Step-by-step guidance, templates, and scorecards to design pilots, evaluate vendors objectively, write practical contracts, and operationalize winners.
Interactive checklist and scorecard for evaluating AI vendors and third-party models. Includes structured fields for technical fit, model provenance, security, data handling, SLAs, liability/indemnity, exit/portability, sandbox testing, weighted scoring, and a final recommendation.
A practical, fillable pilot plan template with clear objective and hypothesis framing, measurable success metrics and SLAs, dataset and sampling guidance, user acceptance criteria, monitoring and rollback rules, vendor evaluation checkpoints, and an actionable scaling and handoff checklist. Includes suggested experiment durations and sample-size guidance for common business scenarios.
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.
An interactive, saveable vendor evaluation form that standardizes scoring across capabilities, security, data handling, SLAs, and commercial terms; includes a contract-clause checklist and space for red-line requests and negotiation notes.