Adaptive AI: Market Overview and Applications
Introduction
Artificial intelligence (AI) is the simulation of human intelligence processes – especially learning and adaptive behavior – by machines. AI is presently powering a wide variety of business and consumer applications, such as:
- Sifting through mountains of Big Data to extract precious business intelligence;
- Answering questions via a conversational assistant called a chatbot;
- Researching and writing reports (and performing other intellectual functions); and, perhaps most impressively,
- Permitting a vehicle to drive itself.
Although still in its evolutionary phase, AI is already being delivered in multiple forms, like:
- “Generative AI,” the technologies behind the wildly popular ChatGPT utility; and, more recently,
- “Adaptive AI,” a continually self-learning – and, thus, self-improving – version of artificial intelligence.
An Adaptive AI Is Continually Learning and Improving
As analyst Helen Zhuravel explains, “Traditional AI algorithms, while powerful, struggle to adapt to rapidly evolving market conditions. That’s where adaptive AI steps in, revolutionizing decision-making with its ability to learn, adjust, and evolve alongside the ever-changing business landscape. Adaptive AI represents a fundamental shift in how AI systems are designed, implemented, and applied. Traditional AI models are static, relying on pre-defined rules and parameters. Adaptive AI, on the other hand, is dynamic, continuously learning and evolving based on new data and experiences.”1
What Comprises Adaptive AI
As might be anticipated, Adaptive AI employs multiple technologies, including:
Machine learning;
Natural language processing;
Predictive analysis;
Transfer learning, using lessons learned conducting one task to inform the performance of other, related tasks; and
Reinforced learning, which analyst Shafaq Naz declares is a “critical technique for enabling adaptability in AI systems. It involves training an AI agent to take actions in an environment to maximize a reward. The agent learns by receiving feedback on the outcomes of its actions, allowing it to adapt its strategies over time. This architecture is well-suited for applications like robotics and game playing.”2
Adaptive Vs. Traditional AI
Suffescom Solutions, a leading adaptive AI development firm, compares the characteristics of adaptive AI with traditional (or original) AI.3
Characteristic | Adaptive AI | Traditional AI |
Definition | Possesses the ability to adapt
(or improve itself) over time. | Unable to adapt. Is “well-versed o
nly in a fixed data set.” |
Maintenance | Requires ongoing maintenance and
monitoring to ensure continuous learning. | Requires less maintenance and monitoring
(usually to detect data or algorithmic corruption). |
Performance | Ever-changing | Fixed |
Scalability | High | Limited |
Table 1. The Differences Between Adaptive and Traditional AI
Adaptive AI Market
Early estimates of adaptive AI growth reflect the potential of a market where AI models are not only smart but getting smarter by the day. Precedence Research, for example, predicts that the global adaptive AI market will expand from $0.73 billion in 2023 to $24.63 billion by 2033, exhibiting a remarkable compound annual growth rate (CAGR) of 42.10 percent.
Among the major market drivers are:
- An ever-improving AI.
- The ability to better analyze customer behavior and “adapt” products and services accordingly.
- The opportunity to better utilize Internet of Things data, “[enhancing] decision-making intelligence in domains such as smart homes, healthcare, and industrial applications.”
Sector Penetration
In terms of industry adoption:
“Financial services stand at the forefront of opportunities for the adaptive AI market. Adaptive AI plays a pivotal role in optimizing risk management, fraud detection, and customer service within financial institutions. Moreover, the integration of adaptive AI enables personalized financial services, tailoring recommendations and solutions to individual customer needs. From dynamic investment strategies to adaptive customer interfaces, adaptive AI fosters a more responsive and customer-centric financial ecosystem.”
Healthcare and Life Sciences is also fertile ground for adaptive AI. “This sector harnesses adaptive AI to … predict disease trends and enhance treatment plans. Trends include the integration of adaptive AI in medical imaging for precise diagnostics, drug discovery acceleration through adaptive algorithms, and the development of personalized medicine. The adoption of adaptive AI in healthcare aims to revolutionize patient outcomes by tailoring medical approaches based on individual needs and evolving health conditions.”4
Prominent Providers
Presently, the prominent providers of adaptive AI development services are:
- RisingMax
- Suffescom Solutions
- Markovate
- Dynam.Ai
- LeewayHertz5
Adaptive AI Applications
The applications for adaptive AI are literally unlimited, especially as adaptive AI emerges as the principal enabling technology (or technologies) for “digital transformation,” a current enterprise passion project.
Digital transformation (DT) is the utilization of digital technologies (basically, any electronic system or device that generates, stores, or processes data) to create new products, services, or processes or to enhance existing ones. DT projects are aimed at streamlining enterprise operations, gaining new operational capabilities, reducing operational expenses (especially personnel costs), and developing business opportunities.
Among today’s high-yield adaptive AI use cases are:
Autonomous Transportation
As analyst Shafaq Naz observes, self-driving cars, trucks, and other vehicles “are an embodiment of adaptive AI. These vehicles need to navigate unpredictable environments, making split-second decisions to ensure the safety of passengers and pedestrians. Adaptive AI algorithms allow these vehicles to learn from their experiences and continuously improve their driving capabilities. For instance, they can adapt to different weather conditions, traffic patterns, and road layouts.”6
Gaming
In addition to transportation, analyst Naz views adaptive AI as “a cornerstone of modern gaming experiences. Video games that adapt to a player’s skill level and style of play provide a more engaging and personalized experience. AI algorithms monitor player actions, learning their strategies, strengths, and weaknesses. This information is then used to dynamically adjust the game’s difficulty, pacing, and challenges, keeping players immersed and challenged.”7
Fraud Detection
Adaptive AI helps protect financial institutions (and the customers they serve) by recognizing new forms of fraudulent activity in real time.
Predictive Maintenance
Adaptive AI can analyze IoT-enabled sensor data to detect deteriorating parts, and predict and schedule maintenance activity as required, thus reducing operational downtime, and extending the lifespan of industrial equipment.
Supply Chain Resilience
Adaptive AI can improve supply chain resilience by monitoring operational data for signs of potential disruptions. Adaptive AI can also optimize supply chain operations by “[moving] the right products to the right place at the right time, and in the right quantity … to meet customer demand.”8
Energy Management
“Adaptive AI is used in energy systems to optimize power distribution, predict energy demand, and manage renewable energy sources efficiently, adapting to fluctuations and changes in the energy grid.”9
Agricultural Surveillance
Analyst Mary K. Pratt reveals that “adaptive AI can help scan agricultural areas. Drones and other equipment can collect data, such as images or soil samples, that adaptive AI systems can analyze to make decisions about the best action to take next.”10
Personalized Learning
Adaptive AI can facilitate education by creating individual “learning experiences” based on a student’s scholastic performance and preferences. “It adapts the curriculum to match the student’s [learning] pace and learning style.”11
Personalized Recommendations
In e-commerce, “Adaptive AI is used to analyze user preferences, [purchasing] history, and browsing behavior to provide personalized product recommendations, enhancing the overall shopping experience.”12
Cybersecurity
Adaptive Ai is utilized to help surface unusual network traffic or user behavior, thus helping prevent – or, at least, mitigate – cyber attacks.
Adaptive Vs. Generative
As must be evident, the field of artificial intelligence – and its principal subdomain, machine learning – is extremely volatile, with new understandings, new capabilities, and new applications emerging every day.
Today’s current “hot property” is “generative AI,” which refers to programs, like the wildly popular ChatGPT, that can “generate” original content, literally creating new digital images, video, audio, text, and code in response to a user prompt.
Given that both generative AI and adaptive AI are following a positive trajectory, and enterprises are likely to invest in both, it’s prudent to consider the differences.
As compared by RisingMax, a leading adaptive AI development firm:
Adaptive AI practices self-learning; generative AI is constrained by a strict rule set.
Adaptive AI can work with a small data set and expand its knowledge over time; generative AI requires a huge amount of data from the start.
Adaptive AI “is capable of handling certain levels of complexity yet might be unable to provide original outcomes;” generative AI “can provide detailed and unique results but takes more computational power.”13
Implementing Adaptive AI
Prior to pursuing adaptive AI (or any AI initiative), an enterprise should establish their Artificial Intelligence Strategy, which may involve, for example:
- Upgrading to “AI-enabled” or “AI-powered” versions of commercial software;
- Deploying “intelligent process automation” (IPA) programs;
- Utilizing ChatGPT, or other generative AI program, to create procedures documents; and
- Experimenting, at least initially, with adaptive AI. Starting with a pilot program is always prudent.
When an enterprise is ready to design, develop, and implement an adaptive AI project, they should first engage an experienced development firm, such as RisingMax or Suffescom Solutions.
Previewing the actual process, analyst Shakeel Ahamed suggests a seven-step implementation:
1. “Define clear objectives – Specify the desired outcomes, such as image or text categorization, user behavior predictions, or market analysis.
2. “Gather relevant data – Regularly update the data to maintain its relevance and utility for model development.
3. “Develop the algorithmic model – Choose the appropriate machine learning algorithms based on the problem at hand.
4. “Make real-time decisions – Leverage the potential of adaptive AI by enabling real-time decision-making. Integrate data from various sources, preprocess it on the fly, and use predictive analytics to make immediate decisions. Implement a feedback loop for continuous system refinement.
5. “Enhance and refine the model – Even after deployment, continuously update and adjust the model to adapt to changing conditions and user needs.
6. “Deploy the model – Transition the model from a testing environment to real-world use.
7. “Monitor and improve – Establish ongoing monitoring mechanisms to ensure the system’s longevity and effectiveness.”14
Web Links
Dynam.Ai: https://dynam.ai/
Leewayhertz: https://www.leewayhertz.com/
Markovate: https://markovate.com/
RisingMax: https://risingmax.com/
Suffescom Solutions: https://www.suffescom.com/
US National Institute of Standards and Technology: https://www.nist.gov/
References
1 Helen Zhuravel. “Adaptive AI: The Key to Dynamic Decision-Making.” Binariks Inc. January 29, 2024.
2 Shafaq Naz. “Adaptive AI: A Game-Changer in the World of AI.” E2E Networks Limited. August 31, 2023.
3 “How To Integrate Adaptive AI In Your Business?” Suffescom Solutions Inc. June 29, 2023.
4 “Adaptive AI Market (By Component: Platform, Services; By Application: Real-time Adaptive AI, Offline Learning and Adaptation, Context-aware Adaptation, Autonomous Decision-Making, Others; By Technology: Machine Learning, Deep Learning, Reinforcement Learning, Natural Language Processing, Computer Vision; By End-use: BFSI, Healthcare & Life Sciences, IT & Telecommunications, Aerospace & Defense, Others) – Global Industry Analysis, Size, Share, Growth, Trends, Regional Outlook, and Forecast 2024-2033.” Precedence Research Pvt. Ltd. January 2024.
5 Derek T. Belford, “Top Ten Adaptive AI Development Companies in 2023.” Linkedin. July 11, 2023.
6 Shafaq Naz. “Adaptive AI: A Game-Changer in the World of AI.” E2E Networks Limited. August 31, 2023.
7 Ibid.
8 Mary K. Pratt. “Explore 14 Real-World Use Cases for Adaptive AI.” TechTarget. October 2, 2023.
9 ChatGPT. March 7, 2024.
10 Mary K. Pratt. “Explore 14 Real-World Use Cases for Adaptive AI.” TechTarget. October 2, 2023.
11 ChatGPT. March 7, 2024.
12 Ibid.
13 “Generative AI Vs Adaptive AI: Which Is Best for Business.” RisingMax. May 23, 2023.
14 Shakeel Ahamed. “Adaptive AI Development: A Guide to Building Adaptive AI Systems.” Linkedin. January 31, 2024.