Adaptive Artificial Intelligence (AI) is able to update its own code to incorporate what it has learned from its experiences with new data. This means Adaptive AI can be used to continually improve the customer experience with each iteration of an interaction between a customer and a brand. AI-driven chatbots will continually refine their conversational skills, and recommendation engines will become more refined, making recommendations that truly resonate with customers. Let’s look at adaptive AI, the ways brands are using it today, and the future of adaptive AI for improving the customer experience.

How Does Adaptive AI Work?

The traditional machine learning (ML) model consists of training and prediction pipelines. A pipeline can be thought of as an interconnected and streamlined collection of operations. The training pipeline aggregates and ingests data throughout the various stages of data cleaning, grouping and transformation. The prediction pipeline then analyzes the data to generate accurate insights and predictions that will be used for fruitful decision making. 

Adaptive AI, on the other hand, consists of a single pipeline that monitors and learns the new changes made to the input and output values and their associated characteristics. Additionally, it learns from the events that may change the behavior of consumers and businesses in real time and is able to consistently maintain its accuracy. Adaptive AI incorporates the feedback it has received from the operating environment and then uses it to create data-informed predictions. This allows for super-fast solutions for the verification of ideas and simple deployment functionality in production. 

Mike Gozzo, chief product officer at Ada, an AI-based automated brand platform provider, told CMSWire adaptive AI relies on the regular training and extension of ML and Natural Language Understanding (NLU) capabilities, and said this will increase the quality of CX. “It works best when trained on millions, or even billions, of customer interactions across different geographies, industries and use cases,” Gozzo said. “This creates a rich data set that drives personalized and proactive experiences for each customer, in every interaction.” Gozzo explained when a global ML model is combined with brand-specific models, the time it takes to train conversational AI and increase reliability is reduced.

The three tenets of adaptive AI are said to be robustness, efficiency and agility.

  • Robustness refers to the ability of adaptive AI to accomplish high algorithmic accuracy.

  • Efficiency refers to the ability of adaptive AI to pull off low resource usage.

  • Agility refers to the capacity of adaptive AI to change operational conditions based on current requirements.

Synergistically, these three core elements of adaptive AI comprise the key metrics for extremely efficient AI-informed actions for many applications.

Ricardo Zuasti, chief product officer at Technisys, a leading global provider of next-generation digital banking platforms, told CMSWire being able to deliver truly tailored experiences customers will increasingly want and expect will depend on a business’ ability to fuel AI-powered decision making in every channel, in real time. Zuasti’s business is moving toward the use of AI to power context and behavior-sensitive mechanisms beyond conversational interactions but more broadly to adapt the user experience dynamically to what the person needs and wants in near real time. 

Related Article: If You Want to Succeed With Artificial Intelligence in Marketing, Invest in People 

Adaptive AI and Chatbots

AI-driven chatbots are commonly used on websites as a way for customers to instantly locate the goods or services they are searching for, as well as for customer service needs. Adaptive AI is still an emerging technology. However, there are already chatbots using it to enhance the chat experience. 

Hyro, for instance, offers an adaptive AI-driven chatbot being used in healthcare, real estate and government industries. Hyro automatically scrapes a variety of data sources including websites, databases, application programming interfaces (APIs) and more, and when content is updated, the conversation is also updated. The unstructured data is mapped to a knowledge graph made to be “queryable” by Natural Language Processing (NLP). 

Adam Dorfman, vice president (VP) of product at Reputation, an online reputation management solution provider, told CMSWire through adaptive AI and ML, an AI-powered chatbot can continually improve service in a number of ways without human intervention. “For instance, a chatbot using adaptive AI can improve the accuracy of its replies and learn how to give more personalized responses based on each customer’s needs (instead of generic, pre-formulated answers),” adding Adaptive AI also promises to help chatbots become more human and accessible by learning to give answers in a more natural way by improving a chatbot’s conversational skills. “This is important because when a chatbot can successfully emulate nuances of tone and language style, people are more likely to emotionally trust the customer experience they’re getting with a business.”

Related Article: The Role of Journey Orchestration Engines in 2022 

Learning Opportunities

Adaptive AI Improves Edge Computing

One of the areas where adaptive AI is shining is in edge computingIBM defines edge computing as a distributed computing framework that physically locates applications closer to data sources such as Internet of Things (IoT) devices or local edge servers. A 2022 Gartner Report predicted by 2025, more than 50% of enterprise-managed data will be created and processed outside the data center or cloud using edge computing.

By using adaptive AI, edge systems are able to dynamically adjust their computing needs, effectively lowering compute and memory resource requirements. Adaptive AI allows edge applications to adapt and adjust to their workloads based on their requirements and environments. By using attention and context to use only the parts of its neural network it needs, adaptive AI is able to do the processing locally on edge devices. Adaptive AI is a new method for neural networks to operate that dynamically minimizes the amount of memory and compute horsepower required. 

The Future of Adaptive AI and Customer Experience

Brian David Crane, founder of Spread Great Ideas, a digital marketing fund, told CMSWire adaptive AI is the next big thing in automation and AI, as programs and machines become self-sustaining mechanisms that continually learn and adapt to human behavior. “The self-driving car is based on adaptive AI and is a glaring example of how brands are using AI today. Brands like Amazon, Netflix and Google are already using adaptive AI to provide a better user experience,” said Crane. 

“Companies are exploring how they can use adaptive AI to deliver customized learning solutions to students based on their individual learning capacities and behavior,” said Crane, who added cybersecurity companies are also exploring adaptive AI to create an automated self-sustaining protocol that learns and models itself with continual iterations to fight digital threats and cyberattacks in real time.

The implications of adaptive AI on customer experience are vast and game-changing. “By analyzing social, behavioral and past interactions, adaptive AI uses continuous interactions to predict and anticipate customer behavior and provide highly personalized solutions to improve the customer journey and deliver positive CX,” said Crane, who explained adaptive AI focuses on feelings and emotions and analyzes sentiments to create perfect interactions on a real-time basis. 

As an example of such an experience, Crane said to think of hyper-interactive and customized displays at retail points that use facial interpretations, voice analysis and body language analysis to identify shoppers' emotions and mindsets and offer solutions in real-time to deliver a positive experience. 

Adaptive AI will enable amazing customer experiences that will be unique, positive, and emotionally connected. “With adaptive AI, businesses can predict the next buyer experience and offer personalized recommendations, discounts and individualized offers, thus helping build an emotional connection with the brand through these experiences over time,” said Crane.

The Takeaway: Adaptive AI Can Improve CX in Real Time 

Adaptive AI takes AI-driven chatbots to the next level through the use of ML, NLU, NLP and real-time decision making, and has the potential to improve the customer experience in real time, enhancing hyper-personalization and recommendation engines, and improving edge computing by minimizing compute and memory resource requirements.