GenAI is poised to make a significant economic impact, with estimates suggesting it could contribute between US$2.6 trillion and US$4.4 trillion annually to global GDP by 2030 across various sectors. The future of GenAI is agentic, where AI agents collaborate in real-time to automate complex tasks and enhance decision-making. This executive playbook explores how organisations can harness agentic AI to boost efficiency, improve customer experiences, and drive revenue growth.
Key aspects of agentic AI
Autonomy
Autonomy Agentic AI systems can operate independently, making decisions based on their programming, learning, and environmental inputs.
Goal-oriented behaviour
These AI agents are designed to pursue specific objectives, optimising their actions to achieve the desired outcomes.
Environment interaction
An agentic AI interacts with its surroundings, perceiving changes and adapting its strategies accordingly.
Learning capability
Many agentic AI systems employ machine learning or reinforcement learning techniques to improve their performance over time.
Workflow optimisation
Agentic AI agents enhance workflows and business processes by integrating language understanding with reasoning, planning, and decision making. This involves optimising resource allocation, improving communication and collaboration, and identifying automation opportunities.
Multi-agent and system conversation
Analysing governance and organisational structures and performing programmes and organisation performance reviews.
