By the end of the program, your leaders know where AI can create measurable operating leverage, your teams know how to use AI inside real work, and you walk out with a prioritized roadmap of agent-ready workflows. Built for industrial mid-market companies where work is fragmented across people, documents, spreadsheets, inboxes, and legacy systems.
Generic AI literacy programs leave teams able to use ChatGPT and nothing else. This curriculum is different. We start by teaching what AI can do today, then shift the question from tools to work: which repetitive, document-heavy, rules-heavy workflows should shrink, disappear, or become exception-only.
Executives leave with a leverage map and a pilot charter. Teams leave with reusable prompts, workflow maps, and an agent design canvas. The company leaves with a ranked portfolio of agent-ready workflows — the same kind we build under our Sprint and Build engagements.
What modern AI actually does today — extraction, classification, multimodal document understanding, tool use. The difference between chatbots, copilots, workflows, and agents, taught with examples from real operations.
Start with work, not tools. The core question we teach every team: what work should shrink, disappear, or become exception-only? Identify the repetitive, document-heavy, rules-heavy workflows where AI moves a number.
Brief AI with context, goals, constraints, examples, and output formats. Reusable prompts become the first draft of SOPs, workflow templates, and agent instructions.
Approved tools, data boundaries, human review, auditability, permissions, escalation. Useful AI adoption without exposing sensitive data or high-risk decisions.
CEOs, COOs, CFOs, and ops leaders learn to identify AI leverage through business outcomes — throughput, cycle time, error reduction, margin, working capital. Vague ideas become a ranked portfolio of bounded workflow candidates with owners, KPIs, and risk levels.
Pilot charter for 1–2 workflows: owner, KPI, scope, access, timeline, deployment path.
Knowledge workers, managers, and power users learn AI for real work — drafting, research, document handling, workflow mapping, exception handling, and agent design. Templates and canvases they keep using long after the program ends.
Prompt library, workflow maps, exception queue design, and draft agent instructions for a candidate workflow.
One frame the whole company shares. It moves AI from individual time savings to operational leverage that runs without a human in every step.
Trigger → Context → Judgment → Action → Review → Escalation → Feedback → Measurement. Simple enough for executives, useful enough for builders.
Experiment → Standardize → Integrate → Govern → Operate → Improve. Keeps the program from stopping at experimentation.
Ten questions per workflow: trigger, info, location, judgment, rules, exceptions, approvals, output, quality, improvement.
Workflows ranked against company KPIs.
Scored, sequenced, owned.
Decision framework per candidate.
Review modes, escalation, audit.
Triggers, decisions, exceptions, owners.
Role-specific, reusable, versioned.
Job, boundaries, tools, escalation.
Real examples, edge cases, failure modes.
From experiments to operating cadence.
A short fit call covers your operation, the workflows you'd want a pilot pointed at, and which mix of executive and team modules makes sense.