For chief marketing officers, the list of targets has not changed much in years. They still have to grow awareness, reduce acquisition costs, improve conversion, defend market share and prove that every rupee of spend works harder. What has changed is the toolkit. In 2025, artificial intelligence is moving from side project to operating layer, helping CMOs move faster on the same key performance indicators that boards and CEOs have always watched.
MVS Murthy, Chief Marketing Officer at Federal Bank, described the shift in an industry discussion by comparing AI led marketing to a constant campaign cycle. AI, he said, allows a marketer to be in “election mode” rather than planning for only one season, so teams can strike campaigns for a clear reason instead of relying on long, fixed bursts. The comment reflects a broader trend. Indian and global CMOs are not using AI to chase novelty. They are using it to run more experiments, allocate budgets with greater precision and compress the time between insight and action.
Across markets, adoption numbers show that AI is now embedded in mainstream marketing. Industry surveys of senior marketers report that more than half of organisations are already using AI in some part of their marketing stack, with many in partial integration rather than pilot mode. In India, a separate study focused on AI in marketing found that almost three quarters of respondents believed AI would significantly enhance marketing capabilities while still requiring human creativity, and that skilling and training were among the biggest barriers to wider deployment. Retail specific research from Infosys shows that around six in ten retail marketing leaders globally are already using AI across five to seven activities, including ad spend management, content creation and ecommerce personalisation. Other reports from Europe find that over 60 percent of marketers who have adopted AI associate it directly with revenue gains.
For CMOs, these numbers matter because they shift the discussion from whether AI should be used to where it moves KPIs most clearly.
From reporting to real time decisions
One of the clearest changes is in media efficiency. Traditional planning relied on historic reports and periodic post campaign analysis. AI driven tools now ingest live performance data and recommend budget shifts across platforms and formats while campaigns are still running. Programmatic platforms optimise impressions toward audiences and contexts that are most likely to respond, and predictive models can flag when a campaign is saturating an audience before waste sets in.
This has direct impact on cost per mille, cost per click and cost per acquisition. Indian CMOs in sectors such as ecommerce, fintech and mobility report that AI assisted bidding and creative optimisation have helped them hold or even reduce acquisition costs despite rising media prices. The combination of multivariate creative testing and dynamic budget allocation means that poorly performing combinations are retired sooner and winners are scaled faster, often within hours rather than days.
Upper funnel metrics have also become more granular. Instead of broad reach estimates, CMOs can now see which micro segments in which cities and cohorts are driving incremental brand searches or app installs. This allows better conversations with finance teams, since scenario models can link changes in media mix directly to expected changes in key KPIs.
Where AI moves the needle on KPIs
On awareness and consideration, generative AI is being used to rapidly adapt master campaigns into multiple language and format variants that fit regional channels and local contexts. Large consumer brands in India now routinely use AI assisted workflows to produce and test dozens of social edits, banners or email subject lines from a single base concept. The goal is not to replace agencies, but to compress production cycles so that more variants can be tried within the same budget.
When AI helps speed up this creative testing loop, brand lift and engagement metrics tend to respond. Studies tracking AI use in content creation and optimisation show that a majority of marketers see measurable gains in click through and engagement when AI is used to refine, rather than fully automate, messaging.
On performance metrics such as lead quality, revenue per user and churn, AI’s impact is even more direct. In retail, banking and subscription services, models trained on historic behaviour now score leads, predict propensity to buy or lapse and suggest interventions. Instead of sending the same offer to every customer in a segment, teams can prioritise those with the highest likelihood to respond and suppress messages to those who are unlikely to convert or who may react negatively to frequency.
This is where the idea of an “always on, self learning funnel” becomes tangible. Each campaign provides new labelled data. Every interaction, from an abandoned cart to an app uninstall reversal, feeds back into the models that drive future journeys.
Udit Agarwal, Global Head of Marketing at cloud communication firm Exotel, captured this mindset when he called AI in marketing “a very exciting trend” and framed the challenge as teaching AI “how to do marketing” rather than treating it as a separate entity. For many CMOs, the task is now to encode their playbooks into systems that can act at machine speed while still reflecting brand logic.
Anuradha Aggarwal, Chief Marketing Officer at Amazon Pay, has highlighted the potential of AI driven dynamic marketing that enables more personalised advertising. For a payments platform that sits inside everyday transactions, this means connecting signals from shopping, bill payments and recharges to decide which offers to show and when. The customer may see only a single notification or card. Behind that surface, AI is working through combinations of timing, value and channel designed to improve activation and retention KPIs.
B2B and long cycle businesses
While much of the visible experimentation is in consumer categories, B2B CMOs are applying similar ideas to pipeline health and sales velocity. AI powered lead scoring systems in SaaS and enterprise technology firms analyse firmographic and behavioural signals to rank inbound leads. Prospects who match the profile of previous successful deals rise to the top of the queue, improving sales conversion rates and shortening cycles.
Content performance is also under closer AI assisted scrutiny in B2B. Large volumes of webinars, whitepapers and case studies are summarised and tagged automatically, allowing marketing teams to build more precise nurture tracks. Instead of a generic sequence of emails, prospects receive content mapped to their sector, role and stage in the buying process. KPIs that matter to B2B CMOs, such as marketing sourced pipeline, opportunity to win ratio and deal size, are tracked against these AI orchestrated journeys.
Here too, the emphasis is on augmentation rather than replacement. AI surfaces patterns that might be hard to see manually, but final decisions on tier one accounts and pricing still rest with human sales and marketing leaders.
New expectations from the CMO office
The spread of AI across planning, creative, media and CRM is changing what is expected from CMOs and their teams. First, there is a stronger requirement for data fluency. AI models are only as reliable as the data and definitions they are trained on. Many CMOs report that a large share of their AI related work involves aligning marketing, sales and technology teams on a single view of the customer and clear definitions of events and outcomes.
Second, governance has become part of the job. Industry research on AI in marketing in India shows that while a majority of marketers believe AI will enhance their capability, a similar majority also recognises the ethical implications of its use. As AI systems make more automated decisions about targeting, pricing and personalisation, CMOs need frameworks for fairness, consent and brand safety. This is especially important under India’s evolving data protection regime, where the basis for using personal data for marketing must be clearly documented.
Third, talent and skills are emerging as both a constraint and an opportunity. The same MMA India survey found that skilling and training were named among the top challenges for AI inclusion in marketing. CMOs are responding by creating hybrid roles that combine marketing strategy with analytics, experimentation design and AI operations. Agencies and martech partners are being brought into this skilling effort, with some large advertisers running dedicated training tracks on prompt design, AI literacy and experiment interpretation.
Practical playbooks and gaps
On the ground, the CMOs who report the most benefit from AI describe three practical habits.
The first is to tie AI projects tightly to a small set of KPIs rather than treating them as innovation showcases. A retail CMO evaluating an AI powered recommendation engine, for example, will often define success in terms of basket size, cross sell rate and return rate, not only engagement. A bank deploying AI in its contact centre will track resolution time, net promoter score and cost per resolution alongside volume metrics.
The second is to start in parts of the funnel where latency is known to hurt results. This includes lead response time in high value categories, frequency management in apps with high uninstall risk and cross sell timing in subscription products. Here, even modest improvements in reaction time or message relevance can move KPIs enough to justify investment.
The third is to keep a human in the loop for interpretation and exception handling. AI models can highlight that a certain creative variant improves click through, but brand teams still decide whether that creative is on equity. Models can signal that a segment responds better to discount led messaging, but CMOs must balance short term volume with long term brand and profitability considerations.
Despite growing maturity, gaps remain. Some CMOs note that AI led optimisation can overfit toward easily measurable outcomes such as clicks or short term sales, neglecting brand health and long term equity. Others point to the risk of homogenisation if many brands use similar optimisation tools and data sources. In India, where cultural nuance and regional diversity are central to marketing, there is also concern that models built primarily on English or metropolitan data may not fully capture the richness of local behaviour.
For now, the direction of travel is clear. As digital media and martech investments take a larger share of India’s expanding advertising market, AI is becoming part of how CMOs look at every KPI on their dashboard rather than a separate box. The CMOs who are furthest along are not those with the most experimental pilots, but those who have quietly woven AI into everyday decisions on reach, frequency, creative, leads and loyalty.
In that sense, AI is not changing what success looks like in marketing so much as changing the speed and confidence with which CMOs can pursue it.
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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