For many marketers, the customer funnel no longer feels linear. People move from a social reel to a review site, from a chatbot to a retail store and back to a brand’s app in the same day. Artificial intelligence is increasingly the system that connects these dots. Instead of running isolated campaigns, brands are trying to build always on, self learning journeys that adjust in real time to what a customer does next.
Indian marketing leaders describe this as a structural shift rather than a short term experiment. “For us in B2B, the marketing funnel is no longer a funnel. AI helps us show up wherever the customer is,” says Anuradha Gupta, Executive Director of Marketing at Deloitte. That view captures how many large organisations now think about discovery, consideration and loyalty as a continuous loop.
Industry studies focused on India indicate that a strong majority of marketing and communication teams already use AI for content tasks, and more than four in five use it for research, insight generation and audience interpretation. Global surveys suggest that a large share of marketers worldwide now use AI in their day to day work, from drafting copy to analysing performance. India also ranks among the more active markets in generative AI adoption, with employee usage of such tools consistently reported above global averages.
At the same time, maturity is uneven. Indian association reports on AI in marketing note that many organisations are still in the experimentation phase and that only a smaller share consider AI to be deeply integrated in their processes. The battle for the AI customer is therefore as much about execution and governance as it is about technology.
From static campaigns to living funnels
In a traditional funnel, teams planned a campaign, launched it across a handful of channels, then waited for reports at the end of the month. In an AI assisted model, the loop is tighter. Signals from each touchpoint feed into models that decide who to speak to, what to say and when to intervene.
In insurance and banking, this often starts with renewals and cross sell journeys. Life insurers, for example, use behavioural and transaction data to identify policies at risk of lapsing and send personalised nudges anchored in the customer’s past interactions. Rahul Talwar, Chief Marketing Officer at Max Life Insurance, has described how the company uses AI personalised renewal messages with brand ambassador Rohit Sharma to lift engagement at the point of renewal and improve creative performance. The renewal moment becomes part of a larger loop where each response informs the next offer.
Retail and e commerce brands are building similar systems around baskets and wishlists. Instead of treating every abandoned cart the same way, AI models classify intent. One user might be price sensitive, another may be unsure about fit, a third might simply be distracted. Follow up emails, app notifications or wallet offers vary accordingly. Where customers do convert, the system logs which combination of timing, message and incentive worked best, improving the next recommendation cycle.
Telecom operators and subscription platforms apply the same logic to churn risk. Engagement scores, service tickets and payment behaviour feed into models that predict which customers might leave. Those flagged as high risk are exposed to retention offers or proactive service calls. Over time, this creates what practitioners describe as a living funnel that moves between acquisition, usage and retention without clear boundaries.
Global research suggests that this kind of AI assisted marketing can increase the productivity of the function by improving personalisation, testing cycles and content production. In many cases, it translates into efficiency gains on marketing spend in the single digit to low double digit range. Marketers are quick to point out that most of the impact comes from better orchestration of existing channels, not from entirely new ones.
What powers self learning funnels
The first ingredient is unified data. For an always on funnel to work, brands need to combine web analytics, app usage, CRM records, call centre logs and sometimes offline point of sale data into a shared view. Indian AI maturity studies show that many companies fall into the middle stages of adoption, where pilots exist but data foundations are still being strengthened. In practice, this means many AI projects now focus on cleaning, labelling and stitching data before any advanced modelling is attempted.
The second ingredient is predictive intelligence. Rather than segmenting customers only by demographics, models score each profile by purchase probability, churn risk or readiness for an upgrade. These scores then drive next best action systems that suggest whether to send a price alert, a how to video or a reminder to complete KYC. In financial services, such models are increasingly used to prioritise leads for relationship managers and to decide which customers should receive human outreach versus automated flows.
Ishan Kaul, an AI researcher and investor who advises consumer brands on deployment, argues that scale is where AI makes the difference. “In the past, personalisation was reserved for a handful of top customers. Today, AI enables that kind of hyper personalisation for millions of users,” he says, pointing to models that can read unstructured data such as search history and email engagement alongside structured CRM fields.
The third ingredient is generative content. Once a system knows who to talk to and when, it still needs something to say. This is where AI writing and design tools are now embedded into campaign production. Marketers in India and globally use them to generate subject line variations, translate copy into regional languages and adapt master creatives to different segments. Vikas Nair, Head of Marketing for Century Real Estate, notes that “content is where the biggest disruption has occurred” and that AI has compressed tasks “from scripting to performance testing” from weeks into hours.
The result is a continuous test and learn loop. Small changes to creative, sequence or incentive levels are rolled out, measured and either scaled up or rolled back. Over time, the system learns which paths produce the highest value customers, not just the fastest clicks.
Human judgment in an automated funnel
Despite the automation, marketers emphasise that AI systems do not run themselves. Someone still has to decide what outcomes matter, what data is acceptable to use and where the line sits between relevance and intrusion.
That tension is visible in sectors like retail and food delivery, where frequency can quickly become fatigue. Always on journeys make it easy to send daily prompts, but teams now track long term metrics such as unsubscribe rates, dormant apps and brand favourability to ensure that short term gains do not damage equity.
Esha Gupta, a marketer who has worked on AI driven projects at companies such as Airtel and Myntra, describes the relationship succinctly. “AI is a co pilot, not the pilot,” she says, adding that marketers must still own the narrative and ethics of how the technology is used. That sentiment is widely shared. Models can do pattern recognition at scale. Humans still frame strategy, set values and interpret context.
Surveys of Indian enterprises underline the point. Many businesses say they are exploring more advanced agent style AI capabilities, but also identify governance and talent as the main barriers to scale. Industry research from marketing associations finds that more than half of respondents feel AI adoption is still not fully understood inside their organisations, even as pilots multiply.
Indian use cases shaping the next wave
In e commerce, large marketplaces now run AI systems that watch inventory, pricing and search behaviour to auto adjust promotions within guardrails set by category teams. Smaller sellers tap into these systems through marketplace dashboards that suggest keywords, hero images and discount bands to improve visibility.
In mobility and food delivery, AI powered support agents handle high volumes of routine queries such as ETA checks, refund status and address changes. Upstream, models predict demand surges in specific neighbourhoods and trigger targeted offers or supply shifts, helping manage wait times without blanket discounting.
In BFSI, banks and fintechs are experimenting with self learning funnels that move customers from awareness to usage by chaining together education content, nudges and support. After a customer explores a new investment product in the app, for example, the system may show a short explainer video, offer a calculator based on their profile and then follow up a week later if no action is taken. Responses feed back into the model so that future sequences can be tuned.
Across these examples, the common pattern is that AI is embedded into existing journeys rather than bolted on as a separate channel. That integration is what turns campaigns into continuous systems.
Where the battle goes next
Looking ahead, marketers expect more activity around AI agents that can take actions across tools, such as booking meetings, updating CRM entries or triggering personalised journeys off a single signal. Early pilots in India already show agents helping sales teams respond faster to inbound leads or summarise complex histories before a call.
Anuradha Gupta’s observation that the funnel “is no longer a funnel” but a set of touchpoints where AI helps brands “show up wherever the customer is” hints at the organisational change required. Departments that once worked in sequence now need to share data and objectives so that models do not optimise for one metric at the expense of another.
For many practitioners, that means redefining what success looks like. Instead of judging a funnel only by last click conversions, teams are starting to track how often the brand appears in AI summarised answers, how many customers complete journeys without human intervention and how satisfaction scores change when loops are adjusted. Early evidence from global and Indian surveys suggests that organisations piloting generative AI in customer facing journeys are beginning to see improvements in engagement and satisfaction in the areas where it is deployed.
The tone from Indian marketing leaders remains pragmatic. There is recognition that a lot of AI talk is still ahead of practice, but also that the direction is set. Systems will become more autonomous in handling routine interactions, and funnels will look more like feedback loops that run all the time.
In that environment, the battle for the AI customer is less about who has the most advanced model and more about who can build the right loops. Brands that invest in clean data, clear guardrails and connected teams are creating self learning funnels that can adapt to changing behaviour without losing sight of the human at the centre. Those that treat AI as a one off add on risk being invisible in the next generation of discovery experiences, where the first answer a system gives may be the only one a customer ever sees.
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.