Score workflow readiness before tool readiness
A 100-person software implementation partner usually has many AI candidates: project-status notes, ticket triage, configuration documentation, knowledge search, quote support, and client onboarding. RSM middle-market AI survey and San Francisco Fed analysis of AI and small businesses are useful context because smaller and middle-market companies need practical adoption tied to everyday work.
The first readiness question is whether the workflow has stable sources, a named owner, measurable outcomes, and a review point. If those are missing, AI will expose operating gaps instead of solving them.
Use the eight-dimension readiness assessment to score the first candidates.
Protect client and implementation data
CISA AI Data Security Best Practices matters because implementation partners handle client configurations, tickets, data mappings, timelines, access notes, and sometimes sensitive operational information. The workflow should respect client permissions, data boundaries, and retention rules.
NIST AI Risk Management Framework gives the governance model: define the context, measure risk, and manage controls. In practice, that means approved sources, reviewer roles, output logs, and an exception path before any AI workflow reaches client-facing use.
Start with an internal workflow before exposing AI-assisted outputs to clients.
Move from assessment to a 90-day proof
Deloitte State of AI in the Enterprise 2026 reinforces the importance of getting AI out of experiments and into managed production. For a 100-person implementation partner, the first proof should be narrow enough to launch and measure within 90 days.
Good candidates include project-status reporting, service desk escalation, implementation QA packets, and internal knowledge search. Measure cycle time, rework, adoption, and review burden.
Use the 90-day implementation plan to convert readiness into a governed first workflow.
