Learn more – Title – How we can help
AI agents will transform the way businesses operate, leading to unprecedented levels of productivity, scalable efficiency and innovation.
According to our research, most organizations agree that AI agents promise smoother automation and enhanced productivity: AI agents will help drive higher levels of automation in our workflows (71%) or significantly improve customer service, leading to improved satisfaction (64%). Still organizations meet key challenges such as integration complexity, accountability, governance, and ethical concerns.
We help organizations navigate the complexities of implementing agentic AI solutions, bringing robust governance frameworks that allow compliance, scalability, and consistent performance. We also address strategic objectives such as:
Rapid prototyping and deployment
Accelerating AI agent rollouts with pre-configured workflows and optimized infrastructure, reducing time-to-market.
Seamless integration
Combining AI agent capabilities with existing business applications to unlock new levels of process automation, efficiency and data-driven decision-making.
Scalability and governance
The dedicated agentic capabilities of Capgemini RAISE, including governance, real-time monitoring and orchestration, enables unified control of agentic solutions with tangible results.
How we can help – Title – How we can help?
Our offer includes three possible implementation routes: Off-the-shelf agents for common tasks, custom AI agents tailored to specific processes, and embedded agents integrated into existing platforms. This flexibility ensures that our AI agents fulfill the unique requirements of your organization for optimal performance and scalability. In addition, our dedicated agentic gallery simplifies the procedure of developing AI agents from the ground up for each business process, resulting in significant time savings and cost reductions.
Partnering with NVIDIA, Capgemini is building over 100 bespoke AI agent-driven solutions tailored to various industry use cases, from life sciences, telco or financial services to manufacturing and retail.
Factories are transforming and becoming smarter through the introduction of powerful multi-agent AI systems.
Coinciding with this, education and knowledge became specialized as well – people were only trained on their individual part of a process. Eventually, the innovation of machinery introduced automated reactivity to factory processes. Machines could now use condition-based “if this, then that” actions to complete a task.
Unlocking new ways of understanding data in smart factories
A second example: if you are processing rubber in an extrusion line, the composition of the raw materials, their current mechanical and thermal characteristics, and the line parameters all influence the quality and speed of extrusion.
A lesson from history
In today’s factories, we are seeing the emergence of innovative multi-agent AI systems, which reflect the above themes in many ways, while also exhibiting some differences. In this blog, we’ll take a closer look at some of these new developments.
