Field Briefings
Research-led notes on AI workflows, implementation readiness, automation strategy, agentic systems, and deployment failure patterns.
Latest Reports
- AI Governance Review: When A Workflow Is Ready For Production: A production-readiness report for AI workflows, covering evidence quality, approval boundaries, exception handling, metrics, and launch criteria.
- AI Reporting Workflow: From Manual Updates To Reviewable Operating Briefs: A reporting-workflow guide for turning scattered updates into structured operating briefs with source evidence, owner review, and clear executive decisions.
- AI Customer Health Scoring Workflow For Retention Teams: A retention-team guide to using AI for customer health scoring, evidence packets, renewal risk detection, and owner review without creating false churn signals.
- AI Proposal Workflow: Drafting, Compliance, And Human Approval: A proposal-operations report on using AI for drafting and compliance review while preserving human control over claims, pricing, scope, and final submission.
- Speed-To-Lead AI Workflow: What To Automate And What To Keep Manual: A revenue-operations guide to automating lead intake, qualification evidence, owner tasks, and draft follow-up without letting AI make unsupported customer commitments.
- Human-In-The-Loop AI Workflow Examples: Where Review Belongs: A field report on human-in-the-loop AI workflow design, where review should block action, and which business decisions should never be handed to automation without an owner.
- AI Workflow Readiness Checklist For Service Businesses: A checklist for service businesses deciding whether a workflow is ready for AI deployment, including intake quality, owner review, system fit, and exception handling.
- How To Choose The First AI Workflow To Automate: A practical selection method for choosing the first AI workflow: visible friction, clear evidence, low-risk review, measurable output, and a realistic path to production.
- The Difference Between AI Adoption and AI Deployment: A practical operating model for separating casual AI usage from governed workflow deployment, including ownership, systems, review points, and measurable production outcomes.
- Why AI Pilots Fail Before They Reach Operations: A field report on the operational gaps that keep AI pilots trapped in demos: weak workflow selection, missing owners, unclear data, and no production decision path.