AI Monetisation

In this episode of the AI Monetisation Podcast, Jayeeta Putatunda, Lead Data Scientist and Director - AI Center of Excellence at Fitch Group, joined Co-founder Binny Mathews, to discuss how financial institutions can successfully evaluate and generate ROI from GenAI initiatives.Guest Background: A recognized AI leader and NVIDIA Enterprise Platform Advisor, Jayeeta has been named to the AI 100 for GenAI and honored among the Top 25 Visionary Women in FinTech AI. At Fitch Ratings, she leads strategic AI efforts, balancing cutting-edge innovation with the rigors of regulatory finance.What’s Inside?How to evaluate and prioritize high-ROI GenAI use cases in financeBuilding structured GenAI pipelines: From PDF chaos to productivityThe art of combining causal analysis and LLMs for quantitative accuracyCrafting custom evaluation frameworks with business alignmentBuy vs. Build: Navigating third-party solutions vs. internal developmentKey Takeaways:1. Document processing and customer service are the biggest GenAI ROI drivers in finance2. Start simple: Evaluate models incrementally and build from real business problems3. Use SME-guided few-shot learning and lightweight fine-tuning for enterprise accuracy4. Define evaluation and ROI metrics before building any solution5. Focus on explainability and expectation-setting to gain business trust and adoptionThis episode is essential listening for data science and business leaders looking to implement GenAI solutions in finance with a clear ROI and strong business alignment!For full show notes, visit- https://bit.ly/44LTTqLProjectPro helps you tackle your AI projects with ease. Upskilling the team for specific AI projects, collaborating with experts to co-build AI solutions, or opting for end-to-end project outsourcing, ProjectPro has you covered. With ongoing 1:1 support, enterprises can overcome roadblocks and seamlessly take their projects from PoC to production. Learn more- https://bit.ly/3IIEMFX

In this episode of the AI Monetisation Podcast, Boris Tvaroska, AI Technical Lead for Financial Services at Google, joined Co-founder Binny Mathews to discuss how leading financial institutions can build ROI-driven AI co-pilots, while navigating one of the most heavily regulated industries on earth.Guest Background: With a career spanning Google, AWS, Lenovo, and HP, Boris brings deep expertise in applied AI, NLP, and cloud-based transformation. He’s led AI implementations across global financial enterprises and previously co-founded an NLP startup, giving him a unique perspective at the intersection of innovation and governance.What's Inside?Top 2 AI use cases in finance: Intelligent document processing & frontline AI copilotsWhy traditional OCR and RPA fall short in modern financial workflowsHow banks can scale personalization while managing regulatory riskThe role of RAG vs fine-tuning in real-world AI deploymentsWhat’s blocking full-scale automation, and how governance must evolveKey Takeaways:Document-heavy processes are ripe for AI-driven unstructured-to-structured data transformationCopilots can boost efficiency for call centers, loan officers, and advisorsGovernance, not model performance, is the biggest blocker to adoptionMost real-world success comes from RAG + embedding fine-tuning, not full model retrainingRegulation needs to catch up to unlock end-to-end automationThis is a must-watch for anyone building or deploying AI in finance, especially if you're focused on measurable ROI, risk mitigation, and scaling innovation responsibly.For full show notes, visit- https://bit.ly/3GByYNVProjectPro helps you tackle your AI projects with ease. Upskilling the team for specific AI projects, collaborating with experts to co-build AI solutions, or opting for end-to-end project outsourcing, ProjectPro has you covered. With ongoing 1:1 support, enterprises can overcome roadblocks and seamlessly take their projects from PoC to production. Learn more- https://bit.ly/4lrlnaR

In this episode of the AI Monetisation Podcast, Anita Luthra, Global Technology Director of AI and Cloud Labs at HCLTech, joins Co-founder Binny Mathews to explore one of the most overlooked challenges in enterprise AI: systemic bias and its multi-billion dollar impact.Guest Background: A veteran in AI architecture, Anita has led major transformations at JPMorgan Chase, IBM, and now HCLTech. She pioneered the first Watson solution integration at IBM and is a recognized speaker on hallucination and bias in AI, recently featured at the Worldwide AI Summit.What’s Inside?The trillion-dollar blindspot: How bias infiltrates AI training data and algorithmsReal-world implications: Personalization failures and reduced efficacy for diverse usersPractical strategies: Detecting and mitigating bias through data, model tuning, and governanceGovernance in AI: Why accountability lies with every member of the development lifecycleContrarian AI thinking: Why small models might hold the key to scalable, equitable solutionsKey Takeaways:1. AI models reflect the societal and demographic imbalances present in their training data2. Personalization without representation can lead to massive financial and brand risks3. Bias correction must start early at data selection, architecture, and prompt engineering levels4. Responsibility for bias mitigation cannot lie with a single team or function5. Enterprises must rethink traditional hiring and development mindsets to stay relevant in the AI eraThis episode is essential listening for enterprise leaders, AI engineers, and product owners looking to build fair, inclusive, and commercially successful AI systems in the real world.For full show notes, visit- https://bit.ly/44d07QhProjectPro helps you tackle your AI projects with ease. Upskilling the team for specific AI projects, collaborating with experts to co-build AI solutions, or opting for end-to-end project outsourcing—ProjectPro has you covered. With ongoing 1:1 support, enterprises can overcome roadblocks and seamlessly take their projects from PoC to production. Learn more- https://bit.ly/44JxR7W

In this episode of the AI Monetisation Podcast, Kevin Laughridge, Principal at Deloitte Consulting, joins Co-founder Binny Mathews to delve into how Deloitte and Google are shaping the future of enterprise AI with agentic systems, multimodal platforms, and practical enterprise frameworks.What’s Inside?Agentic AI: How 100+ prebuilt agents are redefining enterprise workflowsInside “AgentSpace”: Google’s new single pane of glass for enterprise AIThe Deloitte + Google + ServiceNow partnership and agent-to-agent protocolsBuilding trust-based strategic alliances at enterprise scaleAI Assist: Deloitte’s full-stack AI solution for accelerating SDLCHow to measure ROI beyond cost savings, speed, quality, NPS, and moreThe evolving role of humans in an AI-augmented enterpriseKey Takeaways1. Agentic AI enables exception-based reasoning across enterprise processes2. Reliable AI outcomes require continuous evaluation and strong guardrails3. Successful alliances are built on trust, clarity of purpose, and shared value4. AI adoption is shifting from isolated use cases to full-process optimization5. Data modernization, not just model performance, is the real bottleneck6. Enterprise upskilling must include data literacy as a core competencyThis episode is essential listening for business and tech leaders navigating enterprise AI transformation, from strategy and tooling to trust and talent!For full show notes, visit- ProjectPro helps you tackle your AI projects with ease. Upskilling the team for specific AI projects, collaborating with experts to co-build AI solutions, or opting for end-to-end project outsourcing—ProjectPro has you covered. With ongoing 1:1 support, enterprises can overcome roadblocks and seamlessly take their projects from PoC to production. Learn more- https://bit.ly/461c6Bx

In this episode of the AI Monetisation Podcast, Jayney Howson, Senior Vice President of Global Learning and Development at ServiceNow, joined Co-founder Binny Mathews to share how ServiceNow is transforming enterprise learning through AI and the science of play.Guest BackgroundWith 20+ years of experience spanning marketing, sales, and talent development, Jayney leads global learning initiatives at the $200B software leader. Previously at Salesforce, she supported over 30,000 team members and now spearheads ServiceNow University, a bold new platform delivering AI-powered, personalized, in-the-flow learning at scale.What’s Inside?✅ The vision behind ServiceNow University and how it powers AI-driven, personalized learning✅ Science of play: How neuroscience and gamification help overcome fear and drive real skill-building✅ AI Coaches & Playground Environments: What modern learning looks like at scale✅ How ServiceNow embeds learning in the flow of work with context-sensitive, just-in-time content✅ The bold idea of a “Human Renaissance” — unlocking creativity through automationKey Takeaways:1. Personalization and context are non-negotiables in enterprise learning2. True growth happens in psychologically safe, exploratory environments3. Learning must shift from static content to adaptive, AI-generated skill development4. Strong data foundations are essential for tracking performance and enabling tailored upskilling5. Future talent strategies must account for early-career learning disrupted by AI automationThis episode is essential listening for L&D leaders, talent strategists, and enterprise executives looking to harness AI to unlock deeper human potential at work.For full show notes, visit- https://bit.ly/4jzLdYvProjectPro helps you tackle your AI projects with ease. Upskilling the team for specific AI projects, collaborating with experts to co-build AI solutions, or opting for end-to-end project outsourcing—ProjectPro has you covered. With ongoing 1:1 support, enterprises can overcome roadblocks and seamlessly take their projects from PoC to production. Learn more- https://bit.ly/4jGosSJ

In this episode of the AI Monetisation Podcast, Arun Ravi, Senior Director of Health AI at Microsoft, joins Binny Mathews, CEO and Co-founder of ProjectPro, to explore how AI is revolutionizing the healthcare landscape by improving physician efficiency and reducing system-wide costs.What's Inside?Dragon Co-Pilot: Automating clinical note-taking to free up physician timeReal-time agentic AI: Auto-handling reimbursements between providers and payersLLMs in research: De-identifying clinical data to safely train healthcare modelsHealthcare GTM insights: Startup lessons on scaling within Big Tech ecosystemsThe future of preventive and behavioral health powered by AIKey TakeawaysWorkflow integration is critical—AI must fit naturally into the physician’s existing toolsReimbursement alignment drives faster adoption than cost savings aloneHuman-in-the-loop remains essential, especially for high-stakes clinical decisionsEarly AI wins lie in unblocking administrative burdens and improving accessFocused, vertical-specific models (SLMs) are key to real-world healthcare impactThis episode is essential listening for healthcare innovators, AI entrepreneurs, and anyone exploring how AI can empower providers and drive systemic change across the care continuum.For full show notes, visit- https://bit.ly/4mjzbFpProjectPro helps you tackle your AI projects with ease. Upskilling the team for specific AI projects, collaborating with experts to co-build AI solutions, or opting for end-to-end project outsourcing—ProjectPro has you covered. With ongoing 1:1 support, enterprises can overcome roadblocks and seamlessly take their projects from PoC to production. Learn more- https://bit.ly/4jQgAPA

In this episode of the AI Monetisation Podcast, Anurag Bhagat, Senior Generative AI Strategist at the Generative AI Innovation Center, Amazon Web Services (AWS), joins Binny Mathews, Co-founder of ProjectPro, to share how enterprises can identify and scale high-ROI AI projects with confidence.What's Inside?Why executive demand for GenAI is unprecedented—and what it means for enterprisesA practical framework for prioritizing AI use cases across industriesHigh-ROI use cases like intelligent document processing (IDP) and GenAI-powered contact centersBuilding internal roadmaps vs. point solutions: The key to sustainable ROIWhy data quality, metadata, and risk guardrails still matter in the GenAI eraWhat great data scientists do differently: Bridging business understanding with technical depthKey Takeaways:Prioritize low-hanging fruit early to build momentum and buy-inTrue ROI comes from a portfolio of use cases, not isolated pilotsEnterprise GenAI adoption must account for workflow fit, latency, accuracy, and costExpertise remains critical—AI augments, not replaces, human judgmentBuild agile pipelines to swap in better models as the space rapidly evolvesThis episode is essential listening for business and tech leaders aiming to unlock meaningful value from GenAI without falling for the hype!For full show notes, visit- https://bit.ly/3YUPoXoProjectPro helps you tackle your AI projects with ease. Upskilling the team for specific AI projects, collaborating with experts to co-build AI solutions, or opting for end-to-end project outsourcing—ProjectPro has you covered. With ongoing 1:1 support, enterprises can overcome roadblocks and seamlessly take their projects from PoC to production. Learn more- https://bit.ly/4lQ0k2j

In this episode of the AI Monetisation Podcast, Lexy Kassan, Lead Data and AI Strategist at Databricks, joins Binny Mathews to share how enterprises can build a practical, scalable, and enterprise-ready AI prioritization strategy.Guest Background:Lexy brings a rich and diverse background to the table. She started her career as a business analyst and evolved into a data scientist, risk analyst, and now a global thought leader in responsible AI. At Databricks, she works closely with CXOs to guide enterprise AI adoption, drive responsible AI frameworks, and reshape data governance and operational models.What's Inside?✅ AI strategy playbook: How to balance hype, risk, and value✅ The new role of data foundations in enabling successful AI✅ What "last mile AI" really means and why it’s business-critical✅ Build vs. Buy: How to assess enterprise AI solutions smartly✅ Responsible AI: From compliance to cross-functional accountability✅ The evolving role of dashboards, agents, and embedded insights✅ When NOT to use generative AI: A contrarian takeKey Takeaways:1. AI success starts with robust data governance and business alignment.2. Don’t over-engineer—sometimes simpler solutions outperform GenAI.3. Shift from dashboards to actionable, workflow-integrated insights.4. Prioritize use cases based on business value, not just technology excitement.5. Responsible AI is everyone’s job—from IT to legal to business units.This episode is essential listening for enterprise leaders, data strategists, and AI practitioners looking to drive real impact from their AI initiatives—without losing sight of cost, governance, or long-term value.For full show notes, visit: https://bit.ly/4lwsEH3ProjectPro helps you tackle your AI projects with ease. Upskilling the team for specific AI projects, collaborating with experts to co-build AI solutions, or opting for end-to-end project outsourcing—ProjectPro has you covered. With ongoing 1:1 support, enterprises can overcome roadblocks and seamlessly take their projects from PoC to production. Learn more: https://bit.ly/3Y2b8QV