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AI has changed dramatically over the past seven years. When the Applied AI Summit first launched, practitioners were focused on machine learning, NLP, data science, and the challenge of getting models into production. Today, the conversation has expanded to foundation models, generative AI, RAG, autonomous agents, healthcare AI, and increasingly important questions around evaluation, safety, reliability, and measurable impact.  Anna and David explore which technologies genuinely changed the field, what organizations have learned about moving from prototypes to production, and why the industry's focus is increasingly shifting from asking “What can AI do?” to “Can you prove that it works?” Key Highlights: How the industry has evolved from traditional machine learning and NLP to foundation models, GenAI, RAG, and agentic systems.Why creating an impressive AI prototype has become easier while building dependable production systems remains difficult.What organizations have learned about data quality, infrastructure, evaluation, monitoring, workflow integration, and human oversight.Why proving that an AI system actually works has become a defining challenge for practitioners in 2026.Why traditional accuracy metrics aren't enough for generative and agentic systems, particularly in high-stakes industries.What the next three years could bring and which fundamental AI challenges may prove much harder to solve.🎧 Tune in to Episode 69 for a look back at seven years of applied AI and a conversation about what needs to happen next as the industry moves from experimentation toward proving that AI systems are reliable, measurable, and ready for the real world. Register for FREE: https://appliedaisummit.org/  Writer and Host: Anna Anisin Produced, edited, and mixed by the Formulatedby Team

Ritika brings eight years of cybersecurity experience together with hands-on work in LLM security, GenAI applications, AI red teaming, RAG security, and agentic AI risk. Today, her work at the Institute for Security and Technology focuses on emerging AI security risks and translating technical findings into practical approaches to risk management and governance. We explore what changes when AI isn't simply another technology organizations need to protect but becomes a new attack surface capable of accessing data, using tools, interacting with systems, and taking action. Key Highlights: How prompt injection, jailbreaks, adversarial attacks, and other emerging vulnerabilities challenge traditional cybersecurity approaches.What changes when AI systems gain access to tools, data, applications, and the ability to act autonomously.Where vulnerabilities can emerge across retrieval, proprietary data, model interactions, and enterprise GenAI pipelines.Why testing AI systems requires new offensive security techniques and what organizations should actually be testing.How AI can also help defenders automate vulnerability management, compliance, detection, and remediation.Understanding how increasingly autonomous AI systems introduce new questions around monitoring, containment, governance, and human oversight.🎧 Tune in to Episode 68 to hear Ritika Verma's perspective on securing the next generation of AI  and what enterprises need to understand as LLMs and agents become an increasingly important part of the cybersecurity landscape. Be sure to mark your calendars for the annual ALD SF on Nov 5. Join us for the Enterprise Builder’s conference to hear from experts on how AI is shaping the enterprise AI. Apply to attend for FREE:https://ailovesdata.com/san-francisco/    Writer and Host: Anna Anisin Produced, edited, and mixed by the Formulatedby Team

We explore what it takes to turn powerful AI capabilities into products customers actually use and trust, from conversational intelligence and context management to human-AI collaboration, product metrics, and moving beyond AI pilot purgatory. Key Highlights: What separates an AI feature or assistant from a truly agentic product capable of taking action.How product teams should think about context, permissions, user control, reliability, and human oversight.Turning calls, messages, and customer interactions into actionable insights and intelligent workflows.Why promising AI pilots often struggle in production  and what product teams can do differently.Why task completion isn't enough and how teams should think about quality, customer experience, trust, and business outcomes.🎧 Tune in to Episode 67 to hear Tanvi Kopardekar’s perspective on designing the next generation of AI-first products  and what changes when software stops simply answering questions and starts taking action. Be sure to mark your calendars for the annual ALD SF on Nov 5. Join us for the Enterprise Builder’s conference to hear from experts on how AI is shaping the enterprise AI. Apply to attend for FREE:https://ailovesdata.com/san-francisco/    Writer and Host: Anna Anisin Produced, edited, and mixed by the Formulatedby Team

With more than 25 years of experience across real-time machine learning, distributed computing, high-availability systems, and enterprise AI, Ravi brings a deeply technical perspective to one of the biggest questions facing organizations today: How do we move AI agents from impressive demos to systems businesses can actually trust to do work? We explore how enterprises can operationalize AI agents, why industrial systems of record are critical to agentic applications, and what organizations need to consider around context, security, evaluation, governance, and human oversight. Key Highlights: Agentic AI: Hype vs. Reality: Where enterprise AI agents are delivering value today — and where vendor promises are still ahead of what's realistically achievable.From Copilots to AI Workers: What separates a conversational AI assistant from an agent capable of reasoning, taking action, and participating in real enterprise workflows.Agents + Systems of Record: Why integrating AI agents with ERP and other operational systems is essential for reliable, context-aware decision-making.Engineering for Reliability: Lessons from distributed computing, real-time analytics, and high-availability systems that today's agentic AI teams can't afford to ignore.Security, Evaluation & Guardrails: How enterprises should approach permissions, observability, failure detection, governance, and human oversight as AI systems become increasingly autonomous.🎧 Tune in to Episode 66 to hear Ravi Bulusu's perspective on moving beyond the agentic AI hype and building AI systems that are reliable, secure, and ready for the complexity of the real enterprise. Check out: https://theloops.io/  Be sure to mark your calendars for the annual ALD SF on Nov 5. Join us for the Enterprise Builder’s conference to hear from experts on how AI is shaping the enterprise AI. Apply to attend for FREE: https://ailovesdata.com/san-francisco/  Writer and Host: Anna Anisin Produced, edited, and mixed by the Formulatedby Team

James Robson works with charities, research institutions, and public-impact teams to help them navigate complex challenges around privacy, accountability, and ethical data use, ensuring that data is not only compliant but genuinely trusted. We explore how organizations can move beyond “compliance theatre” to build systems that balance privacy, usefulness, and public value — especially when working with sensitive data in high-stakes environments. Key Highlights: Responsible Data in Practice: What ethical and lawful data use actually looks like inside organizations. Beyond Compliance Theatre: Why checkbox compliance fails — and what to do instead. Data Sharing & Trust: How to safely enable collaboration while protecting sensitive information. AI & Accountability: How AI is changing the landscape of governance, risk, and decision-making. Leadership & Decision-Making: Communicating risk and building trust across technical and non-technical teams. 🎧 Tune in to Episode 65 to learn how to build trustworthy data systems and responsible AI practices that stand up in the real world. Be sure to mark your calendars for the annual ALD NYC on Dec 11, where we will focus on THE FUTURE OF APPLIED AI IN Finance and Banking. Join us to hear from experts on how AI is shaping the future of finance, banking and insurance: https://ailovesdata.com/newyork/ Use code ALDPODCASTNYC to get 25% off your registration.  Writer and Host: Anna Anisin Produced, edited, and mixed by the Formulatedby Team

Joyjit Roy shares his experience leading large-scale initiatives across insurance and eCommerce, including multi-year modernization programs that integrate AI into core enterprise systems. We explore what it takes to move from legacy platforms to intelligent, scalable systems that can support real-time decisioning, MLOps, and next-generation automation. Key Highlights: Enterprise AI Modernization: How to integrate AI/ML into legacy systems while maintaining execution speed, governance, and business alignment. Cloud-Native Transformation: Lessons from building scalable, event-driven architectures that improve agility, observability, and resilience. MLOps in Practice: Insights into feature engineering, inference workflows, and operationalizing AI for real-world enterprise use cases. Agentic AI in the Enterprise: Joyjit’s perspective on how agentic systems can enable adaptive decisioning, contextual workflows, and more intelligent automation at scale. Leading at Scale: Strategies for aligning large, cross-functional teams across product, architecture, engineering, and business stakeholders. 🎧 Tune in to Episode 65 to hear Joyjit Roy’s insights on bridging strategy, delivery, and AI innovation to drive measurable enterprise impact. Be sure to mark your calendars for the annual ALD NYC on May 13, where we will focus on THE FUTURE OF APPLIED AI IN Finance and Banking. Join us to hear from experts on how AI is shaping the future of finance, banking and insurance: https://ailovesdata.com/newyork/ Use code ALDPODCASTNYC to get 25% off your registration Writer and Host: Anna Anisin Produced, edited, and mixed by the Formulatedby Team

Key Highlights: Production-Scale LLMs: Deploying and scaling recommender systems powered by large language models. AI Infrastructure Challenges: Building reliable, high-performance search and ML platforms at global scale. MLOps & Model Optimization: Lessons learned from optimizing and monitoring complex AI pipelines. Future of AI at Scale: Trends in GenAI, multimodal AI, and recommender systems that will shape the industry. 🎧 Tune in to Episode 63 to hear Rahul Raja’s insights on creating scalable, reliable, and cutting-edge AI infrastructure for production-grade systems. Be sure to mark your calendars for the annual ALD NYC on May 13, where we will focus on THE FUTURE OF APPLIED AI IN Finance and Banking. Join us to hear from experts on how AI is shaping the future of finance, banking and insurance: https://ailovesdata.com/newyork/  Writer and Host: Anna Anisin Produced, edited, and mixed by the Formulatedby Team

Vaishali shares her experience leading global data teams, partnering with executive leadership, and building strategies that connect cutting-edge technology to real business value. We explore her insights on operationalizing AI, scaling analytics across enterprises, and overcoming challenges in data governance, stakeholder alignment, and innovation adoption. Key Highlights: Bridging Tech and Business: How Vaishali connects AI and analytics innovations to organizational strategy and measurable outcomes.Global Team Leadership: Lessons from managing cross-functional, geographically distributed teams and driving collaboration.Operational Optimization: Examples of initiatives that reduced operational complexity while improving efficiency.Scaling Analytics and AI: Best practices for governance, workflow, and embedding AI into enterprise decision-making.Emerging Trends: Vaishali’s perspective on the next wave of AI, analytics, and enterprise data strategies.Tune in to Episode 61 to learn how Vaishali Lambe drives data-driven transformation, operational excellence, and AI innovation across global enterprises. Be sure to mark your calendars for the 10th annual ALD NYC on May 13, where we will focus on GENAI AND INTELLIGENT AGENTS IN THE FINANCE AND BANKING. Join us to hear from experts on how AI is shaping the future of the enterprise. https://www.datascience.salon/new-york/ Writer and Host: Anna Anisin Produced, edited, and mixed by the Formulatedby Team

Dushyanth shares his journey into AI, the challenges of building complex pipelines, and how to integrate responsible and ethical practices into machine learning workflows. Key Highlights: Scaling AI Systems: How to design and deploy pipelines that handle real-time inference, multimodal data, and production-level demands.Model Interpretability & Explainability: Strategies for making complex models understandable and accountable.Optimizing AI for Real-World Impact: Balancing performance, robustness, and human oversight in AI systems.Responsible AI Practices: Embedding ethics, fairness, and transparency in machine learning workflows.🎧 Tune in to Episode 61 to hear Dushyanth Sekhar’s insights on bridging technical innovation with responsible AI practices, and learn how to build AI systems that deliver both accuracy and real-world value. Be sure to mark your calendars for the 9th annual DSS ATX on Feb 18, where we will focus on GENAI AND INTELLIGENT AGENTS IN THE ENTERPRISE. Join us to hear from experts on how AI is shaping the future of the enterprise. https://www.datascience.salon/austin/ As Data Science Salon celebrates 10 years the community unveils a new brand: AI Loves Data Writer and Host: Anna Anisin Produced, edited, and mixed by the Formulatedby Team

In this episode of the Data Science Salon Podcast, we sit down with Carlos Aguilar, Head of Product at Hex and former founder of Hashboard, to discuss a topic critical for every data team: how to properly evaluate AI analytics tools. Carlos shares why traditional checklist-based evaluations fail for conversational AI and generative analytics tools, and how focusing on context, workflow, and real user testing can dramatically improve the chances of success. Drawing on his experience leading the Data Insights team at Flatiron Health, he provides practical guidance for both end-users and data teams. Key Highlights: End-User vs Data Team Evaluation: Why both perspectives are crucial for measuring AI effectiveness.Context Management: How setting up reference questions ensures accurate and relevant answers.Workflow & Observability: Why monitoring and iterating on AI outputs is essential for real-world success.Lessons from the Field: Examples of tools that look good in demos but fail in production—and how to avoid those pitfalls.🎧 Tune in to Episode 60 to learn how to evaluate AI analytics tools the right way and ensure your data teams deploy solutions that actually work in practice. Be sure to mark your calendars for the 9th annual DSS ATX on Feb 18, where we will focus on GENAI AND INTELLIGENT AGENTS IN THE ENTERPRISE. Join us to hear from experts on how AI is shaping the future of the enterprise. https://www.datascience.salon/austin/ Writer and Host: Anna Anisin Produced, edited, and mixed by the Formulatedby Team

Together, Leon and Oscar share how applied EDA practices remain the backbone of trustworthy analytics pipelines in both academic and industry settings. Their discussion highlights the challenges and lessons learned from building the EDA Toolkit, and why reproducible workflows are more important than ever in the age of AI and ML. Key Highlights: Reproducible EDA: How to standardize exploratory data analysis workflows for consistent and trustworthy insights.Open-Source Innovation: The design and impact of the EDA Toolkit, bridging research, healthcare, and education.Best Practices for Analytics: Lessons learned from creating tools that make EDA more intuitive and scalable across projects.The Future of Data Science Workflows: Why reproducibility and standardization matter in modern AI/ML pipelines.🎧 Tune in to Episode 59 to hear Leon Shpaner and Oscar Gil’s insights on building reproducible, reliable, and effective data science workflows, and how open-source tools can transform analytics practices across domains. Be sure to mark your calendars for the 9th annual DSS ATX on Feb 18, where we will focus on GENAI AND INTELLIGENT AGENTS IN THE ENTERPRISE. Join us to hear from experts on how AI is shaping the future of the enterprise. https://www.datascience.salon/austin/ Writer and Host: Anna Anisin Produced, edited, and mixed by the Formulatedby Team

Kelly discusses how ethical considerations influence machine learning, NLP, and data quality, and how organizations can integrate human-centered thinking into technical decision-making. They also share insights from their upcoming book, The Friendly Guide to Data Science, aimed at making the field accessible, ethical, and practical. Key Highlights: Ethical AI in Practice: How to incorporate ethics and human-centered principles into data science projects.Behavioral Economics & Decision-Making: How understanding human behavior informs AI and tech strategies.Making Data Science Accessible: Kelly’s approach to mentoring, writing, and teaching the next generation of data scientists.🎧 Tune in to Episode 58 to hear Kelly Vincent’s insights on ethical AI, data science, and technology for good. Be sure to mark your calendars for the 7th annual DSS NYC on Dec 11, where we will focus on THE FUTURE OF APPLIED AI IN Finance and Banking. Join us to hear from experts on how AI is shaping the future of the enterprise. https://www.datascience.salon/newyork/ Writer and Host: Anna Anisin Produced, edited, and mixed by the Formulatedby Team

Swarnendu De dives into frameworks like the SaaS Product Success Strategy™ and TechBlueprint Architecture Framework™, and shares insights on turning product ideas into execution-ready roadmaps, designing modular and scalable systems, and creating AI-powered business automation. Key Highlights: Building Scalable SaaS & AI Products: Learn Swarnendu’s approach to designing modular, cloud-native systems that meet investor expectations and real-world demands.AI for Business Automation: Discover how AI can be integrated where it drives real value—automating workflows, improving decision-making, and enhancing product experiences.Mentoring & Leadership: Insights into coaching tech leaders and founders, aligning technology with business goals, and accelerating team performance.From Vision to Execution: Explore Swarnendu’s process for taking a concept through rapid workshops, validation, and execution-ready roadmaps.🎧 Tune in to Episode 57  to hear how Swarnendu De is turning ideas into high-performing, scalable software products, and creating frameworks and strategies that empower teams, founders, and enterprises alike. Be sure to mark your calendars for the 3rd annual DSS NYC on Dec 11, where we will focus on GENAI AND INTELLIGENT AGENTS IN FINANCE & BANKING. Join us to hear from experts on how AI is shaping the future of the finance, banking and insurance. https://www.datascience.salon/newyork/ Writer and Host: Anna Anisin Produced, edited, and mixed by the Formulatedby Team

In this episode of the Data Science Salon Podcast, we sit down with Nitin Kumar, Director of Data Science at Marriott International, to discuss how AI agents are transforming customer support. Nitin shares his experience designing enterprise-scale AI and Generative AI solutions across 30 global brands, creating intelligent, proactive, and human-centered customer care systems. He dives into AI-powered pipelines that monitor incoming emails, analyze sentiment and issues, and draft contextual responses for human agents to review. Beyond individual cases, these systems continuously feed real-time trend data, helping teams identify emerging issues before they become widespread. Key Highlights: -AI Agents in Customer Support: Learn how AI agents automate routine processes while empowering human agents to focus on complex interactions. -Human-in-the-Loop Design: Explore strategies for balancing AI efficiency with human oversight and empathy. -Scaling AI Solutions: Insights into LLM integration, prompt engineering, and global deployment of enterprise AI systems. 🎧 Tune in to Episode 56 to hear how Nitin Kumar is revolutionizing customer support with AI agents and Generative AI to create more efficient, empathetic, and scalable systems. Be sure to mark your calendars for the 3rd annual DSS SF on Nov 6 [FREE!] thanks to our amazing sponsors, where we will focus on GENAI AND INTELLIGENT AGENTS IN THE ENTERPRISE. Join us to hear from experts on how AI is shaping the future of the enterprise. https://www.datascience.salon/san-francisco/ Writer and Host: Anna Anisin Produced, edited, and mixed by the Formulatedby Team

In this episode of the Data Science Salon Podcast, we sit down with Swati Tyagi, an AI/ML expert and responsible AI advocate. With deep expertise in large language models (LLMs), generative AI, and AI automation, Swati has led AI-driven innovation in FinTech, healthcare, and finance, helping organizations build scalable, ethical AI systems. Currently at JPMorgan Chase, Swati’s work focuses on automating financial applications and leveraging LLMs for real-time inferencing. Her passion for responsible AI is central to her approach, ensuring that AI systems are not only powerful but also ethical and scalable. Key Highlights: -AI and FinTech: Swati discusses her work in credit risk and predictive modeling to optimize financial decision-making and risk assessment. -Responsible AI: Insight into how to design AI systems that are both scalable and ethical, addressing challenges around bias and NLP. -AI Automation in Finance: Learn how LLMs are transforming FinTech, and how MLOps and cloud solutions play a role in scaling these applications. 🎧 Tune in to Episode 55 to hear Swati’s insights on the future of AI/ML, responsible AI, and how AI is reshaping industries like finance and healthcare. Be sure to mark your calendars for DSS SF on Nov 6, where we will focus on GENAI AND INTELLIGENT AGENTS IN THE ENTERPRISE. Join us to hear from experts on how AI is shaping the future of the enterprise. FREE COMMUNITY EVENT (space is limited) https://luma.com/dsssf Writer and Host: Anna Anisin Produced, edited, and mixed by the Formulatedby Team