The marketing function in Lentra includes brand strategy, demand generation, and go-to-market for the company’s digital lending and technology solutions. In an exclusive conversation, Pritish Asthana, Vice President – Marketing, Lentra tells Bhavya Bagga, Business Reporter, CXO Media that his marketing approach is rooted in three principles of clarity, curiosity, and a deep respect for audience intelligence. He further advocates early adoption of AI, data, and content tools in marketing while staying anchored to the human story at the centre of every great brand.
As VP – Marketing, what are your key priorities when it comes to driving brand visibility, demand generation, and market leadership in the SaaS space?
For me, brand, demand, and category leadership are not three separate playbooks — they are three-time horizons of the same growth engine. Brand earns consideration. Demand converts that consideration into pipeline. Category leadership compounds both by making your point of view the default reference in the buyer’s mind, that helps us simplify the narrative, sharpen the ICP, and stay relentless about consistency across every touchpoint.
In enterprise SaaS, where buying cycles routinely stretch across months or quarters, marketing has to act to show up early, often, and with conviction. This means investing in narrative-led brand work that travels across channels (LinkedIn, podcasts, and analyst circles). Brand visibility in 2026 is no longer just about Share of Voice, it is about share of conviction.
Demand generation works only when the demand has been shaped to begin with: that is why thought leadership, founder POV, customer storytelling, and product-led proof points have to march in lockstep, which means building an account-based engine that treats accounts and buying groups and not just leads — as the unit of value.
Lastly, in a category where every vendor claims to be AI-first, cloud-native, and end-to-end, the differentiator is not what you say — it is whether the market can repeat it back to you in their own words. Owning a category point of view that re-frames how the market thinks about the problem we solve. Marketing’s role is to be the most trusted explainer in our category.
In an increasingly crowded technology market, what are the biggest growth levers for B2B SaaS companies today, and where does marketing and branding differentiate to build brand positioning?
Horizontal SaaS is mature, but vertical SaaS platforms are built for purpose where growth lives. Lending technology is one such vertical; healthcare, logistics, real estate, and legal are others. The growth lever here is depth or having domain expertise, knowing your customer’s business, and that earns the trust. For a vertical SaaS player & its marketing, this is a structural advantage – vertical depth is becoming the marketing asset.
As search itself is becoming a conversation, buyers no longer start on Google’s ten blue links and they start inside ChatGPT, Perplexity, Gemini, Claude, Copilot, and Google’s AI Overviews. The interface has shifted from a list of links to a synthesised answer with a handful of cited sources, and the brand that gets cited is the brand that gets considered. The rise of Answer Engine Optimisation and Generative Engine Optimisation, disciplines that are now reshaping B2B discovery faster than SEO ever did.
What is changing underneath is the ranking signal itself and how Indian consumers are finding information, discovering brands, and making purchase decisions. A recent industry study* show that classical domain authority correlates only weakly with whether an LLM cites you; what correlates strongly is topical depth, named-expert credibility, entity consistency across the web, structured answer-format content, and original data the model cannot manufacture on its own. In other words, generic content does not work and often fails. Win by writing, speaking, and convening as industry insiders, not as a software vendor.
With the shift in B2B marketing — generative AI in every workflow, buying journeys that begin inside an LLM, and committees that have grown to double-digit stakeholders — will personalisation and customer experience still play a role in influencing buyer trust, adoption, and long-term retention? And what are the trends we will see?
Personalisation in enterprise SaaS has been misunderstood for a decade. Too often it has meant first name tokens in cold emails. The real opportunity now, with AI as an enabler, is personalisation at the buying-group level — recognising that a single account contains procurement, IT, security, business owners, and end users, and that each persona needs a different proof point, delivered through a different channel, at a different stage.
Done well, this looks like a coordinated stream of relevant content, micro-experiences, and human moments that land for the right person at the right time. Done poorly, it looks like noise. The discipline that separates the two is data.
With the shift in B2B marketing — generative AI inside every workflow, buying journeys that begin inside an LLM — personalisation and customer experience will remain at competitive surface, however buyers can now spot AI-generated content within seconds and have learned to discount it accordingly. So the brands that win are not the ones using AI the most, they are the ones using AI most invisibly, in service of an experience that feels intentional, relevant, and human.
I see four trends defining this next chapter.
- Personalisation moves from contact level to buying-group orchestration: Multi-agent AI stacks are now coordinating messaging across an entire committee — one agent maps stakeholders, another tracks engagement signals, a third generates the right asset for the right persona at the right moment. The unit of personalisation is no longer the email; it is the buying group’s collective journey, and industry data suggests B2B leaders who get this experience right close deals around 31% faster than those who do not.
- Retention shifts from reactive to predictive: The customer success motion is becoming more human, not less. AI handles the prediction; humans continue to handle the relationship.
- Brands that will compound are the ones with the cleanest consented data. The richest behavioural graph, and the discipline to use it sparingly enough that it does not feel like bombardment.
- Buyers are already using AI tools to find vendors, draft RFPs, and even rehearse negotiation; sellers are deploying agents to qualify, nurture, and follow up.
From a marketing and leadership standpoint, what do you see as the biggest opportunities and challenges for SaaS brands operating over the next few years?
The biggest opportunity is buyer enablement. Buying committees in B2B span across multiple stakeholders and self-research has become the dominant motion. Therefore, Brands need to enable buyers smarter by providing vertical depth content and assets which will shift from ‘selling-to’ to ‘enabling-to.’
As I had mentioned earlier, search itself is becoming a conversation – buyers no longer starts on Google’s but are relying on LLMs – or Google’s AI Overviews.
The risk is that when marketeers produce a sea of well-written, well-designed, well-targeted, but utterly indistinguishable content. The way through is to invest in the inputs that AI cannot produce- original customer research, founder conviction, real-world stories, controversial-but-defensible takes.
In an age where AI generates content at scale and buyers discover vendors through large language models, what should SaaS startups and growing companies do to build a real brand? How will buyers come to know, learn about, and trust a brand in this environment?
Buyers are increasingly using tools like ChatGPT, Perplexity, and AI Overviews to research solutions, which means they are getting synthesized answers rather than a list of blue links. In this ocean of generated content, trust becomes the only currency that matters. To earn that trust, companies must shift their mindset from traditional SEO to Generative Engine Optimization, or GEO.
We need to stop pumping out keyword-stuffed fluff and start producing citation-first content. AI engines prioritize and trust original data, clear definitions, and verifiable claims, so that is exactly what we must feed them to ensure our brands are cited as definitive sources.
But technical optimization is not enough on its own. Because AI can write a perfectly competent, generic article in seconds, however as buyers we rely on information that also is authentic with human perspective. So, we have to lean heavily on our employee ambassadors—our founders, engineers, and customer success leaders—to act as our primary distribution channels. People trust people, and opinionated, lived experiences shared on platforms like LinkedIn will heavily outperform faceless corporate content every time.
Finally, growing companies need to double down on what I call “un-AI-able” experiences. You can automate a nurture sequence, but you cannot automate a handshake or the collaborative energy of a room. We are seeing a massive return to high-value, in-person, and hybrid events simply because they offer a level of reality and accountability that digital channels now lack. Trust is built where AI cannot reach.
AI is now everywhere in marketing. From a B2B marketer’s standpoint, how is AI changing demand generation, and what tools and approaches can marketers use to create and convert leads through the funnel?
From an operational standpoint, AI is no longer just a neat tool we use to write emails faster; it is if not already has become the infrastructure of the entire marketing function. The traditional, linear funnel is becoming irrelevant and is replaced by dynamic, intent-driven ecosystems. Take lead tracking, for example. The old model was built on capturing a single mid-level manager who happened to download a gated whitepaper. Today, with AI one can synthesize intent signals across an entire buying group.
Marketing platforms can connects those dots in real time. It can alert the sales/business development teams about who is researching about the product/solution. Its shifting us from waiting for a single form fill to initiating strategic, account-based engagement.
To convert those accounts, one can deploy AI agents to execute hyper-personalization at a scale that was simply impossible a few years ago. Instead of basic automation, these agentic workflows autonomously orchestrate campaigns, dynamically adapting website content, email copy, and follow-up timing based on real-time firmographics and website behaviour. This ensures every prospect gets a highly relevant, hyper-personalised experience the moment they interact with the brand.
