Don’t we all know that one person? Like, every Monday, that one person on the team pastes the latest export into a report template and then makes the same corrections, such as a region label that changed or a date range that ran one week long. Claims of efficiency are made, but who are we kidding? The corrections take longer than the paste. Which parts of that weekly report, or of the meeting prep that follows it, could be delegated to AI, and what would need checking before anyone uses the result? The examples below are illustrative, built around that single report.
A workflow is worth delegating when its input, output, owner, frequency, and acceptable result fit on one page, and when the time spent reviewing and correcting the output is clearly lower than the time the task takes by hand. Tasks that meet both tests are good candidates for AI for work. Tasks that fail either one need a better specification before they need a better tool.
AI for work starts with a task you can describe
Any AI power user knows that vagueness is an enemy that drives a wedge between AI and the user. “Help with reporting” is too vague to delegate because it names no input and no finish line. Two people reading it would produce different results, and neither could say whether the result was right. A delegable task is described by five fields, written down before any tool is chosen.
For the weekly report, the input is the Monday sales export (a CSV) and last week’s report. The output is a one-page summary in the existing template, with any week-over-week change above 5% flagged. The owner is the analyst who sends it today, and the frequency is weekly. The acceptable result is that every figure matches the export, no figure was altered, and the flagged changes match a recomputation.
Meeting prep fits the same pattern. The input is the calendar invite, the previous meeting’s notes, and the open items in the tracker. The output is a one-page brief listing decisions needed, and the acceptable result is that every open item appears with its current status. Fields stated this way can be handed to a colleague or to an AI system and checked against the same standard.
AI delegation needs boundaries before tool access
An assistant connected to email, shared drives, and a reporting tool can do more than the task requires. Before connecting anything, write down what the system may draft, what it may change, and what it may send. For the weekly report, a workable set is read access to the export and the template, write access to a drafts folder, and no ability to send.
Some actions should need approval every time. Sending anything outside the team, editing a source file, and posting to a shared channel belong on that list. Altering a figure belongs there too, since a suspect number should be flagged and left as found. The approval list can shrink later if the review record shows few errors, and each change to it should have a named owner.
AI workflow automation needs a review-cost estimate
Time savings from automation are usually quoted as execution time. The number that decides the case also includes review and correction. ODSC’s framework for agent costs makes the same point about completed work: “An unreviewed agent draft does not equal a completed human task.”
An illustrative worksheet for the weekly report follows. The figures are assumptions to be replaced with timed ones.
| Step | By hand | Delegated |
|---|---|---|
| Preparing the draft | 90 min | 5 min |
| Checking figures against the export | included above | 20 min |
| Correcting errors found | included above | 15 min |
| Total per week | 90 min | 40 min |
On these numbers, delegation saves about 50 minutes a week. The decision rests on the middle two rows. If checking grows to 45 minutes because the output cannot be trusted, the total reaches 65 minutes, and the saving falls to 25. If one run in five has to be redone by hand, add 18 minutes to the weekly average (0.2 times 90). Time the review during the first four runs, and use those figures in place of the estimates.
An AI workflow checklist for the first test
The first test should use inputs chosen to fail. ODSC’s article on testing agents before release notes that “Historical examples only capture failures teams already know about,” so at least one test case should come from outside the team’s memory. The checklist below is illustrative and uses the weekly report.
- Write the five fields and the acceptable result before the first run.
- Run a normal week and compare every figure with the export.
- Remove the export and run again. The system should stop and report the missing file, and it should not fill figures from last week’s report.
- Give an ambiguous instruction, such as “summarize the big movers,” with no threshold. The system should ask for the threshold or apply the stated 5% rule and say that it did.
- Supply an export with the wrong date range, or a figure copied from an outdated source. The system should flag the mismatch.
- Record each run as pass or fail with the reason, and note whether the system or the reviewer caught each failure.
- Confirm that no draft can be sent without approval.
A failure that nobody notices is the costly kind, which is why step 6 records who caught each one. The pass-fail record also sets the sampling rate for later runs. A task with few failures can be spot-checked, and a task with frequent failures needs full review until the specification improves.
AI for work training at ODSC AI NYC
The method above is a short version of what the Business Workshops track at ODSC AI NYC 2026, the AI for Work Summit, covers in practice. The summit runs December 2 and 3 at Jay Conference Center, Bryant Park, 109 West 39th Street, New York, NY 10018. The track is built for sales, marketing, finance, operations, and HR roles, and no coding is required. ODSC lists the schedule as preliminary, and session times have not been published.
Sheamus McGovern, founder of ODSC AI, leads “AI for Work: Full-Day Training” on Day 1, in four 90-minute parts. Participants write a task specification that, in the listing’s words, “holds up when someone other than you executes it,” then delegate a task from their own backlog and review the result.
The remaining parts cover connections and permissions (demonstrated against a prepared workspace, so nobody connects a corporate account in the room), building and testing a multi-step workflow with human checkpoints, and review practices, access boundaries, and a baseline for team rollout, ending with a one-page operating agreement for the team. The listing asks participants to bring a real task and files they are permitted to use.
Greg Rosner, founder of PitchKitchen and host of the AI Workforce Clinic, leads “Your AI Agent Needs a Job Description” on Day 2. The interactive session, which needs no code, treats an AI agent like a new hire and covers writing a role, operating context, and success criteria. Inside ODSC AI NYC 2026 describes the wider program.
The free Open Pass covers keynotes, open talks, select breakouts, and networking, and it does not include workshops. The Training Pass adds every Business Workshop and function lab. Compare passes for the hands-on business workshops. Teams of three or more can also ask about team passes.
