From GenAI to Agentic AI: Build your roadmap for Industrial transformation
For the last 18 months, ‘Generative AI’ has dominated every C-level strategy session. But what if that’s already the old conversation?
In the energy and industry sectors, the discussion is rapidly shifting from GenAI to Agentic AI. This isn’t just an incremental update; it’s a new paradigm, one that moves from simply augmenting tasks to completely reinventing core industrial processes.
For leaders, the question is now how to build a roadmap for this new transformation.
This was the central theme of our recent exclusive C-level gathering in Houston, “Shaping AI strategy in Energy & Industry”. The consensus in the room was clear: 92% of energy executives believe organizations that embrace AI will develop a competitive edge , tapping into an estimated $390B-$550B in value creation in the coming years.
The stakes are high, and leaders are already making bold moves. We see companies like ExxonMobil pioneering autonomous drilling , BP leveraging digital twins for maintenance , and Chevron using predictive AI and drones.
But this is just the beginning.
Why the Energy sector is ready for an AI revolution
Based on our work, we’ve identified five key ” fueling factors” that make this industry the perfect candidate for AI adoption:
The new AI lexicon: From GenAI to AgenticAI
To capture this change and its value, it’s crucial to understand the technology’s evolution.
– Traditional AI uses data to interpret, analyze, and predict (like forecasting equipment failure).
– GenAI leverages existing data to produce entirely novel content (generating a safety risk assessment for a new procedure).
– And now, a new concept emerged: AI Agents. So, what is an AI Agent? It’s a system that can perceive its environment, reason to make decisions, and act to complete tasks.
AI is about use caes. AgenticAI is about process reinvention.
While GenAI helps an engineer perform a task (like analyzing a report), an AI Agent can orchestrate an entire workflow. It can detect a pipeline anomaly, notify the correct team, and generate a work order, all while interacting with multiple legacy systems.
From theory to Industrial impact
That is why I believe we’re on the verge of an entirely new data paradigm. These AI assistants don’t simply store information in silos, they synthesize it. They connect the tone of our messages with our past decisions, our creative drafts with our browsing patterns, our voice with our heart rate. The boundary between the advertising environment and the human environment starts to dissolve.
From Targeting to Understanding
We are already implementing these innovative GenAI and Agentic solutions for leading energy and industrial companies. Here are a few real-world examples:
- GenAI for Process Safety: For a seamless tube manufacturer, we built a GenAI tool to create more complete and reliable Risk Assessments. Current RA data is often siloed and incomplete. Our solution harmonizes RA grids and uses cross-site learning to suggest missing risks. The results: +15% missing tasks and +37% missing risks were identified, with 92% validated as relevant by HSE teams.
- GenAI for Process Engineering: We developed a tool that uses GenAI to digitize, interpret, and automate Piping and Instrumentation Diagrams workflows. This solution successfully extracted structured data from over 30 P&IDs and accurately answered complex technical questions , accelerating a process that was slow and prone to human error
- Agentic AI for Operations: To tackle complex fiber outages, we built an Agentic tool to accelerate resolution. Operators were struggling with tickets that could exceed 400 lines of comments. The AI Agent now automates ticket analysis , summarizing intricate cases and spotlighting key information. This led to a 75% reduction in ticket analysis time and potential yearly savings of around $5M.
- Upskilling the Workforce: Technology alone isn’t enough. For a global E&P leader, we helped create momentum on GenAI through a hackathon, targeted masterclasses, prompt challenges, and functional workshops. By focusing on real-life use cases, like summarizing market trends or preparing for a trading committee, we achieved a 94% participation rate and 91% satisfaction.
How to start or accelerate your AI journey
Every organization is at a different place on its AI maturity journey. The right next step depends on where you are today.
The leaders who start mapping their processes and building their roadmaps today will be the ones who redefine efficiency, safety, and value in the years to come.
Discover our expertise in Energy & Industry sectors here
Contact our experts team:
– Ghadi Hobeika, Managing Partner, CEO Artefact North America
– Julien Stalla-Bourdillon, Data & AI Partner, Industry Lead
– Antoine Peix, Data & AI consulting Director
– Alexandre Lachkar, Senior Data & AI Manager
– Juliette Campbell, Data & AI Consultant
