The influence of AI on the advertising and media industries is already significant and accelerating. While many headlines focus on the tools themselves, the broader implications are starting to reshape the way we approach strategy, optimisation, and creativity.
Generative and predictive AI brings operational benefits: increased efficiency, cost savings, and creative automation. But it also presents challenges that go far beyond media buying. As businesses lean harder into AI-led processes, key questions arise. Are we over-relying on AI? Can it really solve everything? And if so, what are we leaving behind?
The Current Phase of AI Adoption in Advertising
The evolution of the internet has always brought waves of change to the advertising ecosystem. From the banner ad era through to the rise of programmatic, we have adapted to each new wave of technology. AI, however, represents something structurally different.
Where previous shifts required human input at every stage, from strategy to execution, AI is reducing the need for that entirely. Today, brands and agencies are already experimenting with end-to-end campaign workflows powered by AI. Meta has publicly shared its ambitions to deliver fully AI-led media plans, from brief to execution, by the end of 2026. Website builders, copy tools, media automation and optimisation software are all becoming more powerful and accessible, with little technical or strategic background required.
This democratisation of tools presents a levelling effect across the industry. Smaller agencies and in-house teams can now start to close the gap with large networks in both output and scale. However, this also raises important questions about quality, control, and what we consider “expertise”.
Implications for Skills, Learning and Professional Expertise
How can we assess genuine expertise and what expertise is it we should be assessing? The challenge is increasingly about separating genuine skill from AI-assisted output. My experience in recent interviews is that the use of AI is openly acknowledged. The question is no longer whether AI was used, but how thoughtfully it was applied.
Traditionally, learning has come through trial and effort by reading, interpreting, and forming opinions. AI rewires this process. It can summarise, analyse and recommend in seconds. While this offers huge advantages in productivity, it also changes the role of curiosity, problem-solving, and independent reasoning.
It raises a key question for the next decade: if AI can answer almost everything, what does it mean to know something?
We have all heard stories about students using AI to write assignments and tutors using AI to detect them. The language around this implies cheating or wrongdoing. But if AI is part of how we will be working, thinking and expressing ourselves, is it wrong to use it?
From an agency perspective, the same logic applies. As long as the outcome is effective, the ad performs, the brand lifts, does it matter how it was created?
Generated Content and Issues of Authenticity
To indulge the philosophical rabbit hole further for a moment, one of the most dramatic and immediate impacts of generative AI is its effect on our understanding of what is actually true.
In the media space, this plays out in several ways. AI-generated influencers, deepfake ads, and entirely synthetic YouTube content are now live and being monetised. We feel advertising is at the vanguard of AI disruption, but in fact Hollywood has already felt the impact, with actors such as Harold Ramis and Ian Holm needlessly (in my opinion) digitally resurrected. This is not just a technological issue, it is a creative and ethical one. Applied to our world of advertising, as campaigns become increasingly automated, we need to make sure that how we communicate still connects meaningfully with real people, not just algorithms.
Emerging Changes in Search and Content Value
Looking more directly at those Large Language Models (LLMs), we are already seeing the knock-on effect of altered search and browsing behaviours. As users increasingly turn to the likes of ChatGPT or DeepSeek for answers, traditional search volumes are falling. This has led to calls for “pay-per-crawl” compensation models, where publishers may charge AI platforms for ingesting their content. Similarly, attention is now turning towards what a future of LLM Results Optimisation (LLRO), the AI-era equivalent of SEO, could look like for brands and businesses.
Where we are right now is not a tipping point, it is right at the beginning. Much like the days when the internet was dial-up and populated by websites of dancing hamsters, the technology is there and growing, but we are still grappling with how to use it.
At Tomorrow, we see AI as a tool to enhance, not replace, the value of human insight and creativity. While AI will continue to unlock new efficiencies and creative opportunities, it is vital that brands, agencies and platforms take a long-term view and go beyond just learning how to prompt a tool.
