While the world was initially stunned by AI chatbots’ ability to hold an intelligent conversation and answer search queries, adaptive AI is often the unsung hero of existing enterprise use cases. This article will explain what these AI models do, what makes them so powerful, and how to leverage them for your business.
Adaptive AI is a nascent category of AI models. True to their name, these models adapt as they perform, learning not just from the data they are fed, but even changing their own code as they go. You can think of it as AI with executive function, capable of acting like an administrator of a library of AI models, using whichever kind is appropriate for a task, and even designing new types as it goes. So how exactly does adaptive AI differ from its generative cousin?
Adaptive AI can learn from new data, adapting its behavior and updating its models based on new inputs and feedback from its environment. Generative AI, on the other hand, is only as good as its training.
Adaptive AI optimizes its functionality for narrowly-defined goals and metrics, including accuracy, speed, task customization, and user preferences. This is how it learns. For instance, in a recommendation system, the goals could be to maximize click-through rate or user engagement scores. Contrastingly, generative AI is not inherently goal-oriented for metrics optimization, but rather is designed to produce novel outputs based on the patterns it discerns during training.
Adaptive systems provide customized results and UX by optimizing according to differences between individual users, while generative AI provides personalized experiences primarily through conditioning on specifics provided with a given input prompt rather than adapting underlying models to users.
Experts expect adaptive AI to prove transformative for digital industries. For instance, Gartner has named adaptive AI as one of the top ten trends to have emerged last year, and predicts that by 2026, companies that have adopted AI engineering practices to build and manage adaptive AI systems will outperform their competitors by 25%. Another study found that by 2026, 30% of new applications will use adaptive AI to drive personalized user interfaces, up from under 5% today.
Given the exponential acceleration of new applications for adaptive AI, it is likely that these technologies will be deeply integrated into nearly all digital experiences before the decade is out. As users become accustomed to the personalized and streamlined UX enabled by adaptive AI’s real-time tailoring of platforms’ functionality, interfaces, and recommendations, it’s likely that web applications lacking adaptive functionality will struggle to compete.
The benefits that adaptive AI has brought businesses around the world range from the macro to the impossibly specific. Here, we’ll briefly address the broad brush advantages.
The ability of adaptive AI models to alter their behavior in real time means that they can respond to problems far faster than their generative counterparts, sometimes before any human actors are aware of an issue.
For instance, Adaptive AI-driven cybersecurity systems can update threat detection models within milliseconds based on real-time monitoring, equipping them to outpace non-adaptive platforms and immediately counteract emerging attacks before significant damage occurs.
Due to the self-maintaining nature of adaptive AI, you and your employees will have a lot less work to do. You can expect: (i) lower repeat issues due to proactively identifying problems (ii), faster processing of core business workflows and documents (iii), and less risk of low quality or irrelevant outputs.
For example, adaptive inventory management systems can dynamically optimize order timing and volumes by analyzing supply chain data patterns. This reduces overstocking, product shortages, and manual oversight needed relative to non-adaptive systems; the AI autonomously adapts for efficiency gains as conditions change.
Studies show that 89% of marketers experience positive ROI when using personalization in their campaigns. Adaptive AI models excel at personalization, learning and tailoring their behavior to individual customers.
Adaptive recommendation systems analyze first party data and feedback in real-time to tailor product suggestions based on thousands of data points such as location, device, browser, and even the weather in their area. Using this data, adaptive AI models sort users into cohorts, before making predictions about their tastes based on the past behavior of members of that cohort. This kind of personalization drives higher engagement and conversion rates.
Charles Darwin said ‘It is not the strongest of the species that survives, nor the most intelligent that survives. It is the one that is most adaptable to change’. The same is true of business. But while we once had to rely solely on our wits for adaptability, now advanced AI models can lend us this quality. Responding to crises, strategising inventory, personalizing content, and generating promotions in real-time, this technology is fast becoming indispensable for online businesses.
