The term ‘Composite AI’ might be new to everyone, but the concept is similar to AI, i.e., the combination of various AI techniques to achieve the best results. A composite AI includes packaged business capabilities that enable users to be flexible & adaptable amidst rapidly varying requirements & systems. A simple machine learning algorithm is sufficient to solve a problem that is straightforward. However, in the case of a complex problem without any disruption, a combination of different AI techniques is required.

Selecting the right AI technology and technique to solve a problem is not always easy. Once the problem is identified & defined, i.e., a crucial part of composite AI, it becomes easier to select which technique should solve that problem. In view of the growing demand for AI-based cognitive solutions and the burgeoning need for evaluating complex data sets in data-sensitive companies, a recent research report by MarkNtel Advisors has revealed that the Global Composite AI Market is likely to grow at a CAGR of 35% during 2023-28.

The Working of Composite AI and its Ever-Growing Importance

Composite AI is not a pre-decided cluster of techniques, which is why businesses can create their own analytical methods as per their requirements & problems. For example, for improving customer experience, a composite AI approach would involve several ML algorithms, NLP, & descriptive statistics, and the key will be to integrate these diverse methods to generate value consistently.

In addition, enterprises can also utilize composite AI platforms that take business data, generate insights, provide customization of different analytical techniques, & learn from feedback. Since it's a new approach, a limited number of such platforms are available presently. However, the world is likely to witness more such techniques & approaches integrated with improved efficiency & features in the future.

AI (Artificial intelligence) has opened new potentials for businesses with a wide range of use cases across different sectors. In order to attain the best results in solving complex problems, enterprises often use a combination of different AI & other analytical techniques. Composite AI has emerged as a promising technique that integrates different AI technologies into a single solution in a systematic manner to tackle complex business problems.

As AI adoption is rapidly increasing across industries, standalone approaches can fall short of solving complex issues. In addition, successful AI applications need large datasets that are not always available. Hence, composite AI is emerging as a unified approach to different analytical techniques that can help companies address complex problems and minimize costs by facilitating human-like decision-making, reducing the need for large data science teams, and enabling users to gain insights from small datasets.

Conclusion

The rapid growth of the Composite AI industry has opened up numerous possibilities for companies to leverage advanced AI techniques with benefits like combining ML, image recognition, deep learning, NLP, and essentially no limitations in dataset size. Rather than focusing on one particular AI technique, companies should gradually develop their composite AI strategy based on the problem(s) they are most willing to solve.

As business needs change & evolve, a wide range of AI techniques shall become readily available worldwide. By integrating the most relevant combination of AI techniques to solve a specific problem and continuously deploying models & decision flows to generate value, companies can yield the benefits of composite AI and realistically tackle their most complex challenges.