Most growth research is operational. Someone shows up with a slide deck, interviews a few stakeholders, and delivers a 40-page PDF that sits on a shelf. The group feels busy for three weeks, and nothing has changed. I’ve been on both sides of that bargain, and I’m tired of it.
At my growth show, we run 90-day growth competitions for private equity (PE)-backed venture capital firms. Auditing is the first stage. It used to take two to three weeks of manual work just to get a clear picture of what was going on within the company’s marketing organization. Now, with AI integrated into every step, we’re compressing that discovery into days and spending the rest of the time fixing things.
Here’s how we do it.
Why Traditional Development Research Fails
Classical auditing has a structural problem. People who do it are motivated to find complexity because complexity implies greater interaction. So the deliverable is a list of all the things that can be improved, not counting anything in particular, without connecting to what the business needs in the next quarter.
I worked in marketing at companies ranging from Fortune 200 to startups before starting my own company. At one company, a 30-minute meeting with the CEO required two or three meetings before polishing the deck. The decision was made in minutes. The deck went into the cabin. All those hours, gone.
That experience shaped the way I think about auditing. The output should be a working document that becomes a blueprint for what happens next. It is not a memorial.
AI-Assisted Audit Framework
Our research covers three areas: the advertising organization itself, the technology stack, and what I call AI readiness. The last one was missing two years ago. Now it is a very important piece, because it determines how much traffic the company can do without hiring five more people.
Each area follows a specific process, and the AI appears differently in each area.
Phase 1: Intake And Context Building
Before we talk to anyone on the client team, we feed everything we can to Claude. Floors for investors. Board presentations. Corporate social marketing. The creation of a competitor. Job postings from the past six months. Glassdoor reviews. Product screenshots. Pricing pages.
Two years ago, putting all that together required a senior strategist to spend a week reading, interpreting, and creating a briefing document. Now, we create a complete content package per day. Claude processes the raw materials and produces a structured brief that includes the company’s positioning gaps, messaging differences across channels, competitive white space, and questions to ask in stakeholder conversations.
The output is not a snapshot. It is a diagnostic framework designed for that particular company. We revise it, challenge it, add our own sense of duty, and enter prospecting calls from a point of view instead of a blank notebook. That changes the conversation immediately. Clients can see when you’ve done your homework.
Phase 2: Technology Stack and Workflow Mapping
This is where things become clear. We pull a full list of all the tools used by the marketing team. Customer relationship management (CRM). Email platform. Mathematics. Attribute. Ad platforms. Content management. Design tools. Project management. The average mid-stage startup has between 15 and 30 marketing tools, and in almost every study, at least a third of them overlap or are often underutilized.
We document the entire workflow: how a campaign goes from concept to life, how leads are routed, how reporting happens, who touches what, and when. We then map each workflow against what is currently possible with native AI alternatives.
A real-life example: One client had three people spend 40 hours a week collectively on creative production for a paid community. To inform the designer. Awaiting rounds of updates. Resizing different areas. It exports. It’s loading. We’ve replaced that workflow with a combination of AI creation and customization tools that handle content generation, conversion, and platform-specific formatting. The same creative volume now takes about eight hours of a person’s time per week, and more of that is strategy revision than production.
Tools like HeyGen and ElevenLabs handle the video and audio production that the studio needed. Custom AI agents built on open source AI frameworks such as OpenClaw and Hermes automate research, competitive monitoring, and content drafting. The point is not to drop the name of the software. That the state of automation has grown exponentially in the last 18 months, and many marketing teams are still not there.
Phase 3: Assessing AI Readiness
This section is the one that surprises customers the most, because it is not so much about technology but about people.
We examine three things. First, does the team have the curiosity and willingness to use AI tools? Other groups are willing. Others are scared. Knowing where people stand before you start pushing new workflows prevents the kind of resistance that kills transformation projects. I spoke about the readiness of AI to a group of large marketers in a high-growth consumer app, and the first question asked was: “Isn’t it magic in our human work and interactions?” They are afraid.
Second, does the company’s data infrastructure actually support AI-driven optimization? If your CRM is crap, your attribution is broken, and your analytics are built on vanity metrics, no AI tool is going to save you. Garbage in, garbage out still works. We highlight the data cleanliness issues that need to be addressed before any AI application will produce reliable results. And testing acknowledges data gaps and how (and why) they can be fixed.
Third, where are the opportunities for automation at the highest level? Not everything should be automatic. Creative strategy still requires human judgment. Product decisions still require a person with taste and context. Research identifies which workflows will benefit the most from AI and which require a human firmly in the loop. AI readiness is not about replacing all humans with AI tools and agents.
What the Deliverable Actually Looks Like
We do not provide a deck. We produce a shared document with four sections: a current situation diagnosis, a key opportunity map, a 90-day implementation guide, and a list of tool and tool recommendations with estimated cost savings.
The roadmap divides the 90 days into three phases. The first month focused on quick wins, workflows where AI can be connected with minimal disruption and immediate impact. The second month deals with structural changes, things like rebuilding attribute models or redesigning the content production pipeline. The third month is about training and hands-on, to ensure that the team can run the new programs independently.
The document is interactive. Clients can comment, undo, and repost content. It becomes an effective engagement plan, not a PDF that gets emailed and forgotten.
Where the Real Savings Come From
Savings are not always where people expect them to be. Most founders think that AI will reduce their advertising spend or lower their agency fees. Sometimes it does. But the big points are often found in time.
A marketing team that was spending 60% of their week on production and reporting and 40% on strategy got those numbers turned around. People focus on work that requires taste, judgment, and relationship building. AI handles repetitive tasks that have been eating up their calendars.
One collaboration reduced a client’s production cycle from three weeks to four days. One has completely automated its weekly reporting, freeing the senior analyst to focus on actual analysis instead of pulling numbers from slides. Third they rebuilt their email lifecycle from scratch using AI-generated content segmentation, which reduced their cost per acquisition by 30% in the first 60 days.
None of those results required anyone to be fired. They need to move people from low-level jobs to high-level jobs. That’s the part of the AI discussion that gets lost in the layoffs.
What I Can Tell Any Marketing Leader Reading This
You don’t need to hire a company to get started. Choose one workflow for your team that is repetitive, time-consuming, and doesn’t require deep creative judgment. Step by step map. Then ask if an AI tool can handle any of those steps today.
Start by dealing with reporting. Next, focus on competitive research. Consider pre-draft content production an early win. Finally, start the process wherever the pain is greatest and the risk is lowest. Get the victory. Show the group what is possible. Then expand.
The companies that will struggle are the ones that wait for someone to hand them the playbook. The companies that will win are the ones that are running their own tests right now, even the weird ones, and learning what works within their particular context.
An audit is a systematic way of doing what every marketing team should already be doing: taking an honest look at how time is being spent and asking if there is a better way. AI just made “better” more accessible than it was 18 months ago.
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