Rather than replacing AV teams, AI used in combination with strategic judgement is making experienced AV specialists even more important, writes James Weinberg, SVP, Activation & Delivery at WPP Media. In this article, Weinberg explores the impact of AI on planning, optimisation and measurement.
“Have you triiiiiiied Wheaties? They’re whole wheat with all of the bran.”
Almost 100 years ago (Christmas Eve 1926, to be precise), WCCO Radio in Minnesota aired the world’s first commercial radio jingle, and an audio-visual (AV) advertising revolution was born.
In the century since, the AV ecosystem has both proven its value tenfold and become fragmented beyond recognition. Its commercial power remains clear: Thinkbox’s Profit Ability 2 study found that TV – linear and BVOD combined – generates 54.7 percent of all advertising-driven profit, with an average full profit ROI of £5.61 for every £1 invested.
But extracting that value is becoming more complicated. From linear TV to CTV, streaming to socials, and now even wearables, new platforms, viewing environments and audience signals are placing more pressure on marketers to make effective decisions across an increasingly fragmented ecosystem.
AI represents a significant opportunity to help AV teams manage that complexity. Specialists have always combined deep marketplace knowledge with significant manual analysis to plan, buy and optimise media across channels. AI can augment that expertise: reducing the manual burden, connecting more signals, and giving specialists more time and intelligence to make better decisions.
Having worked in AV for almost 20 years, I’ve seen that complexity grow first-hand. The fundamentals of great AV planning haven’t disappeared, but the number of platforms, signals and decisions specialists need to navigate has multiplied.
1. AI will transform planning by helping teams navigate complexity
As audiences splinter across linear TV, BVOD, CTV, YouTube, social video and other emerging platforms, understanding their incremental reach – and avoiding duplication – is becoming harder than ever. AI is fundamental in navigating this huge shift. It can help analyse vast amounts of data to identify patterns humans would struggle to uncover, while augmenting team members’ expertise to help them make more effective media buying decisions.
WPP Open offers a practical example of this large-scale analysis in action. Powered by AI agents, the platform enables teams to use consistent audience definitions, from communications planning through to AV planning and buying. It supports TV buying specifically by analysing multiple data sources and identifying which spots are most likely to drive unique reach – not replacing planners, but helping them make more informed decisions, more quickly. In WPP testing, an automated spot-picking approach has delivered an average increase of three percentage points in reach per campaign compared with standard industry tools and practices used to optimise – a huge efficiency gain.
Increasingly, the value of AV specialists will lie not simply in managing individual channels, but in orchestrating an increasingly complex ecosystem – using technology to inform decisions while applying the marketplace knowledge and judgement that technology alone cannot provide.
2. AI will improve optimisation by moving campaigns closer to real-time decision-making
One of AI’s most valuable contributions is its ability to help campaigns learn and adapt continuously.
Historically, many linear TV campaign decisions, from supplier selection and phasing to creative lengths and dates on air, have been largely fixed before launch, with limited scope to respond to early performance once live. AI alone cannot yet enable wholesale changes to a linear campaign once it is live, but it can help teams interpret performance signals even faster and identify where optimisation is possible, improving an ad’s effectiveness.
Across much of the wider video ecosystem, in-campaign optimisation is already fundamental, with performance signals informing decisions around audiences, inventory, investment and delivery. Linear TV presents different challenges; its trading and delivery mechanics mean many of the biggest decisions still need to be made before a campaign goes live. But that is beginning to evolve.
AI can already help teams process and interpret signals at speed, supporting more informed spot-level decisions. At the same time, agencies and broadcasters are developing greater flexibility across linear and addressable TV, creating more opportunities to respond to what is happening during a campaign.
ITV’s launch of Live Addressable+ is one example of addressable capabilities moving further into live linear broadcast. Looking further ahead, the proposed acquisition of ITV’s Media & Entertainment business by Sky could create further opportunities to bring together linear, streaming, data and technology, creating the foundation for a huge transformational change in the way TV and broadcast video is planned, bought and optimised.
The longer-term opportunity is to make AV more responsive to the signals generated during campaigns. That does not mean treating linear TV like a digital performance channel – its role, buying mechanics and strengths are fundamentally different. Instead, AI can help AV specialists identify emerging patterns faster, understand where optimisation is possible and feed those learnings back into the wider video plan.
As these capabilities mature, the gap between planning, activation and measurement should continue to narrow. The result could be a much faster feedback loop for AV teams, while retaining the strategic judgement needed to decide when, where and how to act.
3. AI will make measurement more connected and outcome-focused
Post-campaign measurement remains one of video advertising’s biggest challenges. More and more brands want to understand business outcomes, not just delivery metrics. Did the ad spots drive greater consideration, purchase and retention, for instance, rather than just deliver reach? Here, AI helps us connect far more signals across platforms and environments than we’d be able to manually, so we can comprehensively grasp ads’ impact:
- Incremental reach and deduplicated reach show what each part of the video ecosystem contributes
- Attention provides a richer indication of exposure quality and likely impact than metrics like completion rates alone
- Linking exposure to business outcomes reveals new insights into sales, search, acquisition, footfall, brand growth and other client-specific measures
Marketing mix modelling (MMM) – a statistical technique used to measure how different marketing activities and factors affect commercial results – remains incredibly valuable, particularly for understanding longer-term and cross-channel effectiveness, but it shouldn’t be the only answer. AI can complement MMM with more granular experimentation, incrementality testing and sophisticated outcome measurement – creating a feedback loop between planning, activation and measurement that informs the next cycle of decisions.
Combining craft expertise with technological fluency
Ultimately, the future of AV is not humans versus machines, but humans with machines. The strongest teams will combine strategic judgement with technological fluency, using AI to reduce manual work and surface campaign learnings and marketplace intelligence.
This combination makes experienced AV specialists even more important, moving from ‘processor of information’ towards more of an ‘orchestrator, interrogator and decision-maker.’
AI should give specialists more time to challenge briefs and KPIs, develop hypotheses and test-and-learns, negotiate and build relationships with media owners, interpret why something happened rather than simply reporting that it happened, and turn all of that into better client recommendations.
AI can democratise access to knowledge – but people provide the context needed to apply that knowledge well.
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