There was a time when making an advertisement was an expensive proposition. Shoots meant crews, locations, equipment, actors and weeks of co-ordination, while even a simple visual required a photographer, designer or illustrator. The cost and effort meant not every idea made it to production. Artificial intelligence (AI) is dismantling that filter. Today, images can be generated in minutes, storyboards created before production meetings, and campaigns adapted across languages, formats, markets and audiences at a fraction of traditional costs. Agencies and brands are using AI across research, ideation, production, post-production and personalised content.
Bata India offers a recent example, moving from an agency-dependent creative model to an AI-led system built with Zocket. Its autonomous AI agents will handle the workflow from briefing and content creation to publishing and post-performance learning, while the in-house team will retain strategic and final decision-making control. Bata says the shift is saving more than USD 1 million annually in agency and production costs while significantly reducing turnaround time. The wider numbers also show how AI is becoming embedded in advertising. WPP expects AI-powered advertising revenue in India to cross USD 2.5 billion in 2026, up 9 per cent year-on-year. A 2025 Gartner survey showed 77 per cent of marketing organisations that have adopted GenAI use it for creative development, while Adobe found 52 per cent of marketers were already using it across multiple stages of content production by 2025.
The opportunity is clear—greater speed, experimentation, personalisation and lower costs. But abundance brings its own challenge. If AI enables brands to produce ten times more ads, consumers could also be exposed to ten times more forgettable ones—the phenomenon is described as ‘AI slop’.
AI slop has a recognisable aesthetic—glossy but generic, technically impressive but emotionally hollow, and often built on familiar visual tropes such as unnaturally perfect faces and skin, golden-hour lighting, characters who appear to be speaking to each other without quite occupying the same space, floating products surrounded by exaggerated ingredients or effects, spotless homes, body parts that appear to morph into other objects, and cinematic backdrops that could belong to almost any brand. Even when the execution is visually impressive, the underlying creative grammar remains familiar because the same prompts tend to produce the same kind of spectacle. The problem is not that the ads are AI-generated; it is that when such tropes become easy and cheap to reproduce, brands can end up mistaking visual novelty for an idea. A flood of polished but emotionally distant advertising makes it harder for consumers to distinguish one brand from another, and, over time, can strip advertising of the cultural or emotional specificity that makes people care.
Globally, several brands have faced criticism for pushing AI content and ads that lacked ‘soul’. For example, Gucci faced backlash when they released AI-generated images to promote their Milan Fashion Week show earlier this year, which the audience said was not in line with their bid for creativity and craftsmanship. Coca-Cola has been previously criticised for their AI-generated Christmas campaigns, which consumers said lacked the warmth that’s generally associated with the brand.
The irony is hard to miss. The technology that promises to democratise creativity could also make advertising more homogeneous. According to Rohit Malkani, Chief Creative Officer, Saatchi & Saatchi India, advertising has always had two jobs—generate work efficiently and generate work that people remember. “AI dramatically improves the first. It doesn’t automatically solve the second,” he says. That gap—between producing something and producing something people remember—is where the industry’s worries around AI slop begin.
From production tool to creative collaborator
AI’s first major impact on advertising was not necessarily visible to consumers. It sat behind the scenes in areas such as targeting, media planning, analytics and optimisation. Now, it is moving much closer to the idea itself. Abhinay Bhasin, EVP – Product & Technology, dentsu India, describes a broader transformation taking place across the marketing ecosystem. “AI today has moved upstream in being an integral part of consumer understanding and downstream in execution, like content versioning, etc. The creative process today is no longer just about crafting assets, but about designing intelligent systems that continuously learn and adapt.”
This essentially means that AI is not simply being inserted into an existing creative workflow. It is beginning to alter the workflow itself. For example, dentsu is working with systems that begin with first-party data or content-consumption signals and end with creative and content plans, along with iterations designed to scale across formats. Bhasin says the adoption of AI-driven creative-generation tools has accelerated sharply over the past 18–24 months, moving from experimentation towards scaled deployment.
The technology is also expanding the range of creative possibilities.
Dipshika Ravi, National Creative Director, Schbang sees production and exploration as the biggest areas of value. “The biggest value AI creates today is in production and exploration. It helps us generate hundreds of visual directions, storyboards or copy routes in hours instead of days. That means creative teams spend less time executing and more time evaluating, refining and elevating the strongest ideas,” Ravi explains. The difference is not merely that AI can create an image faster. It changes the number of options a creative team can consider before committing to one. Ravi offers a useful comparison. “Instead of commissioning 10 visual mock-ups for a campaign, we can explore a hundred possibilities using AI and invest our energy in crafting the one that truly stands out. AI accelerates the journey, but human judgment still decides the destination.”
That final sentence is likely to become one of the defining principles of AI-assisted creativity.
Efficiency gains?
AI is changing not just the range of creative possibilities but also the economics of producing them. In India’s diverse, content-hungry market, campaigns must adapt across languages, formats, platforms and audiences, often requiring dozens of variations from a single master asset. Tools such as Canva, Midjourney and Adobe are making this process more accessible, enabling marketers and smaller teams to create, edit and adapt content while lowering production barriers and raising the volume of content that brands can produce.
Shubhika Jain, India Brand Lead at Canva reveals, “A year ago, most of the conversation around AI in advertising was about speed. Today, it is just as much about taste. Canva is now one of the most used AI products in the world, with over half a billion designs created using Canva AI, and India is one of our biggest markets for both Canva Code and Canva AI. That growth tells us something—people don’t just want AI that is fast, they want AI that actually solves their creative problem.”
The question of taste also extends beyond the final creative output to the systems and processes that produce it. Agencies are increasingly experimenting with AI at multiple points in the workflow, using it to make research deeper, production faster and optimisation more responsive. Shikha Davessar, Managing Partner, 22feet, part of Omnicom Advertising India, reveals the agency is seeing AI’s biggest gains in research, production and optimisation. The agency is using Agentic AI for deep research and proprietary tools that carry brand and work context, while node-based AI tools are being used for content production. The resulting efficiency is significant. “At this point in time, AI can reduce content creation and production time by anywhere between 30 and 40 per cent, which is significant in a market like India where brands are expected to create content across multiple languages, formats, and platforms almost simultaneously,” she says.
Puneet Das, Chief Marketing Officer, Britannia, states, “AI has become an integral part of how we approach creativity and marketing. We use it across different stages of the process, from identifying consumer insights to exploring ideas and bringing campaigns to life. Campaigns like Little Hearts’ ‘Share the OG Heart’, which celebrated World Emoji Day through an AI and XR experience, or Milk Bikis Adengappa, which allowed stories to be generated using common day objects, reflect how we’re leveraging new-age technology to create enriching experiences for our consumers.”
Further highlighting how the brand approaches AI in creative processes, he says, “AI also gives us the flexibility to respond to cultural moments with greater agility and enable scale-up of hyper-personalised experiences. While AI supports the creative process, the thinking behind every campaign continues to come from people. Strategy, storytelling and creative judgement remain firmly human, with AI helping our teams bring ideas to life more efficiently.”
Essentially, as creative AI tools become more accessible, the value shifts from execution to the thinking that makes AI-enabled work distinctive. Brands are finding ways to make the technology their own. WforWoman offers an interesting example. AI currently contributes to approximately 65 per cent of the brand’s creative workflow, and its AI film earlier this year extended its Paris Fashion Week story through AI-generated visuals, music, vocals and lyrics. The campaign generated over 2 million organic views, 53.6 million paid impressions and 60–70 per cent video completion rates. AI also helped reduce production costs by nearly 70 per cent.
Yet Meghna Zutshi, Head of Marketing & Visual Communication, TCNS & Jaypore, says the point was not simply to demonstrate AI’s capabilities. “At WforWoman, we see AI as a creative enabler rather than a creative replacement. Technology can generate content, but it can’t define a brand’s identity–that has to come from people.” That meant ensuring that the AI-generated representation remained faithful to the actual collection. “For our AI film, a great deal of effort went into ensuring every garment remained an accurate representation of the actual collection. The AI was carefully directed so that the colours, silhouettes, textures and overall styling stayed true to the products. That attention to detail was essential because the story could only work if the fashion itself remained authentic,” she adds.
The approach demonstrates an important principle—the more synthetic the production process becomes, the more carefully brands may need to protect the real-world truths underneath it.
Baskin Robbins India’s approach shows how the question of AI adoption can also come down to the nature of the product itself. AI supports around 20 per cent of the brand’s creative process, including idea refinement and video content. It is also being explored for marketing analytics and personalised loyalty communication. But hero product imagery remains real.
Aleesha Desai, Deputy General Manager – Marketing, Baskin Robbins India reveals, “One area where AI still falls short is product imagery. Ice-cream is a highly visual, indulgent category, and AI simply can’t match the texture and richness of the real product. That’s why all our hero products are still photographed in real life, with AI playing a supporting role rather than replacing the process.”
Davessar backs this, saying efficiency should not be equated with creativity. “AI should be seen as an amplifier, not a replacement. It helps us move faster and make smarter decisions, but the strategic thinking, the emotional insight and the creative judgement that build enduring brands still come from people. The real value lies in combining machine efficiency with human imagination.”
As AI moves further upstream, the industry needs to pay heed to the fundamental difference between using AI to execute an idea and using AI to decide what the idea should be.
Escaping sameness
As AI commoditises creative execution, differentiation is shifting from how an idea is made to how distinctive the idea itself is. Production capabilities once offered a competitive edge, but GenAI is making craft faster and more accessible.
Malkani elaborates, “Every day, we are using AI to commoditise craft. Storyboards, mood films, image generation, and adaptation can now be produced at crazy speeds. If every agency has access to the same capabilities, this is no longer a competitive advantage. That’s where original thinking comes in.”
As execution becomes easier, the value of thinking rises.
Vishal Prabhu, Creative Director – Strategy at White Rivers Media, sees the same shift happening inside agencies. “Agencies can now spend more time understanding audiences, identifying sharper insights and building stronger creative platforms instead of getting caught up in repetitive production work.”
He argues that this is changing what agencies are ultimately valued for. “Execution is becoming more accessible, while original thinking and strategic judgement are becoming even more important.” In other words, AI may not make the creative department less important. It may make the thinking within the creative department more important.
Sumeet Bhojani, Head – Brand & Strategic Insights at Godrej Enterprises Group, frames the issue around the way generative models learn. “Most AI models are trained on existing information, behaviours and patterns. Their strength lies in identifying what has worked before and generating variations of it. This is useful. However, if every marketer is using similar tools, is trained on similar datasets and optimising for similar outcomes, there is a risk of creating a sea of content that is technically sound but creatively indistinguishable.”
The bigger concern is homogenisation. Bhasin agrees that the risk is real. “There is a real risk that AI, if used naively, can lead to a homogenisation of output because most models are trained on the same broad corpus of existing content and tend to optimise towards patterns that have already worked.” The technology, in other words, can become very good at producing what already looks plausible. But advertising’s most memorable work has often depended on the opposite impulse—finding a perspective that does not look like everything else.
Prabhu believes the problem is not necessarily the technology itself. “As more people use the same models and similar prompts, it’s natural for ideas, visuals and even writing styles to start feeling familiar. We’ve already begun seeing that across digital content.”
If sameness is partly caused by everyone using similar models, the answer is not simply to find another model. It is to give the technology better inputs.
Rajiv Dingra, Founder and CEO of ReBid, calls this ‘proprietary brand context’. “Brands should not rely on a public AI model, a one-line prompt and a logo pasted onto the final output. They need to build a structured brand intelligence layer containing their tone of voice, visual codes, product truths, consumer insights, cultural boundaries, regulatory requirements, previous campaigns and performance learnings.”
This approach moves AI from being a generic production tool towards becoming part of a brand’s own creative infrastructure. Dingra recommends codifying distinctive verbal and visual assets, grounding AI systems in approved brand material and first-party creative performance data, defining clear creative territories and non-negotiable brand rules, and evaluating outputs for brand distinctiveness, cultural relevance and originality rather than just clicks and conversions. Most importantly, he says, “Keep creative directors and brand custodians involved throughout the process, rather than using humans only for final approval.”
That distinction—whether AI is the beginning of the creative process or a tool within it—is crucial. Ravi explains, “The difference between AI-assisted creativity and AI slop is the quality of the idea and the people behind it. Great campaigns don’t begin with a prompt, they begin with a human insight. AI should help us bring that idea to life faster and in more relevant ways, not replace the thinking that made it powerful in the first place.”
Why human judgment becomes more valuable
The more possibilities AI generates, the more important selection becomes. Culture, intuition, taste and lived experience remain central to identifying ideas that resonate. As Bhasin puts it, “AI can inform, amplify and scale creativity, but the spark itself remains deeply human. AI can be the best apprentice but not the master.”
This is especially true in categories where trust and authenticity matter. Erum Kidwai, Chief Marketing Officer, Ageas Federal Life Insurance, says, “Trust is the most valuable currency in life insurance, and it is built through every interaction a customer has with us.” The brand uses GenAI across ideation, pre-visualisation, adaptation and language versions, but the extent of its involvement varies by project. “The constant is that AI supports the process, while the creative thinking, judgement, and final call rest with our teams.”
Accuracy and compliance remain essential, regardless of how an asset is produced. Prabhu notes, “Understanding culture, identifying genuine consumer truths, making creative choices and knowing whether an idea truly feels right for a brand are decisions that go beyond pattern recognition.”
In the AI era, the creative director may be defined less by what they make and more by what they choose to make, or reject. Malkani remarks, “The future creative isn’t the person who uses AI the most. It’s the person who can ask better questions, reject obvious answers and recognise the one idea that feels fresh.”
From more assets to smarter systems
The next step could be to make AI accountable not simply for producing more creative, but for learning which creative works. This is where performance, media and creative are beginning to converge. Arjit Sachdeva, Co-founder, VDO.AI believes the real opportunity is not simply producing more variants, but making creative respond to the granular way in which audiences are targeted. “Stop treating creativity as a finished artefact handed to the media and start treating it as a system that learns.”
The company’s AI-powered DCO technology generates campaign variations and adapts them to audience behaviour, context, device and location. Creative thus becomes a continuously optimised system rather than a static asset. But when outputs run into thousands, traditional human approval becomes impossible, forcing a rethink of quality control. Sachdeva argues that AI slop is ultimately a failure of constraints. “Slop isn’t caused by machines making content. It is caused by the loss of a constraint that used to enforce discipline. When producing an asset was expensive, someone had to justify it. Remove that cost and the filter disappears with it.”
His answer is to build quality checks into the system rather than rely on manual approvals. “You can’t manually review thousands of creative variations and a final approval step at that volume is a formality, not a safeguard.”
AI-first execution, creativity-first thinking
The evidence suggests the industry need not choose between AI and creativity, but define their roles. Malkani says, “Creativity is and will always be at the heart of our business. Now look at AI as a tool to get to where you want. The agencies that win over the next decade will be the ones that become AI-first in execution and creativity-first in thinking.” In simple terms, AI handles scale while humans handle meaning—deciding which insights matter, which ideas represent the brand and whether performance translates into meaningful work.
As Bhasin puts it, “The goal should be to treat AI as an amplifier of differentiation, not a shortcut to it.”
The emerging principle is clear: use AI to make creative work faster, more flexible, personalised and effective, while keeping the idea rooted in human insight.
