News
IAB AI Growth Summit 2026: Confidence, Challenge and Letting Go
9th July, 2026
Summary
- AI in marketing means using artificial intelligence to research, create, target and measure marketing. It ranges from assistants built on large language models (LLMs), such as ChatGPT, Microsoft Copilot and Gemini, to AI built into ad platforms, such as Google Ads’ Smart Bidding.
- At The Media People’s (TMP) reAImagine AI summit in Leeds in January 2026, clients discussed what AI is and how they use it.
- Clients saw AI’s main uses as saving time, research, data analysis, first drafts and creative back-and-forth.
- Its limits: it can’t replace human creativity, it depends on good prompts, and its output needs human checking.
- The session’s view: AI is a useful marketing tool when used strategically and responsibly, under a clear AI policy.
When you talk to colleagues, friends or family about AI, how often do your views on what it is, and what it can do, actually match? If you work in marketing, that gap matters. This recap of TMP’s reAImagine summit discussion explains what AI in marketing is, what our clients said it can and can’t do, and the challenges your team should plan for.
What does AI mean in marketing?
AI in marketing is the use of artificial intelligence to help plan, create, target and measure marketing, from drafting copy and analysing data to setting bids in ad auctions. AI itself is a broad field of technologies, not a single tool. The UK government’s AI Playbook (February 2025) uses the OECD definition: an AI system is a machine-based system that works out, from the input it receives, how to generate outputs such as predictions, content, recommendations or decisions.
Term | What it means | Example |
AI | Machine-based systems that turn inputs into outputs such as predictions, content or | Streaming |
Generative AI | A type of AI that creates new text, images, video or other content | A first |
Large Language Model (LLM) | Generative AI trained on large amounts of text to produce text-based answers | The models |
AI in ad platforms | AI features inside media-buying tools that make campaign decisions, such as bids | Google Ads |
First three definitions paraphrased from the UK government’s AI Playbook, February 2025. The ad-platform row is our own summary, with the example from Google Ads Help.
AI also sits inside the platforms marketers already buy media through. In paid search, Google says its Smart Bidding strategies use Google AI to optimise for conversions or conversion value in every auction (Google Ads Help).
AI today looks very different from what science fiction led us to expect. Using it now feels as routine as making a cup of coffee: it powers smartphone features, shapes streaming recommendations and helps people search for recipes, DIY projects and even new homes.
Is AI one single technology?
No. AI is an umbrella term for many technologies with distinct functions, even though we often talk about it as one thing. The tools most of us use day to day are assistants built on LLMs, such as ChatGPT, Microsoft Copilot and Gemini. Customers increasingly use them as search engines and businesses rely on them at work, which is why they sat at the centre of day one.
For marketers, that creates two jobs: keeping up with a technology that keeps changing, and deciding where your business stands on how it’s used.
What can AI do for marketers?
Five uses emerged when we put a simple question to clients: when you hear the word “AI”, what comes to mind first?
Saving time. AI removes many repetitive tasks, freeing people for work that delivers meaningful outcomes.
A creative sounding board. Faced with a blank page, creatives can use AI for a back-and-forth that gets ideas flowing.
A fast research assistant. LLMs can find and summarise existing material far faster than a manual search, and automate repetitive tasks such as citation, leaving you to review the results.
Data processing and analysis. Data analysis is one of AI’s strongest capabilities, delivering useful insight almost instantly.
Quick drafts and edits. If you struggle to self-edit or structure your writing, AI is good for a first draft or a round of edits.
What can't AI do?
AI can’t replace human creativity, work without human direction or be relied on without human checks. Those were the three limits the discussion identified.
It can’t replace human creativity. AI recombines existing information well, but it can’t create original perspectives, draw on lived experience or explore emotional nuance.
It needs human input. The quality of its output depends heavily on the quality of the prompt.
Its output needs human quality assurance. AI makes mistakes, so human scrutiny and critical thinking remain essential. The government’s AI Playbook makes the same point: AI systems currently lack reasoning and contextual awareness.
What are the biggest challenges of using AI in marketing?
The discussion raised five challenges of using AI in marketing.
Using prompts properly. Do your teams know how to get the best from AI? Prompting is a skill, and most users still have a knowledge gap. Vague prompts get vague answers.
Trust and authenticity. People use AI for convenience but stay sceptical of what it produces. Marketing still depends on human connection, so decide carefully where AI fits in your funnel.
Diluted marketing quality. Widespread AI use could saturate the market with similar content: when businesses use the same tools, trained on the same data, marketing risks becoming repetitive. You need an AI policy that uses the technology while protecting human craft.
Misinformation and post-purchase dissonance. AI often acts like the ultimate “yes man”, answering confidently even when it’s wrong. That opens a gap between what customers expect and what they get, which can damage confidence in the product.
The endless “next best thing”. AI speeds up comparison, so another option always seems one click away. Brands need to reinforce value and buyer confidence before, during and after the purchase.
Another focus of the session was looking at how we can effectively integrate AI into search strategies. Clear frameworks for tracking performance are essential, and our new AI product offering (Semrush Enterprise) allows us to understand visibility within AI models. Instead of guessing, we can see what’s appearing, where it appears and how frequently. This insight makes ongoing optimisation possible. Teams can refine content, strengthen technical foundations and make sure campaigns are positioned to perform in an AI-influenced search environment.
What should a marketing AI policy cover?
Three areas of UK guidance are directly relevant to a marketing AI policy: human oversight of AI output, personal data, and the advertising rules. None of these sources sets out what a marketing AI policy must contain, and the AI Playbook was written for government teams, not businesses. The session recommended a clear AI policy but didn’t draft one, so the table below adds context from each source (checked 30 September 2026).
Area | What the UK source says | Source |
Human oversight | Principle | UK government AI Playbook, |
Personal data | If you use | |
Advertising rules | The | Advertising Standards Authority (ASA), |
Treat this table as context and a starting point, not legal advice.
What did the session conclude?
The session concluded that AI is a powerful marketing tool when you use it strategically and responsibly. The aim was never to chase hype or spread fear. It was to ask better questions and define responsible AI use in our industry.
Decide AI’s role deliberately. Shape AI’s role in your workplace, or it will shape you by default.
Keep human oversight. A clear AI policy, with a strong quality assurance process, keeps people accountable for AI outputs.
Keep people at the centre. Human perspective and creativity remain central to persuasive marketing. The skill is knowing when AI should assist and when human instinct should lead.
The summit’s second session turned to search: read our write-up on how to measure AI search visibility, or see the full reAImagine AI summit in Leeds.
