Walk into two different marketing war rooms in India right now and you could be looking at two different decades.
In one, a large brand team has an AI stack wired into everything: first party data from apps and loyalty programs, automated media buying, creative variations generated at scale, and dashboards that update by the hour. Decisions move fast because the inputs are machine-processed and the workflows are increasingly agent-led.
In the other, a small brand or mid-sized agency team is still running marketing on spreadsheets, WhatsApp groups and manual reporting. They may be experimenting with ChatGPT or Canva, but they do not have clean data pipes, governance processes, or the time to train people properly. Their “AI” looks like a few tools, not a system.
This gap is becoming one of the most consequential divides in marketing. It is not only about who has access to advanced models. It is about who can operationalise them and who will be left working around them.
Several recent studies show how quickly AI usage is rising, but also how uneven readiness remains. In India, 79% of marketers said they plan to use AI more over the next year, yet only 46% said they feel fully confident in their teams’ ability to use AI effectively against KPIs. That confidence gap is where the new class divide shows up most clearly.
The AI Haves: Data, Talent, and Systems
The brands at the “AI haves” end of the spectrum tend to share a few structural advantages.
First, they have data that is usable. Large consumer businesses with apps, loyalty programs, omnichannel commerce, and CRM infrastructure can feed models with cleaner signals. That makes personalisation, measurement and automation far easier. They can build internal knowledge bases, train models on brand-safe assets, and maintain audit trails.
Second, they can pay for talent and training. AI adoption is not just a software line item. It requires prompt literacy, analytics skills, experimentation habits, and people who can work with legal and compliance teams on privacy, consent, and policy.
Third, they can run pilots long enough to learn. Many large marketers can afford to test, fail, and iterate without immediate pressure to prove ROI in a week.
That is why enterprise-facing adtech and martech platforms are seeing faster uptake among bigger advertisers. In the MiQ report highlighted by Storyboard18, Indian marketers were already using AI heavily for social media management, visual design and content creation. Even the tool choices reflect maturity: Google’s Performance Max and Canva were cited among the most-used AI-based tools by Indian marketers.
Varun Mohan, Chief Commercial Officer India at MiQ, summed up how AI advantage compounds: “AI tools will have a tremendous role to play in the future of marketing as a force multiplier, and teams that prioritise early adoption and upskilling for AI will find themselves significantly ahead of the curve.”
That is the crux. This is no longer a “nice-to-have” layer. For teams who invest early, AI starts to create speed advantages, output advantages, and learning advantages that stack over time.
The AI Have Nots: Tool Access Is Not Capability
On the other side are smaller brands, newer D2C players, local retailers, and many independent agencies. They are not necessarily rejecting AI. In many cases, they are using it every day. But their usage is fragmented.
They may use generative tools to write captions or build quick creatives, but they still lack the underlying systems that make AI reliable: structured product catalogs, unified customer views, clean event tracking, or governance processes that prevent off-brand outputs. They also face the operational reality that one person often does five jobs. There is little time to rewire workflows.
The MMA Global India “State of AI in Marketing” report captures a key part of this challenge: 69% of respondents flagged skilling and training as one of the top challenges for AI adoption in marketing, and 54% said AI adoption is not effectively understood. This is the gap between experimenting with tools and building organisational capability.
There is also a cost issue that gets understated. While some AI tools are affordable, the full stack is not. Cloud usage, data engineering, premium licenses, measurement solutions, and brand safety layers can add up quickly. For many smaller teams, the hurdle is not buying an AI tool. It is integrating it and keeping it compliant.
A Divide That Runs Through Bharat Too
This divide is not only between big and small companies. It also plays out across audiences and geographies.
Gunjan Khetan, CMO at Perfetti Van Melle India, described a campaign approach built for rural contexts, where conventional digital assumptions do not hold. In an Exchange4media interview, he said that in rural markets “where TV reach is limited and smartphone usage is below 50 percent in states like UP and Bihar, conventional media cannot go far.” His team partnered with WPP and Google to use Bharat GPT-like capabilities via IVR-style engagement, using voice and local dialects rather than relying on apps and screens.
That example matters because it shows two truths at once. AI can widen divides if only urban, app-heavy brands benefit. But it can also narrow divides when designed for low-infrastructure contexts. The difference is not the model itself. It is who has the partnerships, the budgets, and the strategy to deploy it in-market.
The Consumer Pressure Cooker
Consumer expectations are accelerating the divide. When customers start expecting hyper-personalisation, better service, faster discovery, and smoother journeys, brands with better AI systems meet those expectations more easily.
Adobe’s India-focused study reported that 66% of Indian brands were already utilising generative AI and another 26% were experimenting, while 81% of Indian consumers expected brands to adopt gen AI by the end of 2024.
Anindita Veluri, Director of Marketing at Adobe India, framed the challenge in simple operational terms: “Gen AI is transforming consumer expectations at an unprecedented pace, particularly in India… while Indian brands are frontrunners in adopting this technology, they must also prioritise transparency, ethics, and responsible usage.”
This consumer pressure creates a second-order effect. If the market begins rewarding speed, relevance and responsiveness, the brands with AI maturity pull further ahead. Brands without it do not just lose efficiency. They risk losing relevance.
The Hiring Gap Is Becoming a Strategy Gap
The sharpest part of the class divide is increasingly talent.
AI is changing what “good marketing” looks like inside organisations: the ability to brief models, evaluate outputs, interpret signals, and run experiments. That shift is creating a new fault line between teams that can recruit or train for AI fluency, and teams that cannot.
Ruchee Anand, India Head, LinkedIn Talent Solutions, captured the workforce tension in late 2024: “Businesses are increasingly prioritising AI adoption, alongside meaningful investments in upskilling and reskilling their people.”
At the leadership level, IBM’s CMO Study 2025 puts numbers on the gap. Only 26% of Indian CMOs said they believe they have the talent needed to achieve their goals over the next two years. The same study language is telling because it positions AI not as a tool upgrade, but as an operating model shift. Tuhina Pandey, Director, APAC Communications & Marketing at IBM India and South Asia, said: “While the potential of AI is clear, what’s needed now is a bold new playbook, one powered by trusted data, skilled talent, cultural reset, and AI augmentation.”
For smaller brands and agencies, hiring is harder because AI-skilled talent is scarce and expensive. For larger brands, the advantage is not only higher pay. It is also the ability to offer scale, data access, and learning opportunities that attract talent.
This is where the “marketing class divide” becomes real. AI maturity is not just a technology decision. It is a labour market advantage.
Startups vs Enterprises: Two Very Different AI Journeys
Startups are often assumed to be the “AI haves” because they move fast. In reality, their position varies sharply.
Some martech and ecommerce startups are native to AI workflows. They build with automation in mind, use AI for content and customer support early, and run lean growth loops. If they have a strong data pipeline, they can be surprisingly effective.
But many young brands remain “AI tool users” rather than “AI system builders.” They use AI to increase output but still rely on paid platforms for targeting, measurement, and reach. Without deep first party data, they are dependent on ecosystems.
Enterprises, meanwhile, often adopt slower but scale more powerfully once the foundations are in place. They can centralise brand governance, integrate AI into media, and build shared services for creative and analytics. The output gains show up across multiple teams, not just one campaign.
The divide between startups and enterprises is therefore not about who uses AI. It is about who can institutionalise it.
What This Means for Indian Marketing in 2026
The AI haves vs have nots divide is unlikely to disappear. If anything, it may widen as agentic workflows become more common and marketing becomes more machine-assisted across planning, creative, execution, and measurement.
But it is not a fixed hierarchy. There are paths for smaller teams to compete, especially when they focus on a few high-impact use cases, build stronger first party signals, and invest in skilling. Partnerships also matter, as shown in Bharat-focused deployments where AI is used to reach audiences without conventional digital access.
The next phase of marketing advantage may look less like who has the biggest budget, and more like who has the best system: trusted data, trained people, and workflows that can scale without breaking trust.
That is the new class divide. And it is already reshaping how brands grow in India.
Disclaimer: All data points and statistics are attributed to published research studies and verified market research. All quotes are either sourced directly or attributed to public statements.
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