Context is king: How AI redefines personalised ads
By reading signals such as tone, emotion, and engagement, AI-powered systems can deliver ads that feel timely, relevant, and respectful – but only if brands balance innovation with transparency, cultural nuance, and ethical design.
Artificial intelligence (AI) is transforming the way audiences experience advertising – not just by targeting who they are, but by understanding the context in which they engage. Today’s machine learning (ML) models are capable of interpreting viewing habits, content tone, and even emotional cues, enabling advertisers to deliver messages that feel relevant, timely, and – crucially – respectful.
By using contextual signals rather than intrusive personal data, AI promises a new era of personalisation that blends seamlessly into the content it supports. However, with that promise comes a new set of questions around ethics, creativity, and trust.
How can AI create advertising that feels human rather than manipulative? Further, what does this evolution mean for the balance between relevance and responsibility?
Building trust through transparency and ethical design
As personalisation deepens, so does the scrutiny. AI’s ability to interpret emotion and intent raises legitimate concerns about manipulation and overreach. For advertisers, the challenge is balancing technological sophistication with ethical responsibility – all within a clearly defined context.
“Trust is at the heart of the broadcaster-viewer-advertiser relationship,” expounds Paul Davies, Head of Marketing at ad insertion company Yospace. “It’s imperative that it is maintained when exploring new levels of personalisation. That comes from trust around viewer data and how it is used and handled, and it also applies to ad measurement and providing reliable metrics.”
For Davies, getting the technology right isn’t just a technical issue – it’s a trust issue. “If the streaming tech doesn’t support the adtech in the right way, you undermine the benefits of addressable advertising because it won’t scale,” he explains.
Avi Yampolsky, Vice President (VP) of International Accounts at order management software company Operative, adds that privacy and precision don’t have to be opposing forces. “The key is leveraging privacy-enhancing technologies like clean rooms and secure data collaboration platforms that enable precise targeting through anonymised data matching,” he posits.
He emphasises that advertisers don’t need to know everything about a person to reach them effectively: “Effective targeting doesn’t require hyper-personalisation. Cohort-based approaches using viewing patterns, content preferences, and contextual signals can deliver strong results while respecting privacy boundaries.”
When it comes to mood detection and sentiment analysis, Yampolsky stresses clear ethical limits. “The safest thing to leverage these technologies is for identifying the context of the program itself, rather than the viewer. Presenting a comforting advertisement following a sad scene, or avoiding a cheerful ad during a sombre moment, is a responsible use of context. However, the industry should establish strict prohibitions against targeting vulnerable emotional states for exploitative purposes.”
This ethical line – between understanding emotion and manipulating it – is expected to be the defining boundary of AI-led advertising in the decade ahead.