Adaptive AI in 2023: Components, Use Cases, Ethics & Potential of Adaptive AI

AI is no longer optional for most businesses — and it’s far from a differentiating factor. In fact, researchers found that over 95% of companies have AI initiatives underway. To get ahead of the competition, leaders need to:

  • Advance their AI capabilities.
  • Get more from their investments.

Adaptive artificial intelligence (AI) is the next generation of AI systems. It has the ability to adjust its code for real-world changes, even when the coders didn’t know or anticipate these changes when they wrote the code.

Machine learning (ML) and deep learning (DL) innovations have advanced AI technology by leaps and bounds. AI algorithms can ingest and interpret data more accurately and effectively than ever. As a result, adaptive AI generates staggering insights and better predictions to help organizations make better decisions, become more resilient, and work more efficiently.

As businesses shift away from the fixed, historical data of traditional AI and embrace adaptive frameworks, technology will evolve even more to fit into an increasingly digital world. That is why Gartner proclaimed that adaptive AI is the top strategic technology trend of 2023. Gartner experts predict that organizations that embrace adaptive AI will outperform competitors by 25% by 2026.

In a rapidly evolving digital world, adaptive AI is a critical tool to meet the needs of today’s customers and continue to create crucial innovations. But what is it, and how is adaptive AI transforming the future? Let’s dive into the basics of adaptive AI and why it’s crucial for businesses today.

What is Adaptive AI?

Adaptive AI is the latest form of artificial intelligence that learns, adapts and improves as it encounters changes, both in data and the environment.

While more traditional forms of AI follow set rules and algorithms and tend to fall apart when faced with obstacles, adaptive AI systems can modify their behavior based on their experiences. It adjusts its own code without input from a person, providing a level of adaptability and resilience not seen before. This enables AI to deliver better outcomes faster.

Some key characteristics that distinguish adaptive AI systems are:

  • Ability to learn. Machine learning algorithms allow the technology to process and analyze new information. As a result, adaptive AI can acquire knowledge, identify patterns and make predictions.
  • Adaptability. Adaptive AI systems adjust their algorithms and decision-making processes when they encounter changes in input data or the context in which they operate. This flexibility makes them practical and relevant even in dynamic and unpredictable situations.
  • Self-improvement. Systems improve their capabilities over time by analyzing their own performance, identifying weak or inefficient areas and refining their algorithms in response.
  • Problem-solving capabilities. Adaptive AI systems develop sophisticated and nuanced approaches to problems by learning from experience and adapting to new information. These enhanced problem-solving capabilities often surpass traditional AI systems, leading to more innovative solutions to even the most challenging issues.

Traditional vs adaptive AI

While traditional AI lets companies work faster and become more productive, the technology is limited in its real-world application. The development of adaptive AI lets organizations become more flexible, resilient and effective when faced with complex challenges in daily business.

Key components of Adaptive AI

Adaptive AI has critical components that help it learn, adapt and improve over time. These are the most critical components…

Machine learning algorithms

These algorithms allow AI systems to learn from data and make predictions or decisions based on their learning. Machine learning is categorized into three main types:

  • Supervised learning. This is where the system receives labeled data and learns to map input data to known outputs.
  • Unsupervised learning. The system learns patterns and structures in unlabeled, often identifying hidden relationships or clustering similar data points.
  • Reinforcement learning. Through trial and error, the system adjusts its actions based on feedback in the form of rewards or penalties.

Neural networks and deep learning

The structure of the human brain inspired the creation of neural networks, which have interconnected layers of nodes or neurons. Deep learning involves using large neural networks with multiple layers to learn complex patterns and representations in data. AI systems learn hierarchical features with these techniques and perform tasks such as image and speech recognition, natural language processing and more.

(Follow our recurrent neural network tutorial.)

Transfer learning and meta-learning

AI systems leverage the knowledge it learns from one task or domain and apply it to another related one with transfer learning. It significantly reduces the required training to speed up the learning process.

Meta-learning, sometimes called learning to learn, trains AI systems to optimize their learning algorithms, improving their ability to learn new tasks or adapt to changing environments.

Evolutionary algorithms

These algorithms use natural selection and involve optimization through successive generations of candidate solutions. Adaptive AI uses evolutionary algorithms to optimize AI models, select features and tune hyperparameters, enhancing the system’s adaptability and performance.

Working together, these components enable adaptive AI systems to learn from experience, adjust based on new information, and continually improve performance over time.

Real-world applications of Adaptive AI

The implications of adaptive AI and its capabilities on the world are vast and revolutionary. It can potentially transform nearly every aspect of business, bringing innovation and resilience.

The healthcare industry

Adaptive AI will likely prove to be lifesaving and can improve how the healthcare industry functions. Because it can analyze data related to millions of patient vital signs and symptoms, it can optimize disease diagnoses and treatment. Patients can get the most accurate diagnosis and treatment that considers their individual needs and challenges.

It can take vital factors such as disease prevalence to fine-tune its performance and learn the subtle differences between healthcare institutions, like how often they perform blood tests which would be challenging for medical providers to calculate.


As finance becomes more digital, adaptive AI has the potential to transform how the financial industry runs. For example, Equifax uses it to assess customer risk with deep learning of customer behavior. By studying each customer’s financial decisions over the past 24 months, it approved 92,000 more loans — without any more losses.

In addition to risk management, adaptive AI offers more effective fraud detection by learning customer patterns and identifying anomalies. It can alert institutions faster than ever without inconveniencing customers.

(Consider what emerging AIs mean for cybersecurity.)


Organizations can optimize their supply chain using adaptive AI. Algorithms analyze market trends and sales data to predict demand and give manufacturers and leaders deeper insights into production, inventory and distribution. With optimized inventory, companies can avoid shortages and overstocking their warehouses.

Adaptive AI also predicts when equipment will fail, minimizing failures and enabling proactive maintenance. It also predicts other potential risks by analyzing:

  • Delivery times
  • Supplier performance
  • Economic indicators

This way, leaders can proactively tackle threats and minimize disruption to the supply chain.

Environmental sustainability

By analyzing complex climate data, adaptive AI can create more accurate models to predict climate change impacts. With these advanced models, policymakers make informed decisions about mitigation and adaptation strategies, such as:

  • Setting emissions reduction targets.
  • Planning for sea-level rise.

Adaptive AI also can optimize farming practices to reduce waste. Cameras, drones and sensors use AI to scan fields and detect areas over- or under-irrigated, pests, crop diseases, and vital soil health to eliminate the need for helicopters and enhance food security. These tools help farms improve resource management while reducing the environmental impact of waste.

(Read more about what technology means for sustainability.)

Ethical considerations & challenges

While adaptive AI has incredible potential, it’s vital to recognize and account for its challenges. Access to more data doesn’t necessarily mean the new technology will improve performance. It can negatively impact users and organizations if it is fed the wrong information and is taught the “wrong” lessons from the data given.

Bias is one of the most significant problems of adaptive AI. For example, Amazon had to abandon its recruitment engine that was supposed to select the best applicants with AI. The technology showed it was heavily biased against women. The technology was trained with resumes submitted over the ten years prior. However, since tech has long been male-dominated, the tool accidentally picked up and continued this bias. It penalized resumes that mentioned anything to do with women, such as “woman’s chess club,” and downgraded any applicants from two all-women universities.

Likewise, without proper controls in place, the technology can also be heavily biased against race. AI has mistagged photos based on racist tropes and failed to recognize Black and Brown faces because the images the algorithms were trained with were mostly white. Understanding and rectifying potential biases is critical to avoid harm and more fully utilize adaptive AI.

(Learn more about the ethics of AI.)

Embracing the potential of adaptive AI

Adaptive AI innovates a more resilient, effective and faster framework by adapting to real-world circumstances. It continues to adapt and problem-solve, improving its capabilities over time, making it ideal across numerous industries, including healthcare, finance, manufacturing, and environmental sustainability. These applications demonstrate the transformative potential of adaptive AI in:

  • Addressing complex, real-world challenges.
  • Promoting more sustainable practices.

As the development of adaptive AI continues, it is essential to address some of the critical ethical challenges it raises, such as bias, for responsible AI development and usage. By embracing the potential of adaptive AI and addressing its challenges, we can harness its power to drive innovation, enhance efficiency, and create a more sustainable and prosperous future for all.

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Kayly Lange
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Kayly Lange

Kayly Lange is a freelance writer. As a tech and SaaS specialist, she enjoys helping companies achieve greater reach and success through informative articles. When she’s not writing, she enjoys being out in nature, cooking, and reading a wide range of novels. You can connect with Kayly on LinkedIn.