In the back offices of India’s fastest-growing consumer brands, a quiet rearrangement is underway. The change is not a new agency, a new celebrity face, or a new media channel. It is a new kind of “teammate” that sits inside dashboards and chat windows, watches data streams, makes recommendations, and increasingly tries to execute actions.
Marketers are calling it “agentic”. The claim is bigger than marketing automation and bigger than chatbots. It suggests something closer to a team: multiple AI agents with specialised roles, connected to customer data and media platforms, capable of planning, deciding, and acting toward a goal, with humans supervising and approving.
But in India, is this real transformation, or another shiny layer on top of the same old performance marketing playbook?
The answer, right now, is messy. The hype is loud. The pilots are real. The transformation is uneven, but it is starting to show up in the places where India’s marketing economy is already most intense: FMCG, e-commerce, fintech, BFSI, and telecom, sectors that spend heavily, run always-on campaigns, and live and die by incremental conversion.
The pressure cooker that makes “agentic” plausible
To understand why agentic marketing is even on the table, start with the scale of India’s digital ad machine.
In 2025, FMCG alone accounted for 32% of India’s digital advertising spends, totalling Rs 23,243 crore, up from Rs 16,606 crore in 2024, when FMCG held 34%. E-commerce followed as the second-largest digital advertiser, rising to Rs 15,836 crore in 2025 and taking 22% share, up from Rs 10,131 crore and 21% share in 2024. Other sectors also climbed: consumer durables from Rs 2,569 crore to Rs 3,625 crore, automotive from Rs 2,309 crore to Rs 3,425 crore, and pharmaceuticals from Rs 2,367 crore to Rs 3,328 crore.
This is the context in which “agentic teams” sell well. When spends are measured in thousands of crores, small efficiency gains matter. When customer journeys run across search, social, apps, WhatsApp, email, call centres, retail, and now connected TV, manual optimisation starts to look like a bottleneck.
The second force is mix shift. Telecom remained the most digitally skewed category in 2025 with digital at 74% of its total media mix, up from 66% in 2024. E-commerce’s digital allocation rose from 65% to 72%. FMCG’s digital share jumped from 53% to 64%, and pharmaceuticals from 54% to 67%.
In other words, budgets are moving to channels that are measurable, reactive, and algorithmic. That is exactly where AI agents can plausibly add value, at least on paper.
The third force is adoption readiness inside organisations. A nasscom AI Adoption Index 2.0 summary reported an index score of 2.47 on a 4-point scale in 2024, up slightly from 2.45 in 2022. It also reported that 87% of companies sit in the middle stages of “Enthusiast” and “Expert” AI adopters, with a twofold rise in the number of companies in the Expert stage compared to 2022. The study referenced 500 surveyed companies across seven sectors covering 75% of India’s GDP.
That does not prove agentic marketing is mainstream. But it suggests the plumbing for AI inside enterprises is improving, which is usually the prerequisite for anything “agentic” to work.
What “agentic marketing teams” actually mean in practice
Strip away the buzzwords and most “agentic marketing” deployments in India currently fall into three buckets.
Bucket 1: Insight agents that shrink analysis time. These agents monitor performance, identify anomalies, answer questions in natural language, and recommend fixes. They reduce the time a marketer spends slicing data across dashboards.
Bucket 2: Orchestration agents that trigger journeys. These systems decide the next best action across channels based on user signals, then trigger messages, offers, or experiences. This looks like advanced automation, but the agent framing is about continuous decisioning rather than static rules.
Bucket 3: Creative and media agents that generate, test, and iterate. These agents produce variants, route them into tests, watch performance, and suggest what to scale.
Even supporters admit this is not fully autonomous marketing. In regulated categories, most brands still insist on approvals. The team metaphor is aspirational: agents do more of the work, but humans remain accountable for brand, risk, and budget decisions.
That accountability constraint is why Indian BFSI leaders tend to speak about AI in operational terms, not sci-fi terms. At a Martech Summit in New Delhi, Deepak Oram, Head of Martech at HDFC Bank, put it bluntly: “The biggest field that generative AI is going to disrupt is marketing.” He also framed AI’s value as breaking bottlenecks in testing, creative iteration, and personalised communication, arguing that the future is less campaign thinking and more continuous optimisation.
That line captures the practical Indian promise of agentic systems. Not robot CMOs. Faster iteration.
Where the pilots are real
A useful way to measure reality is to follow who is shipping products and who is paying for them.
On the vendor side, Indian and India-focused customer engagement platforms have started packaging AI features explicitly as agents. Netcore, for instance, positions its offering as an Agentic AI Marketing Team and an extension of your marketing team in its product messaging. Netcore also announced an Insights Agent in 2025, describing it as a way to provide actionable, real-time marketing insights.
CleverTap has also been publishing explainers around marketing AI agents and intelligent automation, framing agents as the next step beyond static automation.
The buyer side is harder because most brands do not publicly disclose how much autonomy they allow, or what sits behind the scenes. But agencies and specialised firms are beginning to talk more openly about agentic workflows because it differentiates them.
In a Financial Express report on agentic AI in marketing, Shashwat Vatsa, AVP Brand at Olyv, described the direction of travel: “Within the marketing workflows, we are incorporating it to take control of routine but complex processes, from interpreting data to segmenting audiences and recommending content that drives engagement and conversions.”
The same report quoted Rajiv Dingra, founder and CEO of Mumbai-based agentic AI agency ReBid, saying his firm is already using multiple agentic AI functions across layers like data, media, creative and insights, and describing deployments for clients such as PNB Housing, Axis Direct, HDFC Sky, and Xiaomi. These are not proofs of a fully autonomous team, but they are tangible examples of agentic language moving from conference stages into client work.
A second sign of reality is how marketing is being reshaped by channel shifts that demand more automation.
A 2024–25 India digital marketing report by Ipsos noted that digital media spending in India overtook television spending in FY 2024, and highlighted growing adoption of AI and automation in marketing as an emerging trend. The same report also pointed to the rise of connected TV, citing over 20 million CTV users in India in 2023, expected to reach 40 million by 2026.
CTV is not agentic by itself. But it increases the number of surfaces where brands need to plan creative variants and optimise outcomes. As Reliance AJIO’s marketing head Arpan Biswas put it in the report, “CTV has become a critical channel to reach hi-end users,” describing how CTV helps brands avoid skip behaviour and target premium and family customers.
When channel complexity rises, the incentive to hand routine optimisation to machines rises too.
The adoption curve inside organisations
If the market story is about more channels, more data, and more pressure, the enterprise story is about workforce transformation.
Microsoft’s Work Trend Index 2025 found that 93% of Indian business leaders intend to use AI agents to extend workforce capabilities in the next 12–18 months. It also reported that more than three in four knowledge workers in India already use AI at work, and that 59% of leaders are using AI agents to automate workstreams or business processes across entire teams, the highest share globally in that report’s framing.
Marketing leaders read those numbers and see two immediate implications.
First, adoption is likely to start as assist rather than autonomy. Leaders try agents in contained workstreams like reporting, segmentation suggestions, campaign QA, and creative versioning.
Second, once teams get comfortable, the unit of change becomes workflow design, not tool adoption. The new advantage goes to organisations that can connect data, define objectives clearly, and build governance that allows faster action without breaking compliance.
That governance point matters disproportionately in India. BFSI, healthcare, and telecom have real constraints around customer trust and regulation. That is why HDFC Bank’s Oram stressed that banks operate in an environment where users are cautious about automated decision-making involving money, which shapes how MarTech leaders evaluate emerging capabilities.
So, hype, pilot, or transformation?
All three are present, depending on where you look.
It is hype when agentic is used as a synonym for any AI feature. Many products marketed as agents are still rule-based automation with a chat interface. The branding is ahead of the autonomy.
It is pilot in most Indian enterprises today, especially where brand risk is high. The systems are being tested in narrow lanes: insight generation, creative drafts, audience segmentation, and performance diagnosis. Humans still approve most actions that touch spend, customer messaging, or compliance.
It is transformation in specific environments where the economics reward it and the risk is manageable: performance-heavy categories, app-first businesses, and digital-native marketers running high-volume experimentation. That is where continuous optimisation, always-on journeys, and rapid variant testing are already the operating model. Agents fit because they accelerate what teams already do.
A simple litmus test is whether the organisation changes roles and cadence.
If a team still runs weekly reporting, quarterly campaign calendars, and siloed channel managers, agentic becomes another tool in the stack.
If a team shifts toward daily experimentation, real-time journey tuning, and shared KPIs across marketing, product, and growth, agentic systems start to behave like a multiplier.
This is why the most honest description of India’s first agentic marketing teams is not AI replaces marketers. It is AI eats the backlog.
The backlog in India is enormous: regional language variants, hyperlocal creatives, segment-specific offers, compliance checks, platform-specific optimisation, and measurement across fragmented journeys. India’s scale creates marketing work that is repetitive, data-driven, and unglamorous, which is exactly where agents perform best.
There is also a broader AI ambition running through Indian corporate narratives. At Reliance’s AGM, Mukesh Ambani said, “AI has become integral to everything we do,” adding that Reliance is embedding AI into all processes and offerings, creating end-to-end workflows with real-time, data-driven insights and automation. That is not a marketing-only statement, but it reflects how India’s largest companies are positioning AI: as workflow automation at scale.
The next 12 months: what will decide the outcome
Three practical factors will determine whether agentic marketing becomes mainstream transformation or stays in pilot mode.
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Data readiness and identity resolution. Agents are only as useful as the context they can access. Brands with clean first-party data, unified customer profiles, and consent-aware pipelines will move faster.
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Governance that enables speed. The winners will define where agents can act automatically, where they need human approval, and how the system logs decisions for audit.
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Talent that can run human-agent teams. India’s marketers will not need fewer skills. They will need different skills: prompt discipline, experimentation design, measurement literacy, and the ability to translate brand strategy into machine-operable objectives.
This is why, right now, the most credible transformation stories are not about AI writing taglines. They are about AI compressing decision cycles. Less time to find what went wrong. Less time to build variants. Less time to coordinate across channels. More time to decide what the brand should do next.
In 2026, India does not yet have agentic marketing teams in the way the term is sold on stage, with fully autonomous pods running budgets end-to-end. What India does have is the beginning of something more realistic, and potentially more disruptive: marketing organisations reorganising around workflows that assume machines will do the repetitive thinking, so humans can focus on judgment.
The hype will continue. But the pilots are already rewriting the default setting of Indian marketing: always-on, always-optimising, and increasingly, always assisted by agents.
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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