ProfitComm • 4–5 min read
Outcome first
Anchor every candidate to a business result you can measure in 90–120 days. Write one sentence that names the metric, target, timeframe, and team: “Cut average support response from six hours to one hour in 60 days for Tier-1 email.” If you can’t state it cleanly, the scope isn’t ready.
Where the first wins live
Decide and implement
Pick with speed, not committees. Give each idea a quick read on business value, feasibility, time-to-impact, and risk/change; choose the top two to pilot. Before you build, confirm the data path: inputs exist, least-privilege access is in place, and activity can be logged and retained. Implement the lightest solution that works and then start with augmentation (routing, summarizing, drafting, retrieval) stitched into a small workflow. Keep ROI math plain: items per month × minutes saved × adoption rate = hours saved; convert to dollars with a blended rate. For revenue, compare baseline and post-AI conversion at typical deal values, then subtract setup and run costs.
Prove and scale
Run a 30–60–90 rhythm. In 30 days, benchmark against the baseline with one team. By 60, expand users or hours and fix quality gaps. By 90, lock the workflow, publish results, and decide to scale, iterate, or retire. Assign a business owner for the metric and an operator for the workflow. Build guardrails into the design where outcomes affect customers or compliance, clear data boundaries, logging, retention, and fallbacks when confidence is low. Use the NIST AI Risk Management Framework (Govern, Map, Measure, Manage) to keep responsibilities explicit without slowing delivery (National Institute of Standards and Technology [NIST], 2023).
Good starting points: support triage with reply drafting to lift first response and cut handle time; a sales follow-up copilot to summarize calls, update CRM, and propose next steps; permission-aware search over SOPs and policies to reduce escalations and time spent hunting for documents.
Takeaway: pick a problem that matters, confirm the data path, use the smallest solution that moves one metric, prove it in weeks, then expand the same workflow to more volume.
References
National Institute of Standards and Technology. (2023). AI Risk Management Framework (AI RMF 1.0).https://www.nist.gov/itl/ai-risk-management-framework