In an exclusive conversation with CIO&Leader, Venugopal Ganganna, Co-Founder & CIO of LS Digital, outlines how the company is reimagining digital transformation for the evolving B2B landscape. He shares insights on building AI-native platforms, modular tech architectures, and trust-driven systems that align with modern enterprise needs.
CIO&Leader: How has LS Digital’s technology strategy evolved to meet changing B2B needs?
Ganganna: We’ve seen a radical shift from running siloed digital projects to building a tightly woven integrated ecosystem. We have well and truly recognized that B2B buyers today demand more than transactional interactions—they expect seamless, data-rich journeys tailored to their business goals. So, our strategy has expanded to include integrated platforms that blend AI, data insights, UX design, and automation.
Today, our six-pronged Digital Business Transformation (DBT) framework unites media, creative, data & insights, technology & innovation, UI/UX, and CX/CX. Each vertical collaborates tightly to build platforms that buyers find intuitive and sellers find smart. For instance, a central pillar is our AI stack—research, generation, prediction—it’s all deeply embedded. We believe that B2B buyers and sellers don’t just want a product, they want a partner powered by intelligence and trust. Our AI Marketing Stack is embedded across these verticals—research, content generation, and predictive modelling—to empower B2B clients with insights and efficiency.
So, our strategy has matured from tactical execution to a platform-led, innovation-first, data and AI-enabled model, designed to build trusted relationships between buyers and sellers in the B2B space.
CIO&Leader: What are LS Digital’s biggest digital transformation priorities?
Ganganna: Three things that drive our agenda right now.
- Democratizing AI. We aren’t just experimenting; we’ve made AI the default. Our AI Marketing Stack is built to scale across research, generation, and prediction, and is standard for all clients, with early access free for existing ones The goal is to make AI not just accessible but practical for B2B buyers and marketers.
- Modular yet integrated platforms. Our DBT architecture emphasizes clear, specialized units—Tech & Innovation, Data & Insights, UX, etc.—but connects them seamlessly through shared APIs, UX patterns, and data pipelines. This supports both rapid innovation and end-to-end client journeys.
- Trust by Design. We’re building transparent, ethical, privacy-centric systems. Whether it’s AI that’s explainable, data that’s anonymized and GDPR-ready, or UX that clearly shows how recommendations are made, we want clients to trust not just what the system does, but how it does it.
CIO&Leader: How do you ensure technology aligns with growth and customer needs?
Ganganna: I firmly believe alignment starts with empathy. We conduct deep discovery sessions with clients across marketing, sales, operations to understand not only their KPIs but their daily criteria for efficiency and trust. Our Integrated Digital Solutions (IDS) team then maps these needs to tangible metrics before looking at technology options.
Once we have a blueprint, we adopt a test‑learn scale model: launch a small AI‑powered pilot, track metrics, iterate based on real feedback, and then scale across the account. Encouragingly, our Research AI alone contributes 5–7% of revenues today. And we expect it to grow to 15–20% this year.
In parallel, our group structure enables cross-functional review: Tech & Innovation sits alongside Data & Insights and CX, ensuring any solution supports the full journey. This not only fosters buy-in but keeps us accountable to business outcomes, not just tech adoption.
CIO&Leader: How are you using AI and automation to enhance efficiency and customer experience?
Ganganna: We’ve woven AI and automation right into our platforms:
Research AI crawls market trends, segment insights, and competitive movements, turning them into dashboards and prompts for marketers. Generative AI supports content creation, screenplay ideas, personalized ad drafts, social creatives. Predictive AI forecasts campaign outcomes, enabling smarter media allocation and customer retention strategies.
But here’s what we always say: “AI speeds things up, but humans keep it real.” So, while a bot might draft your first ad, a marketer fine-tunes it for the brand fit. And when something about a campaign looks off, we’re ready to pause, check the data, and course correct. We maintain data quality and trust through built-in traceability, privacy checks, and advisory dashboards. We believe trust doesn’t happen by accident; it’s engineered.
CIO&Leader: What are your top security concerns, and what tech challenges have you faced in scaling?
Ganganna: Info Security is absolutely non-negotiable for us. A few of my biggest concerns are the risk of data leakage, especially when you’re running shared infrastructure across multiple clients. We’ve had to be extremely intentional about building role-based access control, encryption layers, and adopting a zero-trust architecture. Another one is AI bias. With AI becoming so deeply embedded into how we operate, we must make sure the inputs don’t quietly corrupt the outputs. We’ve built continuous monitoring into our stack to catch any signs of adversarial data or optimization loops that could lead to skewed or unfair outcomes. I always say: AI needs a conscience, and that starts with clean, transparent data.
Then, of course, there’s compliance. Between GDPR, India’s local data policies, and increasing client scrutiny, you can’t afford to wing it. Every layer of our tech stack is auditable, traceable, and built with privacy in mind. We’ve engineered our systems so that compliance isn’t a hurdle, it’s the very foundation.
Scaling this whole beast came with its fair share of battle scars. Stitching together our verticals—Media, Tech, UX, Data—meant untangling a maze of custom integrations and standardizing everything from APIs to data schemas. AI adoption was another journey: we had to build trust first, with onboarding frameworks and pilot programs before it could scale. And then, of course, with data, pulling clean insights from fragmented sources like CRM, marketing, and social was pure chaos. But it taught us this: real scale only happens when standardization meets empathy, and governance is designed in, not added as an afterthought later.
CIO&Leader: What skills and mindsets matter in the next generation of enterprise tech leaders?
Ganganna: We look for people who are T-shaped: deep in one area—say AI or UX—but curious and fluent in adjacent disciplines. What gets me excited is when someone is both curious and sceptical: “Hey that tool is cool, but what does it do to my KPIs?” I also value product-thinking—treating platforms as living products with roadmaps and feedback loops. Collaboration is non-negotiable; no one can drive innovation alone. And a human-first AI mindset: seeing AI as a teammate, not a replacement. I’d say the biggest differentiator is adaptability. If you can pivot and stay aligned through constant change, you’ve got what it takes.
In essence: blend technology passion with business savvy, ethical awareness, and human empathy.
CIO&Leader: How do you see the future of B2B commerce evolving with GenAI and edge computing?
Ganganna: I really think we’re on the cusp of a revolution. Imagine GenAI-powered dialogue that understands all the context of your last purchasing cycle. Instead of form fills, buyers will interact with intelligent assistants that understand context across industry, account history, and intent, supporting self-serve experiences and consultant-style engagement.
Add in edge computing for low-latency personalization or pricing in sensitive environments, and you get real-time responsiveness with privacy embedded. Now thanks to Agentic AI, within eighteen months, we’ll see AI autonomously run end-to-end workflows—market research, media activation, reporting—triggering human intervention only when exceptions arise. Due to which, clients will expect plug-and-play modules, pricing engines, ABM orchestration, commerce catalogues, all interconnected and customizable, rather than being rigid suites.
This shift will make B2B commerce faster, smarter, and more responsive to real-time business needs.
CIO&Leader: What advice would you give to CIOs navigating platform‑led models in India?
Ganganna: I’d say start small, scale fast. Launch high-impact pilots—e.g.: Research AI, a content assistant—track value early, then layer automation and analytics. Make your systems GDPR-ready, build audit trails into every AI insight, anonymize PII. Indian clients respect transparency. You can’t build future-ready platforms in silos. Create a hub that brings UX, data, tech, and media teams together early on.
Build adoption frameworks, not just tech. Create workshops, toolkits, communication campaigns. Make adoption measurable. Cultivate hybrid leaders. Tech leaders must deepen in their craft but also think like product owners, business strategists, and ethical custodians. Adopt a composable architecture—APIs, microservices—so you can plug in new AI capabilities without overhauling systems.
In short: iterate quickly, build trust, foster cross-pollination, invest in people, and keep your architecture flexible. That’s the winning recipe for CIOs building B2B commerce platforms in India today.