In the technology space, artificial intelligence and machine learning continue to be rapidly growing by making a deep impact on a wide range of industries and use cases. 

Studies and surveys present a forecast of the AI market reaching $500 billion by the end of 2023 and touching an estimated $1,600 billion by 2030, indicating the continued interest in these technologies in the upcoming years. In addition to the ChatGPT, a generative AI model that can challenge the existing search engines and new automated machine learning tools, AI is finding applications in many new domains, expanding the use of AI across segments and sectors. 



Artificial Intelligence is categorized in many different ways, but one broad classification looks at AI variants in the following ways:

  • Generative AI: This variant of AI is designed to generate content in the form of images, videos, and texts. 
  • Adaptive AI: This helps continuous learning to generate new data insights, by modifying its own code to consider unpredictable changes. 
  • Edge AI: This helps overcome the need for high latency and network bandwidth usage by IoT-enabled connected devices. 
  • Explainable AI; This is an artificial intelligence development that enables human users to understand and trust the results, data transparency, fairness, and accuracy, generated by machine learning algorithms.


Let us now explore the trends in AI and ML that will revolutionize many things in most segments in the coming years.


AI and ML Trends for Upcoming Years

To leverage the future potential of artificial intelligence and machine learning in areas that include autonomous systems, enterprise management, and cybersecurity, business leaders and CIOs will have to work on strategizing an approach to align AI and ML with the business goals and interests of stakeholders including employees, by using internal talents or exploring Open Talent platforms.


Let us go over some of the top AI and ML trends that will impact industries and society in varied manners in the coming years.

Automated Machine Learning (AutoML)

Automated machine learning, aka AutoML, basically involves automating the application of machine learning to real-world problems and is useful for people who are not machine learning experts. Gartner forecasts a change in focus to enhancing a range of processes required to operationalize these models, such as PlatformOps, MLOps, and DataOps, collectively known as XOps. The Global Automated Machine Learning Market is expected to reach USD 5,406.75 Million by 2027, at a CAGR of 42.97% during the period of 2022-2027.


Conceptual Design

AI is traditionally used to automate repetitive tasks by using data, visual images, and analytics. New models such as CLIP tools work as a bridge between computer visions and natural language processing (NPL) to generate new visual designs from texts. The AI trends are expected to impact architecture, fashion designing, and similar creative industries disruptively, in the near future.


Multimodal Learning

AI developments such as Google DeepMind support multiple modalities to perform visual, language, and robotic tasks within a single ML model. Using multi-modal learning, for example, healthcare systems can extract and process patients’ medical data that include clinical trial forms, visual lab results, genetic sequencing reports, and other documents in scanned formats. 


Multiple Objectives

The traditional AI approach of targeting a particular metric can result in businesses missing out on related objects, like new revenue opportunities, focusing only on customer conversion rates or the increasing significance and awareness of environmental, social, and governance (ESG) aims. This means CIOs are expected to work on models that balance ESG objectives with traditional business goals, such as optimal inventory, improved revenue, and reduced costs.



An increasing number of enterprises have begun to use AI to detect and mitigate cyber-attacks, defensively and proactively. IBM’s Cyber Security Intelligence Index Report of 2021 found that "human error was a major contributing cause in 95% of all breaches." AI can help achieve better security, sifting through large amounts of information and identifying threats in an otherwise seemingly difficult and routine activity, like Microsoft’s Cyber Signals which is being used to analyze 24 trillion security signals, 40 nation-state groups, and 140 hacker groups.


Language Modeling

While ChatGPT exhibited a new interactive experience with the use of AI and ML for diverse use cases in wider fields, there has been a lot of criticism against defective and incorrect results provided by the tool. Similarly, product companies will face negative feedback against imperfect product descriptions and suggestions, for example. This scenario will prompt product companies to explore better methods to clearly explain the chances and scenarios of these their products and tools generating errors.


Computer Vision

Computer vision models, enabled by cameras and AI, will open up opportunities for the automation of processes that otherwise need humans to examine and decipher objects in the real world visually. The use cases can include analytics, streamlining document workflows, and digitizing the physical objects of operations. It is forecasted that the computer vision market will reach $41.11 billion by the year 2030, at an annual growth rate (CAGR) of 16.0% between the years 2020 and 2030.


Democratized AI

The involvement of subject matter experts in the development of AI helps reduce the need for high-level AI expertise. This helps accelerate AI development and enhance accuracy in results. The benefit of the democratization of AI is the shift in the use of technology and data from a few AI experts to wider user groups across businesses and society. 


Removal of ML Bias

One of the challenges coming from the wider adoption of AI is ML bias, which needs to be addressed to warrant that the AI predictions are accurate so that people won’t be discriminated against during various transactions like loan applications, online shopping, and medical treatment. Mitigation of ML bias will be crucial for businesses to ensure credence in their ML products and this will be one of the key challenges CIOs will face in the coming years. 


Intelligent Digital Twins

With the convergence of traditional industrial simulations and AI, digital twins are now used to model and simulate human behaviors and evaluate alternative scenarios. Digital twins will also become a critical technology for organizations to predict disease progression, economic changes, consumer behavior, design ESG modeling, and smart cities, among others. A recent study estimated the market would reach $90 billion by 2032, an increase of 25% from 2023.



Many core AI technologies like language models, multimodal machine learning, and transformers, will attract considerable interest and gain importance in the near future. This will make standardized software and devices that organizations use daily smarter. They are, however, not free from the likelihood of ethical questions arising from the use of AI and ML in complex use cases, which includes concerns around data governance, bias, and transparency. Ideal model validation and audit frameworks will be in demand as the standard practice to prevent ethical gaps in the adoption of these technologies in the future.

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