In the past, media planning meant large spreadsheets, weekly calls with broadcasters and publishers, and long nights spent balancing reach, frequency and budgets. In 2025, many of those routines are being rewritten as artificial intelligence enters the planning room. Global forecasts suggest that nearly 60 percent of all ad spend this year will be routed through algorithmically enabled channels, a share expected to rise to about 79 percent by 2027. The question inside Indian agencies is no longer whether AI belongs in media planning, but how far it can go without losing human judgment.
India’s ad market gives that debate a sharp edge. The country crossed the one lakh crore mark in total advertising spend in FY25, with digital media accounting for around 46 percent of the pie. A separate industry outlook estimates that India’s digital advertising market stood at roughly 49,251 crore rupees in 2024 and could reach about 59,200 crore rupees in 2025. As more of that money flows into biddable and data driven channels, the planning task is shifting from choosing individual spots to steering algorithms.
Senior media leaders say the shift is structural. Prasanth Kumar, CEO of GroupM South Asia and president of the Advertising Agencies Association of India, has repeatedly framed AI as central to how Indian media businesses prepare for growth. He has pointed to India’s scale and data richness, noting that the country combines a large consumer base, a young population and a strong appetite for experimentation, which makes it fertile ground for AI driven planning. At the same time, survey work from MMA Global India finds that about 73 percent of Indian marketers believe AI will significantly enhance marketing capabilities but will not replace human creativity and expertise. The industry is therefore treating AI as a planning copilot rather than an autopilot.
In practical terms, current AI media tools sit on top of familiar programmatic and performance platforms. A planner working on a large consumer brand might feed historic campaign data, audience profiles and channel costs into an AI system that then proposes budget splits between television, connected TV, digital video, search, social and retail media. Global trade bodies note that these systems can already build media plans, generate audience segments, select media partners and run scenario forecasts at a speed that would be impossible manually. In India, this is visible in the way large FMCG and ecommerce advertisers now review multiple machine generated scenarios before locking a final plan.
At agency level, AI is increasingly embedded in workflows rather than presented as a separate product. In recent commentary, Shashi Sinha, CEO of IPG Mediabrands India, has summed up the current phase by saying that AI is mostly about efficiency, helping teams eliminate grunt work, iterate plans across languages and touchpoints, and generate acceptable quality content at scale. His comment captures a wider reality. Algorithms are handling repetitive optimisation and basic forecasting, but strategy, risk appetite and creative direction remain with humans.
The most visible change is in audience planning. Instead of relying primarily on age, gender and SEC cuts, planners are using AI models to build behavioural cohorts based on content consumption, time of day habits and recent purchasing patterns. In digital, these clusters often inform lookalike or interest based targeting. In traditional media, they help decide which shows, genres and dayparts are likely to aggregate the right mix of viewers. For example, an auto brand planning a new SUV launch can now simulate how different combinations of sports, regional entertainment and connected TV placements might influence test drive sign ups by region before the first GRP is booked.
Out of home has emerged as a test bed for AI augmented planning in India. OSMO Advertising’s LOC8 platform, which combines recce videos, audience data and AI driven recommendations, is positioned as a way to move from gut based buying to attention led planning in OOH. The system uses on ground imagery and traffic data to estimate how likely it is that a site will actually be noticed, rather than simply counting impressions based on location. Mangesh Shinde, founder and CEO of OSMO, has described this as a transformation moment for outdoor media, arguing that AI can help marketers answer a basic question more rigorously: will people really see this ad. For categories such as real estate and auto, where outdoor still plays a major role in discovery and recall, this kind of tool is becoming part of the standard planning discussion.
Inside digital agencies, AI is also reshaping how plans are assembled and revised. A cover story on AI in Indian agencies earlier this year highlighted how many networks and independents have built internal copilots that sit on top of platforms like Meta, Google and programmatic buying tools. One example is a Mumbai based agency where planners now query an internal model to pull historic performance benchmarks, recommended channel mixes and suggested frequency caps for a given budget and target audience. This does not remove the need for a planner, but it compresses hours of manual report pulling into minutes.
Rohit Sakunia, founder of ArtE Mediatech, a creative and media agency, describes their approach in similar terms. “At ArtE, we use AI tools as force multipliers. This means the first draft is quick and the team can explore more creatively. More content routes can be thought of, providing more references and ideas to the client. As far as media planning is concerned, tools help in audience insights and performance prediction. However, all final calls are still human. We look at AI as an enabler,” he said, noting that the agency combines generic tools like ChatGPT with custom dashboards built on top of Meta and Google APIs.
Marketers are equally focused on the limits of AI plans. One of the biggest constraints in India is patchy and siloed data across channels. While digital touchpoints provide granular feedback, television, radio and large parts of OOH still depend on panel based or modeled metrics. Companies that have stitched together customer data platforms, CRM systems and media logs are better positioned to train planning models. Others have to work around gaps. Consulting firms tracking the sector point out that internet advertising in India is expected to grow at more than 15 percent annually over the next few years, faster than traditional media, which means these integration challenges will only become more important.
Skill gaps are another recurring theme. The MMA India State of AI in Marketing report highlights that while marketers are optimistic about AI’s potential, about 69 percent cite skilling and training as a top challenge in adoption. Agencies and brands are responding by setting up internal AI councils, training media teams on prompt design and model interpretation, and hiring data analysts who can work alongside planners. The job description of a media planner is slowly expanding from channel negotiation and schedule building to include scenario modelling and model audit.
The question of replacement versus augmentation is usually answered with caution. On one hand, global forecasts show that more than half of all ad spend in 2024 is already algorithmically enabled and that this share will keep rising. On the other, agency leaders in India are clear that client expectations, brand risk and cultural nuance still require human oversight. At industry forums, several have echoed a similar line that AI is a copilot that can accelerate routine work, but that final accountability sits with people. This sentiment is reinforced by survey data which suggests that most Indian marketers do not expect AI to replace human creativity and expertise in the foreseeable future.
There are also risks to manage. Fraud detection firms warn that AI is being used not just to optimise campaigns, but also to generate synthetic traffic and manipulated media, which complicates measurement. As planning models become more complex, teams need robust governance frameworks to ensure that the data feeding them is clean and that outputs are audited for bias. This is especially important in sensitive categories such as finance, healthcare and political advertising, where incorrect or skewed allocations can have reputational and regulatory implications.
For Indian marketers, the opportunity lies in treating AI media planning as part of a broader marketing operating system rather than as a standalone tool. Brands that are seeing early gains typically follow a few common steps. They start with a clearly defined use case, such as optimising budget splits between connected TV and digital video, or improving leads from dealer campaigns. They ensure that campaign, cost and outcome data from different partners is centralised and made available in a consistent format. They run controlled tests where an AI suggested plan is compared with a human plan, measure the differences in reach, cost per outcome and attention metrics, and then scale what works.
The answers that emerge from these experiments are rarely absolute. In some cases, AI recommended mixes deliver better cost per acquisition but slightly lower brand metrics, prompting teams to hybridise the plan. In others, AI may identify overlooked regional inventory or niche publishers that improve incremental reach. Over time, planners learn where models are strong and where local knowledge or client dynamics still dominate.
The long term direction is clear, even if the pace will vary by organisation. As more of India’s ad spend migrates to digital and biddable formats, AI will take on a larger share of the work involved in forecasting, optimisation and reporting. Media planners, in turn, will spend more time on upstream tasks such as defining audiences, interpreting signals and aligning plans with business goals. The industry’s own data points to this blended future rather than to a zero sum outcome.
In that sense, the dawn of AI media planning is less about machines replacing planners and more about changing what planning means. For advertisers trying to reach fragmented, mobile first audiences at scale, the practical question is not whether AI should sit at the table, but how to design teams, contracts and data pipes so that it can do useful work without eroding trust. The planners who thrive in that environment will be the ones who can read both a model output and a room, translating probabilities into decisions that still reflect the brands, cultures and markets they serve.
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.
Related Articles
