As marketing races to embrace AI, the promise of efficiency has never been greater – but neither has the risk of losing what makes a brand truly human.
Time saved. Budgets stretched. Content scaled. AI is delivering on its early promises – but what’s the cost to brand integrity? As Sarang Paraskar, head of strategy US at GUT Network, puts it: “You’ve saved time. You’ve saved money. But have you lost your brand in the process?”
It’s a provocation that should stop CMOs in their tracks – but also redirect their focus. Because, as Paraskar says, it’s not just about what AI generates but what it’s trained on in the first place. “If you don’t have the right kind of data at the foundational layer, no matter how amazing your prompt engineering is, it’s still going to come off as generic, robotic or disconnected.”
It’s a wake-up call that’s echoed across a growing number of creative and digital leaders as AI enters daily workflows – and content creation becomes a high-speed conveyor belt rather than a system built on intentional, strategic data. And these short-term wins could come at the cost of long-term resonance. “You’re trying to build a long-lasting brand, but you’re using technology in a way that lacks emotional nuance,” says Paraskar. “You need to start from the foundational layer.”
Advertisement
Yet, under pressure to get solutions out there fast (in our AI Marketing Pulse, 58% of CMOs place ‘speed to market’ as the top signal being used to justify AI investment), that foundational thinking is often a missing part of the puzzle that’s being overlooked.
“The speed at which CMOs are trying to go to market with some of this is not matched with the speed of actually setting up the right data structures to enable that,” Paraskar explains. “The hype is all about being ready to roll out AI solutions – without doing the foundational work. And very few people know how to deal with data structures and architecture. That’s not a CMO’s realm.”
It could be leading CMOs to lean into surface-level automation – scaling content, streamlining production and chasing quick wins – without pausing to ask how their brand is actually showing up. “They're focused on just automating, getting to market faster, reducing the amount of operational inefficiency... but they’re not focused on what’s sitting underneath,” he says.
This idea of a “data-first brand strategy” ran throughout The Drum’s conversation with Paraskar. In his view, marketers have become fixated on prompt engineering and rapid iteration – the majority (62%) of CMOs say, right now, they are seeing the biggest AI-driven impact in creative development – but few are asking the harder question of how their brand is encoded into their tech stack.
“Everyone wants to be first, everyone wants to be fast. But what happens when your outputs start looking the same?” he asks. “If you don’t invest in what sits beneath the AI, you’re essentially outsourcing your brand identity to a tool without thinking about what the tool has been taught.”
The result? Homogenized outputs with no cultural nuance or brand voice. “If you just focus on complete automation of creative, then in many ways, you're commoditizing your creative assets,” says Paraskar. “There's no distinctiveness that brands can add in.”
That creative sameness becomes especially dangerous when every brand is built on the same base models. “Every CMO I've met is truly a creative at heart. They deeply care about building brands – that’s what got them into marketing. But they also need to ensure they’re still delivering what makes users fall in love with a brand. They need to pause and make sure they haven’t lost sight of that in this race to AI.”
That’s not to say Paraskar is anti-AI – far from it. Instead, it’s about being “pro-brand”. He sees massive potential in using AI to test messaging across markets, to simulate feedback loops, to stretch creative possibilities. But only when the brand’s DNA is intentionally embedded in the system.
Advertisement
“You have a clear sense of ownership that is human related to your brand,” he says. “It should not be completely in the hands of a machine that is not going to be thinking with creativity, that is not going to be helping your brand evolve over time.”
AI, in Paraskar’s view, should augment human creativity – not replace it. “You could achieve a lot with models if you gave them the right prompts,” he adds. “But that human inventiveness – of constantly trying to refresh, reframe, rethink – that’s what makes marketing interesting. And that’s what would be lost if we just kept relying on pure automation.”
It’s also a question of performance – not just principles. “It’s very important that CMOs can demonstrate the commercial value of those investments,” says Paraskar. “But that’s where they’re struggling today – because again, it comes back to data.” And poor data quality or access to data (46%) tops the list of biggest barriers to AI adoption for CMOs.
The issue? Fragmented systems and broken tracking. “The backend structures of data at companies are usually riddled with inaccuracies. Even if a company is investing in a marketing initiative that might be AI-enabled... there’s very little they can do to prove the results.”
Advertisement
“They might be using AI to create all of this content and blogs and pull in traffic – but where that traffic goes, how it converts into a sale or a lead – oftentimes, they don’t have the right tools in place to measure and track that. And that’s where they struggle to communicate the commercial impact. That’s where their relationship with their CEO becomes heated – they’re asking for all this investment money, but struggle to prove it.”
His advice? Don’t wait. “Companies should pause, revisit their data and marketing ops, clean it up, and run all of these AI strategies in parallel,” he says. “Not next year. Now.”
Because if AI is the new creative engine, your brand is still the driver. And the brands that win won’t just automate faster – they’ll build smarter, with humanity at the core.
Explore more in the AI Marketing Pulse hub.
