How One Team Saw An 80% Increase in Enterprise Leads By Leveraging AEO
AI Start: Marketing leaders struggle to begin AI projects due to overwhelming choices and uncertainty on first steps.
High-Impact Improvements: CroMetrics uses small, high-impact AI improvements to address workflow challenges rather than pursuing perfect strategies.
AI Search Optimization: CroMetrics optimizes content for LLMs, seeing increased enterprise referrals from AI-driven platforms like ChatGPT.
Human-AI Balance: AI aids research and early development at CroMetrics, but human oversight ensures quality and brand consistency.
Identify Friction: Leaders should prioritize easing workflow friction points to gain efficiency before implementing broader AI capabilities.
Most marketing leaders know they should be doing something with AI. The problem isn't lack of awareness. It's not knowing where to start.
“I find leaders and organizations are almost so overwhelmed by AI that it becomes paralyzing,” says Gwen Hammes, co-CEO of CroMetrics, a digital marketing agency working with brands like Bombas, Starz, and Intuit. Her advice is to start small.
Hammes refers to this as the “1% better every day” rule, a mindset built on consistent, incremental progress rather than the pursuit of a comprehensive plan before taking any action at all. Instead of chasing a perfect AI strategy, CroMetrics is focusing on small, high-impact improvements and solving workflow friction points.
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When AI Becomes Your Acquisition Channel
Last year, CroMetrics faced the same question every marketing organization was asking, how do you do search well when search is fundamentally changing?
Google traffic was declining and LLMs were answering questions directly in chat interfaces. Zero-click behavior started becoming the norm and the old SEO playbook stopped working as well.
We embedded AEO/GEO into our technical SEO offering and began by testing it on our own business first.
Hammes explains, that their intent was to optimize content not just for search engines, but for the LLMs themselves.
The team embedded Answer Engine Optimization (AEO) and Generative Engine Optimization (GEO) into their technical SEO approach, then tested it on their own business before rolling it out to clients.
She describes her work at CroMetrics, saying "our work spans complex customer journeys, multi-channel ecosystems, and enterprise-level growth programs where strategy, execution, and data must operate as one system."
The work focused on making content more interpretable to AI systems with structured data, clear summaries, strong author attribution. These are all signals that help LLMs understand and cite content with confidence.
From Philosophy to Process
The tactics are straightforward enough. By starting with structured data, TLDRs, and detailed author attribution, they saw momentum. "A lot of companies are saying, okay, well, how do we optimize our website for people, as well as the bots, as well as the LLMs," she notes. What her team saw as a result is nothing short of impressive.
We saw an 80% increase in AI-driven referral traffic alongside measurable lifts in search visibility and direct traffic translating into qualified inbound and pipeline.
For context, most organizations are still treating AEO as a future consideration, something to revisit once the SEO fundamentals are locked in. CroMetrics treated it as a present-tense competitive advantage, and the traffic data reflects that timing.
But what surprised Hammes most is the referral traffic type.
We’re seeing referrals coming directly from tools like ChatGPT and Claude. And interestingly, a lot of that traffic is from large enterprises.
Enterprise buyers aren't just experimenting with LLMs. Instead, they're using them for vendor discovery and evaluation. So, if your content isn't optimized for AI search, you're invisible to a growing segment of high-value prospects.
Building A Decision Engine
The AEO success reflects a broader philosophy at CroMetrics, so they can use AI where it creates leverage, keeping humans where judgment matters.
The company built ImpactLens, a custom predictive prioritization engine. "We use it to evaluate behavioral signals, historical experimentation data, and client-specific inputs to predict which experiments are most likely to drive measurable growth," Hammes explains. "This allows us to prioritize with far greater precision before committing resources."
But there's a boundary, in her team doesn't rely wholly on AI. Instead, it informs where to focus and what may work, but the strategy, client leadership, creativity, and final decisions are owned by people. Hammes reasons that "growth still requires context, judgment, and original thinking."
This is the pattern that scales from experimentation to productive AI adoption. AI handles pattern recognition at scale. Humans take care of the parts that require context, relationship management, and judgment calls when data is ambiguous.
Where AI Still Falls Short
Hammes notes that when it comes to brand clarity, people also maintain responsibility for quality assurance. She draws the line by sharing how, for CroMetrics, "AI has not yet delivered the level of reliability required for fully autonomous execution in building production-ready experiments or high-quality creative at a standard suitable for enterprise brands." However, there are areas where her team is using it to make huge strides.
AI meaningfully accelerates research, synthesis, and early development, but human oversight remains essential to ensure high-quality and brand integrity.
For agencies working with enterprise clients, or companies targeting this group, AI can still skirt the quality bar required for production-ready creative or strict brand standards. Meaning, output still requires a human pass before it goes anywhere.
It’s about the work and the impact, but it is so much about relationships. I always say, as long as there’s human clients on the other side of the table, companies like ours will always exist.
Professional services run on trust, on reading a room, understanding what a client truly needs versus what they said they need, and making judgment calls that data doesn’t resolve. These are not gaps AI is likely to close anytime soon, and the smart move is to treat it as the differentiator it is rather than a limitation to work around.
Start With Friction
For organizations trying to figure out where to start, Hammes suggests leaders focus on friction first. "Rather than a specific marketing system, leaders should identify the recurring friction points across workflows where small inefficiencies compound and consume disproportionate time," she advises.
CroMetrics surveyed their own team to identify where AI could have the biggest impact. The answers weren't glamorous. They came in the form of approvals, compliance reviews, and early-stage content development.
One of our first objectives was simple. Eliminate the paper cuts that drain capacity so teams can spend more time on strategic thinking, creativity, and higher-impact work that drives growth.
This is the opposite of the AI strategy that most organizations pursue. Instead of starting with capabilities, asking what can AI do, start with pain points, asking where your time is wasting time and not seeing the ROI.
This is where a lot of AI adoption efforts go sideways. Leaders start with capability, noting what a tool can do, but struggle to connect that to a tangible business need for every department.
You don't need executive buy-in to test AI for compliance review acceleration or budget approval to use ChatGPT for first-draft synthesis. If the need is already there, you’re just finding the right tool for it.
Scrappiness Over Best Practices
Hammes' comfort with experimentation comes from an earlier chapter in her career, working with teams in Mexico who operated without established playbooks. She recalls how, "it was such a level of scrappiness and of everybody coming together and figuring it out and getting it done." Instead, the team was were maniacally focused on the objective and the overarching direction.
Nothing really scares me or overwhelms me. Maybe because I’ve had that experience, but also being over 20 years in this industry, you’ve seen and gone through a lot and you always know at the end of the day, we’ll get there. There’s a solution to everything and perseverance is real.
A builder's mindset is particularly valuable now. There are no established AI best practices yet—just experiments, some successful, most not. The organizations that wait for the playbook will be years behind the ones willing to test and iterate.
The Micro-Action Mandate
Hammes' experience at CroMetrics offers a blueprint for organizations who feel stuck amidst AI hype.
First, start with friction, not strategy (hint—you can survey your team to unearth this). The advice she offers to leaders feeling overwhelmed is to keep things simple and start small.
What are those little micro actions? And what you find is that they quickly start adding up. The worst thing is just no action, you're just paralyzed and you're kind of perpetuating the problem that you have.
Next, test on yourself before rolling to clients. Search your own brand in ChatGPT, Perplexity, and Claude. Do it more than once, and run several different queries to see where your brand emerges.
Then, search your company name directly. Follow it up by searching for the problems you solve, the category you compete in, and specific questions your customers tend to ask before they buy. Document your findings with screenshots.
You’re looking for a pattern, not just presence.
You'll see whether you're showing up in the right context, or if your company is being surfaced in ways that align with your actual positioning. The organic search footprint you’ve built over years may have almost no overlap with how LLMs are currently representing your category.
The gap between where you show up and where you want to be is your AEO starting point. It’s also a faster way to make the case for content investment than most attribution models will ever give you.
The 1% better philosophy works because it removes the pressure to have a complete AI strategy before starting. You don't need to know where you'll be in a year. You just need to know what you'll try today.
"I am a big believer in 1% better every day," Hammes says. Take the first step."
The 80% increase in enterprise leads her team saw didn't come from a rigid AI strategy, and it didn't happen overnight. It came from taking one step, measuring the result, and taking the next one.
This is not just good advice for AI adoption. It's good advice for anything worth doing.
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