Most SMBs don’t need another AI tool. They need AI to become part of how work actually gets done.
I’ve been thinking about one question for a long time: how can smaller organizations move beyond demos and isolated technical experiments—and turn AI into a capability their own teams can operate and improve?
The Forward Deployed Engineer (FDE) model has proved highly effective in large enterprises. Engineers embed with customer teams, understand the business context, and build against real workflows.
But many SMBs don’t have the conditions the classic model often assumes: ready data, clear processes, governance frameworks, internal AI talent, or enterprise-sized budgets. As a result, even promising pilots can stall. They work as demonstrations, but never become secure, measurable, team-owned business capabilities.
That’s why I’m building FFDE.ai — Fortified Forward Deployed Engineering for SMBs.
FFDE builds on the FDE concept and adds the capabilities SMBs most often need around the core engineering work:
- sharper use-case selection and prioritization;
- data, knowledge, and tool integration;
- security and governance boundaries;
- cost visibility and evaluation;
- training, operating routines, and a clean handover.
The goal isn’t another impressive demo.
It’s practical AI capability embedded in workflows that fit the business, generate evidence of value, and can be run and continuously improved by the client’s own team.
In other words: AI Freedom—the ability to choose, use, govern, and grow AI without permanent dependence on external experts.
FFDE.ai is still early, and I’m building it in public.
If you’re leading AI adoption in an SMB—or have seen FDE-style delivery succeed or fail—I’d genuinely value your perspective:
- Where does AI adoption usually get stuck: use-case selection, data, governance, cost, or team ownership?