Quebec Public Sector: Why AI Projects are Being Scrapped

Government-led AI initiatives in Quebec are hitting a wall, with several automation projects being scrapped across the public sector. It's a stark reminder that throwing LLMs at a bureaucratic problem doesn't automatically equal efficiency.

When you look at why these deployments fail, it usually comes down to a few recurring technical and operational bottlenecks:

• Data Silos: Public sector data is notoriously fragmented. Trying to build an AI workflow on top of legacy databases that don't talk to each other is a nightmare.
• Integration Friction: Moving from a "cool demo" to a real-world production environment in government often fails because the existing infrastructure can't handle the API overhead or the security requirements.
• Lack of Specialized Talent: Without deep expertise in prompt engineering or LLM agent orchestration, these projects often drift into "feature creep" without ever delivering a tangible ROI.

For those of us building in the private sector, this is a lesson in starting small. Instead of massive, sweeping automation overhauls, the move should be toward modular, beginner-friendly tools that solve one specific friction point at a time.

If you're designing an AI workflow for a large organization, focus on the data pipeline first. If the data is messy, the most expensive model in the world won't save the project.