Discover Dialogues

From Infrastructure To Insight: Redesigning Leadership Ft. Abhishek Mehrotra, CHRO At Yubi

In this episode of TechDogs Discover Dialogues, host Vikramsinh Ghatge sits down with Abhishek Mehrotra, Chief Human Resources Officer at Yubi, to explore how HR is fundamentally evolving, from a backend support function to a strategic decision engine. This conversation cuts through the noise around AI in HR and gets to what it actually means for leaders, teams, and the future of work.Organizations today are investing heavily in HR technology, yet most still treat it as operational infrastructure rather than a source of strategic intelligence. The real opportunity, and the real gap, lies in reimagining people systems as proactive decision-making platforms. As AI becomes embedded in hiring, performance, and workforce planning, the question isn't whether to adopt it, but how to do so without losing the human judgment that makes organizations sustainable.Abhishek brings over 25 years of people leadership across technology, telecom, social media, and fintech to this conversation. Having navigated transformations at global enterprises and fast-scaling platforms alike, his perspective spans both the architectural and the deeply human dimensions of organizational change. What makes his viewpoint distinctive is the rare combination of data-driven discipline and empathetic leadership philosophy, and the conviction that HR must own business outcomes, not just enable them.Here is a core insight that reframes how most organizations approach AI adoption in HR: Abhishek argues that rather than launching company-wide skilling programs and waiting for training to drive adoption, organizations should start by creating internal champions, and build success stories that others naturally want to replicate. Adoption, in his model, is pulled by curiosity and proof, not pushed by mandates.For most HR leaders, this reordering is significant. It means democratizing access to AI tools across all functions, not just engineering, and trusting that on-the-job experimentation will outperform structured classroom training. The shift from "training first, then adoption" to "access first, then learning" fundamentally changes how organizations build AI fluency at scale.In this episode, we cover:• How Abhishek's 25-year journey, from executing people processes to architecting people intelligence, shaped his view of HR's strategic role• Why HR is no longer an enabler but a co-pilot with skin in the game for business outcomes• How AI is being embedded in hiring, performance management, and workforce planning, and what the human in the loop actually does now• Why roles are converging across functions and what a skills-based talent strategy looks like in practice• How the challenges of building a 0–100 organization differ fundamentally from scaling a 1,000-to-5,000 one• How to think about role reconstruction, reimagining team structures for a world where AI handles the what and how• Why leaders must be hands-on AI users themselves before they can credibly lead transformation• How psychological safety, system literacy, and human-centric resilience define leadership in an AI-first era• What budding HR professionals should do today to future-proof their careers, starting with their own daily workflows Key Takeaway:The most important shift in HR leadership isn't about adopting the right tools, it's about owning the right outcomes. Abhishek's core argument is that HR becomes a decision system when its leaders stop waiting for a seat at the table and start earning one by demonstrating measurable business impact. As AI accelerates the convergence of roles, compresses time-to-market, and reshapes the skills landscape, organizations that pair algorithmic intelligence with human judgment, curiosity, critical thinking, emotional🔔 Subscribe to our channel now: https://tinyurl.com/TDYTSub🔔 Subscribe to stay ahead of enterprise tech trends: https://www.techdogs.com/newsletter 🌐 Visit us at: https://www.techdogs.com

Securing The Future Of AI Ft. Sandip Wadje, MD, Global Head Of Emerging Tech, BNP Paribas

In this episode of TechDogs Discover Dialogues, host Vikramsinh Ghatge sits down with Sandip Wadje, Managing Director and Global Head of Emerging Technology Operational Risks & Intelligence at BNP Paribas, to explore how enterprises can securely embrace AI and emerging technologies while navigating an increasingly complex risk landscape. From overlooked vulnerabilities in agentic systems to the fragmented global regulatory environment, this conversation cuts through the hype and gets to what actually matters for leaders on the front lines of innovation.AI adoption is accelerating faster than most governance frameworks can keep pace with. Organizations are deploying generative and agentic models at scale yet the foundational questions around accountability, data quality, and output-level risk remain largely unresolved. One of the sharpest insights in this conversation: Sandip argues that organizations rushing into high-impact AI use cases; fraud detection, security operations, without addressing the basics first are setting themselves up for failure. His recommendation is to start with summarization, which accounts for roughly 50 60% of corporate knowledge work, build ROI there, and only then move into higher-complexity applications. This sequenced approach: summarize, write, reason, reframes AI adoption as a disciplined business case rather than a technology experiment.For enterprises already stretched across multiple emerging technology domains, this reordering of priorities is significant. Fixing data quality, cleaning up access permissions, and establishing output-level accountability before scaling AI is not just risk management; it is the foundation that makes AI ROI sustainable.In this episode, we cover:• How Sandip's career, from government officer to global risk leader, mirrors the evolution of technology itself• Why emerging technology is defined by the absence of regulatory clarity, and what that means for AI governance• The most underestimated risks in quantum computing, digital assets, robotics, and autonomous AI systems• Why the 'run the bank vs. change the bank' model is reaching an inflection point in large institutions• How organizations should sequence AI adoption: summarize first, then write, then reason• Why starting from AI output failures not model guardrails is the right approach to risk management• The compensatory control mindset: using existing security infrastructure before chasing new tools• Why global regulations are fragmented and what prescriptive, harmonized guidance should look likeKey Takeaway:The biggest risk in AI adoption is not moving too slowly it is deploying AI on a foundation that was never designed to support it. Sandip's core argument is that organizations must address data quality, access permissions, and output accountability before scaling AI into high-stakes domains. As agentic systems grow more autonomous and quantum computing edges closer to practical disruption, the institutions that build this disciplined foundation now will be far better positioned to lead, not just survive the next wave of technological change.About the Guest:Sandip Wadje is the Managing Director and Global Head of Emerging Technology Operational Risks & Intelligence at BNP Paribas, where he leads the firm's strategic approach to identifying, assessing, and mitigating risks across AI, cloud, digital assets, and quantum technologies. His background spans more than two decades, beginning as a Class 1 government officer in India's Ministry of IT before transitioning through KPMG, Deloitte, JP Morgan, and Risk IQ into his current global leadership role. What distinguishes Sandip is his ability to bridge deep technical expertise with board-level risk strategy, having operated across both the practitioner and advisory sides of some of the world's most complex financial systems.🔔 Subscribe to our channel now: https://tinyurl.com/TDYTSub🌐 Visit us at: https://www.techdogs.com

The Evolving Role Of HR: Leading People, Culture And Change

The role of HR is evolving faster than ever before, and AI is at the center of that transformation. In this exclusive TechDogs Discover Dialogues HR Roundtable, we bring together three senior HR leaders to discuss how organizations can balance people, performance, technology, and the future of work in an AI-driven world. Featuring: Nancy Hauge Chief People Experience Officer, Automation Anywhere Lisa Sherwell Chief People Officer, SUSE Arpit Shelat Director - Human Resources, Genesys India As AI reshapes how businesses operate, HR leaders are being asked bigger questions than ever before: How do you stay people-first while driving business performance? Can AI make HR more human? How should companies build future-ready workforces? Is work-life balance outdated? What do employees expect from modern workplaces today? This conversation explores how HR is moving beyond traditional functions and becoming one of the most strategic drivers of business transformation. From AI-powered employee experiences to internal mobility, hybrid roles, workforce personalization, and leadership transformation, this roundtable dives deep into what the future of HR really looks like. Whether you're a CHRO, HR leader, business executive, founder, talent leader, or someone curious about the future of work, this episode is packed with actionable insights. Key Takeaways: ✔ Why people-first companies often outperform others ✔ How AI is improving employee experience ✔ Why HR must become more strategic ✔ The future of workforce planning✔ Why internal mobility matters more than ever ✔ How Gen Z is reshaping workplace expectations ✔ Why work-life balance may need redefinition Subscribe to TechDogs for more conversations with global leaders shaping the future of business, technology, leadership, and innovation.🔔 Subscribe to our channel now: https://tinyurl.com/TDYTSub🔔 Subscribe to stay ahead of enterprise tech trends: https://www.techdogs.com/newsletter 🌐 Visit us at: https://www.techdogs.com

Scaling AI With Guardrails: FinTech Innovation FT. Justin Stottlemyer, Director Engineering, Intuit

AI is changing how products are built, how teams operate, and where bottlenecks show up inside organizations. In this episode of TechDogs Discover Dialogues, Nikhil Sonawane speaks with Justin Stottlemyer from Intuit about scaling AI responsibly, getting data AI ready, integrating agentic systems, and building the evaluation strategies that make non deterministic systems dependable in production. Justin shares lessons from decades of building at scale and explains why today’s AI shift is not just another digital transformation wave. It is a deeper disruption that forces new thinking around architecture, collaboration, governance, and leadership. In this episode, we cover: • Justin’s journey across early internet and commerce platforms and what it taught him about availability and scaling • Why thinking in 1000x forces better architectural decisions than 10x planning • What changes when LLMs, agents, and multimodal systems enter the production stack • Data readiness for AI and why access, security, and structure matter more than ever • The real architectural challenge: coordination overhead across teams, not just tools • FinTech AI: combining non deterministic reasoning with deterministic APIs for accuracy and trust • Human in the loop and validation strategies to reduce risk in AI powered workflows • Build vs buy in AI: what to develop internally and what to leverage externally • Evals and the Agentic Development Lifecycle, and why evaluation strategy is now a core competency • Leadership for AI first organizations: gateway mindset with guardrails Key takeaways: • AI changes the pace of delivery, so leaders must measure where the next bottleneck is. • Data must be AI ready to unlock real value, without compromising security. • Non deterministic systems still need rigor, testing, and evaluation loops across the lifecycle. • The winners are teams that democratize access safely and invest in education and adoption. About the Guest: Justin Stottlemyer is a Distinguished Engineer at Intuit and part of the FinTech AI Center of Excellence, where he focuses on AI transformation, developer productivity, and building scalable foundations for agentic experiences. If you’re exploring enterprise AI, agentic workflows, FinTech innovation, developer productivity, eval strategy, or AI leadership, this episode is packed with practical insights Subscribe for more conversations on AI, data, cloud, cybersecurity, and enterprise technology.

Modernizing Banking Tech Without Disruption FT. Mike Stawchansky, EVP & CTO At Finastra

Core banking modernization is not about shiny new tech. It is about upgrading systems without breaking uptime, trust, or compliance. In this episode of TechDogs Discover Dialogues, Nikhil Sonawane speaks with Mike Stawchansky, EVP and CTO at Finastra, about practical modernization playbooks, AI in mission critical banking, and how to scale reliability across global engineering teams.Mike explains why modernization starts with measurement, why compliance needs to be built into the foundation, and why incremental change using the Strangler Fig approach beats big bang rewrites for core systems.We also discuss AI in banking, where it adds value today, why it should complement core systems rather than replace them, and why production automation still requires human checks and the four eyes principle.In this episode, we cover:• How to modernize legacy banking platforms without disruption by measuring what exists first • Why non-functional requirements must be explicit before migration or re-architecture • Compliance by design and how to make it difficult to do the wrong thing in production • Why big bang rewrites are risky for core banking and why Strangler Fig works better • Where to start modernization: user journey, adoption, and what actually improves customer outcomes • The modernization mistake many teams repeat: delaying tech debt until it becomes urgent • AI in mission critical banking: where it adds value, and why it cannot replace deterministic core systems • AI governance: where to draw the line on automation and why humans must stay in the loop • How global engineering teams stay aligned on reliability, delivery standards, and ownership • Reliability culture: breaking the “throw it over the wall” model across engineering, DevOps, and support • Leadership lessons for sustaining momentum during long transformation cyclesKey takeaways:• Measure first. Modernization should preserve or improve reliability, not reduce it. • Modernize incrementally. Strangler Fig reduces risk and builds trust through small wins. • AI complements core systems. Its probabilistic nature makes deterministic banking cores difficult to replace. • Keep checks in production. Treat AI like any engineer. Use validation and the four eyes principle. • Communication is the scaling factor. Clear, explicit communication and playback prevent misalignment in global teams. About the Guest:Mike Stawchansky is EVP and Chief Technology Officer at Finastra, where he leads technology strategy with a focus on product and infrastructure modernization and cloud transformations for mission critical financial services platforms. He brings deep experience across platform engineering, reliability, and large scale modernization, with a practical operating mindset focused on measurable baselines, shared accountability, and execution at scale.🔔 Subscribe to our channel now: https://tinyurl.com/TDYTSub🔔 Subscribe to stay ahead of enterprise tech trends: https://www.techdogs.com/newsletter 🌐 Visit us at: https://www.techdogs.com