GenerationAI Paris 2025 | How Agentic AI is Reinventing Organizations

Is your organization ready for the shift from "tools" to "agents"?

While traditional AI follows scripts, Agentic AI introduces autonomous, goal-driven systems that plan, act, and collaborate with humans in real time. The presentation, led by Geoffroy Petit (Partner, Data & AI at BearingPoint), explores why Agentic AI marks a watershed moment for the modern enterprise.

About the Speaker: Geoffroy Petit is a Partner at BearingPoint and lead of the Data & AI community in France. He specializes in helping global organizations harness Generative AI to streamline operations and drive meaningful digital transformation.

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Conference Details:
Conference: GenerationAI Paris 2025, part of FOST (Future of Software Technologies)
Theme: Enterprise GenAI-readiness with the API mindset
Date: 1 - 3 December 2026 • CNIT Forest – Paris
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Transcript

1. How Agentic AI is reinventing organizations Generation AI
2. © BEARINGPOINT 2
3. © BEARINGPOINT 3 A disruptive, unprecedented and accelerating shift in the workforce
4. © BEARINGPOINT 4 Agent/Human Collaboration is at the Heart of GenAI Transformation ✓ Simplified customer journeys ✓ Redesigned processes to optimize the respective added value of employees and agents. ✓ A revised operating model to avoid overcapacity. ✓ Democratize AI fluency for smooth collaboration Vision 2030: a symbiosis of people + AI for performance
5. © BEARINGPOINT 5 • Loss of control and fear of professional devaluation • Lack of understanding and feeling incompetent • Distrust in reliability and compliance • Damage to collaboration and collective dynamics (1) Recognize & address concerns
6. © BEARINGPOINT 6 (2) Design future workforce scenario Task (greatly shortened)​ Impact of GenAI How AI will change the task? Future Focus Area Setup and Maintenance of Development and Test Environments High​ The setup and maintenance of development and test environments can be largely automated using automation tools based on generative AI, including the configuration of software and hardware as well as the simulation of test data. • Business alignment & high-level solution modelling • AI-orchestrated development & code review expertise • Supervision of automated pipelines & quality gates • Test strategy design & supervision of AI-generated tests • Advanced diagnostics, validation of AI-proposed fixes • Knowledge curation & lifecycle governance Creation and Updating of Documentation for the Software Lifecycle High​ The creation and updating of documentation can be strongly supported by generative AI, as it can automatically generate documents from code and test results and update existing documents. However, quality assurance and final review should be performed by humans. Development and Implementation of Software Applications and Interfaces High​ Generative AI can have a significant impact on the development and implementation of software applications and interfaces by generating code, suggesting design patterns, and helping with code optimization. Nevertheless, human oversight is necessary for quality assurance and fine-tuning. Modeling of Data, Functions, and Objects for Software Projects High​ Generative AI can effectively assist in modeling data, functions, and objects by providing templates and patterns. It can also work interactively with developers to refine models. However, the final decision and review of the models require human expertise. Support for Integration Testing and Maintenance of Software Applications Medium​ Generative AI can support integration testing by generating test cases and helping diagnose issues. For maintenance, it can suggest optimizations. However, final assessment and decision-making require human expertise. Analysis of Business Processes and Identification of System Requirements Medium​ Generative AI can assist in analyzing business processes by recognizing patterns and relationships in data and generating suggestions for system requirements. However, human expertise and contextual understanding are necessary to assess the relevance and accuracy of the requirements. Analysis and Correction of Software Errors and Processing of Change Requests Medium​ Generative AI can help analyze software errors by analyzing log files and suggesting possible causes. It can support the correction of errors and the processing of change requests, but the final solution and implementation of changes require human expertise.
7. © BEARINGPOINT 7 (3) Adopt progressive embedding strategies Design Agent/Robot Create the Agent Onboarding and Continuous Improvement Identify opportunities 1. Instantiating the identity and role of the agent 2. Connect to Data and Tools (MCP) 3. Define Agent-to-Agent triggers and flows 4. Define decision-making guidelines for agents 5. Test with process and data experts 1 2 3 4 5 Define "what" the agent will do • Scope of responsibility • Inputs, Outputs • Performance ✓ Friction analysis: support, network, operations, sales ✓ Identification of repetitive, constrained, non-scalable tasks ✓ Detection of potential gains: reduction of OPEX, QoS, time- to-resolution ✓ Prioritization via a common framework (impact × feasibility × risk) Target areas where AI can generate a rapid and measurable impact. Precisely define the role, interactions, and business value of each agent. Instantiate an operational agent, connected to data and capable of acting according to a controlled framework. integrate the agent into real processes and drive their improvement over time. Define "how" the agent will do • Technology Platform • Business & Ethics Guidelines • Interaction with employees / agents / systems • Risk & Mitigation
8. © BEARINGPOINT 8 Addressing organizational transformation in GenAI Recognize & Address Concerns Design future workforce scenario Adopt progressive embedding strategies