U.S. Chamber CO — Small Business AI Guide
TL;DR
- Define goals, pick a pilot, measure outcomes, train teams.
- Set basic governance and data practices.
- Iterate and scale what works.
Highlights
- A practical playbook for adopting AI with benefits, risks, and checkpoints, designed for small teams.
- Focus on business outcomes over tools: tie pilots to KPIs like response time, qualified meetings, CPA/ROAS, and AOV.
- Establish lightweight governance early (human‑in‑the‑loop, QA checklists, privacy practices) to reduce rework and risk.
- Training and enablement matter: prompt libraries, examples, and office hours raise consistency across staff.
- Scale with a rolling roadmap—sunset experiments that miss targets and double‑down on proven workflows.
Case study anecdote
A wellness studio documented an intake → triage → follow‑up flow. They created a short prompt sheet for common scenarios and a two‑step QA (facts and tone). Within four weeks, first‑response time fell, no‑shows dropped, and the owner recovered several hours per week from manual inbox work. The team used the time to run educational posts and loyalty offers, which stabilized repeat bookings during slower months.
Guidance for SMBs
- Pick one workflow with weekly cadence (e.g., campaign brief → draft → review → schedule). Capture a baseline for cycle time and error rate.
- Define 1–2 KPIs tied to that flow (first‑response, qualified meetings, AOV, CPA). Publish the target and review weekly.
- Create a one‑page prompt playbook (inputs, tone, length, examples) plus a short QA checklist to keep outputs consistent.
- Assign owners and backup owners; run a 15‑minute weekly retro to prune steps and record learnings.
Lessons & metrics
- Leading indicators move first: response time, content cadence, edit time.
- Documented prompts cut rework and handoffs; QA reduces errors while preserving speed.
- A simple dashboard and clear owners keep momentum as additional workflows are added.
Learn more
Read the full article: CO — Small Business AI Guide
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