Intelligent automation is entering a new phase with agentic AI. By combining the execution capabilities of automation with the reasoning of generative AI, it enables autonomous agents to act directly at the core of business processes. Organizations expect three major benefits: cost reduction, faster turnaround times, and improved service quality.
Led by Simon LUSINCHI (Partner at Artefact) and Marie-Astrid de Foucauld (AI Ecosystem Manager at OUTSCALE) during the OUTSCALE EXPERIENCES event, this session analyzes the major obstacles related to deploying agentic AI in companies. It highlights the importance of going beyond a purely technological vision to jointly orchestrate human, business, and technical levers, which are key to sustainable transformation.
Understanding the stakes of agentic AI
The emergence of agentic AI is part of the ongoing evolution of technological uses, following RPA automation, traditional analytical AI, and then generative AI. Characterized by its ability to reason and chain actions, this new form of AI marks a turning point: it no longer limits itself to content generation, but decides, acts, and adapts based on its context.
This technological shift fundamentally transforms how projects are approached. The objective is no longer simply to optimize the time allocated to an isolated task, but to rethink and transform the overall functioning of an entire process.
Three methods to get started
To initiate an agentic transformation, organizations can choose between three methods:
- Global strategy: map all processes, quantify potential value, and prioritize use cases to build an enterprise-wide agentic roadmap.
- Function-based approach: first target a cross-functional area (HR, legal, marketing, administrative) and identify processes to optimize with agents.
- Targeted pilots (PoCs): launch pilots on process fragments to build a methodology and quickly demonstrate value (e.g., commitment management).
A progression of benefits
The deployment of agentic AI can be understood across three levels of maturity: individual use, mutualization, and end-to-end autonomy. At each level, ROI becomes clearer, more collective, and more strategic.
Individual level : An assistant automates specific tasks for a person or function. The gain is real but remains diffuse, as it mainly improves local productivity.
Mutualization level : An agent orchestrates multiple actions around a common objective, serving a team or shared workflow. The value created becomes collective and easier to measure in operations.
Autonomous level : The agent takes charge of an entire business process end-to-end. This is where the impact becomes systemic, with performance indicators focused on execution speed, quality, cost, and scalability.
A systemic approach to transformation
To avoid the common mistake of trying to deploy artificial intelligence systematically, Artefact structures its approach around a global vision. Very often, the ideal solution does not lie in integrating an autonomous agent, but rather in simplifying automation or restructuring an existing process.
This multidimensional approach is structured around three strategic axes:
- The human dimension: defining a global vision, building the internal narrative, setting appropriate safeguards, and continuously developing skills.
- The business dimension: relying on precise process mapping to determine whether the added value will truly come from AI, an RPA solution, or simple organizational optimization.
- The technological dimension: focusing on rigorous data management, robust architecture, and the establishment of clear governance.
Data governance and security requirements
The effectiveness of an AI agent is closely tied to the quality of its data architecture. To be fully usable, information must follow a rigorous process of collection, cleaning, structuring, and classification. However, it is crucial to avoid paralyzing the system through excessive classification into “confidential” or “restricted” categories, which could block access to useful data and significantly degrade overall performance.
At the same time, security is a fundamental pillar of the approach. Before considering the deployment of an agentic solution, any organization must anticipate and formalize its requirements in terms of network constraints, hosting conditions, integration protocols, and access rules. Beyond purely technical aspects, this reflection represents a true challenge of trust and overall governance.
Selecting the right solution
To address different levels of maturity and organizational constraints, OUTSCALE offers three complementary approaches based on the desired level of autonomy.
- GPU as a Service is aimed at highly technical profiles seeking full control over infrastructure and models.
- LLM as a Service enables rapid deployment of a private API within a sovereign and secure framework.
- OUTSCALE offers a turnkey software solution designed to be accessible to non-technical users and to accelerate large-scale adoption.
OUTSCALE also relies on an ecosystem of cloud and consulting partners, including Artefact, to support clients at every stage of the project. This support covers initial assessment, use case scoping, deployment, and ongoing optimization of usage and performance.
Key Takeaways
1. Agentic AI fundamentally changes how processes are executed. It no longer simply assists users: it acts, orchestrates, and can make certain decisions within a defined framework. This requires organizations to rethink business processes as a whole, rather than automating only isolated tasks.
2. Success relies on a truly cross-functional approach. Three complementary dimensions must be aligned: the human dimension, with adoption and upskilling; the business dimension, with the selection of the most relevant use cases; and the technological dimension, with data quality, architecture, and integration. The challenge is therefore not only technical, but also organizational and operational.
3. The main obstacles relate to data and governance. Data quality, accessibility, and security directly determine agent performance. Choosing solutions adapted to the organization’s level of maturity is also critical to delivering reliable, measurable, and sustainable results.
