What we really do with AI (and what we don’t)

Over the past few months, we have been working hard internally on further refining our processes, our output, and our way of collaborating with clients. Not only at the campaign level, but especially at the strategic level.

We notice that online marketing is becoming increasingly technical, but at the same time demands more and more direction, choices, and clear priorities. That is precisely why we have devoted a great deal of attention to improving our working methods behind the scenes. Think of better analyses, tighter briefings, smarter checks, faster switching between disciplines, and more control over further development.

In addition, we have started integrating AI much more actively into our daily operations. Not as standalone experiments, but as a structural part of how we conduct research, optimize campaigns, prepare content, improve websites, and identify opportunities.

• New SEO projects in which we have scaled up content structures, landing pages, and local findability.
• Google Ads accounts that have been cleaned up and re-targeted based on better search intent and conversion data
• Websites and funnels that have been improved in terms of usability, loading speed, and conversion
• New design and development projects in which brand, UX, and performance are more closely aligned.
• Strategic processes where we not only handle execution but also actively contribute ideas regarding positioning, proposition, and growth opportunities.

AI is changing the market rapidly. Just like you, we notice this every single week.

At the same time, we do not believe in blindly automating everything. For us, the real value lies in the combination of human and machine. AI can be of enormous help with speed, scale, and analysis. But without context, direction, and quality control, it often remains superficial.

We therefore use AI to enhance our profession, not to replace it.

In concrete terms, this means that we use AI to gain better insights faster, investigate more variants, recognize patterns, perform technical checks, and work more efficiently. However, all important choices, interpretations, creative decisions, and final checks remain with our team.

SEO is arguably one of the fields currently experiencing the greatest impact from AI. Not only because content production is changing, but primarily because search behavior is changing. People search differently, search engines display increasingly different results, and AI overviews and conversational search mean that traditional SEO is no longer sufficient.

That is precisely why we do not use AI in SEO simply to quickly fill pages. In fact, that is exactly what we are critical of. Bad AI content harms your brand, adds little value for users, and will actually work against you in the long run.

We use AI in SEO in a much smarter way.

First and foremost, we use AI for research and analysis. For example, to analyze large volumes of search terms, competitor pages, search intent, and SERP patterns more quickly. This allows us to see faster which topics are relevant, where there are gaps in the market, and which content opportunities can really make a difference.

In addition, AI helps us structure content. This includes setting up logical page layouts, developing supporting subtopics, formulating FAQ directions, and clustering topics within a clear site architecture. As a result, we work more consistently and strategically, especially for larger websites or SEO projects with many pages.

AI also helps on a technical level. For example, by identifying technical issues, interpreting crawl data, detecting internal inconsistencies, checking metadata at scale, and recognizing patterns in lagging pages. This makes it easier to identify areas for improvement more quickly and set priorities.

Furthermore, we use AI to strengthen content briefings. Instead of just looking at a keyword and volume, we can build a complete picture of what a good page should contain much faster. What questions are being asked, which angles are important, which competitors dominate, what tone fits, what structure works, and where is there room to be better than what is already there?

It is important to emphasize that the output that ultimately goes live is not blindly created and published by AI. We use AI to make the preliminary phase smarter and to arrive at better frameworks more quickly. After that, the real work actually begins. That is when we look at the nuance, the brand, the positioning, the commercial relevance, and the quality.

Additionally, within SEO, we are increasingly focusing on the rise of AI search engines and AI-generated search results. This means that SEO is no longer just about positions in Google, but also about visibility in a landscape where answers are increasingly summarized directly. As a result, authority, clarity, source quality, and semantic completeness are becoming even more important.

For clients, this means that we approach SEO less and less as simply writing content around keywords, and more and more as a combination of structure, authority, technical quality, search intent, and substantive added value. AI helps us execute this faster and more precisely, but the strategy behind it remains human.

We are now also actively deploying AI within SEA and paid advertising, but again in a way that goes beyond simply “the tools will do the work”.

Platforms such as Google Ads and Meta naturally already run largely on machine learning. That is not new. What is new, however, is how we as an agency can align better with this through additional analysis, better input, and smarter interpretation.

We use AI within SEA, among other things, to analyze larger volumes of search terms, ad variants, performance data, and account structures more quickly. This allows us to see faster where inefficiencies lie, which campaigns are too broad, where budget is leaking, which search intents are not being captured effectively, and where opportunities to scale up actually exist.

AI also helps with ad copy, but not simply to automatically generate standard texts. It primarily helps us test multiple angles faster. For example, different value propositions, tone of voice directions, call to actions, hooks, and variations per target audience or stage in the funnel. From there, we translate this into strong ads that align with the brand and strategy.

In addition, we use AI to recognize patterns in campaign data more quickly. Sometimes, the difference does not lie in a single metric, but in a combination of signals. For example, declining CTR in combination with specific search terms, or conversion loss related to landing pages, device type, time, or message. AI helps to form hypotheses faster, which we then evaluate and translate into concrete optimizations.

Another important area is landing page analysis. Good ads are not just about bidding and targeting, but also about the page you send traffic to. AI helps us assess more quickly whether the ad’s message flows logically across the page, whether there is friction in the user journey, whether the structure is correct, whether objections are sufficiently addressed, and where conversion potential is being untapped.

Within larger accounts or more complex campaigns, AI can also assist in preparing scenarios. Consider different budget allocations, new campaign logic, audience segmentation, or expanding into additional search directions. This allows us to compare multiple routes more quickly and make better-informed choices.

What we emphatically do not do is rely blindly on platform automation without critical scrutiny. Especially now that so many systems are automated, it becomes more important to remain vigilant regarding input, tracking, conversion quality, commercial feasibility, and strategic choices. An algorithm can optimize for a goal, but only if that goal is properly configured and the context is well understood.

That is where the added value lies for us as an agency. AI accelerates analysis and broadens possibilities, but interpretation, direction, and control remain essentially human work.

AI has now also been given a clear place in our working methods within design and development.

In design, AI helps us not by replacing creativity, but rather by making the creative process smarter. For example, in the exploration phase of a concept, when analyzing the competition, gathering style directions, structuring brand principles, or accelerating the development of initial ideas and lines of thought.

That does not mean that a brand identity or website design is “made by AI.” On the contrary. What AI primarily does is help us arrive at a richer starting point more quickly. More references, more scenarios, more visual routes, and getting a feel for what works and what doesn’t faster. As a result, we have more time left for the real design choices that make the difference.

AI is also interesting at the UX level. For example, for analyzing page structure, user flows, friction points, and information hierarchy. It helps to more quickly assess whether a structure is logical, whether content blocks are well-constructed, and where users might drop off or experience confusion. In combination with data, experience, and common sense, this helps to make websites more user-friendly and effective.

Within development, we primarily use AI as an accelerator and an additional layer of control. For example, when writing technical logic, checking code, finding potential errors, accelerating repetitive development tasks, or preparing technical solutions. As a result, we can work faster and more frequently compare multiple solution approaches side by side.

In addition, AI also assists with technical documentation, debugging, and QA. Especially for larger websites or custom projects, this can save a significant amount of time. Not by deploying code without thinking, but by identifying bottlenecks faster and organizing development work more efficiently.

On content-driven websites or platforms, we are also seeing increasing opportunities to functionally deploy AI for end users. Think of smart search functions, recommendations, internal assistants, content enrichment, or workflow automations. We critically assess this on a project-by-project basis. Not because AI has to be everywhere, but because it sometimes adds genuine functional value.

For us, the same applies here: AI is not a substitute for good design or solid development. Bad choices remain bad choices, even if they are made faster. But if you use AI well as a tool, you can research faster, test better, build more efficiently, and maintain a greater focus on quality.

For us, AI is no longer a thing of the future. It is something we are already using in our work today.

What you can expect from us is that we continue to actively develop in this area. We closely monitor changes, test new applications, further expand our processes, and constantly critically assess where AI does and does not add value.

That also means being honest about it. Not every AI solution is smart. Not every trend is relevant. Not everything that is new is automatically better. We only want to deploy what truly delivers value for our clients.

So if you are wondering whether we are already working on this: yes, fully. But not superficially or in a fashionable way, but practically, substantively, and with a focus on results.

Curious what this specifically means for your company?

Every client, website, campaign, and growth phase is different. Therefore, the way in which AI can be smartly deployed also differs.

For some, the greatest opportunity lies in SEO research and content structure. For others, in smarter ad optimization, better landing pages, or more efficient development processes. And sometimes, precisely in refining the strategy before you even start applying AI.

If you would like to brainstorm about how we would apply this to your situation, feel free to send us a message. We would be happy to take a look with you.