Generative AI Assistants & Agents — Opportunity Scan

Practical guidance to find, prioritize, and responsibly trial assistant-style generative AI that augment people — not replace them.

Why this matters

Generative AI assistants and lightweight agents can accelerate routine work, reduce friction between teams, and unlock new service or product capabilities — but only when the right use cases, data foundations, and guardrails are in place. This resource helps you move from broad curiosity to concrete experiment design: find candidate opportunities, map risks, and run short, focused trials that produce clear learning.

Examples: a roofing contractor using an assistant to summarize site photos for estimates; a community health clinic testing a scheduling assistant to reduce no‑shows; a university research team using a drafting assistant to accelerate literature synthesis; a factory piloting an agent that collates machine logs and suggests inspection priorities.

What you'll understand, practice, and accomplish

Using the materials in this scan you will:

• Spot and frame assistant-style opportunities that amplify specific people or teams rather than broadly automate entire roles.
• Assess feasibility: data needs, integration touchpoints, latency and cost tradeoffs, and likely failure modes.
• Map ethical, safety, and compliance guardrails (hallucination risk, bias, PII handling, escalation paths).
• Prioritize candidates with a simple canvas so you invest in fewer, higher‑value experiments.
• Design and run short trials with measurable success criteria, observation checklists, and a decision agenda for next steps.

What's included and how to use it

This opportunity scan bundles concise, actionable assets that teams can use immediately: a one‑page brief for stakeholder alignment, a prioritization canvas to rank candidates, a trial playbook with step‑by‑step trial patterns, and a practical starter guide on agents and automation. Start by running a 30–90 minute discovery huddle to collect candidate tasks, then use the canvas to pick 1–3 pilots and follow the playbook to run short trials and collect evidence.

Platform affordances that support this work: copyable collections and toolkits you can adapt to your organization, and interactive forms to capture trial observations and outcomes as structured JSON for later analysis and reuse.