1. Choose a first use case worth doing
Start with work that is frequent, repeatable, owned by someone in the business, supported by accessible inputs, and assessable at the end. Do not make “company-wide AI transformation” the first scope.
How should SMBs choose their first AI use case?
2. Establish operating conditions first
Before connecting a model, confirm the trusted source of information, inputs and outputs, business rules, exceptions, and owner. If a workflow cannot be described, AI will only scale inconsistent habits.
3. Validate a minimum pilot
Give the pilot a bounded scope, pass line, and human fallback. Use real samples to define evaluation; record exceptions, human rewrite rate, and safety boundaries. The outcome should be a credible decision to expand or stop.
How long does an AI Agent MVP pilot take?
4. Accept business outcomes, not model output
Acceptance is not “the model answered a question.” Define improvements in efficiency, quality, cycle time, cost, or knowledge assets, and verify that data, security, workflow, and operations can be handed over.
How do you accept an AI Agent pilot?
5. Turn the pilot into an operable capability
After acceptance, name an owner for knowledge updates, exceptions, permissions, cost, and daily operations. Without this, a successful pilot decays as information and people change.
How do you run Agent day-to-day operations? · How do you control token costs?
What is FFDE's role?
FFDE works with SMBs to connect use case, data, workflow, security, acceptance, and handoff. A fit begins when a business owner is willing to own one concrete scenario and define what success means.