Scan, prioritize, and trial assistant-style generative AI use cases with practical guardrails and playbooks for safe learning.
A practical, stepwise scan that surfaces high‑value use cases for generative AI assistants, converts signals into testable research questions, describes repeatable trial patterns and safe guardrails, and provides a quick decision checklist plus scaling criteria for discovery teams.
An interactive prioritization canvas to capture, score, and pilot assistant- and agent-style generative AI opportunities. Includes fields for scope, inputs, outputs, data and safety guardrails, feasibility notes, a pilot-readiness checklist, a starter prompt library, and simple scoring guidance to help teams choose high-impact, low-risk pilots.
A practical, step-by-step playbook to scope, run, evaluate, and scale safe, measurable trials of generative assistants and agents. Includes a ready-to-use trial plan template, evaluation rubrics (accuracy, safety, usefulness, business impact), monitoring and logging guidance, privacy safeguards, sample prompts, and governance checklist.
A practical, safety-first playbook to evaluate, prototype, and scale lightweight agents and automation pipelines for discovery workflows. Includes use-case selection criteria, safe experiment patterns, monitoring and observability checklists, minimal orchestration blueprints, evaluator metrics, and starter templates for hypotheses and runbooks.