The AI Marketing Audit is a $1,500 written diagnostic from Stan Consulting LLC. It reviews AI readiness across an existing marketing operation: tool inventory, workflow gaps, data quality, content production bottlenecks, and compliance mapping. The deliverable is a written audit report with a prioritized AI implementation plan. Delivery is within 7 days of read-only account access. One-time engagement. The fee is final on submission.
Home Services AI Operator Lane AI Marketing Audit
Stan Consulting · F6.1 · AI Operator Lane
You have channels running, a team in place, and AI tools somewhere in the mix. What you do not have is a written account of what works, what conflicts, what your compliance exposure is, and what to build first. That is what the audit produces.
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Quick answer
The AI Marketing Audit is a $1,500 written diagnostic of AI readiness across an existing marketing operation. Tool inventory, workflow gaps, data quality, content production bottlenecks, and compliance constraints are reviewed. The deliverable is a written report with a prioritized AI implementation plan. Delivered within 7 days of read-only account access. One-time engagement. The fee is final on submission. No retainer is required.
AI replaces production. It does not replace diagnosis.
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These are the recognition moments. Each one describes an organization with marketing running and AI adoption stalled or scattered. If one of these matches, the audit is the correct first step.
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ChatGPT, Jasper, Midjourney, a transcription tool, a social scheduler with an AI tab. Each one purchased by a different team member for a different reason. No one knows what the full list is, which tools have data access, or which contracts are active. The AI stack is a list of subscriptions, not a system.
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Individual contributors use AI for their own output. The process is personal, not shared. There is no prompt standard, no output review, no quality gate. Two people in the same role produce AI-assisted work at completely different quality levels. The gap is invisible to leadership until it shows up in a campaign.
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The organization operates in a regulated category: financial services, healthcare, legal, or another supervised sector. AI-generated content has been going out. No one has mapped which output types require human review, which claims trigger disclosure requirements, or where the data handling creates regulatory exposure. The compliance gap is assumed to be small. It is usually not.
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There is no documented protocol for what AI can write without review, what requires sign-off, and what cannot be AI-generated at all. Brand voice is maintained manually by a few senior people. The team is growing. The manual enforcement does not scale. The first AI brand incident is usually what triggers the audit.
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Marketing reports take three to six weeks to compile. By the time the data reaches the decision maker, the campaign it describes has already run. The team knows AI can close this gap. The specific integration point, the data sources required, and the tooling sequence are unclear. The audit maps this before the build starts.
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Leadership has approved AI integration. The team does not know where to start. Tool vendors have been contacted and each one recommends their own product first. An internal working group has been meeting for four months. The decision tree is genuinely complex: data readiness, tool compatibility, team training, compliance clearance, and sequencing all interact. The audit resolves the sequence.
Operating principle
Tool selection before diagnosis is the most common way AI integration fails inside a funded marketing operation.
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The audit reviews what is in place before recommending what to add. Tool recommendations without a current-state diagnosis produce redundancy, conflict, and wasted spend. Every recommendation in the audit report is grounded in the specific stack and workflow already running.
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AI integration has a correct order. Data quality before automation. Compliance mapping before content generation at scale. Shared protocols before individual tool adoption. Getting the sequence wrong creates technical debt that costs more to unwind than the original integration did to build.
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The audit delivers a written report, not a presentation, a workshop, or a verbal debrief. A written instrument can be reviewed by multiple stakeholders, shared with legal or compliance, and referenced at the start of a build engagement. A slide deck cannot do any of those things reliably.
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The coverage is fixed. Every audit reviews all six areas regardless of the operator's current AI adoption level. The depth of each area varies based on what is found.
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Every AI tool in active use across the marketing function is catalogued: name, purpose, access level, data inputs, subscription status, and who owns it. Overlapping tools are flagged. Tools with undocumented data access are named as compliance risks.
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Current workflows are mapped against the tasks AI can handle reliably at the operator's scale. Gaps are named: tasks done manually that should be automated, automations running without oversight, and workflow steps where AI output quality is inconsistent because the input is not structured correctly.
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AI performance depends on the quality of the data it is fed. First-party data availability, CRM hygiene, analytics tracking completeness, and audience data structure are reviewed. The audit identifies which data gaps will limit AI integration before a build begins.
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Where content production is the rate-limiting factor in campaign velocity, the audit identifies which production steps AI can take over and at what quality threshold. Brief creation, first-draft generation, copy variations, asset metadata, and review cycle management are each assessed.
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For operators in regulated categories, the audit maps where AI-generated output creates regulatory exposure. Output types requiring human review, claim categories triggering disclosure obligations, and data handling constraints specific to the operator's regulatory environment are documented in the report.
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The six findings areas are consolidated into a sequenced action plan. Each item is ranked by impact, implementation cost, and prerequisite dependencies. The plan names which products in the AI Operator Lane apply and in what order. Some operators implement from this plan directly. Others use it to scope a build engagement.
Access method
The audit is conducted on read-only access. No tools are installed. No credentials are stored beyond the audit window. The operator grants view access to marketing platforms, analytics, and relevant documentation. Access is revoked after delivery.
Interview protocol
One intake call is conducted before the audit begins. No additional client interviews are required during the 7-day window. The written report is the output of the document and platform review, not a summary of what the team told us.
What is not included
Implementation work is not part of the audit. Tool subscriptions are not purchased or managed. Ongoing advisory is not included. The audit is a diagnostic instrument. If the findings warrant a build, that is scoped as a separate engagement under the AI Workflow Build or AI Stack Retainer.
Compliance categories served
Financial services (SEC, FINRA, FTC) · Healthcare (HIPAA, FTC health claim rules) · Legal (state bar advertising rules) · General commercial (FTC endorsement guidelines, GDPR where applicable). Compliance annotation is included in every audit where the operator declares a regulated category at intake.
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The report is delivered as a PDF within 7 days of read-only account access being confirmed. No slide deck. No verbal debrief as the primary output. The written document is the deliverable.
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A factual account of the AI tools in use, how they are being used, by whom, and at what frequency. Gaps between stated usage and actual usage are noted. The summary is written for a non-technical executive reader and for the technical team lead simultaneously.
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The workflows where AI can be inserted and those where it cannot are named. Each gap entry includes the current manual step, the AI task that replaces or augments it, the tooling requirement, and the estimated time saving at the operator's current volume.
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Data sources assessed for AI readiness: structure, completeness, freshness, and accessibility. Findings are named as either prerequisites for integration or as constraints that will limit AI output quality. Each finding has an estimated remediation effort.
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Each content type the team produces is assessed: feasibility for AI generation, minimum quality bar, review requirements, and brand risk. The assessment includes a recommended protocol for AI-assisted content at the operator's team size and output volume.
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For regulated operators: a section-by-section annotation of where AI output creates regulatory exposure. Each exposure is named with the applicable rule, the content type at risk, and the required human review step. Operators in unregulated categories receive a general FTC and data handling note.
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The action document. Every finding from all five preceding sections is consolidated into one sequenced list. Each item has: priority tier (1–3), implementation effort estimate, prerequisite flag, and the relevant AI Operator Lane product if external support is needed. Implement in-house from this plan, or use it to scope the next engagement.
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The methodology is fixed. The audit is conducted the same way on every engagement. Read-only access, no installs, one intake call, written output only.
The audit does not require embedding a consultant in the organization. It does not require a discovery workshop, a stakeholder interview series, or a multi-week engagement before findings are available. The 7-day window is a constraint, not a feature: it forces the audit to focus on what is observable from the documentation and platform data, not on what the team believes is true.
Read-only access means the audit team reviews without modifying. No tools are installed on the operator's stack. No data is retained after the report is delivered. The access credential list is agreed at intake and is scoped exactly to what the audit requires.
One intake call happens before the 7-day window opens. That call covers the operator's context, the platforms in scope, and the compliance category. The call is not a strategy session and does not extend the audit scope. The written report answers the questions that arise after the call.
Step 01
Submit the intake form. Marketing stack, channels currently running, current AI tool usage, and compliance category are confirmed. The $1,500 fee is collected. Fee is final on submission. The intake call is scheduled within 48 hours.
Step 02
Read-only access credentials are provided by the operator for the agreed platform set. The 7-day delivery window opens when access is confirmed. No installs. No credential storage beyond the audit window.
Step 03
Tool inventory, workflow gap analysis, data quality review, content production assessment, and compliance mapping are conducted during the 7-day window. No additional calls or interviews are required. The intake call and the document and platform review are the sole inputs.
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The written audit report is delivered as a PDF at the end of the 7-day window. An optional 30-minute clarification call is available within 14 days of delivery if specific findings need clarification. The call does not extend the scope of the audit.
Specimen · From a 2026 AI Marketing Audit
Finding 2.4 · Workflow gap analysis
Healthcare operator, Series B. Marketing team of 11. Compliance category: HIPAA.
Finding 2.4 · Content production workflow · Gap identified
Campaign brief production takes an average of 4.2 working days per brief across the content team. Of that time, 2.8 days is spent on research aggregation: pulling competitor references, assembling audience data, and formatting the brief template. This task is suitable for AI augmentation. The constraint is that two of the five data sources currently used in brief research contain identifiable patient context from the CRM. Those sources cannot be fed to a third-party AI tool without a Business Associate Agreement in place. The remaining three sources are not HIPAA-restricted and can be automated immediately.
Recommendation · Priority 1 Separate the two restricted data sources from the brief research workflow immediately. Build the AI-assisted research step using only the three non-restricted sources. This alone reduces brief production time from 4.2 days to an estimated 1.9 days. The restricted sources can be re-integrated after a Business Associate Agreement is executed with the AI vendor; that path is scoped in the compliance section of this report (Section 5, Finding 5.2).
Brand and patient identifiers withheld under NDA. One excerpt of a 22-section AI Marketing Audit deliverable. Numbers are from the documented case.
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These are the structural findings from completed AI Marketing Audits. Client identifiers are withheld under NDA. The findings and the post-audit implementation outcomes are documented.
Vertical · B2B SaaS · Series B
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AI tools found with overlapping function
An AI stack audit found seven tools performing partially overlapping jobs with no shared data layer between them. Four of the seven had access to customer data under separate and inconsistent terms of service. The audit produced a consolidation plan. Post-build: two integrated tools replacing the seven, documented workflows, and reproducible output quality across the team.
Case · documented · NDA
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Compliance incidents post-build
A regulated healthcare operator had no AI compliance framework before the audit. The audit mapped every AI output type against applicable HIPAA and FTC constraints. The workflow build was scoped inside those constraints. Zero compliance incidents in the 8 months following the build.
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Reporting lag eliminated
A DTC operator with a 6-week reporting lag identified the specific integration point where automated data aggregation could close the gap. Post-build: weekly reporting delivered within 48 hours of the period close. No additional headcount required.
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Audits end without further engagement
Approximately one in four AI Marketing Audits concludes at the report itself. The in-house team implements the prioritized plan directly. The audit is not a pre-sale for a retainer. Some engagements end here. That is the design.
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This audit is for
This audit is not for
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The AI Marketing Audit reviews six areas: tool inventory, workflow gap analysis, data quality, content production bottlenecks, compliance mapping, and a prioritized implementation plan. The report is typically 18 to 24 pages, delivered as a PDF within 7 days of read-only access being confirmed. Price is $1,500 one-time. The fee is final on submission.
The $1,500 covers the full written audit report: current state summary, workflow gap map, data quality findings, content production assessment, compliance annotation, and the prioritized implementation plan. One intake call before the audit begins and one optional 30-minute clarification call within 14 days of delivery are also included. No implementation work, tool subscriptions, or ongoing advisory is included.
No. The $1,500 fee is final on submission. The scope of what is audited and what is delivered is specified in full before payment. The 7-day delivery commitment and the written report are what the fee covers. The scope does not change after payment is made.
The Conversion Second Opinion ($999, 72-hour delivery) diagnoses structural problems in paid advertising and conversion architecture. It is for operators whose paid campaigns have stopped producing results. The AI Marketing Audit ($1,500, 7-day delivery) diagnoses where AI integration fits into an existing marketing operation: what tools are in use, where workflows can be automated, what compliance constraints apply, and what the correct build sequence is. They address different problems and serve different buyer contexts. Some operators complete both at different stages of their engagement.
The audit is led by the principal at Stan Consulting LLC. The team has operated inside funded marketing organizations at Series A through C scale across multiple verticals. Compliance annotations are produced against the operator's declared regulatory category, and the written report represents the audit team's findings, not a software-generated assessment or an AI-summarized output.
The compliance section covers the category declared at intake: financial services (SEC, FINRA, FTC advertising and disclosure requirements), healthcare (HIPAA, FTC health claim rules), legal (state bar advertising rules), and general commercial (FTC endorsement and testimonial guidelines, CAN-SPAM, GDPR where applicable). The audit maps where AI-generated output creates regulatory exposure and where human review is required. It does not constitute legal advice.
The audit delivers a written implementation plan that identifies which products in the AI Operator Lane are relevant and in what order. Some operators implement from the plan directly using their in-house team. Others move to the AI Workflow Build, AI Operator Training, or the AI Stack Retainer based on the findings. Some engagements end at the audit. The audit does not auto-enroll into any subsequent engagement. Each subsequent tier is a separate agreement.
Yes. Ad-hoc tool adoption is the most common state found in AI-curious marketing operations at the time of audit. Individual contributors using ChatGPT for one-off tasks is not an AI-integrated operation. The audit identifies what is being done manually that should be automated, what is being automated in ways that create quality or compliance risk, and what tooling gaps exist between the current state and a coherent AI layer. The audit frequently finds that tools already in place are sufficient but are not connected or governed correctly.
Begin here
The form collects what the audit needs to scope the review correctly: your marketing stack, the channels currently running, current AI tool usage, and your compliance category. The $1,500 fee is collected after the intake is reviewed and fit is confirmed.
The audit team reviews every intake before confirming. If the engagement is not a fit for the current context, the fee is not collected and a note on the correct first step is provided at no charge.
F6.1 · AI Operator Lane
Tool inventory, workflow gaps, data quality, compliance mapping, and a sequenced implementation plan. $1,500. Fee final on submission. No retainer required to start.
Begin the Audit · $1,500Not ready for the audit? Read the full AI Operator Lane first, or write with a specific question before committing.