AI Training for Business Operations Teams

A shared starting point for what a language model can and can't do in day-to-day operations work. Participants sort the day's tasks into what's worth delegating, drafting, summarizing, classifying, and what stays with an accountable owner.

• What a language model can and can't do in operations work
• Business operations vs. IT AIOps: where this program draws the line
• Drafting first, then classifying
• What customer or vendor information stays out, including financial data
• Reviewing AI output before it becomes an internal document

Capturing a process that lives in people's heads so the next hire could follow. Participants capture triggers, actors, and system touchpoints, and practice using AI to speed up the first draft without skipping the people who run the work.

• The trigger and actors, plus every system touchpoint a process runs through
• Where handoffs stall
• Building a first-draft map from interview notes and screen walkthroughs
• Workarounds and shadow spreadsheets get flagged for review
• An evidence log of who said what, and where accounts disagree
• Validating the draft map with the people who run the process day to day

Uses a validated process map to draft an SOP a new hire could follow on day one, complete with role-specific variants and exception clauses, every draft routed through its owner before it becomes policy.

• Drafting a step-by-step SOP straight from a validated process map
• Requester view, handler view
• Writing the exception clause straight into the SOP
• Every SOP needs a source and owner, with a freshness date before it publishes
• A senior employee's tribal knowledge, turned into a reviewable first draft
• No SOP becomes policy until its owner has reviewed the AI-assisted draft

Not every case fits the SOP, and that is fine as long as there is a repeatable way to handle the ones that don't. Participants classify recurring exceptions and draft response templates in advance. They also set the trigger that sends a case out of the AI-assisted lane and to a person. The taxonomy gets tracked over time, so it flags a process gap instead of just another individual mistake.

• A taxonomy for the exceptions that keep coming back
• A response template drafted before the exception shows up again
• Where an exception leaves the AI-assisted lane and becomes a human call
• Proposing a root-cause hypothesis from case notes, for someone else to check
• Checking an AI-assisted response against the service standard before it goes out
• Exception volume gets tracked to find process gaps

AI structures the recurring reporting grind, the KPI narrative, the action log, the meeting pack, and the workload scenario, without ever picking the plan itself. Decision ownership stays exactly where it already sits, with the manager.

• Turning a KPI dashboard into a narrative a stakeholder can read
• An action log and a meeting pack, drafted from the notes and decisions already made
• A workload or capacity scenario, built with its assumptions stated up front
• Comparing scenario options without letting the model pick one
• Who owns the decision, and why it stays the manager
• The recurring operations update, ready before the meeting starts

Moving from one team's pilot to a rhythm the whole shared-services function runs. Leaders prioritize process changes and set the governance rules everyone follows. They also draw the line between where operations work ends and automation begins.

• Value, effort, risk
• Handoff to automation: when to bring in AI Automation and n8n
• Running a pilot against one metric and one review date
• Governance rules for approved tools, data boundaries, and review steps, written down once
• A champions network that spans shifts and shared-service teams
• A 90-day roadmap from one team's pilot to shared-services-wide adoption