The last few years have seen AI evolve from curiosity to capability. For marketing teams, that transition has been rapid. The tools that once felt experimental are now powering daily operations. From campaign ideation to visual asset production, AI has become a practical companion in everyday workflows.
But adoption is only part of the story. What matters now is how we scale and integrate AI in ways that are sustainable, observable, and secure. At NordVPN, our experience experimenting across dozens of tools has led to clear lessons about what it really means to bring AI into marketing.
From experimentation to structured integration
A few years ago, in our early experimenting stages, we embraced a spirit of exploration. At one point, the marketing team were testing over 80 different AI tools, including large language models, data analysis tools, and video and audio platforms. The goal was to discover what worked and where AI could bring speed and flexibility to routine processes.
That phase helped shift mindsets. Initially, some marketers were concerned they might have to compete with AI. But as experimentation progressed, it became clear that AI tools did not replace creative people – they empowered them. Teams began spending more time on high-value challenges, while AI took over monotonous or tedious tasks.
How AI reshaped marketing workflows
For visual content, AI tools like Midjourney, Nano Banana, Veo and others allow teams to generate on-brand images quickly and at scale. In a lot of scenarios AI-generated visuals often require additional refinement, which may seem like it defeats the efficiency purpose. But in cases where campaigns need custom imagery that stock banks simply do not have they proved to bring impactful solutions. As well as making the storyboard building process a much less of a burden for creative teams.
Other tools have helped streamline repetitive production work. Remove.bg automates background removal, turning what used to be a manual process into a task that takes seconds. Our teams estimate this has saved around 30 hours each month. LetsEnhance.io improves image resolution, helping recover low-quality assets for use in high-resolution digital formats.
LLMs proved to be a rapid helper when it comes to helping draft technical SEO details, like meta-data or HTML tags.
A framework for scaling AI adoption
As more tools entered daily workflows, managing them became more difficult. Costs began to rise, visibility declined, and it became harder to track who was using what, and how. These exact challenges inspired our co-founders to launch nexos.ai, an all-in-one AI platform designed to bring structure and control to AI usage across different teams.
The broader takeaway is that AI adoption needs oversight. An endless list of tools may be exciting during the testing phase, but over time it becomes unsustainable. It is critical to understand where tools are making a real difference and where they are simply adding complexity. Otherwise, teams risk losing focus and efficiency as they scale.
Guardrails, data visibility and responsible use
A key learning in our experience has been the importance of observability. As AI tools become more integrated into daily work, marketing teams must stay aware of how and where they are being used. That includes tracking performance and setting clear boundaries around how sensitive company data is handled.
The goal is to ensure teams feel empowered to try new tools while maintaining appropriate safeguards. That means monitoring usage and avoiding accidental leaks of proprietary or confidential information. Guardrails and clear evaluation criteria help keep AI useful, sustainable and safe, without slowing down innovation.
Advice for startups adopting AI in marketing
For early-stage startups, AI can accelerate marketing in measurable ways. It reduces the manual time spent on content creation, speeds up iteration cycles and brings structure to data-heavy tasks like analyzing A/B tests. This applies not only to B2C brands, but also to B2B teams, especially where growth depends on efficient lead generation and targeting.
Some practical guidance from our journey:
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Dedicate time to experimenting with a variety of tools
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Track how much effort is saved and whether the quality improves
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Do not keep tools that add complexity without value
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Monitor AI usage carefully to avoid security issues or data leaks
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Ask of every process, “Can AI help us with this?”, and act on the answer
These approaches make it easier to build a tech stack that truly supports the business, rather than adding noise or overhead.
Where AI is headed next in marketing
The shift toward AI in marketing is not theoretical. A Mailchimp survey found that nearly 90 percent of marketers believe their organization must increase AI usage to stay competitive. Another study by Influencer Marketing Hub showed that over 60 percent of marketers have already integrated AI into their work.
Still, one of the biggest challenges is separating hype from impact. AI is everywhere, in webinars, on LinkedIn, in pitch decks, but practical use cases are what make the difference. Teams that focus on real outcomes, such as reduced turnaround times, improved targeting or better content performance, are the ones getting the most out of these tools.
The buzz around AI will continue to grow. But underneath it lies a core truth: if used thoughtfully, AI does not just help marketers do more, it helps them do better.
