When AI writes part of your marketing, the real risk is a wrong price or promise going out under your name. Trojan Digital Marketing, based in Indianapolis, offers AI agent setup. When an agent we set up answers phone calls, each call has a ten-minute limit and a defined human handoff. A vague 'check the AI output' step is easy to skip and hard to audit. To map your sign-off steps with us, the first strategy call is free, and during business hours we aim to reply within five minutes.
We design the review around the action. Drafting an outline is low risk, while publishing a service promise, changing a campaign budget or sending a customer commitment each needs its own permission and evidence. We put a person at the step where a mistake would reach a customer or cost money.
Map the workflow before adding automation
- Business
- Start from the real business.
- Coverage
- Identify the places it actually serves.
- Office
- Do not turn a service area into an invented office.
Write down the current steps from input to final action. Identify where facts enter, where they're changed, and where something becomes public or reaches a customer. Mark who owns each step. We want that map before adding any automation, because AI layered on an unclear handoff only makes the same mistakes faster.
For a hypothetical marketing article, the steps might be gathering approved information, drafting an explanation, checking claims, editing for clarity, approving publication, and verifying the published page. An AI system could assist at several points. The person who approves the service facts should still have a name. On our own site, the reviews and figures we publish come from Jeff Clerisier, our founder.
NIST's AI Risk Management Framework is voluntary guidance for incorporating trustworthiness into AI design, use, and evaluation. It provides a useful broader reference, while the workflow below is a practical operating recommendation rather than a claim of certification. See the NIST framework overview.
Classify tasks by consequence and uncertainty
Ask two questions: what happens if this output is wrong, and how easy is it to verify? A formatting suggestion is usually easy to reverse. A claim about a customer's results may require source records and permission. A message committing the business to a delivery date may need a capacity check that the drafting system cannot perform.
| Task | Useful review focus |
|---|---|
| Organize approved notes | Check that meaning and important conditions were preserved |
| Draft public service copy | Confirm claims, scope, ownership, and current availability |
| Prepare a customer reply | Verify context and any commitments before sending |
| Change a live campaign | Check authority, budget boundaries, and the intended effect |
| Summarize an inquiry | Preserve the customer's words and flag uncertainty |
Use this classification to allocate attention. Requiring the same approval for every comma and every commercial promise makes the process noisy. A reviewer should understand why an item reached them and which decision they must resolve.
Provide evidence beside the draft
We ask the system, or whoever prepares the draft, to keep sourced facts apart from interpretation and suggested wording. Link each material claim to an approved source, and flag a missing fact so the model can't fill it with a guess. Our own house rule is that a number needs proof and the conservative claim wins, so a polished paragraph can't hide an unverified price.
Give the reviewer a concise change summary. Show what is new, what evidence supports it, and what remains uncertain. If the task updates an existing page, provide the relevant prior wording. Review becomes faster when the person can assess the decision without reconstructing the entire history.
The knowledge base governance process helps maintain approved source material. Without that foundation, an AI workflow can repeatedly retrieve contradictory information and present the contradiction in increasingly confident prose.
Define what approval authorizes
Make the approved action explicit. Approval to use an outline is not approval to publish the final article. Approval to draft an email is not approval to send it. Approval of a proposed budget does not necessarily authorize changing every related campaign setting. The workflow should keep those boundaries understandable to the person using it.
For routine work, set standing rules with a clear scope. A business might let AI format approved content into a draft layout and keep publishing for an editor. In the agent plans we offer, the written scope sets ceilings before onboarding, such as 1,000 processed runs a month on the Focus plan. The exact boundary should match the organization's actual responsibilities and the capabilities of the system.
Keep a record of the approved version and the resulting action. If the content changes materially after review, route the changed parts through the appropriate check. Otherwise, approval becomes a historical event attached to an output the reviewer never saw.
Test the awkward cases
Use realistic examples that challenge the workflow. Include missing information, contradictory source material, outdated pricing, ambiguous requests, and a customer asking for a commitment outside the normal process. The desired behavior may be to produce a limited draft, request a specific fact, or hand the item to a person.
Do not test only whether the system produces fluent text. Check whether it preserves exclusions, marks uncertainty, routes exceptions, and stops at its authority boundary. For customer inquiries, the lead triage handoff process provides a more specific operating pattern.
Start with a small workflow in your AI automation program. We keep the old process running while we inspect real outputs, so a broken step can be fixed without stopping unrelated work.
Review the review process
We track the kinds of corrections reviewers make. When reviewers keep fixing facts, look at the source library first. Repeated scope corrections may point to unclear instructions. Long queues may indicate that too many trivial tasks require the same person. Use these patterns to improve the workflow rather than blaming individual reviewers for every delay.
Check the published or sent result too. A sound draft can still break in a template, an integration or a wrong recipient list. We count a review as done when the live page or sent message is right, and a preview screen doesn't count.
When public marketing content is involved, align the workflow with your search content strategy. Your business stays responsible for what it claims and promises. In our view, AI earns its place when it hands a person a clearer decision and leaves a record someone else can follow.