How Digital Marketing Agencies Should Actually Be Using AI

A year ago, I watched one of our senior strategists spend a Tuesday morning doing something he shouldn’t have had to do. Not because it was beneath him, because it was beneath the point of him being in the room at all.

He was digging through a client’s paid media dashboards, slicing spend by campaign, checking landing page performance, cross-referencing it against search data. Important, necessary work, but a complete waste of his best hours.

That’s what nobody says clearly enough about AI in agency life. The problem has always been smart people spending enormous amounts of time on work that didn’t require them to be smart. AI solves for that.

At Wheelhouse DMG®, AI is now integrated into core Digital Strategy workflows, including automated technical SEO auditing, landing page performance monitoring, and competitive intelligence. Rather than replacing senior judgment, these workflows deliver faster data analysis so strategists can focus on the insights that drive client outcomes.

How AI Gives Digital Marketing Strategists More Leverage

We’re not interested in doing the same work with fewer people. For us, AI exists to make our best people better.

Our clients are complex. Healthcare and medical device brands operate in regulated, high-stakes environments where the wrong strategic call has real consequences. It’s exactly the kind of work that gets better when experienced people have the tools and resources to dive deeper, make data-informed decisions, and provide evidence-backed guidance without spending countless hours to get there.

Great digital strategy is built on knowing the data deeply, thinking clearly about what it means, and translating insight into action that actually moves a business. None of that changes with AI.

What does change is the ratio of time our team spends doing those things versus the time they spend collecting, cleaning, and organizing the inputs. What I don’t want is a series of AI capabilities managed by junior employees. The goal is to put more data, delivered faster, into the hands of smart, capable people, so that they can exercise their judgment, expertise, and experience to know what to do with the insights AI helps us uncover.

That’s a different approach than many agencies are taking right now, and I think it’s the right one.

Use Case #1: AI-Powered Landing Page & UX Conversion Analysis

What it does: Structured conversion audits delivered in a fraction of the time.

How it works: A multi-step Claude workflow applies the LIFT Model and 7 Levels Heuristic frameworks with built-in strategist logic at every stage.

Client benefit: Faster identification of conversion barriers and prioritized, actionable recommendations tied directly to business goals.

Evaluating a landing page for conversion performance sounds like it should be straightforward. In practice, it rarely is. A thorough audit means applying multiple analytical frameworks in sequence, separating what’s actually on the page from what you’re inferring about it, cross-checking for contradictions between frameworks, and then synthesizing everything into recommendations a strategist can act on the same day. Done manually, that’s two to three hours of focused work per page, and it’s easy to do it inconsistently.

We built a structured AI workflow that runs the entire evaluation process the way a senior strategist would. The workflow applies the LIFT Model, which assesses a page’s value proposition, relevance, clarity, distraction, anxiety triggers, and urgency signals, and then immediately follows that with André Morys’ 7 Levels Heuristic, which evaluates the psychological journey a user takes from first impression through post-conversion confidence. Both frameworks are scored with specific evidence from the page, not general impressions.

What makes this more than a template is the verification layer we built in. After the LIFT and 7 Levels analysis runs, the workflow audits its own findings. It confirms that every cited element actually exists on the page, labels anything that’s a best-practice inference rather than a direct observation, and checks the two frameworks against each other for contradictions. Any finding that can’t be grounded gets removed or rewritten before it reaches the report.

The output is a structured report with a quick-glance summary, scored findings across both frameworks, and recommendations separated into quick wins and bigger lifts, all tied back to the specific business goal the client is trying to address. Our strategists aren’t reading a wall of AI-generated text and deciding what to do with it. They’re reading a document built with the same logic they’d apply themselves, delivered in a fraction of the time. This doesn’t go directly to the client. It goes in front of a senior strategist who has a thorough starting point, analyzed rigorously, so they can hit the ground running.

Use Case #2: AI-Powered Technical SEO Auditing

What it does: Automated overnight crawls flagged and prioritized by morning.

How it works: Screaming Frog + Claude, with encoded senior-strategist triage logic.

Client benefit: Issues caught in hours, not weeks.

Running a crawl with Screaming Frog and combing through hundreds of flagged issues to find the ones that actually matter is one of the more time-consuming and frankly tedious parts of managing a complex web presence. It’s not glamorous. But a website with undetected crawl errors, broken redirects, or degrading Core Web Vitals is a website quietly losing ground, and in competitive categories, quietly losing ground means ceding territory to someone else. Our research into how top-ranked healthcare provider sites compete technically shows exactly how much ground there is to lose.

We now schedule automated crawls to run in the background, often overnight, and pipe that data into Claude for analysis. The key is how we’ve set it up. We built detailed instructions that tell Claude exactly how to interpret the output: what to prioritize, what thresholds matter, how a senior strategist would triage these issues, what context makes something urgent versus routine. We encoded our methodology, so the output isn’t generic AI analysis. It’s our thinking, applied systematically.

Our strategists wake up to a prioritized readout of what’s new, what’s critical, and what needs attention. Issues that might have gone unnoticed for weeks or months get flagged the next morning. They no longer have to spend time digging through the data. They read the diagnosis and decide what to do about it.

Use Case #3: Automating Campaign Performance Monitoring with AI

What it does: Recurring performance digests that automatically collect and analyze campaign data across platforms, sliced by campaign, creative, and audience.

How it works: Automated workflows pull multi-platform data through the same analytical framework a senior strategist would apply manually.

Client benefit: Underperforming landing pages and shifting campaign signals get flagged before they compound.

Keeping a close eye on how campaigns are performing is essential. It’s how you know when messaging is losing resonance, when a landing page is underdelivering, when spend needs to shift. But pulling data across multiple platforms, slicing it by campaign, creative, and audience, and identifying where the lever is? That’s hours of work, done properly.

We’ve built recurring performance digests that do the data collection and analysis automatically. Again, the how matters. We gave our AI workflows the same framework a senior strategist would use to review this data: what benchmarks to compare against, what patterns signal a real problem versus normal variance, what questions to ask before recommending a change.

That means the output isn’t generic AI commentary. It’s actually our thinking, applied at scale and speed. Our strategists get the signal, and then they apply the judgment. The time they used to spend getting to the question is now spent answering it.

These aren’t isolated examples. The same principle runs through everything we’ve built. Monitoring competitor positioning in real time, synthesizing user behavior data across markets, cross-referencing organic and paid coverage to surface gaps. In every case, the system is only as good as the thinking that went into designing it.

How AI Delivers More Value for Digital Marketing Clients

This is what I want to be clear about. We are not in the business of delivering deliverables. The goal is never the slide deck or the audit or the report. It’s to grow our clients’ businesses.

AI helps us work faster. But what it really does is let us deliver more value, more consistently, and at a level of depth that would have required significantly more time to achieve before. When a client gets a proactive insight about a competitor’s search strategy shifting, or a flag that a major technical issue appeared overnight, or a sharper set of recommendations at the quarterly business review, they’re not thinking about the tools that made it possible. They’re experiencing a team that seems to always know what’s going on.

That is the result of building systems that keep our strategists informed, without asking them to spend their best hours staying informed.

What We’ve Learned Building AI Workflows for Digital Marketing

It takes real investment to do this well. Not just in the tools, but in the design and in the process of taking how a great strategist thinks and translating that into something a system can execute consistently. It requires governance, so the outputs get reviewed and refined over time. It requires the humility to admit when something isn’t working and rebuild it.

The temptation is to stand up a few prompts, call it a workflow, and move on. The ones that actually change how your team works are the ones built with the same care you’d put into a client strategy.

AI as a shortcut produces shortcuts. AI as infrastructure produces compounding advantage.

To learn more and stay up to date with our AI workflow implementations, follow along on LinkedIn, where I will continue sharing examples of AI workflows and what they look like in practice. If you’re curious about building AI systems that work, check out our three-part framework for effective AI implementation.