In the past year, AI has shifted from a buzzword to an everyday companion for digital marketers and analysts. Google, Meta, Microsoft, TikTok, Amazon: every major platform now embeds AI deeply into its products. And while that creates huge opportunities, it also forces us to rethink how we work, how we add value, and how we maintain control.
At Semetis, AI has become a catalyst for efficiency, creativity, and strategic decision-making. But it also comes with new responsibilities: knowing how to prompt, how to validate, and how to combine human expertise with machine intelligence.
Here are the five biggest things we learned about AI this year, and practical ways digital marketers can use AI to work smarter, not harder.
1. AI is everywhere, and that means we’re giving up some control
AI is no longer a standalone tool. It’s built directly into Google Ads (PMAX asset suggestions, bid strategy explanations), into Meta Advantage+, into GA4’s insights, and even into dashboards like Looker Studio.
This is both exciting and scary.
What we learned
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Platforms push automation aggressively: we no longer manually optimise every keyword, bid, or placement.
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Transparency decreases. We often see what happened but less of why.
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The marketer’s role shifts from operator → strategist → quality controller.
Practical tips
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Define strong inputs for automated systems (creative variety, audiences, product feeds, business goals). AI performs only as well as your input structure.
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Use AI tools to understand black-box systems. Ask ChatGPT to explain changes in performance, differences in attribution, or why a bid strategy behaves in a certain way.
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Challenge the machine. Always validate AI’s decisions with your own expertise, business context, and platform knowledge.
2. AI helps launch campaigns faster, without sacrificing quality
One of the most immediate benefits of AI is reducing “blank page time.” Copywriting, brainstorming, ad variations, audience hypotheses, what used to take hours now takes minutes.
What we learned
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AI accelerates ideation dramatically.
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It ensures consistency in tone of voice across campaign assets.
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It doesn’t replace your creativity, it boosts it.
Practical tips for campaign creation
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Ad copy generation
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Ask AI for multiple versions of headlines and descriptions, adapted to:
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Google RSAs
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Meta primary texts
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Different tones (informative, playful, premium, urgent)
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Creative testing ideas. Use AI to propose hooks, angles, visuals, and A/B test concepts.
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Audience definitions. AI can help structure interest groups, personas, or behaviour-based segments.
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Landing page suggestions. Ask AI how to improve clarity, structure, or conversion focus.
Example prompt to include in your workflow
“Create 10 RSA headlines under 30 characters that highlight our USP: free next-day delivery. Keep the tone modern and simple.”
This doesn’t replace your expertise, it speeds up the path to high-quality results.
3. AI supercharges reporting and insight generation
Reporting has always been one of the most time-consuming tasks for analysts. Pulling numbers is easy, explaining them, finding insights, and crafting the narrative is what takes time.
AI helps tremendously here.
What we learned
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AI excels at summarising complex datasets.
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It helps spot anomalies or trends faster.
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It improves the clarity of client-facing communication.
Practical tips for reporting
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Paste your data (or a summary of it) into for instance ChatGPT and ask for:
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Performance summaries
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Top insights & next actions
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Executive-friendly explanations
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Anomaly detection (“which metric behaves abnormally and why?”)
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Quarterly or yearly wrap-ups
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Use AI to turn raw tables into clear sentences:
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“ROAS increased by 24% MoM thanks to higher average order value and more efficient PMAX distribution.”
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Ask AI to rewrite your report in:
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simpler language
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a more strategic tone
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bullet-point format
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client-ready wording
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Bonus
AI can help structure a QBR (Quarterly Business Review) by highlighting:
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key wins
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areas of underperformance
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upcoming opportunities
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action plan for next quarter
4. AI makes keyword analysis & creative exploration faster and smarter
Keyword research is still essential, even in an era of broad match and automation. The difference today is that AI can create, cluster, and analyse large sets of queries faster than ever.
What we learned
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AI improves speed and completeness of keyword ideation.
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It helps structure keywords by intent (informational, commercial, transactional).
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It reveals gaps and opportunities based on competitor messaging.
Practical tips for keyword workflows
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Generate long-tail variants for SEO or PPC.
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Cluster keywords into themes for PMAX or SA360 (Search Ads 360).
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Ask AI to analyse:
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search intent
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potential negatives
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category gaps
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Use AI to rewrite keywords into user-friendly ad copy angles.
Example task
“Cluster these 150 keywords into 6 intent groups and suggest matching ad messaging.”
This helps create cleaner setups and stronger creative consistency.
5. AI is becoming a core skill for marketers: training, debugging & collaboration
Beyond campaigns and reporting, AI has become a daily companion that supports upskilling and problem solving.
What we learned
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AI democratizes technical knowledge (SQL, GA4, GTM, metadata, scripts).
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It removes friction when learning new tools or understanding updates.
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It improves collaboration across teams and with clients.
Practical ways to use AI as your partner
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Training buddy. Ask AI to explain a concept (e.g., attribution models, bid strategies) at different difficulty levels.
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Debugging assistant/ When stuck with GTM triggers, GA4 parameters, tracking issues, or simple scripts, AI provides step-by-step logic.
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Brainstorm facilitator
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Use AI during ideation workshops for:
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campaign concepts
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content angles
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product positioning
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creative ideas
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Internal collaboration. Turn long threads or emails into summaries and action points.
In short: AI helps you learn faster and execute smarter.
A note on prompts, inputs and pitfalls
This article deliberately focuses on practical tips for working with ChatGPT and AI tools, because one key lesson stands out:
the better the input, the smarter the machine behaves.
However, AI is not magic. It doesn’t understand your business context, your client constraints, or your strategic priorities unless you explicitly provide them. Well-structured prompts, including objectives, tone of voice, platform constraints, and target audiences, make the difference between generic output and genuinely useful results.
At the same time, AI should never be used blindly. While it excels at speed and pattern recognition, it can still:
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hallucinate facts
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oversimplify complex situations
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miss nuance or business-specific constraints
That’s why human validation remains essential. Every AI-generated output should be reviewed, challenged, and refined by a digital marketer or analyst who understands the data, the platforms, and the client’s reality.
In short:
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AI works best as a co-pilot, not as an autopilot.
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Use it to accelerate thinking and execution, but always double-check the output before acting on it.
Final thoughts: AI won’t replace marketers, but marketers using AI will replace those who don’t
AI is not here to take over digital marketing. It’s here to transform it.
The value of a digital marketer no longer lies in manually tweaking bids or writing every ad from scratch. It lies in:
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asking the right questions
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validating AI’s output
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providing business context
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shaping strategy
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making smart creative and analytical decisions
AI covers the repetitive and operational work. You provide the expertise, experience, and judgment that machines don’t have.
The marketers who thrive will be the ones who embrace AI as a partner, not a threat, learning how to guide it, challenge it, and combine its power with human insight.
This year taught us one clear lesson: AI is now part of the job, and it’s making us better at what we do.
Lisa De Sloovere
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