In marketing teams across India, the AI conversation has moved past curiosity. It is now a line item. New tools promise faster content, smarter media buying, better attribution, cleaner data and “personalisation at scale”. The pitch is familiar: plug in the platform, switch on automation, and watch productivity rise.
But behind the demos and pilot decks, a quieter question is surfacing in brand offices and agency war rooms: are companies actually getting value, or simply accumulating software?
The worry is not that AI does nothing. Many teams are seeing real gains in content turnaround times and campaign workflow. The worry is that too many brands are buying more capability than they can use, paying for overlapping tools, and calling it transformation. Some of that spend is driven by genuine need. Some of it is driven by fear of missing out.
This matters because marketing budgets are not expanding at the same rate as the vendor landscape. And as AI features get bundled into everything from CRMs to analytics dashboards, decision-making is becoming harder, not easier.
A recent India-focused industry survey cited at the Pitch CMO Summit underlined how big MarTech has become inside budget planning. More than 65% of respondents said they were spending over 16% of their marketing budget on MarTech, up from 18% in 2023, according to insights shared by Mihir Karkare, Managing Director at Mirum India.
Spend growing does not automatically mean waste. But it does raise the stakes. The more money that moves into tools, the higher the pressure on marketers to show impact.
Why the “useful tool” is turning into “tool sprawl”
Part of the problem is the sheer number of choices. The State of MarTech 2024 landscape counted 14,106 products, up 27.8% from the previous year, a scale that makes it easy for teams to end up with multiple solutions doing similar jobs.
At the same time, AI has changed buying behaviour. Instead of choosing one platform and building around it, many teams are trying multiple point solutions for copy, design, social listening, audience modelling, media optimisation, chat, CRM automation and analytics. A 2025 survey of martech and marketing operations leaders found that 62.1% are using more martech tools than they did two years ago, and 68.6% said they are using generative AI tools.
The result is a familiar pattern: a marketing team buys an AI tool to solve a workflow bottleneck, then buys another tool because it integrates better with a different system, and then keeps the first tool because someone is still using one feature. Over time, the “stack” becomes a set of subscriptions held together by habit.
This is also where procurement and finance leaders start asking harder questions. In many organisations, software spend is not the issue by itself. Waste happens when tools are under-used, poorly integrated, or purchased without clarity on ownership.
That is why the strongest pushback is not against AI. It is against “AI without a use case”.
Deepali Naair, Group CMO at CK Birla Group, captured a sentiment many teams recognise: there is plenty of conversation about generative AI in marketing stacks, but she pointed out that there are not enough integration use cases yet, and that adoption often stays in the pilot space.
This is the gap where budgets disappear. Pilots are relatively cheap. Scaling is not.
What waste looks like in practice
In interviews and internal reviews, marketers often describe “waste” less as a scandal and more as a slow leak. It shows up in everyday scenarios:
-
Multiple teams paying for separate tools to do similar tasks, because each department bought what they liked.
-
AI content tools producing volume, but brand teams still rewriting most outputs because guardrails were never set.
-
Data platforms generating dashboards, but decision-makers still relying on spreadsheets or gut feel because trust in data quality is low.
-
Media optimisation tools promising automation, but marketers overriding recommendations because they cannot explain the logic to stakeholders.
One reason this keeps happening is that the incentives are skewed. Buying a tool is visible action. Doing the slower work of integration, data readiness, governance and training is less visible.
Indian marketing leaders are increasingly framing the fix as discipline, not more technology.
Aabhinna Suresh Khare, Chief Digital and Marketing Officer and Head of Strategy at Bajaj Capital, described a more grounded approach: aligning tool selection with specific business objectives and audience needs, doing thorough research, and prioritising platforms that integrate with the existing stack.
Rajat Abbi, Vice President Global Marketing and CMO at Schneider Electric, has described a similar filter, saying their approach is built on rigorous evaluation and a steady focus on organisational objectives, identifying essential tools that drive tangible results rather than getting distracted by flashy options.
These are not anti-AI statements. They are budgeting statements.
The global mirror: when AI spend is working, and when it is not
Global cases show both sides of the equation. Some brands are using AI to cut external costs and speed up production. Others are learning that not every AI initiative pays back.
Klarna, for instance, said AI helped it cut marketing agency spend by 25% and reduce sales and marketing spend by 11% in Q1 2024 while running more campaigns. The details matter here: Klarna’s claim was tied to specific operational shifts like faster image production and reduced reliance on external suppliers, not just a generic “AI transformation”.
Unilever has also publicised AI-driven changes to marketing production workflows, including its use of digital twins for product imagery to create content faster and more cost-effectively.
These examples are popular because they are measurable. They show what finance teams want: a link between tool use and cost, time, or output.
But these same examples also highlight why some Indian brands feel their budgets are being eaten: not every organisation has Klarna’s willingness to redesign workflows, or Unilever’s scale to justify industrial-grade content systems. Many companies buy tools hoping for these outcomes, then discover the harder truth that tooling is the easy part.
The hidden cost: talent gaps and governance debt
Another reason budgets feel wasted is that teams often buy technology faster than they build the capability to operate it.
IBM’s CMO Study 2025 India announcement points to a widening execution gap: only 26% of Indian CMOs have established responsible AI guidelines, and only 26% believe they have the talent needed to achieve their goals over the next two years.
This is not just a governance issue. It is a cost issue. When guidelines are unclear and teams are under-trained, organisations compensate by hiring agencies, adding tools, or running parallel processes. Budgets rise because the organisation is paying twice: once for the software, and again for humans to make the software safe and usable.
Tuhina Pandey, Director APAC Communications and Marketing, India and South Asia at IBM, put the emphasis on responsible execution, saying that while AI’s potential is clear, what is needed is a “bold new playbook” powered by trusted data, skilled talent, a cultural reset, and AI augmentation.
That “playbook” language matters in a budgeting story because it reframes the problem. Waste is not always caused by bad tools. It is caused by buying tools without building operating models.
Where Indian brands and agencies are most likely to overspend
In India, the highest-risk categories for “AI ate my budget” stories tend to cluster around four areas:
-
Content generation and creative automation
These tools are easy to try and easy to add. But they can multiply quickly, and quality control becomes the hidden cost. -
Media buying and optimisation layers
Brands may already have platforms through agencies, ad networks, and enterprise stacks. Adding another AI layer often creates overlap unless roles are clearly defined. -
Analytics, attribution, and measurement
This is where vendor promises are biggest and implementation timelines are longest. If data plumbing is weak, tools do not perform, and teams end up adding more tools to “fix” measurement. -
Customer data and personalisation systems
Many enterprises invest here expecting clear ROI. But integration across channels and consent-safe identity resolution in India’s evolving privacy environment can slow outcomes.
None of this means brands should stop investing. It means they need clearer rules for what gets funded.
A simple test finance teams are now using
In more mature organisations, procurement and finance leaders are pushing marketing teams to answer a few practical questions before approving AI tools:
-
What business metric changes if this tool works?
-
Who owns adoption, not just purchase?
-
What existing tool becomes redundant if this is implemented?
-
What data and integration work is required, and what will it cost?
-
What are the governance risks, and who signs off?
In plain terms, the test is shifting from “does it have AI?” to “does it reduce work, reduce spend, or increase revenue in a measurable way?”
That is also why integrated stacks are becoming a stronger preference. Karkare highlighted a gap between executives on martech choices: 72% of CEOs and CMOs preferred tools that are part of an integrated stack, compared to 53% of CDOs or CTOs, and suggested this misalignment contributes to the complexity of choosing the right tools.
When leaders are not aligned, budgets suffer. Duplicate tools survive because no one can agree what to retire.
The more realistic conclusion: AI is not eating budgets, bad buying is
The most useful way to frame this debate is not as a warning that AI tools are useless. Many are clearly valuable. The real issue is that the buying cycle has sped up while the integration and governance cycle has not.
The brands that feel like AI “ate” their budgets are often those that treated AI as a product shopping problem. The brands that see value are treating AI as an operating model change.
For Indian marketers, the next phase looks less like a race to buy and more like a race to simplify: fewer tools, clearer ownership, stronger data foundations, and an honest willingness to shut down pilots that do not scale.
If 2023 and 2024 were about experimentation, 2025 is shaping up to be about accountability. The most competitive marketing teams will not be the ones with the longest list of AI subscriptions. They will be the ones that can explain, in one sentence, what each tool is doing for the business and what it replaced.
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.