A short research playbook using data analysis, process mapping, and stakeholder interviews to surface non-obvious AI use cases.
Find hidden AI opportunities that actually matter. If your team wants breakthroughs instead of incremental ideas, this short research playbook helps you discover non-obvious, testable AI opportunities in a domain without getting lost in speculative brainstorming. What this resource helps you do: run a compact...
A practical, timeboxed playbook to surface non-obvious AI use cases using short stakeholder interviews, a focused process-mapping checklist, light data probes, and a simple scoring template to prioritize candidates.
An interactive, role-aware interview workbook and analysis template: pre-interview consent and checklist, structured question sets, free-text notes, numeric ratings, taggable affinity-mapping fields, post-interview summary, and suggested follow-ups. Saves each interview as structured JSON for comparison, prioritization, and later analysis.
A practical, reusable set of experiment designs, pre-analysis planning guidance, sample-size guidance, randomization and guardrail checklists, and a ready-to-copy A/B experiment template to validate AI pilots and other changes with rigorous evidence.
A practical, step-by-step playbook to design disciplined AI pilots: frame testable hypotheses, define minimal datasets, create an experiment protocol, map success criteria, build a measurement plan, run controlled validation, and use decision gates and handoff checklists to move confidently from pilot to operation.
Interactive experiment protocol and A/B template: hypothesis framing, primary-metric definitions, sample-size guidance, logging & instrumentation checklist, guardrail metrics, analysis plan, and result-interpretation checklist. Saves protocols for reproducibility and handoff.