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For over a decade, Caitlin McCulloch has been focused on growth. Both for the SaaS brands she’s helped to scale and for the marketing teams she’s led. Now Vice President of Performance & Growth Marketing at Contentful, she’s putting her experience to the test in one of the fastest-moving areas of marketing today: the connection between brand and customer.
Before Contentful, Caitlin played a key role at Braze, driving demand generation during its IPO, and also held senior roles at Bazaarvoice and Social Media Today. But it’s never been purely about results, but the people and ideas behind them. Caitlin now gives the same respect to the creative process in the era AI. “Too often,” she told us, “teams expect polished, ready-to-publish content without investing the time to refine prompts or review results critically.”
For Caitlin, the role of AI in marketing comes down to its value as a tool, not just as a buzzword or one-fit-all solution. She makes it clear that AI shouldn’t be treated as “some kind of marketing magic,” but instead a successful use of the technology is a “mastery of timing, brand tone and targeting”.
Whether it’s been scaling campaigns or leading teams, Caitlin has kept the focus on what she feels resonates most with audiences: trust. Authentic marketing isn’t optional in her eyes, it’s the differentiator. And as Caitlin sees it, to earn that authenticity today you need relevance, transparency and a real emotional resonance.
How do you maintain trust when there’s so much pressure to automate everything? With this conflict in mind, we started our interview by asking Caitlin just how effective these AI-driven methods can be.
AI takes understanding and predicting customer behaviour to a new level because of its ability to process vast amounts of data in real time. Traditional analytics relied on historical trends and manual segmentation, whereas AI uses machine learning to detect more nuanced patterns.
However, as everyone knows, AI is far from infallible. Firstly, it is only as good as the data it is trained on — if that data is of poor quality, the AI tools you’re using will never reach their full potential. Secondly, it still lacks the capacity to fully grasp the emotional and cultural factors that drive consumer behaviour.
Because of this, AI is most effective when the technology is paired with human insight i.e. using AI for scalable, data-driven predictions while supplementing it with human-led qualitative research to ensure depth and relevance.
From Contentful’s perspective, the key breakthrough that AI has delivered is hyper-personalisation. The days of broad demographic targeting are gone for good. AI enables our customers to deliver tailored content to individual consumers based on their behaviours and preferences at speed and scale.
With AI, brands can take personalisation one step further to better understand what their audiences might be looking for at that exact time, like a particular product catered to their lifestyle. The brands that stand out today are the ones that serve up something a customer needs, before they even realise they need it. This is not some kind of marketing magic – it is mastery of timing, brand tone and targeting.
For example, Ruggable uncovered that its audience consists of two primary segments – dog people and cat people – and created distinct ad campaigns and moments to suggest more relevant products to each group. They even unified the experience by carrying the segmentation into their website. This is personalisation that is helpful, not intrusive.
Consumers, especially social media savvy generations like millennials, Gen Z and Gen Alpha, have a delicate sense for when a brand is truly plugged into the conversation or just trying to hop on the bandwagon to sell a product. To avoid missing the mark, brands need to understand their audience while also bringing something of value to the table.
Authenticity in AI-driven marketing depends on balancing personalisation with genuine human-centric messaging. While AI excels at analysing behavioural data to predict what a customer might want next, the real magic happens when brands use those insights to craft communications that feel empathetic and contextually relevant.
An example of this is Contentful customer Pets Deli, which supercharged its Black Friday promotions using personalised content. The retailer identified different types of customers and each segment was offered a highly tailored, unique set of prices and promotions that appealed to their specific needs. This approach proved to be immensely successful, as the company was able to increase its conversions by 51% compared to the same day the previous year.
Authentic marketing in the age of AI means staying true to a brand’s values while using technology to enhance genuine human connections. AI can generate content at scale, but authenticity is not just about volume; it’s about relevance, transparency, and emotional resonance.
Maintaining authenticity at scale is possible if AI is used to amplify human creativity, for example drafting personalised follow-ups based on real customer service interactions. The key is maintaining a feedback loop where AI refines messaging based on real human responses, ensuring continuous alignment with audience expectations.
The biggest mistake marketers make with generative AI is assuming it can fully replace human creativity, rather than recognising it as a powerful co-creator. Gen AI isn’t a set-it-and-forget-it solution. It needs context, strategy, and thoughtful input to generate outputs that truly reflect a brand’s voice and resonate with its audience. Too often, teams expect polished, ready-to-publish content without investing the time to refine prompts or review results critically.
Marketers should focus on how Gen AI can enhance, not replace, their creative and strategic processes. It’s exceptionally useful for speeding up ideation, generating variations at scale, and personalising content for different segments or regions. But the best results come when humans bring the emotional intelligence, taste, and brand nuance, while AI brings the speed, efficiency, and pattern recognition. It’s a collaboration, not a handoff.
As teams begin exploring AI, I recommend starting with a focused, measurable initiative—like a one-off campaign or content creation —rather than trying to overhaul an entire marketing workflow. Starting small allows teams to experiment within clear boundaries, quickly evaluate outcomes, and learn what’s working and what’s not. With a defined use case, you can measure impact, iterate with intention, and build internal confidence and capability before scaling more broadly.
Another area some of our customers are exploring is localisation. It can be a major pain point for brands that target global audiences, and translating content at scale can be both expensive and extremely time-consuming. Our customer, DocuSign is a great example of this – they leveraged one of our AI tools, AI Actions, to translate 7,000 pages on their website into 60 different languages. This improvement not only allowed them to provide more relevant content to customers, it also built trust across the marketing department to deploy AI for other activities.
