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Humanising AI with Arundhati Bhattacharya, Salesforce
Humanising AI with Arundhati Bhattacharya, Salesforce

Time of India

time23-07-2025

  • Business
  • Time of India

Humanising AI with Arundhati Bhattacharya, Salesforce

India's digital transformation is at a crossroads, propelled by widespread adoption yet challenged by legacy business infrastructures. Navigating this complex landscape, Salesforce India's president and CEO, Arundhati Bhattacharya , sees both immense potential and critical hurdles, particularly in the adoption of artificial intelligence (AI) and advanced marketing technologies ( Martech ). 'Trust', Bhattacharya emphasised in a candid conversation with ET BrandEquity, 'is fragile'. In the age of AI, where scepticism about data privacy and ethics is heightened, fostering trust requires more than technological innovation. It demands credibility and genuine human connection. This principle underpins Salesforce's strategic choice of Rahul Dravid for its recent AI-centric campaign. Dravid, known for his humility and steadfastness, symbolises the company's commitment to responsible and human-centric 'agentic AI'. Bridging the Digital Divide India, as Bhattacharya pointed out, presents a paradox in digital readiness. On one hand, individual consumers exhibit extraordinary enthusiasm towards digital adoption. A Salesforce survey highlights that India boasts the highest proportion of digitally active individuals over the age of 80 globally, a testament to the country's robust digital public infrastructure, including Aadhaar, UPI, DigiLocker, and ONDC. These initiatives have significantly accelerated digital inclusion, bringing underserved communities online. Conversely, traditional Indian businesses, particularly legacy enterprises, face distinct challenges. Bhattacharya observed that while digital-native startups benefit from agility and cloud-based architectures, legacy organisations must grapple with the complexities of modernising outdated infrastructure without disrupting ongoing operations. Drawing a vivid analogy, she remarked, 'Legacy companies must transform while running the business, much like changing tyres on a moving car.' Yet, these legacy entities also hold invaluable assets, deep institutional knowledge, vast customer bases, and established trust. Bhattacharya believes targeted AI applications addressing specific pain points, such as customer service enhancement or internal decision-making, offer significant opportunities. The banking sector illustrates this well: retail banking is markedly more digitalised compared to corporate banking, where personal relationships and face-to-face interactions still predominate. Martech: From Instinct to Precision In parallel with AI, India's Martech landscape is evolving, fundamentally reshaping marketing practices. Bhattacharya contrasts today's precision-driven, data-informed marketing with earlier instinct-based approaches that dominated her tenure in public-sector institutions. 'Marketing has transformed from a shot in the dark into a scientific discipline,' she noted. Today, companies leverage Martech tools for precise customer segmentation, behavioural targeting, and real-time performance measurement. Salesforce's recent campaign illustrates this shift vividly. A simple print advertisement featuring a QR code seamlessly transitioned audiences into an immersive, mixed-reality experience with Rahul Dravid. Personalisation and Privacy: Striking the Balance While Martech has elevated personalisation to new heights, Bhattacharya stresses the fine line between relevance and intrusion. Genuine personalisation extends beyond superficial customisation, it encompasses context, timing, channel choice, and consumer intent. 'True personalisation knows when and how to engage customers,' she remarked, emphasising that transparency and ethical data usage are paramount to maintaining trust. Her insights align with Salesforce's own research, which consistently underscores consumer expectations for ethical data handling and responsible AI usage. The emphasis is clear: businesses must ensure technology enhances the customer experience without encroaching upon personal boundaries. Humanising AI and Addressing Workforce Concerns Addressing prevalent fears about AI's impact on employment, Bhattacharya maintains a nuanced perspective. 'AI is a tool, not a substitute for human experience,' she clarified. While acknowledging AI's capability in executing routine tasks efficiently, she highlights its limitations in replicating genuine human creativity and nuanced emotional intelligence. For Bhattacharya, AI's true value lies in liberating professionals from repetitive tasks, enabling them to focus on strategic thinking, problem-solving, and innovation. Her own experience at Salesforce illustrates this: AI-driven anomaly detection in mundane tasks such as expense reporting allows her more bandwidth to concentrate on strategic decision-making. Yet, she acknowledges the necessity for continual upskilling. 'If your job is entirely repetitive and you aren't adapting, then there is certainly risk,' she cautioned. Bhattacharya encourages professionals to proactively embrace AI: 'We're at the start of a massive wave. You can either be overwhelmed or learn to ride it smartly.' India stands poised at an exciting juncture. Its digitally enthusiastic population and rapidly evolving Martech ecosystem offer a fertile ground for innovation. Yet, the road ahead requires cautious navigation around ethical pitfalls and digital divides. Bhattacharya's vision for Salesforce, and India at large - is clear: leveraging AI and Martech not merely for technological advancement but to fundamentally enhance human experiences. Ultimately, Bhattacharya advocates an approach grounded in humility, responsibility, and ethical clarity. Her vision encapsulates a balanced ethos for India's digital future-bold yet responsible, innovative yet human-centric. In her words, 'AI's greatest promise lies not in replacing human potential, but in amplifying it.'

7 Hard Truths About Building AI Products That Last
7 Hard Truths About Building AI Products That Last

Martechvibe

time15-07-2025

  • Business
  • Martechvibe

7 Hard Truths About Building AI Products That Last

For years, product innovation has been fueled by excitement: a new framework, a breakthrough model, a rising trend. But in 2025, as AI weaves itself into every interface, the question that separates good AI products from enduring ones isn't 'What can we build?'—it's 'What should we build?' According to the 2025 State of Martech Report by Scott Brinker, product management is now one of the most pivotal roles in the Martech ecosystem tasked with balancing rapid innovation, data complexity, and the promise (and pitfalls) of AI. As boundaries between product, marketing, and customer experience blur, the need for clarity, focus, and intentional design has never been greater. Because behind every buzzword, be it LLM, Web3, or genAI , is a simple truth: if it doesn't solve a real customer problem, it's noise. To ground this exploration, we turn to insights from Sumaiya Noor, Product, AI & Technology Leader, who has built B2B, B2C, and B2B2C SaaS products across emerging tech domains like AI and Web3. Drawing from her product, engineering, and customer experience background, Sumaiya offers a refreshingly pragmatic lens, one focused not on hype cycles, but on human problems. Strategy Can't Be Built in Silos In high-velocity environments, roadmaps shift, features morph, and priorities blur. So how do you keep AI product and marketing aligned ? The answer lies in dissolving the silos entirely. 'We don't build and then inform,' says Sumaiya. 'We build together.' Cross-functional planning, with inputs from sales, marketing, CX, and engineering, not only improves go-to-market timing, it ensures that every feature is designed with customer communication in mind. And yet, even with cross-functional harmony, another trap remains: building for the tech instead of the problem. That's where disciplined product thinking becomes essential. The Case for Problem-First Product Thinking There's a temptation to fall in love with an idea or worse, a technology. But as Sumaiya puts it: 'Even the best idea is irrelevant if no one will pay for it.' The most impactful products today aren't those that chase AI for the sake of AI. They start with deep listening. They define the problem before prescribing the tech. And only then do they decide whether that shiny new model is the right tool. But what happens when customer needs evolve faster than the solutions built to serve them? When Customer Pain Points Evolve Faster Than You Build Technology is changing rapidly but so are customer expectations. The feature they needed last month might feel redundant next week. This is where continuous product discovery becomes non-negotiable. Beta feedback, prototype testing, and agile pivots must be baked into the build process. 'It's not about being right from the start,' says Sumaiya. 'It's about being flexible enough to shift fast, based on what your users actually tell you.' Flexibility is key. Not just in building, but in knowing when to stop building. Because holding on to outdated features can be just as risky as launching the wrong ones. Sunsetting Isn't Failure, It's Focus Great teams know when to quit. That feature your team launched with pride may no longer serve its purpose—maybe a competitor has done it better, or your users have outgrown it. The hardest part? Internal buy-in. 'You're not just retiring code,' Sumaiya notes. 'You're sunsetting people's effort, pride, and belief.' But with clear metrics and shared goals, this becomes a strategic move, not an emotional one. And as AI becomes embedded in more features, another layer of complexity emerges: unpredictability. Especially when the tech behaves in ways even its creators can't fully control. You Can't Eliminate AI Hallucinations But You Can Contain Them As large language models make their way into every workflow, a difficult truth remains: hallucinations are part of the system. 'If someone in the product world says that hallucination can completely be eliminated or can be mitigated, I think they don't understand the technological side of LLMs or AI, artificial intelligence or agents that much,' says Sumaiya. Rather than over-promise, product teams must scope narrowly, train models on proprietary data, and design safeguards to guide behaviour. 'You can't fully control LLMs but you can control how, where, and why you deploy them.' But responsible deployment isn't enough. You also need to know if your AI is actually adding value, which brings us to the challenge of building effective feedback loops. Feedback Loops in AI Products Are Twice As Hard Feedback is already tough in traditional product development. In AI, it's even more layered. 'You need two types of feedback loops. One for validating the feature itself, the service itself, whatever you are trying to provide to the customer. In terms of solving their problem or pain point. If it's an AI integrated or AI-based product, the additional feedback loop is also required to validate what type of value addition this AI integration is adding to your overall solution,' says Sumaiya. You're not only asking whether a feature works, you're asking whether AI is meaningfully improving the experience. This means comparing pre- and post-AI metrics, collecting real-time usage data, and isolating AI's impact on usability and satisfaction. Even with feedback in place, product teams still face a difficult judgment call: which technologies are worth betting on, and which ones are just noise? How to Tell If a Technology Will Stick or Fizzle When everyone's chasing the next 'platform shift,' how do you know what's real? Sumaiya's take: measure cost (financial, environmental, ethical), problem-fit, and long-term sustainability. Her critique of blockchain coin mining, versus her long-term belief in AI, isn't about trendiness. It's about impact. 'Tech that creates more problems than it solves won't last.' In the end, it's not about resisting innovation. It's about choosing it wisely. In a world shaped by AI, what we build is only as good as why we build it. ALSO READ: Brands Use Context Engineering to Appeal to Answer Engines Chandni is an Editor with a keen interest in customer-obsessed ideas. A journalist by profession and a writer at heart, she is committed to martech and CX content that resonates with readers across industries. View More

7 Hard Truths About Building AI Products That Actually Last
7 Hard Truths About Building AI Products That Actually Last

Martechvibe

time15-07-2025

  • Business
  • Martechvibe

7 Hard Truths About Building AI Products That Actually Last

For years, product innovation has been fueled by excitement: a new framework, a breakthrough model, a rising trend. But in 2025, as AI weaves itself into every interface, the question that separates good AI products from enduring ones isn't 'What can we build?'—it's 'What should we build?' According to the 2025 State of Martech Report by Scott Brinker, product management is now one of the most pivotal roles in the Martech ecosystem tasked with balancing rapid innovation, data complexity, and the promise (and pitfalls) of AI. As boundaries between product, marketing, and customer experience blur, the need for clarity, focus, and intentional design has never been greater. Because behind every buzzword, be it LLM, Web3, or genAI , is a simple truth: if it doesn't solve a real customer problem, it's noise. To ground this exploration, we turn to insights from Sumaiya Noor, Product, AI & Technology Leader, who has built B2B, B2C, and B2B2C SaaS products across emerging tech domains like AI and Web3. Drawing from her product, engineering, and customer experience background, Sumaiya offers a refreshingly pragmatic lens, one focused not on hype cycles, but on human problems. Strategy Can't Be Built in Silos In high-velocity environments, roadmaps shift, features morph, and priorities blur. So how do you keep AI product and marketing aligned ? The answer lies in dissolving the silos entirely. 'We don't build and then inform,' says Sumaiya. 'We build together.' Cross-functional planning, with inputs from sales, marketing, CX, and engineering, not only improves go-to-market timing, it ensures that every feature is designed with customer communication in mind. And yet, even with cross-functional harmony, another trap remains: building for the tech instead of the problem. That's where disciplined product thinking becomes essential. The Case for Problem-First Product Thinking There's a temptation to fall in love with an idea or worse, a technology. But as Sumaiya puts it: 'Even the best idea is irrelevant if no one will pay for it.' The most impactful products today aren't those that chase AI for the sake of AI. They start with deep listening. They define the problem before prescribing the tech. And only then do they decide whether that shiny new model is the right tool. But what happens when customer needs evolve faster than the solutions built to serve them? When Customer Pain Points Evolve Faster Than You Build Technology is changing rapidly but so are customer expectations. The feature they needed last month might feel redundant next week. This is where continuous product discovery becomes non-negotiable. Beta feedback, prototype testing, and agile pivots must be baked into the build process. 'It's not about being right from the start,' says Sumaiya. 'It's about being flexible enough to shift fast, based on what your users actually tell you.' Flexibility is key. Not just in building, but in knowing when to stop building. Because holding on to outdated features can be just as risky as launching the wrong ones. Sunsetting Isn't Failure, It's Focus Great teams know when to quit. That feature your team launched with pride may no longer serve its purpose—maybe a competitor has done it better, or your users have outgrown it. The hardest part? Internal buy-in. 'You're not just retiring code,' Sumaiya notes. 'You're sunsetting people's effort, pride, and belief.' But with clear metrics and shared goals, this becomes a strategic move, not an emotional one. And as AI becomes embedded in more features, another layer of complexity emerges: unpredictability. Especially when the tech behaves in ways even its creators can't fully control. You Can't Eliminate AI Hallucinations But You Can Contain Them As large language models make their way into every workflow, a difficult truth remains: hallucinations are part of the system. 'If someone in the product world says that hallucination can completely be eliminated or can be mitigated, I think they don't understand the technological side of LLMs or AI, artificial intelligence or agents that much,' says Sumaiya. Rather than over-promise, product teams must scope narrowly, train models on proprietary data, and design safeguards to guide behaviour. 'You can't fully control LLMs but you can control how, where, and why you deploy them.' But responsible deployment isn't enough. You also need to know if your AI is actually adding value, which brings us to the challenge of building effective feedback loops. Feedback Loops in AI Products Are Twice As Hard Feedback is already tough in traditional product development. In AI, it's even more layered. 'You need two types of feedback loops. One for validating the feature itself, the service itself, whatever you are trying to provide to the customer. In terms of solving their problem or pain point. If it's an AI integrated or AI-based product, the additional feedback loop is also required to validate what type of value addition this AI integration is adding to your overall solution,' says Sumaiya. You're not only asking whether a feature works, you're asking whether AI is meaningfully improving the experience. This means comparing pre- and post-AI metrics, collecting real-time usage data, and isolating AI's impact on usability and satisfaction. Even with feedback in place, product teams still face a difficult judgment call: which technologies are worth betting on, and which ones are just noise? How to Tell If a Technology Will Stick or Fizzle When everyone's chasing the next 'platform shift,' how do you know what's real? Sumaiya's take: measure cost (financial, environmental, ethical), problem-fit, and long-term sustainability. Her critique of blockchain coin mining, versus her long-term belief in AI, isn't about trendiness. It's about impact. 'Tech that creates more problems than it solves won't last.' In the end, it's not about resisting innovation. It's about choosing it wisely. In a world shaped by AI, what we build is only as good as why we build it. ALSO READ: Brands Use Context Engineering to Appeal to Answer Engines Chandni is an Editor with a keen interest in customer-obsessed ideas. A journalist by profession and a writer at heart, she is committed to martech and CX content that resonates with readers across industries. View More

Hypergrowth strategies to scale smarter and faster
Hypergrowth strategies to scale smarter and faster

Time of India

time18-06-2025

  • Business
  • Time of India

Hypergrowth strategies to scale smarter and faster

The Viksit Bharat vision sees India as a $23–35 trillion economy by 2047. To deliver on this ambition, corporate India must shift from incremental thinking to exponential action. The question is no longer 'How do we beat market growth?' but 'How do we double revenue in three years?' With this shift comes a rising appetite for experimentation and bold bets. What does it take to fuel rapid yet sustainable growth? Don't skim, go deep We live in a world of choice overload. From tech stacks to sales channels to marketing platforms, options abound. But fear of missing out cannot drive business strategy. The most successful companies will be those that choose their battles and allocate resources with intention – going deep where it matters most. For instance, many digital natives focused their resources on online channels and only when they gained a strong foothold did they expand their business to offline and other channels. Experience as strategy Customer experience (CX) is no longer just about service – it's a strategic differentiator and growth driver. Businesses are embedding CX into product design, especially in sectors like automotive, where features like personalised interfaces and proactive maintenance are shaped by customer insight. Companies with robust CX execution are seeing up to 30% higher customer lifetime value, realising faster growth. The winners aren't just designing great products – they're creating great journeys. The Martech landscape is evolving rapidly to deliver solutions around hyper-personalisation, omnichannel experiences, AI-enabled chatbots, voice and conversational marketing among other offerings. Though data privacy is still a concern, we are seeing a strong growth in Martech investments (upwards of 15%+ YoY) in the coming years. Growth through tech AI is emerging as a critical enabler of speed and scale. From real-time personalisation to predictive analytics and rapid content creation, technology is helping businesses move faster, engage smarter and unlock new revenue opportunities. By partnering with nimble startups, running incubators or co-developing with niche players, businesses can accelerate innovation cycles, go to market faster and reduce execution complexity while staying focused on their strengths. Many building product companies are integrating VR/AR tools into their websites and mobile apps to drive conversion. AI-powered recommendations on e-commerce or food delivery apps, and IoT (Internet of Things) sensors in auto and consumer durable products are some of the notable examples of this integration. Diversify with synergy In a rush to add new revenue streams, companies looking to diversify into new markets or new product segments sometimes overlook the synergistic capabilities that can help them win. Whether its access to customers, a wide channel network, low-cost manufacturing or a strong supply chain, it's imperative to anchor future growth efforts in current strengths. In the business landscape of the future, organisations that master hypergrowth will stand at the forefront, setting themselves apart from competitors and redefining benchmarks of success. To translate their exponential growth aspirations into reality, companies should start by reimagining their business and encouraging disruptive innovation. For more details, visit - (The authors are Partners at PwC India; Somick: partner and strategic engagements leader, PwC India; Saurabh: Partner, customer transformation, PwC India) (The opinions expressed in the article are those of the authors and do not necessarily reflect the views of the publication. The information provided in the article is for general information purposes only. makes no representations or warranties of any kind, express or implied, about the accuracy, adequacy, validity, reliability, availability, or completeness of any information. It does not assume any responsibility or liability for any errors, omissions, or damages arising from the use of this information. We reserve the right to modify or remove any content without prior notice. The reproduction, distribution, or storage of any content without written permission is strictly prohibited.)

Evolving Martech Stacks: Essential Strategies from MoEngage's GROWTH Summit 2025
Evolving Martech Stacks: Essential Strategies from MoEngage's GROWTH Summit 2025

Time of India

time17-06-2025

  • Business
  • Time of India

Evolving Martech Stacks: Essential Strategies from MoEngage's GROWTH Summit 2025

In today's hyper-competitive and experience-driven economy, consumer brands face the imperative of not just reaching customers but truly captivating them. The answer lies in transforming their Martech stack from rigid systems to dynamic, agile, and AI-powered stacks capable of delivering truly unforgettable brand experiences. This critical paradigm shift was the central focus at MoEngage 's #GROWTH Mumbai Summit 2025, which successfully convened over 450 marketing and product leaders at Taj Lands End, Mumbai. The summit emphasized that a strong Martech foundation, particularly through an integrated Customer Data and Engagement Platform (CDEP), is no longer a luxury but a necessity. A unified CDEP enables brands to operate with exceptional agility and leverage extensive customer data. This integration also empowers them to implement AI-driven personalization at scale, which is essential for cutting through the noise and fostering deep, lasting brand affinity. "The modern Indian consumer expects more than just a product; they expect seamless, intelligent, and deeply personal brand experiences," stated Yash Reddy, Chief Revenue Officer at MoEngage. "An agile, AI-powered Martech stack is the engine that drives this. Our CDEP unifies disparate data points, providing the intelligence needed to personalize every interaction and build truly unforgettable brand moments, ensuring enterprises can adapt and thrive with unparalleled speed." The #GROWTH Mumbai Summit 2025 served as a key forum, featuring over 40 industry experts across multiple insightful panel discussions. Attendees from leading brands such as Adani Digital Labs, Zee5, Airtel, Tata Motors, Apollo Health & Lifestyle, Apparel Group, Borosil Limited, TBZ Originals, EY, Dream11, IHCL, Federal Bank, Aditya Birla Capital Digital, and Godrej explored strategies for leveraging advanced technology to elevate brand engagement. Key discussions and takeaways from the summit provided an actionable blueprint for brands to build future-ready, experience-centric Martech stacks. The panel discussions focused on understanding the modern Indian consumer and mastering two-way, engaging interactions across sectors. Attendees explored leveraging unified customer data and advanced analytics to personalize engagement and foster loyalty. Deep dives into AI's role in achieving personalization at scale and navigating the customer journey were also conducted, optimizing content, timing, and channels. Discussions included the impact of AI on BFSI and broader industry experience transformation, with a strong emphasis on data-driven personalization and building emotional connections with consumers. Raviteja Dodda, CEO and Co-founder of MoEngage, underscored the company's strategic vision, to empower Indian enterprises with a robust AI-driven Martech stack: 'The future of brand leadership lies in consistently delivering superior customer experiences, powered by intelligent and agile technology. MoEngage is committed to providing Indian enterprises with precisely this capability– an AI-powered, agile Martech stack. A stack that allows them to truly understand and engage their customers one-on-one at scale, driving both loyalty and remarkable business growth."

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