Indian marketers have spent the last two years experimenting with public large language models for copy, customer service and campaign planning. The next phase is quieter, more technical and far more strategic. Large enterprises are now asking whether they should train or fine tune their own models, instead of relying only on public tools that sit outside their data and compliance walls.
This shift is not happening in isolation. A joint Intel–IDC study estimates that spending on AI in India could reach about 5.1 billion dollars by 2027, growing at more than 30 per cent a year and outpacing several other Asia Pacific markets. As AI budgets move from pilots to production, more leadership teams are treating model ownership and deployment choices as core to their brand strategy.
At the same time, adoption is already broad. According to Adobe’s 2024 Digital Trends study for Asia Pacific and Japan, 72 per cent of senior executives in India say they have generative AI pilots or solutions in place, and half of practitioners are consolidating their content and marketing tools to work better with AI. What started as isolated experiments is now turning into a question of architecture: do we keep relying on external models, or do we bring AI closer to our own data and systems.
From Public Playground to Private Stack
Public models still play a useful role. They are fast to test, easy to access and ideal for early-stage experimentation. But as soon as brands start applying AI to customer histories, call transcripts, product roadmaps or media plans, the concern shifts from creativity to control.
For BFSI and regulated sectors, that shift is already visible. At an Adobe Summit discussion on generative AI in Indian banking, Federal Bank’s Chief Marketing Officer M V S Murthy summed up the attraction clearly: “Generative AI allows us to do mass customisation at scale and across multiple segments at the same time.” That level of personalisation becomes even more powerful when the model is tuned on a bank’s own knowledge base, product rules and risk policies, rather than just public internet data.
Similar conversations are happening in insurance, telecom and large retail. Many brands are still using foundation models from global providers, but they are increasingly insisting that the model runs inside a virtual private cloud, or is fine tuned on their own data with strict guardrails. For some, the logical next step is a “private LLM” that is customised enough to behave almost like an internal employee, but governed like any other enterprise system.
Compliance and Trust as the Main Drivers
In India, regulation is pushing this conversation forward. The Digital Personal Data Protection Act is forcing companies to rethink how they collect, process and store customer information. That directly affects how they use AI models.
Marketers who once focused on reach and return on ad spend are now expected to show that their AI systems respect consent and data minimisation. As Arvind Lakshmiratan, Chief Marketing Officer at Inspira Enterprise, told exchange4media, “The only sustainable path is strengthening first party data,” using AI to model intent within consent boundaries and relying more on privacy enhancing technologies for measurement. Private or tightly controlled models make that discipline easier, because data does not have to travel across multiple external APIs before it can be processed.
Consumer expectations are also rising. Research on trust and AI in India suggests that more than nine in ten residents want to know if the content they see has been generated using AI. That makes transparency and provenance part of the brand promise. Private models, combined with watermarking or content credentials, give enterprises more visibility into how outputs were created and which data they relied on.
Adobe India’s managing director Prativa Mohapatra has framed the issue in terms of commercial safety. Speaking about the company’s own approach to training models, she said: “Our AI models are commercially safe, which is a key differentiator. From the outset, we ensured that our models were trained on data we own,” and argued that enterprises will increasingly demand AI systems that respect data ownership. The same logic is now being extended by Indian brands to their internal LLMs, especially in sectors that handle sensitive customer information or proprietary content.
What a private model changes for marketers
For marketing teams, a private or enterprise LLM is less about building a rival to global models and more about shaping behaviour.
First, it narrows the model’s “worldview” to the things the brand actually offers. A public chatbot can talk about any product or service. A private model tuned on a retailer’s catalogue, an insurer’s policy library or a bank’s product sheet will default to answers that are aligned to current pricing, eligibility rules and risk thresholds. That reduces the chances of hallucinated offers or off-brand claims.
Second, it embeds brand tone and compliance checks by design. Indian brands that operate across dozens of languages and regions often maintain long style guides, template libraries and escalation rules. A private model can be fine tuned on approved phrasing and creative examples so that campaign drafts and customer responses feel consistent across teams and regions.
Third, it improves traceability. When models are trained or adapted inside the organisation, logs of prompts, outputs and policy decisions can be integrated with existing monitoring tools. That is increasingly important for audit trails, especially when boards and regulators ask how AI is influencing pricing, offers or customer denial decisions.
In practice, many Indian enterprises are starting with narrow domains. A life insurer may build a model that only handles internal queries from agents about product features and exclusions. An automobile brand may train a model on service manuals and customer complaints to help call centre agents resolve issues faster. Over time, some of these domain models get stitched together through orchestration layers, but the core idea remains the same: keep sensitive logic and data inside, even if the underlying architecture still relies on global foundation models.
Indian examples: BFSI, IT services and consumer brands
BFSI is often cited as the early adopter. Large private sector banks have been using AI for fraud detection and chatbots for several years. Now, their focus is shifting to generative models for relationship managers, credit operations and marketing. Case studies of HDFC Bank, for instance, describe how the lender is trying to become an “AI first” institution by weaving AI into customer conversations, risk engines and internal productivity, supported by a unified data platform. In such an environment, the question of where models are hosted and how tightly they are governed is central.
Indian IT services and consulting firms are another important cluster. Many have announced internal AI platforms that combine open source and proprietary models for clients in the United States and Europe. These firms are applying the same stacks to their own marketing: building private copilots that draft proposals, summarise RFPs, benchmark competitors and generate content tailored to specific sectors or geographies. Because these systems are trained on confidential client work and internal knowledge bases, they are almost always deployed as private models behind strong access controls.
Consumer brands are moving more gradually but in visible ways. Large FMCG and personal care companies are using AI to generate localised creatives for different languages and retail formats. Retailers and quick commerce firms are testing models that blend product data, store inventories and loyalty histories to personalise offers in real time. In these cases, the model may still call an external API, but the retrieval and ranking layers that touch first party data are increasingly built and managed in house.
The Economics and Talent Question
Despite the strategic appeal, training a completely new large language model from scratch is still beyond the scope of most Indian brands. That work requires dedicated AI teams, access to high performance compute and very large datasets. For now, these investments are being led by hyperscale technology players and a small number of AI startups.
Enterprise marketers are instead choosing from a spectrum of options. At one end, they simply call public APIs with strict policies and redaction. In the middle, they deploy open source or vendor provided base models inside their own cloud accounts and fine tune them on their data. At the other end, a few large organisations commission domain models that are trained almost entirely on proprietary corpora.
The middle path is where most private LLM conversations are currently anchored. It reduces dependence on any single provider, allows brands to move workloads if pricing or policy shifts, and keeps most sensitive data within controlled environments. It also aligns with a reality highlighted by AI spending reports: India is expanding AI budgets rapidly, but companies are still under pressure to show clear return on investment from each use case.
Talent remains a constraint. Even when cloud platforms abstract away the hardest parts of model training, enterprises still need architects who understand data pipelines, prompt engineering, evaluation frameworks and security. Several Indian companies are responding by setting up internal AI academies and cross functional councils that bring together marketing, technology, risk and legal teams.
What this Means for the Next Phase of Martech
For Indian marketers, the move toward private or enterprise LLMs is less about owning a shiny new toy and more about staying in control as AI becomes embedded in daily work.
It suggests a future where:
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Brands treat models as part of their core infrastructure, not just as external tools.
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First party data strategies and consent management platforms sit at the centre of AI roadmaps.
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Creative and media teams work with AI that knows their category, their regulations and their internal rules, rather than generic assistants.
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Governance and transparency become selling points in their own right, especially for financial services, healthcare and education.
The direction of travel is clear. As Murthy’s comment on mass customisation shows, Indian marketers see AI as a way to serve many segments at once without losing relevance. As Lakshmiratan and Mohapatra argue in their own contexts, that opportunity will only be durable if it sits on a base of trusted data, safe models and clear ownership.
For now, most brands will continue to use a mix of public and private models. But the strategic questions they are asking have changed. Instead of “what can this tool do for us”, the discussion in more boardrooms is now “which parts of our intelligence layer must we keep inside, and what kind of model do we need to build for that”.
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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