Artificial intelligence (AI) has opened new capabilities for businesses with a diverse set of use cases across sectors. 78% of organizations now use AI in at least one function 1
Composite AI integrates multiple AI techniques into unified solutions that can address complex business problems more effectively than any single approach. We explain composite AI, its importance, and how it works.
What is composite AI?
Instead of relying on a single AI model, composite AI aims to solve business problems by combining multiple analytical techniques, such as:
- Machine learning / Deep Learning
- Natural language processing (NLP)
- Computer vision (CV)
- Descriptive statistics
- Knowledge graphs
- Simulation and agent-based techniques
- Optimization techniques
Why is composite AI important now?
AI adoption in business is increasing, but standalone approaches can result in AI failing or falling short in solving complex business problems. Moreover, successful AI applications require large datasets of high quality, 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
Because of these advantages, composite AI is identified as one of the top emerging technologies with a high impact on businesses. 2
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. A composite AI workflow could involve:
1. Data ingestion & preprocessing
- Collect structured (databases, spreadsheets) and unstructured data (text, images, audio).
- Clean, normalize, and prepare data for multiple analytical methods.
- Example: A retailer pulling transaction logs, customer reviews, and product images into a unified data lake.
2. Technique selection & application
- Choose the most suitable AI/analytics methods for each data type and business need:
- NLP for sentiment analysis on customer reviews.
- Computer Vision for image-based quality control.
- Machine Learning for sales forecasting.
- Example: Applying NLP and ML in tandem to detect customer dissatisfaction early.
3. Orchestration & Integration
- Combine outputs from multiple models into a unified framework.
- Use knowledge graphs, rules engines, or decision intelligence systems to connect insights and resolve conflicts between models.
- Example: Linking customer sentiment scores with purchase patterns to adjust marketing campaigns automatically.
4. Insights delivery & action
- Present integrated insights through dashboards, APIs, or automated triggers.
- Feed results into downstream systems (CRM, ERP, marketing automation tools).
- Example: Triggering a customer retention workflow when sentiment and purchase frequency drop.
5. Continuous feedback & optimization
- Monitor model performance and update based on new data and user feedback.
- Automate retraining cycles where possible.
- Example: Adjusting recommendation algorithms as seasonal trends shift.
Vendor examples supporting composite AI
Alternatively, companies can utilize composite AI platforms that leverage business data, provide customization of various analytical techniques, generate actionable 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. However, the following examples can be given to these platforms:
- Dataiku : Supports hybrid AI workflows, enabling orchestration of diverse AI models within collaborative projects.
- IBM Watson Orchestrate : Integrates ML, NLP, and rule-based systems for business process automation.
- SAS Viya : Combines statistical analysis, ML, and decision intelligence tools in a single analytics platform.
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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