Implementation challenges: between technological complexity and organizational maturity.
The promise is great but the road to get there is just as demanding. Before reaping the benefits, organizations must navigate technological constraints, legacy systems, and cultural transformation. This journey requires vision, discipline, and adaptability.
1. Most companies aren’t “digital natives”.
Across industries from manufacturing to education and even in tech, systems are often heterogeneous, outdated, and poorly integrated. Data is scattered, poorly governed, or difficult to access. Yet, data is the foundation of any agentic AI system.
"Data is the foundation of any agentic AI system."
2. Regulated environments add additional complexity.
Banking, insurance, and healthcare are highly regulated sectors where compliance requirements can slow innovation. Agentic AI must operate within strict, transparent, and well-documented frameworks.
3. The cultural barrier: our relationship with error.
This is often the most underestimated challenge. We easily forgive human errors but we’re much less forgiving of errors made by technology.
Yet, every AI system, agentic or not, carries a margin of error. Each component introduces potential risk. Learning to manage, monitor, and continuously improve that risk requires a significant cultural shift.
Where to begin?
The goal should never be “to do agentic AI.” The goal is to improve a process and create true value. Achieving that requires a structured, thoughtful approach where each step builds on the last.
"Test, learn, and then add the next building block."
Here’s how to start your organization’s agentic AI journey:
1. Look beyond the buzzword.
Technology is an enabler. Start by understanding your needs, your current processes, and your business context. Only then should you assess how AI can drive improvement.
2. Master your internal processes.
An inefficient process doesn’t become better with AI. It actually becomes more inefficient faster and at scale. Without optimal internal processes, agentic AI can create more problems than it will solve.
3. Scale ambition gradually.
Don’t try to automate an entire process at once. Identify instead:
- One step in the workflow
- A specific decision block
- A small automation opportunity
- Possibly with initial human supervision
Test, learn, and then add the next building block. This is how robust agentic systems are built.
4. Don’t start with a customer interface.
Many think of AI agents that interact directly with customers but that’s rarely the best starting point. Begin internally, with employees or advisors “augmented” by AI. Make mistakes, validate rules, and stabilize the system behind the scenes before deploying it externally. The quick wins are often found in internal systems and processes.
Conclusion: advancing with pragmatism and ambition.
Agentic AI represents a tremendous opportunity for companies seeking to modernize operations, improve efficiency, and deliver superior customer experiences.
But success requires:
- A clear understanding of internal processes
- A realistic roadmap
- Thoughtful risk management
- Gradual evolution
- And a cultural shift in how we approach errors
Adopting agentic AI means building organizations that are more agile, intelligent, and human-centric. It’s about turning experimentation into impact, and vision into measurable results. And organizations that embrace this with pragmatism and ambition will secure a lasting competitive edge.
Author
Sarah Legendre Bilodeau, Première vice-présidente, Groupe conseil, intelligence artificielle – Videns, propulsée par COFOMO
This article was written with the help of Gemini and ChatGPT based on an interview recording with Sarah Legendre Bilodeau during the AI for Business event, presented by Les Affaires on November 19, 2025 in Montreal.
