The Data Lab

Marketing in the Age of AI

Community & Events, Thought Leadership

Breaking Through the Noise

Every day, people are exposed to thousands of messages across all of their screens, apps, emails, and social media platforms. In this crowded digital space, organisations face a growing challenge: breaking through the noise with communications that reach the right people, with the right message, at the right moment.

From personalising experiences and forecasting campaign performance to speeding up content creation, AI tools have the potential to change how marketing works. But their impact is not automatic or guaranteed. The value of AI depends on the quality of the data behind it, the skills of the people using it, and the care taken to apply it responsibly.

For The Data Lab, marketing is a useful lens for exploring both the opportunities and challenges of AI adoption. It’s one of the areas where data and AI are being adopted fastest, making it a clear example of both what’s possible and what organisations need to get right – particularly around trust, ethics and capability building.

Personalisation

Brands are trying to connect with audiences who differ widely in language, culture, and lifestyle, and whose buying choices are shaped by deeply personal tastes and circumstances. Reaching such a diverse group, while making each interaction feel genuine and relevant, is a challenge that traditional approaches often struggle to meet.

This is where personalisation comes in – this is the practice of using data to tailor messages and offers to individual customers. This approach is increasingly moving from a “nice to have” to something that audiences expect as standard: around 71% of consumers expect personalised experiences, and 76% feel frustrated when they don’t receive them.

AI is now taking this a step further by identifying patterns across large datasets and predicting what each customer is likely to value and when – allowing organisations to adapt in real-time and hyper-personalise at scale in ways that would be impossible to do manually.

Virgin Wines, for example, has partnered with the AI platform Preferabli to develop tailored wine recommendations. Using sensorial AI – which enables machines to process and interpret data through senses like sight, sound, touch, smell, and taste, mimicking human perception – the tool breaks wines down into attributes like acidity, sweetness, tannin structure, body, and flavour profile. These characteristics are then matched with customer signals (including ratings, purchase history, and previous searches) to deliver personalised recommendations based on each customer’s individual taste.

However, this level of personalisation relies on access to significant amounts of personal data. Public attitudes towards this are often conflicted: many people want relevant, tailored experiences, whilst, rightly so, expressing concern about how their data is collected, stored, and used.  In the UK, GDPR and data protection laws set the baseline for responsible data use, but the UK’s pro-innovation approach to AI regulation – which prioritises adoption and flexibility over strict rules – may not fully address emerging risks around automated decision-making and the use of personal data. For marketers and the data teams supporting them, delivering personalised experiences while protecting user privacy isn’t just a technical task – it’s a core part of ethical and responsible marketing, and means making careful decisions about the handling of personal data.

Predictive Insights and Forecasting

AI doesn’t just help brands personalise experiences; it’s also reshaping how they plan, test and optimise campaigns. Traditionally, marketers have relied on attribution models to pinpoint which action drove a particular response, but in today’s multi-channel, fast-paced environment, these approaches are often too rigid. AI enables a more dynamic, learning-focused process: it can detect emerging trends in real time, mimic how different audiences might react to creative or messaging, test multiple strategies in parallel, and continuously update forecasts as new data arrives. This helps teams anticipate what customers are likely to value next, reduce the risk of unsuccessful campaigns, and refine strategies before campaigns even go live.

Black Swan Data is one example. Using AI and data science, its platform analyses vast volumes of social and online conversation data to identify emerging trends, changes in consumer behaviour, and potential growth opportunities. By analysing millions of real-time consumer conversations across social media, blogs, forums, review sites, and news, Black Swan’s models help brands anticipate what customers are likely to care about next. These forward-looking insights can then inform marketing strategy, innovation, and campaign planning, helping clients such as PepsiCo, Unilever, and McDonald’s to move from reactive measurement to more predictive, data-driven decision-making.

In practice, this allows organisations to act earlier and more confidently: deciding where to invest, which messages to prioritise, and how to respond as consumer sentiment evolves, rather than waiting until a campaign ends to learn what worked. But predictive power depends on both data quality and human skill. Poorly managed data or misinterpreted outputs can lead to wasted spend or off-brand campaigns. Human expertise is essential for interpreting AI-generated forecasts, understanding context, and making ethical, responsible decisions – turning raw predictions into meaningful actions.

Creativity and Content

As data and AI play a bigger role in marketing decisions, the way content is created is evolving, too. As marketers, many of us already regularly use AI in our day-to-day work – whether for brainstorming ideas or refining copy, or analysing performance data for insights – with 57% of us using AI for creative production. What’s shifting is that AI is no longer just a support tool or a jumping-off point; it is becoming embedded in the creative process itself, helping teams generate, adapt, and refine content at scale.

H&M, for example, has experimented with generative AI to create realistic digital “twins” of models for its campaigns, alongside traditional photoshoots, allowing it to explore new directions and concepts and to take advantage of greater flexibility and refinement. But these approaches also bring ethical and reputational risks. Questions around representation, diversity, and the use of digital likenesses illustrate that AI content creation isn’t risk-free. A similar debate arose when Vogue featured an AI-generated model for Guess – a first for the magazine – which sparked discussion around unrealistic beauty standards and fairness in representation.

Closer to home, a planned mural in Glasgow also sparked controversy after an initial AI-generated concept was shared online. The design, which included what looked like a bald eagle (not native to Scotland!) and a floating train, drew criticism and was widely seen as “AI slop” – content that missed the mark and felt off-target. While the human artist is creating the final mural, they are working from the AI-derived concept rather than being involved from the very start. Critics argued that any artwork representing Scotland’s heritage should be conceptualised, drafted, and developed by a human from the beginning, emphasising that an artist’s “human voice” is what gives cultural work authenticity. This highlights that AI can contribute ideas, but human judgment, cultural context, and creative interpretation are essential to produce work that resonates authentically and responsibly.

But it’s not all doom and gloom. For smaller businesses, AI can transform workflows and backend processes, enabling teams to achieve more with limited resources. For example, The Original Tamale Company, a family-run business in Los Angeles, used AI to create a meme-based video that went viral in just 10 minutes, generating 26 million views and 1.5 million likes. While the technology enabled rapid production, the video’s concept and creative choices were guided by the team’s judgment and understanding of their audience, underscoring that human creativity and insight remain central even when AI does much of the heavy lifting.

A Final Word

AI is transforming marketing in big ways – from personalising experiences to spotting trends and predicting what customers might value, to making content creation faster and more flexible. But AI isn’t a magic bullet. Its outputs are only as good as the data behind it, the skill of the teams using it, and the care taken to apply it responsibly. Poor data, misinterpreted forecasts, or “AI slop” – low-quality or off-target content – can mislead campaigns or harm a brand’s reputation.

Crucially, making the most of AI in marketing isn’t just about technology – it’s also about people and skills. Smaller businesses, in particular, may struggle to harness AI effectively without the right expertise, from understanding data and analytics to applying AI responsibly and ethically. Addressing this skills gap is essential for organisations to use AI confidently and avoid missteps. Tools like our Data and AI Skills Framework (2025) can help by providing guidance on the capabilities teams need to build, including data literacy and technical skills, as well as ethical decision-making and leadership.

Ultimately, the brands and teams that will thrive are those that combine AI with human judgment, creativity, and ethical insight. When used thoughtfully, AI can be more than just a productivity tool – it can unlock new ways to understand customers, spark innovative ideas, and deliver marketing that is meaningful, responsible, and truly impactful.

Marketing Month at The Data Lab Community

In February, we’re shining a spotlight on marketing – showcasing interesting and innovative uses of data and AI!

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