Decision AI for smarter decision-making

• Multi-agent orchestration
• Generative AI
• Deep learning
• Evolutionary AI
• Trustworthy AI systems

Multi-agent orchestration is a foundational component of decision AI, enabling a network of specialized agents to interact not only with humans but also with each other. Every agent handles a distinct task, such as data generation or prediction, while communicating across domains to ensure decisions are optimized throughout the organization. This real-time interaction allows for adaptive, holistic AI systems that drive efficiency and innovation.

Core capabilities:

• Cross functional collaboration
• Flexible and scalable systems
• Optimized decision-making

Leveraging large language models (LLMs), it generates synthetic data, simulates potential outcomes and explores creative solutions that support decision AI and broader decision-making processes by offering new insights and possibilities. By modeling various scenarios and crafting data-driven strategies, generative AI empowers businesses to make proactive, well-informed decisions across industries, even in the absence of complete data.

Core capabilities:

• Synthetic data generation
• Scenario simulation
• Creative problem-solving

Deep learning is primarily used for prediction and processing large datasets through neural networks to identify patterns that traditional analytics might miss. By learning from data specific to the problem at hand, deep learning enhances decision AI by providing more accurate predictions and insights, enabling businesses to anticipate future trends and make more informed decisions.

Core capabilities:

• High-confidence predictions
• Continuous learning
• Data-driven insights

Evolutionary AI prescribes optimal strategies by mimicking the process of natural selection, retaining only the most effective or “fittest” strategies over time. It tests multiple solutions simultaneously, prescribing actions that meet conflicting objectives like performance, cost and efficiency. By adapting to updated predictor models, evolutionary AI ensures that decision AI models stay relevant and drive continuous business growth.

Core capabilities:

• Discovery of novel solutions
• Multi-objective optimization
• Continuous adaptation

Trustworthy AI is built on essential techniques that ensure decision AI outputs are reliable, explainable and transparent. These include uncertainty estimation to gauge confidence in predictions, explainability methods to clarify decision logic through rule sets, and interactive tools that allow users to explore variations and alternative solutions.

Core capabilities:

• Uncertainty estimation
• Explainability techniques
• Interactive exploration