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.
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. 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.
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All Replies （3） #
G
Probably missing the legacy data silos. Hard to automate when the source data is a mess.
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L
did they even try RAG or just straight fine-tuning? usually where it falls apart.
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C
Seen this before with old government databases; you can't automate a broken manual process.
0