Facing tomorrow’s challenges today.

To respond effectively to today’s uncertain business conditions, organizations need solutions to tackle current and future challenges, while continuing to drive innovation and competitiveness. Agentic artificial intelligence (AI) represents just such a solution. This advanced form of AI is focused on autonomous decision-making and action, promising greater benefits than traditional AI.

Driving sustainability and operational efficiency with agentic AI

In our latest point of view developed in collaboration with Microsoft, we lay out how agentic AI can help organizations reduce waste, optimize processes, and embed sustainability in operations at scale.

A breakthrough in business efficiency through agentic AI

Agentic AI can actively drive better business outcomes, thanks to its ability to process information, learn across interactions, and act autonomously to achieve objectives. When implemented across end-to-end workflows, agentic AI enables better resource allocation, automated sustainability reporting, and real-time emissions tracking. By effectively leveraging these capabilities, organizations can promote resilience and optimize operations, while strengthening governance structures and social responsibility commitments.

From automation to autonomy: redefining operational excellence

AI has unfolded in waves, each expanding what machines can do. Early AI systems were designed for precision and repeatability within narrowly defined tasks. Expert systems in medicine, for example, applied symbolic logic to diagnose diseases based on predefined rules, while early spam filters relied on supervised learning to classify emails. These approaches excelled in stable environments but faltered when conditions changed, requiring human retraining and oversight.

The emergence of generative AI broke through these boundaries. Flexible, able to draw on large data sets, and requiring less expert human direction, generative AI was the next step towards autonomous action in AI. By training large language models (LLMs) on massive datasets, developers enabled machines to create original outputs – text, images, or code – through natural language interfaces that human managers could easily understand. However, by itself, generative AI remains fundamentally reactive and goal-agnostic. It can mimic knowledge and generate convincing outputs, but it lacks capabilities in long-term planning and business objective alignment.

Agentic AI emerges as a powerful enabler. The building blocks of agentic AI are AI agents: intelligent software systems that perceive their environment, reason, and act autonomously to achieve objectives. Equipped with persistent memory, the ability to harness other types of AI and automation, and self-reflection, agents adapt across interactions and coordinate with humans or other agents to undertake complex, end-to-end workflows.

Achieving sustainability goals with agentic AI

Although organizations across sectors are eager to leverage the benefits of AI, they are concerned about its potential environmental impacts. However, agentic AI can actually strengthen organizations’ sustainability ambitions. Through better resource allocation, it can enable greater efficiency, productivity, and environmental performance. With key partnerships and a comprehensive human-AI framework, agentic AI can help organizations reimagine core operations, benefiting both business outcomes and the environment.