Artificial intelligence (AI) has opened new capabilities for businesses with a diverse set of use cases across sectors. Here, we explain Composite AI, its importance, and how it works.
What is Composite AI?
Composite AI is an advanced form of AI for integrating different AI technologies into a single solution in a systematic way to approach complex business problems holistically. Instead of relying on a single AI model, composite AI aims to solve diverse business problems by combining multiple analytical techniques such as:
- Machine learning / Deep Learning
- Natural language processing (NLP)
- Computer vision (CV)
- Descriptive statistics
- Knowledge graphs
Why is composite AI important now?
AI adoption in business is increasing, but standalone approaches can fall short in solving complex business problems. Moreover, successful AI applications require large datasets which are not always available.
A unified approach to different analytical techniques can help businesses solve complex problems and reduce costs by:
- enabling human-like decision making
- reducing the necessity of large data science teams
- allowing users to gain insights from small datasets
Such benefits are why Composite AI is identified as one of the top emerging technologies with a high impact on business over the next several years. 1
How does composite AI work?
Composite AI is not a predetermined set of techniques. Businesses can create their own pipeline of analytical methods according to their business problems. For instance, if improving customer experience is your business problem, a composite AI approach would involve NLP, several ML algorithms, and descriptive statistics. The key point is to integrate these diverse methods to generate value consistently.
Alternatively, companies can use composite AI platforms that take business data, offer customization of different analytical techniques, generate insights, and learn from feedback. There is a limited number of such platforms since the approach is relatively new but we expect the market to grow over the next few years.
Composite AI FAQ
How does composite AI differ from traditional AI?
Traditional AI often focuses on using a single model or algorithm to solve a specific problem. Composite AI, on the other hand, combines multiple AI approaches, such as machine learning, natural language processing, computer vision, and symbolic reasoning, to deliver more comprehensive and accurate outcomes.
What are the main benefits of using composite AI?
Composite AI offers several benefits, including:
Enhanced versatility: It can address a wider range of challenges.
Increased accuracy: By integrating diverse methods, the system can produce more reliable and precise outputs.
Resilience: If one model underperforms, others can help maintain system performance.
Adaptability: It’s more flexible in handling multi-faceted data and evolving problems.
What industries can benefit from composite AI?
Composite AI can be applied across various industries, including:
Healthcare: For diagnostics that combine image analysis with patient history and genetic data.
Finance: To detect fraud using multiple data streams like transaction logs, behavioral analysis, and text mining.
Retail: Enhancing customer experience through predictive analytics, sentiment analysis, and recommendation systems.
Manufacturing: For predictive maintenance and real-time decision-making based on machine learning and sensor data.
How does composite AI improve data analysis?
By combining models that specialize in different aspects of data analysis (e.g., time-series forecasting and pattern recognition), composite AI can extract richer insights from diverse datasets. This multi-angle approach leads to deeper understanding and more informed decisions.
What does the process of building a composite AI system look like?
The process typically involves:
Defining the problem and setting goals.
Collecting and preparing data from multiple sources.
Selecting and integrating different AI models that complement each other.
Training and optimizing each model.
Merging outputs to provide the final, composite result.
Deploying the system and monitoring its performance.
Continuously updating and improving the system based on feedback and new data.
What challenges can arise with composite AI?
Integration complexity: Ensuring different AI models communicate and work well together can be technically demanding.
Data requirements: Composite systems often need varied data types, which can be difficult to source or synchronize.
Resource intensity: More components mean potentially higher computational and maintenance costs.
How does composite AI handle data privacy concerns?
Composite AI systems can be designed to follow strict data handling and privacy protocols. Using synthetic data and federated learning techniques, for example, can help mitigate data privacy risks while still enabling robust model training and analysis.
Is composite AI suitable for small businesses or just large enterprises?
While composite AI may require significant initial investment and expertise, it can be scaled to fit the needs of small to mid-sized businesses. Cloud-based platforms and pre-built composite AI solutions are becoming more accessible, making it easier for smaller organizations to leverage this technology.
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