Artificial intelligence is rapidly changing the way marketing agencies operate.
While much of the conversation focuses on content generation and productivity tools, some agencies are using AI to redesign the systems that power campaign management, reporting, analytics, and strategic decision-making.
One marketer taking that approach is Algis Aukštuolis. Originally from Lithuania and now based in Cincinnati, Ohio, Aukštuolis has spent more than 15 years working across digital advertising, analytics, marketing infrastructure, and business growth. Today, as Director of Digital Media at The Ohlmann Group, he helps organizations build connected systems that enable smarter decisions, faster execution, and stronger marketing outcomes.
Rather than viewing marketing as a collection of separate channels, he approaches it as an interconnected ecosystem where data, technology, workflows, and strategy all influence one another. In this interview, Algis Aukštuolis shares how his team is using AI today, where it delivers the greatest impact, and how he sees the role of agencies evolving over the next year.
At a Glance
Role: Director of Digital Media
Company: The Ohlmann Group
Experience: 15+ Years in Digital Marketing
Specialization: Marketing Systems, Analytics, AI Workflows, Programmatic Advertising
Location: Cincinnati, Ohio, USA
"AI has helped us design better systems and workflows behind the scenes, which means more of our energy goes into framing the actual question instead of assembling the pieces."
Q&A
Which AI tools are most valuable to your team today?
Claude with a custom MCP server stack is the centerpiece. We’re running MCPs for six ad platforms and systems simultaneously, connected into one workspace alongside our operational and reporting layers.
The value isn’t the number of integrations, but that connecting all of them in one place is what makes cross-system insight possible. We can check our thinking against hard-to-find data and client conversations without jumping between tools, which directly accelerates and improves strategy.
Where has AI delivered the clearest productivity gain so far?
Mainly two places.
The first is campaign build time: our vibe-coded campaign builder reads media plans from our planning system and programmatically creates campaigns across platforms, cutting lead time from 2-4 days to roughly 30 minutes, including human QA.
The second is automated reporting. We built a skill that pulls live Funnel.io spend data across various ad platforms, cross-references the National Weather Service forecast, and drafts a client-ready email from a single command.
That used to be hours of manual data pulling and assembly.
Deck production is a close third because we’ve encoded structure into defined Claude skills, we can input our thinking and data without rebuilding layout and formatting every time.
Can you share one example where AI improved a workflow, campaign, or client outcome?
A water park’s weather-triggered campaign is the clearest one.
We built a dual-layer architecture where real-time weather thresholds, temperature and precipitation, drive same-day digital spend for the water park, pushing budget to the upper range on hot, dry weekends and suppressing it when it rains.
That’s a strategy that’s hard to execute manually, and AI is what made building and operating it feasible. It’s an example of AI enabling a new capability, not just speeding up an old one.
On top of that, the same infrastructure lets us quickly identify underperforming ad spend and surface new audiences or behaviors worth pursuing, with reports generated from meeting notes, emails, and internal data, so optimization decisions that used to wait on manual analysis happen in near real time.
How has AI changed the way your team approaches creative or strategic decision-making?
It’s freed us up to focus on the strategic problem rather than the mechanics of execution.
AI has helped us design better systems and workflows behind the scenes, which means more of our energy goes into framing the actual question instead of assembling the pieces.
It’s also made us more willing to make bigger bets: when we need to pivot a client’s strategy, AI helps us pull together the supporting data and rationale quickly, so we’re more likely to pursue the stronger move rather than defaulting to the safe one.
The value has shifted toward solving the problem well rather than producing the underlying work product.
How are clients responding to AI being used in your agency’s workflows and delivery process?
We present the work on its merits rather than leading with the tooling, so clients aren’t reacting to “AI” as a label, they’re reacting to output.
Early on, some outputs raised eyebrows: detectable AI language, some incoherence in presentation.
As we’ve dialed in our skills and added context layers, client meetings have gotten consistently more positive, and in many cases trust has increased.
What they notice is that we move quicker and bring sharper insights.
What has been the biggest challenge when integrating AI into your agency’s work?
Two layers, one technical and one organizational.
The technical challenge is governance and data.
Some of the team still uses AI as a basic chatbot, which yields generic answers.
The hard part isn’t connecting data, it’s making sure the model understands why certain data matters and how to prioritize what to use in a given process or query. The organizational challenge is concentration of knowledge.
Sophisticated AI workflows tend to centralize in one person, and I’ve been the sole technical owner of this infrastructure. That’s why we’ve started building read-only MCP forks and installers for people across the agency, so the capability isn’t dependent on a single point of failure.
What guardrails or best practices are most important for responsible AI use in your team?
Two stand out:
Using models that don’t train on our data outside the workspace, and cleaning and structuring data so it provides genuine context rather than noise.
The discipline is less about access and more about teaching the system how to weigh and prioritize information.
Distributing the infrastructure, so more than one person can operate and maintain it, is the third practice we’re now treating as essential.
Where do you see the biggest opportunity for AI in marketing agencies over the next 12 months?
A shift in the value model.
Our business has run on billable hours, and AI is a real risk to that.
But clients are simultaneously raising the bar on insight quality. Instead of billing 10 hours of research, we produce better research faster and fold it into what we already know about the client, then reinvest those hours into client contact and relationship-building.
The opportunity is moving value away from hours of technical execution toward how well we solve a strategic problem and execute the solution, with results sitting on top of a relationship built on trust.
Final Thoughts
Much of the discussion around AI focuses on content generation, automation, and productivity gains. This conversation highlights a different perspective.
For Algis Aukštuolis and his team, the greatest opportunity lies in building better systems – connecting data, improving workflows, reducing complexity, and helping marketers make smarter decisions faster.
As agencies continue adapting to AI, success may depend less on how many tools they adopt and more on how effectively they integrate those tools into the way they think, collaborate, and solve problems.
The approach shared by Algis Aukštuolis demonstrates that the future of AI in marketing is not simply about automation. It is about building systems that enable better decisions, stronger strategy, and more meaningful client outcomes.
If the last decade was about mastering digital channels, the next may be about mastering the systems that connect them.
Connect with Algis Aukštuolis:
