Applied patterns, governance, and pilot practices for automation, RPA, and AI in operations.
An interactive canvas to evaluate, score, and prioritize AI and automation pilots using dimensions for value, feasibility, data readiness, risk, integration complexity, and change management. Includes fields for required inputs, default weighting guidance, and a recommended go/no-go decision field to capture outcomes.
A practical, step-by-step playbook to help operational teams find safe, high-impact AI and automation pilots, design human-in-the-loop workflows, establish governance, measure both model and operational outcomes, and scale pilots iteratively without creating new operational risk.
A practical, operational playbook for finding high-value AI and automation pilots, calculating ROI and cost-of-waste, running controlled pilots with measurable success criteria, validating models and monitoring drift, designing human-in-the-loop controls, managing change, and handing solutions safely to operations. Includes ready-to-use rubrics, checklists, calculation formulas, and risk-mitigation templates for safety-critical contexts.
A practical, step-by-step calculator and intake guide to estimate time savings, error reduction value, payback period, and realistic total cost of ownership for RPA and AI automation pilots.
A practical, step-by-step playbook to plan, run, evaluate, and scale AI pilots in operations with clear value metrics, human-in-loop validation, safety and bias controls, and a reusable pilot charter and evaluation scorecard.
A practical, operations-focused playbook to design, run, evaluate, and hand off AI pilots. Covers selecting a constrained use case, verifying data readiness, sampling and labeling strategy, baseline metrics and acceptance tests, human-in-loop design, operational monitoring and escalation, governance and roles, and clear decommission or scale-up criteria.
A required, pre-deployment interactive assessment and template to evaluate purpose, data lineage, performance targets, bias and fairness risks, human-in-the-loop controls, failure modes and mitigations, monitoring and retraining needs, gating decisions, and required approvals for operational AI pilots.
An interactive use-case assessment and governance checklist to scope, secure, test, monitor, and decide whether to scale, retire, or rework automations. Collects practical details about frequency, ROI, exceptions, data sensitivity, security controls, change-control needs, monitoring plans, and retire/scale criteria so teams can create maintainable, observable, and safe automations.