For marketers in India, the next phase of artificial intelligence is no longer about whether AI can write a social media caption, draft an emailer or generate campaign copy. That layer of adoption is already underway. The deeper question now is whether organisations have the data, governance, workflows and operating models required to use AI at scale without losing control of the brand.
Across sectors such as hospitality, financial services and broking, digital leaders are beginning to converge around a common view: AI may be advancing rapidly, but the real bottleneck sits inside the enterprise. Fragmented customer data, legacy martech systems, disconnected teams and compliance-heavy workflows are slowing down the shift from experimentation to execution.
For Vikram Singh, Head Digital Marketing, ITC Hotels, the biggest friction to personalization at scale is not the lack of AI tools. It is the lack of connected systems and unified operating models. According to him, “The primary friction is fragmented data and operating models. While AI tools are advancing fast, most brands still struggle with unifying first-party data, legacy martech stacks, and content workflows; making real-time, contextual personalization hard to execute consistently.”
That challenge is becoming more visible as brands attempt to move from campaign-led marketing to always-on engagement. The ambition is clear: deliver more relevant communication, in real time, across channels, formats and customer contexts. But the execution often breaks down when customer information sits across multiple systems and content workflows are still designed for slower, manual production cycles.
Pulak Kumar Singh, Chief Business Officer, Jainam Broking Limited, puts it more bluntly. “The honest answer is not the AI, the AI is ready. The challenge lies upstream, where fragmented data ecosystems and disconnected systems limit seamless decision making.” He points out that a typical large organisation may have customer data spread across a CRM platform, loyalty system, call centre tool, mobile app and other databases, each operating in silos.
For him, the problem is foundational. “The AI is ready. What isn’t ready is the data infrastructure it needs to work from.” In other words, the promise of AI-led personalization depends less on the sophistication of the model and more on the quality of the data environment feeding it.
This becomes even more complex in India, where personalization is not just about sending the right message to the right customer. It is also about language, culture, region and tone. Pulak adds that brands are “not personalising for one market” but for a country with 22 official languages, dozens of dialects and varied cultural registers. A campaign that feels relatable in one region may not land the same way in another. Most content systems, he says, are not built to handle that level of nuance at scale.
This is where AI’s role is expanding beyond copy generation. The first wave of AI adoption in marketing was dominated by content creation. The next wave is moving into content intelligence, asset management, workflow automation, testing, customer servicing and performance optimization.
Singh of ITC Hotels says AI is unlocking value across the entire content and marketing ecosystem. Beyond copy, he sees AI enhancing audience insights, predictive signals and real-time intent mapping, which can help brands target customers more precisely across channels. That intelligence layer can then feed into smarter media planning, dynamic segmentation and performance optimization.
On the execution side, he says AI can accelerate asset tagging, automate personalization rules and power continuous A/B and multivariate testing. It can also reshape production workflows by enabling quicker creation of multi-format and multi-variant content, including images and localized adaptations. The larger shift, in his view, is that AI is helping marketers move from reactive campaigns to “always-on, data-driven engagement” that can improve journeys, conversions and ROI across the funnel.
Boniface Noronha, Head, Digital Business, Axis Mutual Fund, also sees AI’s value moving far beyond content creation. For financial services, he says the real value lies in speed, enablement and consistency. AI is increasingly being used to help non-technical teams create and iterate digital assets such as landing pages without heavy engineering dependency. It is also supporting testing, debugging and code review, reducing development cycles.
He also points to the rise of AI-powered conversational interfaces, including chat and voice, which are improving investor servicing and accessibility. AI agents are being deployed for specific workflows such as investor queries, lead qualification and internal productivity. Internally, AI is strengthening content operations through more structured content hubs that allow faster and more consistent multi-format publishing across channels.
However, Noronha makes an important distinction. For Axis Mutual Fund, AI is used in an assistive manner, with human oversight. That means judgment, accountability and compliance remain central to the process. This is especially critical in regulated sectors, where speed cannot come at the cost of accuracy or suitability.
The operating model around AI-led content is also changing. Rather than choosing between fully in-house teams and external managed services, brands are moving toward hybrid structures.
Singh of ITC Hotels says there is a decisive move toward hybrid operating models. Organisations are bringing core strategy, first-party data and brand-critical content in-house to retain control and differentiation. At the same time, managed services are being used for scale, speed and specialized AI capabilities.
Noronha echoes this view from a financial services lens. He says the clear shift is toward a hybrid model, with strategic ownership moving in-house. Business teams today are closer to customer insights, regulatory nuances and brand priorities. As a result, content strategy, ideation and narrative control increasingly sit within the organisation. AI tools have also lowered the barrier for non-creative teams to develop first drafts and frameworks, enabling faster iteration.
Agencies continue to matter, but their role is changing. Noronha says agencies are now more focused on execution, scale and specialised craft, rather than owning the thinking end-to-end.
Pulak Kumar Singh sees the same shift, but adds that the split often reflects a brand’s maturity curve. Large enterprises with strong brand equity are building internal content intelligence capabilities because they need control over brand voice, data governance and model fine-tuning. They have also seen the risks of third-party tools hallucinating product claims or generating copy that does not clear legal review.
“Managed services give you speed. In-house capability gives you trust. Right now, most organisations need both,” he says.
For challenger brands, regional players and mid-market retailers, managed services can act as a shortcut. They may not have the internal bench strength or time to build everything from scratch. A well-configured managed solution can help them move faster. But Pulak believes the middle ground is where most serious organisations will land: brands owning strategy and guardrails, while outsourcing operational throughput.
That hybrid structure becomes important because AI-led content ecosystems require more than creative output. They require governance, quality control, compliance checks, brand consistency and workflow discipline. Without these, speed can quickly become a risk.
Pulak says one of the most significant areas of AI value is content performance prediction. Instead of publishing content and waiting for results, brands can now assess how a piece of content is likely to perform across audience segments before it goes live. This changes how organisations manage creative risk.
The second area is content operations and workflow orchestration. AI can help route briefs, flag compliance issues, tag assets and auto-archive dated content. For large organisations, this can remove significant manual effort from the back office of content production. As Pulak puts it, “The biggest savings aren’t in writing the first draft, they’re in eliminating the tenth revision.”
The third area, he says, is semantic search and content repurposing. A company sitting on years of customer communication can now mine its archive intelligently and surface relevant existing assets instead of commissioning new ones. In a market where content budgets are closely watched, reuse can become a major advantage.
This is also why the conversation around AI in marketing is shifting from cost reduction to value creation. Cost efficiency remains important, but leaders are increasingly looking at AI through the lens of speed, trust, customer experience and long-term growth.
For Singh of ITC Hotels, the two top priorities this year are operational speed and building a brand-safe AI ecosystem. Speed matters because marketing now operates in a real-time environment, where the ability to respond quickly to signals directly impacts relevance and conversion. But as AI becomes embedded into content and digital marketing workflows, he says brand safety, governance and data integrity are non-negotiable.
He argues that true competitive advantage will come from building trusted AI ecosystems grounded in first-party data, clear guardrails and scalable architectures. The goal is rapid execution without compromising brand integrity or customer trust.
Noronha says that in financial services, cost reduction, speed and brand safety cannot be treated as separate priorities. Every AI use case must be evaluated against a clear business objective, whether improving investor experience, driving efficiency or supporting long-term growth. Short-term cost savings matter, but they must be weighed against long-term business value such as incremental AUM, faster investor acquisition or better retention.
Operational speed, he says, has emerged as a key differentiator. Teams that can execute, learn and refine faster tend to perform better. But in financial services, every AI-driven output must pass through compliance, accuracy and suitability checks before reaching investors. Sustainable value comes from balancing speed and efficiency with trust and governance.
Pulak pushes back on the idea that brands should rank cost reduction, speed and brand safety as separate priorities. In his view, brand safety is the foundation. “If you build an AI content engine that moves fast but occasionally outputs something that violates regulatory norms or damages brand reputation, you have not built speed, you have built a liability.”
He adds, “Speed without guardrails isn’t efficiency, it’s exposure. The brands that move fastest safely are the ones investing in governance first.”
That statement captures the central tension facing marketers today. AI gives brands the ability to create, test, personalize and optimize at a pace that was previously difficult to imagine. But the faster the system moves, the more important the guardrails become. For BFSI, hospitality, retail and other customer-facing sectors, the next competitive advantage will not come merely from using AI. It will come from building the right data layer, operating model and governance structure around it.
For India’s marketers, the message is becoming clear. AI may be ready, but the enterprise has to catch up. The brands that win will not be those that generate the most content. They will be those that connect data, build trust, move quickly and personalize with precision, without losing control of the brand.
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. Opinions expressed herein are personal and do not represent the official stance of the brand/company or its leadership.
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