I’ve been working with my teams on AI since 2017. Now that the real challenges of moving LLM-based systems into production are surfacing, I wanted to share a few thoughts on why this transition is anything but automatic.
While the "demo" feels like magic, the reality of 24/7 operations requires a shift in mindset: Business first, technology second. Without an updated business plan and sound architecture, costs can spiral quickly on a monthly basis, even while the delivered value remains flat.
The move to production is far from automatic for two critical reasons:
a) The SaaS Illusion: unlike traditional software, AI doesn’t follow a flat-fee model. Costs can spiral monthly, with significant incremental costs per user that can eat margins if not carefully architected.
b) The Complexity Gap: building a "cool" prototype is fast; maintaining a robust, reliable, and secure AI system 24/7 requires a specialized skill set that goes way beyond the initial prompt engineering.
For a PME, the goal isn't just to have AI—it's to have a sustainable, profitable business fueled by it. To bridge this gap, the focus must shift to re-engineering the business model for a premium, sustainable experience.
Success in production requires a sound architecture that prioritizes efficiency over hype:
a) Solve the "Tough Situations". Deploy AI where it tackles high-value, complex problems that humans find bottlenecked or repetitive.
b) Focus on reusability – build modular systems with AI first, then ask yourself if each module must be done with AI or not.
c) Own the Intelligence – training your own models can drastically reduce per-user costs.
d) Look for alternatives – keep an eye on evolving partners; telcos, for example, are quickly becoming key AI actors (probably this will be one of the key annoucements @ MWC).
The shift from pilot to production is a business transformation. Start from here.
What’s your view on this? Are you seeing the same "SaaS illusion" in your own projects
?