Most AI training teaches people to use ChatGPT. Ours teaches your office team to find the jobs an AI worker should do, hand the first one over by the end of the workshop, and leave with a practical rollout plan. Built for logistics, construction, industrial, and insurance companies between $50M and $1B where headcount is rising faster than revenue.
Generic AI literacy programs leave teams able to prompt ChatGPT and nothing else. This curriculum is different. We name the work first: which repetitive, document-heavy, rules-heavy jobs should shrink, disappear, or become exception-only — and which ones should be handed to an AI worker.
Executives leave with a leverage map and a pilot charter. Teams leave with reusable prompts, workflow maps, and an AI worker design canvas. The company leaves with a ranked portfolio of AI-worker-ready jobs — 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 AI workers, 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 AI worker 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.
Your office team, managers, and power users learn AI for real work — drafting, research, document handling, workflow mapping, exception handling, and AI worker design. Templates and canvases they keep using long after the program ends.
Prompt library, workflow maps, exception queue design, and draft AI worker instructions for a candidate job.
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.