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Explore candid Interviews, Q&A's with C-suite leaders on strategic leadership and management, Leadership Challenges, and Leadership Accountability.

Oct 8

AI In Customer Experience Needs Trust, Not Just Automation Ft. Anshuman Singh, CEO, EMEA At HGS

In this episode of TechDogs Discover Dialogues, host Jemish Sataki, Senior Content Lead at TechDogs, sits down with Anshuman Singh, CEO, EMEA at HGS, to explore how AI is reshaping customer experience, customer service, trust, enterprise transformation, and leadership.The conversation begins with Anshuman’s professional journey across architecture, product design, IT, user experience, digital marketing, e-commerce, self-service, and customer experience transformation. He explains how his design background naturally pulled him toward the front end of information technology, where customer behavior, service design, and business outcomes meet.A central theme of the episode is that customer experience should not be designed around channels alone. Anshuman explains that enterprises often organize CX around websites, chat, email, or phone support, but the more important distinction is between transactional experiences and deeply meaningful experiences. Routine, mundane, and informational interactions can be automated, but emotionally charged, high-stakes, or deeply meaningful moments still need human empathy and judgment.The discussion also explores where AI is adding value in customer service today. In This Episode, We Cover:• Anshuman’s journey from architecture and product design to CX transformation• Why customer experience should focus on outcomes, not only channels• How the Kano model can help enterprises understand customer satisfaction• Why customer experiences are becoming K-shaped• Which experiences should be automated and which should remain human-led• Why personalization is now table stakes, not a differentiator• How AI can improve informational queries, diagnostics, account questions, and service support• The difference between assistive AI and agentic AI• Why emotionally charged and high-stakes service moments still need humans• Why signal-based escalation matters more than task-based escalation• Why many AI pilots fail when they move to enterprise scale• Why inconsistent knowledge bases can create inconsistent AI responsesKey Takeaway:AI can transform customer experience, but only when businesses use it with clarity, governance, and empathy. The goal should not be to automate every customer interaction. The goal should be to understand which experiences are routine enough to automate and which moments are meaningful enough to require human judgment.Anshuman’s core message is that enterprises must focus on customer outcomes, not just contact metrics. AI can add value in assistive use cases, diagnostics, informational queries, and workflow support, but agentic AI requires stronger governance, testing, cost visibility, knowledge management, reversibility, and careful control of its blast radius.The organizations that succeed will be the ones that automate the mundane, protect the meaningful, design for reversibility, and keep trust at the center of AI-led customer experience.About Anshuman Singh:Anshuman Singh is the CEO, EMEA at HGS. He brings nearly three decades of experience across technology, design, digital transformation, customer experience, digital commerce, customer self-service, and service innovation.His career began with architecture and product design before moving into IT and user experience. After spending nearly 25 years at Mindtree, where he helped build the user experience group, Anshuman moved into customer experience transformation at HGS. His work today focuses on how generative AI, customer service, business process management, and human-centered design can reshape enterprise customer experiences.#CustomerExperience #CXTransformation #AIInCX #CustomerService #AgenticAI #AssistiveAI #AIGovernance #EnterpriseAI #CustomerTrust #DigitalTransformation #ServiceInnovation #BusinessTransformation #HGS #AnshumanSingh #TechDogs #DiscoverDialogues
Sep 17

Accelerating AI Agents Without Losing Control Ft. Nathan Warner, North Director At AND Digital

In this episode of TechDogs Discover Dialogues, host Jemish Sataki, Content Team Lead at TechDogs, sits down with Nathan Warner, North Director at AND Digital, to explore how enterprises can move from AI experiments to real operating outcomes without losing governance, accountability, or business control.This is not a conversation about deploying AI agents for the sake of innovation. It is a practical discussion on why AI transformation must start with the business process, not the technology. Nathan explains that leaders should not begin by asking where to deploy an agent. They should begin by identifying processes that are expensive, slow, inconsistent, or frustrating for customers, then baseline the outcome, risk, cost, and quality before introducing AI.The conversation closes with leadership lessons for teams navigating AI-driven change. Nathan emphasizes clarity, visible trade-offs, faster decisions, and involving people in change rather than imposing it on them. Leaders do not always need to know every step of the roadmap, but they must be honest about what they know, where they are heading, and why adaptability matters.In this episode, we cover:• Nathan’s journey across operations, consulting, enterprise transformation, and digital leadership• Why technology is rarely the biggest blocker in transformation• How good governance helps organizations move faster• Why AI agents should not be deployed before the business problem is clear• How leaders should baseline outcome, risk, cost, quality, and ROI before AI adoption• Why many AI pilots fail when they move into production• How AI exposes weaknesses in operating models, processes, data, and route-to-live systems• Why enterprises should redesign the “to-be” process instead of inserting AI into broken workflowsKey Takeaway:AI transformation will not succeed by adding agents into existing broken processes. It succeeds when organizations redesign the operating model, define the business outcome, understand the risk, make costs visible, and build governance into the lifecycle from the start.Nathan’s core argument is that AI agents should be treated like digital workers. They need identity, ownership, boundaries, monitoring, controls, escalation paths, and a kill switch. The organizations that succeed will not be the ones that run the most pilots. They will be the ones that build trusted routes to production, involve people in change, and keep value, cost, and risk visible at every stage.About Nathan Warner:Nathan Warner is the North Director at AND Digital, where he sits on the executive team and leads a major part of the company’s UK business. He works across clients, teams, and transformation programs, helping organizations connect technology delivery with business outcomes. With more than two decades of experience across operations, digital transformation, consulting, agile product engineering, cloud transformation, and AI-led change, Nathan brings a practical leadership perspective on how organizations can move faster without losing governance, clarity, ownership, or control.#AIAgents #AgenticAI #AITransformation #AIGovernance #EnterpriseAI #DigitalTransformation #ANDDigital #NathanWarner #TechDogs #DiscoverDialogues #AILeadership #AIROI #AIAdoption #DevSecOps #ResponsibleAI #AIInEnterprise #TechnologyLeadership #BusinessTransformation🔔 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
Jul 28

HR In The AI Era Must Stay Deeply Human Ft. Arppna Mehra, VP Of Human Resources At Honeywell

In this episode of TechDogs Discover Dialogues, host Vikramsinh Ghatge sits down with Arppna Mehra, Vice President Human Resources at Honeywell, to explore how HR leadership is changing in an era shaped by AI, transformation, culture shifts, employee experience, and future-ready workforces.This is not a conversation about HR automation alone. It is a grounded discussion on why the future of HR must balance AI-enabled efficiency with human judgment, empathy, trust, psychological safety, and culture-led transformation.Arppna brings over 30 years of experience across HR, business transformation, mergers and acquisitions, people experience, and leadership. Her journey began in an era when HR was often seen as a personnel or policy function. Over time, she made a conscious choice to step out of the traditional HR back office and immerse herself in the business. She spent time shadowing line managers, sales leaders, operational teams, engineering teams, manufacturing environments, supply chain functions, and customer conversations.The conversation closes with leadership lessons for professionals moving into modern leadership roles. Arppna highlights contextual agility, influence without authority, active listening, inclusive environments, and the ability to manage people through uncertainty and macro-level anxiety. Her strongest leadership habit is listening: not to respond immediately, but to make people feel heard, supported, and understood.In this episode, we cover:• How Arppna’s 30-year HR journey evolved from personnel management to people experience• Why HR must operate close to business realities, not from the back office• How shadowing line leaders and operational teams shaped her business-first HR philosophy• Why HR leadership has shifted from control to enablement• Why psychological safety, trust, and commitment matter more than compliance alone• How AI is changing HR workflows, talent screening, skill mapping, and workforce planning• Why AI should augment human intelligence rather than replace human judgment• Why hiring, culture, empathy, and career development still need human involvement• How organizations can balance automation with a human employee experienceKey Takeaway:AI will reshape HR, but it should not remove the human center of the function. The future of HR is not about replacing judgment with automation. It is about using AI to reduce repetitive work, improve insight, personalize experiences, and help leaders make better people decisions while preserving empathy, trust, culture, and human connection. Arppna’s core argument is that HR must stay business-first and people-centered. The function cannot create impact from an ivory tower. It must be close to employees, close to operations, close to transformation, and close to the lived reality of work. In the AI era, the strongest HR leaders will be the ones who combine agility with empathy, transparency with trust, and automation with deeply human leadership.About Arppna Mehra:Arppna Mehra is the Vice President Human Resources at Honeywell. She brings over 30 years of experience across HR leadership, people experience, business transformation, mergers and acquisitions, culture building, organizational change, and future-ready workforce strategy. Her career reflects the evolution of HR from personnel management and policy enforcement to strategic people leadership and business transformation. Arppna’s leadership approach is shaped by operational immersion, active listening, psychological safety, transparent communication, contextual agility, and a strong belief that HR must be deeply connected to the business and the people it serves.#HRLeadership #FutureOfHR #AIInHR #PeopleExperience #WorkforceTransformation #HumanCenteredAI #EmployeeExperience #OrganizationalCulture #PsychologicalSafety #FutureReadyWorkforce #LeadershipDevelopment #TransformationLeadership #Honeywell #ArppnaMehra #TechDogs #DiscoverDialogues
Jul 23

From Software Marketplaces To Agentic Commerce Ft. Andy Sen, CTO and Co-Founder At AppDirect

In this episode of TechDogs Discover Dialogues, host Vikramsinh Ghatge sits down with Andy Sen, CTO and Co-Founder at AppDirect, to explore how software marketplaces, SaaS buying, digital commerce, and AI-led productivity are reshaping the way businesses discover, buy, manage, and scale technology.This is not a conversation about marketplaces as a static distribution channel. It is a practical discussion on how software commerce has evolved from large enterprise sales and on-premise implementations to subscription-based SaaS, self-service adoption, advisor-led buying, marketplace ecosystems, and now AI-assisted and agentic commerce. At AppDirect, this internal productivity wave led to an important realization. If employees are building their own AI-powered tools, organizations need visibility, governance, guardrails, approved LLMs, data protection, and a platform to manage that activity. That thinking led AppDirect to create and productize its AI productivity platform, devs.ai.The episode also explores platform leadership. Andy emphasizes two principles for anyone building scalable, customer-focused platforms: know your customer and build observability from day one. Teams must measure whether the platform is solving the right problem, whether customers are getting value, and whether the product is performing at the right level. Just as importantly, leaders must build great teams, hire for the characteristics that matter, measure what matters, and push decision-making as far down as possible.In this episode, we cover: • How Andy’s career moved from IBM eBusiness to Walmart Labs, Salesforce, and AppDirect • Why e-commerce became the through line of his career • How software purchasing evolved from enterprise sales to SaaS subscriptions • Why marketplaces and ecosystems became strategic priorities for business leaders • How software buying became more decentralized and user-led • Why time to value is now central to software adoption • How trusted advisors influence software buying for small and mid-sized businesses • Why SaaS management has become more important as software stacks grow • How marketplace platforms can help manage subscriptions, assignments, usage, and visibility • Why enterprise software pricing and billing have become more complex • Where AI is creating real productivity impact today • Why AI has not yet produced many category-defining Key Takeaway: The future of software commerce is moving from traditional buying to marketplaces, ecosystems, advisor-led trust, subscription management, AI-assisted productivity, and eventually agentic commerce. Andy’s core argument is that the biggest shift in software buying is time to value. Businesses no longer want to wait months or years before software creates impact. They expect products to deliver value quickly, scale across teams, and remain manageable as software stacks grow. AI is adding a new layer to this shift by allowing people to build tools faster, but it also creates a new governance challenge: organizations need visibility, guardrails, and platforms that help them scale AI productivity responsibly. About Andy Sen: Andy Sen is the CTO and Co-Founder at AppDirect. He brings deep experience across engineering, product management, e-commerce, software marketplaces, subscription commerce, platform strategy, SaaS management, and AI-led productivity. Andy began his career at IBM’s eBusiness division during the early days of internet commerce. He later worked across digital commerce and platform initiatives at companies including Walmart Labs and Salesforce before co-founding AppDirect. At AppDirect, Andy helps build platforms that enable businesses to create marketplaces, manage software subscriptions, support trusted advisor ecosystems, and navigate the shift toward AI-assisted and agentic commerce.
Jul 21

AI Readiness Starts With Trusted Data Ft. Dave Shuman, Chief Data Officer At Precisely

In this episode of TechDogs Discover Dialogues, host Vikramsinh Ghatge sits down with Dave Shuman, Chief Data Officer at Precisely, to explore why AI readiness does not begin with models, platforms, or the latest technology trend. It begins with trusted data. This is not a conversation about AI demos or technical buzzwords. It is a practical discussion on why organizations must get the foundational layers right before expecting AI, automation, real-time analytics, or advanced technologies to create meaningful business value. Dave brings a unique and non-linear career journey to the conversation. He began in broadcasting, running radio stations across Caribbean islands, where he learned how to cut through noise and deliver a signal that connected with audiences. That early experience shaped how he thinks about data today: organizations are constantly transmitting information, but the real question is whether the signal is accurate, trusted, and received in a way that supports action. The conversation closes with leadership lessons for data professionals. Dave explains that future data leaders must own outcomes, not outputs. They must translate technical concepts into business language, stay technically curious without making technology their entire identity, and build trust with business leaders through quick wins, mid-term initiatives, and long-term foundational investments. In this episode, we cover: • How Dave’s broadcasting background shaped his understanding of signal, noise, and feedback loops • What the dot-com era taught him about behavioral data and fast decision-making • Why companies that survived disruption used data to make better decisions faster • How demand signal analytics revealed gaps between internal assumptions and real market movement • Why scale makes data quality and governance more important • Why AI readiness starts with data catalogs and semantic layers • How the OODA loop applies to enterprise AI and data strategy • Why “observe” requires visibility into data, lineage, and quality • Why “orient” depends on shared business definitions and semantic consistency • How organizations should define which AI decisions can be automated and which need humans Key Takeaway: AI readiness does not begin with the model. It begins with trusted data, shared definitions, continuous quality, semantic context, governance, and business alignment. Dave’s core argument is that organizations cannot expect AI to deliver value if the data foundation is weak. AI agents do not always fail loudly. They can produce confident, coherent, and incorrect answers, which makes governance, semantic consistency, and data quality even more critical. The organizations that succeed will be the ones that move through the decision cycle faster because they can observe clearly, orient correctly, decide responsibly, and act with confidence. About Dave Shuman: Dave Shuman is the Chief Data Officer at Precisely, where he leads work across trusted data, data quality, governance, AI readiness, real-time analytics, and enterprise data strategy. His career spans broadcasting, e-commerce, demand signal analytics, big data, IoT, connected industries, smart cities, and enterprise data leadership. Dave’s professional journey began in broadcasting, where he learned the importance of signal, noise, audience feedback, and relevance. He later moved into the dot-com era, where behavioral data shaped real-time business decisions, before moving into demand signal analytics, supply chain intelligence, Hadoop, streaming data, and IoT. At Precisely, Dave focuses on helping organizations build trusted data foundations that support better decisions, stronger governance, and responsible AI adoption.
Jul 16

Building Trusted AI For The Future Of Work Ft. Naomi Lariviere, Chief Product Owner & VP At ADP

In this episode of TechDogs Discover Dialogues, host Vikramsinh Ghatge sits down with Naomi Lariviere, Chief Product Owner and Vice President at ADP, to explore how workforce platforms are evolving from systems of record into systems of guidance, action, and trust.This is not a conversation about adding AI into HR and payroll because it is the latest trend. It is a practical discussion on how AI can create real value only when it removes friction, supports better decisions, protects trust, and helps people accomplish important work more easily. The conversation begins with Naomi’s product philosophy and how her career shaped the way she thinks about building technology. For her, technology is a tool, but understanding people is the real foundation. If product teams understand people deeply, they can build tools around their needs rather than forcing users to adapt to software.Naomi explains that organizations no longer want HCM platforms that simply store information or automate transactions. Employees want fast, clear answers. Leaders do not want another dashboard that requires interpretation. HR teams want to spend less time managing processes and more time helping the organization use its people more effectively. The episode closes with leadership lessons for product professionals. Naomi explains that early-career professionals should stay curious, seek learning over comfort, ask better questions, and build empathy for customers. For individual contributors moving into leadership, the biggest shift is realizing that the skills that earned the promotion are not the same skills that make someone a successful leader. Leadership is about creating clarity, removing obstacles, developing teams, and helping others become the heroes of their own stories.In this episode, we cover:• How Naomi’s 20-year career shaped her people-first product philosophy• Why technology is a tool, but understanding people is the foundation of good product design• How HCM platforms are evolving from systems of record to systems of guidance and action• Why employees want answers instead of more places to search• Why leaders need guidance, not just dashboards• How AI is making workforce software more conversational, contextual, and proactive• Why useful AI starts with customer friction, not technology placement• Why AI should often feel invisible inside the user experience• How product teams should avoid turning AI into just another feature• Why trust and innovation must go hand in hand in HR and payroll• How ADP evaluates AI use cases based on what happens if something goes wrong Key Takeaway:AI will not create value in workforce technology simply because it is added as a feature. It creates value when it solves real customer problems, removes friction, provides guidance, protects trust, and helps people get important work done more easily. Naomi’s core argument is that the best workforce products are built around people, not technology. In HR and payroll, where products influence pay, compliance, employment records, and major life moments, innovation must always be matched with accountability. About Naomi Lariviere:Naomi Lariviere is the Chief Product Owner and Vice President at ADP. With over 20 years of experience in technology, product management, software development, customer experience, human capital management, payroll, data analytics, and AI-powered innovation, Naomi brings a deeply human-centered perspective to product leadership.
Jul 14

Enterprise AI Needs A Strong Foundation Ft. Ameet Joshi, BU Head, YASH Technologies Middle East

In this episode of TechDogs Discover Dialogues, host Vikramsinh Ghatge sits down with Ameet Joshi, BU Head – Enterprise AI, SAP BAIP & Digital Platforms at YASH Technologies Middle East, to explore how enterprises can move from AI pilots and platform investments to real business outcomes.This is not a conversation about adopting AI for the sake of AI. It is a practical discussion on why enterprise AI needs the right foundation, why fragmented data is an architecture problem rather than an AI problem, & why transformation should always begin with business outcomes.Ameet brings a non-linear and deeply practical career journey to the conversation. He started with an entrepreneurial venture at the age of 19, building and scaling a fish food selling business across two cities before formally learning terms such as sales, distribution, marketing, and customer engagement. That early experience shaped one of his lasting principles: build, deliver, and repeat. The conversation turns to talent and the future of technology professionals. Ameet’s advice is clear: stay hungry, stay foolish, keep learning, fail fast, and fall in love with the problem, not the tool. Tools will change, platforms will evolve, and technologies will be replaced. What keeps professionals relevant is deep business knowledge, the ability to solve problems, and the discipline to create outcomes.In this episode, we cover:• How Ameet’s entrepreneurial journey shaped his approach to enterprise transformation• Why transformation should be measured by business outcomes, not technology adoption• How sophisticated technology can fail when the human experience is ignored• Why simple solutions can outperform complex tools when the business problem is clear• Why many AI pilots struggle to scale in enterprise environments• Why fragmented data is an architecture issue, not an AI issue• How SAP BTP helps build the foundation for integration, data harmonization, automation, and AI• Why enterprises should prepare their foundations before chasing more AI use cases• How SAP Joule and SAP Business AI can change enterprise user experiencesKey Takeaway:Enterprise AI will not create value simply because an organization runs more pilots or adopts more tools. It creates value when the enterprise foundation is ready. That means harmonized data, integrated processes, strong architecture, clear business outcomes, and people who understand the problem they are solving.Ameet’s core argument is that transformation is not about technology. Technology creates possibilities, but people create outcomes. SAP BTP, SAP Business AI, Joule, and autonomous enterprise capabilities can help organizations scale intelligence across processes, but only when the business case is clear and the foundation is strong.About The Guest:Ameet Joshi is the BU Head – Enterprise AI, SAP BAIP & Digital Platforms at YASH Technologies Middle East. He leads initiatives across SAP BTP, enterprise AI, SAP Business AI, digital platforms, cloud migration, analytics, mobility, automation, and platform-led business transformation. His journey began with entrepreneurship before moving into technology with SAP Labs India in 2005. Over the years, Ameet has worked across transactional systems, data and analytics, enterprise mobility, cloud platforms, SAP Cloud Platform, SAP BTP, and digital ecosystems involving SAP, Microsoft, Salesforce, and UiPath. At YASH Technologies Middle East, he focuses on helping enterprises modernize foundations, adopt AI responsibly, and create measurable business value through integrated platforms and business-led transformation.
Jul 13

Why Enterprise Transformation Fails? Ft. Anshuman Vedi, Founder At Techspire Consulting

In this episode of TechDogs Discover Dialogues, host Vikramsinh Ghatge sits down with Anshuman Vedi, Founder at Techspire Consulting, to unpack why enterprise transformation programs often struggle to deliver real business impact despite significant investments in technology, advisory, implementation, and modernization. This is not a conversation about technology as the problem. It is a grounded, experience-led discussion on why enterprise transformation often fails because of execution gaps, unclear sequencing, misaligned expectations, complex delivery models, and a lack of hands-on accountability. Anshuman’s core argument is clear: technology rarely fails on its own. Transformation fails when organizations make the journey more complicated than it needs to be. The conversation begins with Anshuman’s professional journey and how his early exposure to engineering, ERP systems, consulting travel, and multinational transformation programs shaped his view of enterprise technology. His experience across Hitachi, Accenture, and EY gave him a front-row seat to the structural gaps that exist between clients, advisors, implementation partners, and delivery teams. A central insight from this episode reframes how organizations should think about transformation. Anshuman argues that transformation must be broken down, sequenced, and delivered with clear ownership. Enterprises often define ambitious benefits before they fully understand the technical and operational reality. When the actual implementation begins, the business case, technology capability, and delivery model may not align. That is where complexity builds and programs begin to drift.In this episode, we cover:• How Anshuman’s journey from engineering to enterprise consulting shaped his transformation philosophy• Why enterprise transformation is rarely a pure technology problem• How consulting, delivery, advisory, and entrepreneurship gave Anshuman a full view of transformation gaps• Why transformation programs fail when benefits are defined before technical realities are understood• How business transformation and technical transformation create complexity when they are not sequenced properly• Why enterprises must maintain continuity, stability, and agility while modernizing• Why AI should be treated as a multiplier, not a standalone solution• How Oracle, SAP, and other enterprise application vendors are embedding AI into real workflowsKey Takeaway:Enterprise transformation does not fail because technology is unavailable. It fails when organizations define ambitious outcomes without enough clarity, sequence business and technical change poorly, overcomplicate delivery, and lose accountability between advisors, implementation partners, technology providers, and internal teams.About Anshuman Vedi:Anshuman Vedi is the Founder of Techspire Consulting, a specialist consulting firm focused on simplifying complex enterprise transformation programs and delivering measurable business impact. With over two decades of experience across enterprise applications, ERP transformation, Oracle Cloud, consulting delivery, advisory, and program leadership, Anshuman has worked across organizations such as Hitachi, Accenture, and EY before founding Techspire Consulting.#EnterpriseTransformation #DigitalTransformation #ERPTransformation #OracleCloud #AITransformation #EnterpriseAI #ConsultingLeadership #TechspireConsulting #AnshumanVedi #EnterpriseModernization #BusinessTransformation #AIInEnterprise #TransformationStrategy #TechDogs #DiscoverDialogues🔔 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

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Explore candid Interviews, Q&A's with C-suite leaders on strategic leadership and management, Leadership Challenges, and Leadership Accountability.

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TechDogs
Years Active

2025 - 2026
Episodes

90
Rating

Clean
Show Website

Discover Dialogues

Discover Dialogues

Episodes

AI In Customer Experience Needs Trust, Not Just Automation Ft. Anshuman Singh, CEO, EMEA At HGS

Accelerating AI Agents Without Losing Control Ft. Nathan Warner, North Director At AND Digital

HR In The AI Era Must Stay Deeply Human Ft. Arppna Mehra, VP Of Human Resources At Honeywell

From Software Marketplaces To Agentic Commerce Ft. Andy Sen, CTO and Co-Founder At AppDirect

AI Readiness Starts With Trusted Data Ft. Dave Shuman, Chief Data Officer At Precisely

Building Trusted AI For The Future Of Work Ft. Naomi Lariviere, Chief Product Owner & VP At ADP

Enterprise AI Needs A Strong Foundation Ft. Ameet Joshi, BU Head, YASH Technologies Middle East

Why Enterprise Transformation Fails? Ft. Anshuman Vedi, Founder At Techspire Consulting

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