locate the path to best signal coverage, based on signal strength.
- Waste water treatment systems to standardize chemical dosing based on real time parameters.
Adaptive AI has the potential to transform and redefine customer experience and has been recognized as one of the top strategic intelligence technology trend. Gartner predicts that by 2026, companies using AI engineering practices to build and manage adaptive AI systems will outperform competitors by atleast 25% in the number and time to operationalize AI models.(Reference source: Modernize Data and Analytics Capabilities, Gartner).
