by Manpreet Gill, Analytics Director, Jellyfish
Adtech and Martech have lived alongside each other for years. Adtech centered on reach, targeting and media buying and Martech owned the customer relationship, CRM, measurement and the lifecycle. The line between the two has been blurry in the past, but the evolution of AI’s role across many processes within this has meant we’re experiencing a fast-growing convergence.
Since the birth of GDPR (followed by CCPA, DMA, DUAA etc.), what followed was tighter controls around data, usage of data and ultimately, a more controlled pool of data points, underpinned by user consent and brand loyalty. Martech and AdTech technologies caught up and revealed enhanced and more regulated ways to capture and utilise data. But while technologies are keeping up to enhance signal feeds and maximise performance, this doesn’t always translate into how teams are functioning around this. Data, Paid Media and Cloud Engineering teams, are best placed to triangulate and combine forces when implementing and integrating said new technologies, but team silos aren’t quite keeping up.
Most brands are looking for a single source of truth – It feels like a comforting way to navigate the sometimes complex world of data, but in reality, data doesn’t always work this way. Your CRM tells you one story, ad platforms tell another. Measurement platforms, data clean rooms, attribution models and brand tracking all maintain very important data points, but as singular sources, only a partial view can be seen, with each source shaped by its own methodology and blind spots.
In reality, a source of truth should be multiple sources of truth, built with the specifics of your organisation, and aligned with business goals and metrics. AI is genuinely good at this kind of work; stitching together signals, reconciling discrepancies, surfacing where the sources agree and disagree – but that only works if you’re feeding it enough of the picture. A brand relying on one platform’s version of performance is optimising against a fraction of reality, and increasingly, that gap shows up in wasted spend and misplaced confidence.
Consumer behaviour itself is ever evolving – Search is unfolding across AI assistants, social platforms, and traditional search engines. New channels are emerging, Rich Communication Services (RCS) being the latest example, offering rich, app-like messaging directly through a phone’s native messaging. Every new channel adds another signal source, another touchpoint to reconcile, and another reason a single source of truth was never going to be enough.
AI vs Your own judgement and creativity of thought – It might feel like the easy thing to do – trust what AI gives you with minimal questioning, but it’s important to contextualise this. In this context, AI is an efficiency layer, a way to process more signals faster, and surface patterns a human would take weeks to find. When we choose to not layer our human judgement on top of this, we can lose our sense of direction and innovation quickly.
What actually makes AI useful is the organisation around it – the data foundations, the right logical processes, and people asking the right questions. Critical thinking, data ethics, and clarity of judgement don’t become optional because a model can now do the heavy lifting. If anything, it matters more, because when decisions move fast, mistakes compound faster too. AI can be used to enhance processes, but ultimately we must steer the ship, decide what ‘good’ actually means, what’s accurate and ethical, and what actually serves the customer, rather than just the model’s objective function.
This is the real reason why Adtech and Martech are a beautiful alliance – AI needs data from both sides to be useful. It needs mMartech’s understanding on who the customer is and where they are in their journey, and it needs Adtech’s reach and real-time signals. Feed it media data only and it’ll optimise for clicks. Feed it CRM data only and it optimises for people you already have, ignoring how they found you.
Put the two together though, and something more useful happens, AI can start connecting acquisition to retention, media exposure to lifetime value, targeting to how the customer actually experiences your brand. The ideal, a suitably joined-up view of the customer, which AI is well placed to help build, as long as the data pipes between Adtech and Martech exist.
This is why paid media decisions made in a silo, disconnected from the wider measurement and optimisation picture, aren’t so powerful anymore. A campaign can look efficient on a platform report and still be quietly eating into organic demand, over-serving existing customers, or missing what’s happening further down the funnel entirely. You need the whole signal set; website and app engagement, media performance, CRM behaviour, brand lift, incrementality, (etc.) before you call a decision optimised. Martech’s job is increasingly to hold that whole picture together.
So where should brands focus their energy?
Here’s what I believe brands need to do:
- Invest in the data infrastructure that lets Adtech and Martech talk to each other
- Designate ambassadors and change leaders across functions to bridge existing silos, ensuring teams adapt to this shift while working toward unified communal goals.
- Encourage curiosity, critical and innovative thinking, and trust. These are important growth factors that AI can’t really replace.
- Build AI capability with the right focus for each team, with governance and ethics baked in from the start rather than being an afterthought.
- Keep humans firmly accountable for the judgement calls, especially when AI takes on more of the execution.
The brands that succeed won’t be the ones with the most AI. They’ll be the ones who’ve built the organisational discipline to point it in the right direction.
