AI Realized Podcast

Jason Williamson shows how to squeeze real intelligence from tiny compute. Hear how MythWorks delivers deterministic, verifiable reasoning on the edge, personalizes assistants to the job, and runs agents like a workforce with owners, logs, and KPIs. Learn the efficiency math that beats GPU sprawl, why managed autonomy keeps humans in charge, and how to measure lift in time to decision, cost per inference, and energy per task. This is AI you can run, trust, and afford.

Harvard’s John Sviokla delivers a sharp wake-up call: most enterprises are giving away their secret sauce—without even knowing it. In this episode, he unpacks how cognitive capital is becoming the new basis of competition, why hybrid AI-native firms are pulling ahead, and what it means to truly “own your intelligence.” If your strategic plan doesn’t include protecting your operational know-how, you may already be behind.

Consultant and AI council builder Bob Mitton shows how to turn generative AI into measurable impact. Hear how he personalizes work with targeted assistants, automates safely with human approvals, and treats agents like a real workforce with owners, logs, and KPIs. Learn the five-day Thumbprint sprint, why guardrails beat gates, and how to move from shadow AI to a governed system your CFO and CISO will trust.

Staffan Truvé explains how to turn AI from a liability into a control system. Learn how his teams personalize defense with outside-in intelligence, automate response with managed autonomy, and anchor every decision to hard KPIs like time to patch and risk reduced. Hear what to lock down, what to test, and how to keep humans in charge while agents do the work.

Lenin Gali shows how to move service from portals to people, with AI agents that resolve issues in seconds, not days. Learn how to personalize help inside Slack and Teams, automate smart handoffs with human approvals, and hit KPIs like ticket deflection, time to access, and cost to serve while keeping security and governance tight. 

Venture partner Shomit Ghose reveals how to scale agentic AI without losing control. Learn where to start, how to guardrail automation, and which KPIs prove real value while your humans stay in charge.

Nitin Gupta, Global Product Manager of AI at Mondelēz International, shows how computer vision, agent based workflows, and firm governance turn pilots into real gains on the shelf. Hear how his teams personalize store execution in near real time, automate smart handoffs with human in the loop controls, and hit KPIs like on shelf availability, forecast accuracy, and waste reduction without losing trust. 

Investor and fractional executive Tallulah Le Merle shows leaders how to cut through the AI hype, pick winning use cases, and deploy agent based systems that lift KPIs without breaking trust. Strategy first, champions engaged, results you can measure.

Fractional CMO Doug Bell reveals how everyday AI fixed bad lists, boring messaging, and slow tests. Learn the Cannonball GTM method that finds real buyer pain, ships permissionless value propositions, and turns RevOps into a growth engine.

Generative AI is great until it goes wrong. Eric Siegel, CEO of Gooder AI and author of The AI Playbook, shows how predictive models become the guardrails that make AI agents safe, profitable, and deployable. Learn how to rank risk, route the right 15 percent to humans, and turn proofs of concept into production wins.

What happens when an MIT engineer, an AI model, and a Renaissance painting meet? In this episode of AI Realized, we explore the bold future of art restoration with Alex Kashkin, whose generative AI breakthrough is bringing damaged artwork back to life. By combining semiconductor precision with neural networks, Alex is helping museums, conservators, and collectors restore pieces that have been lost to time. The result? A new industry is being born at the crossroads of culture and computation.

Inside the bold agent-based AI strategy transforming Fandom’s operations, ad model, and global fan experience. Hear how one CTO is orchestrating trust, scale, and impact through next-gen infrastructure.

Plotly's Domenic Ravita, unveils a breakthrough in enterprise analytics: AI-native data visualization that turns messy datasets into insight-ready narratives. Learn how "Vibe Analytics" is helping teams get to better questions—and answers—faster.

Today’s enterprises aren’t just dealing with data — they’re preparing for a future shaped by intelligent agents, AI workflows, and secure multi-agent systems. In this episode of AI Realized, we unpack what it really takes to operationalize AI at scale, from rethinking infrastructure and data governance to managing hallucinations and securing agent-based architectures. If you’re leading AI deployment in your org, this is the episode you can’t afford to miss.

In our latest AI Realized episode, Kevin Petrie, VP of Research at BARC, explains why AI projects succeed or fail based on governance, data quality, and trust. From human-in-the-loop agents to the power of RAG, this episode is packed with real-world frameworks for enterprise leaders. If you're serious about AI deployment, don’t miss this one.

In this bold new episode of AI Realized, Yogita Parulekar, Founder & CEO of InvaGrid, reveals how her frustration with reactive security led her to pioneer secure-by-design infrastructure for AI deployments. From AI hallucinations to output risk, she shares the governance-first playbook every enterprise needs. 

AI Realized explores the evolution from automation to agent-based AI systems with guest Ashish Bhatia. The episode unpacks why deep reasoning and memory context are critical for managing complex edge cases, outlines a three-phase roadmap from human-assisted tools to fully autonomous digital employees, and examines the breakthroughs required for safe full automation. Listeners will discover why context engineering is set to surpass prompt engineering, and how curiosity, experimentation, and precise evaluation metrics are key to building effective AI systems. 00:00 Introduction to AI Realized00:42 From Automation to Gentrification02:12 The Future of Autonomous Agents06:11 Technological Breakthroughs Needed09:19 Model Routing and Enterprise Impact14:25 Rapid Fire Round16:47 Leadership in AI17:33 Conclusion and Farewell

In this episode of AI Realized, hosted by Christina Ellwood, guest Al Shanmugam, Head of Product at EchoStar, discusses the impact of AI on enterprise executives, focusing on AI deployment in streaming services like Dish Network and Sling TV. Shanmugam elaborates on AI's role in personalization, automation, and agent AI, describing how EchoStar uses AI for streaming challenges, customer recommendations, and ad placements. He also touches on the importance of having a strong, centralized AI committee within the enterprise to manage AI strategies and the significance of setting KPIs to measure AI’s effectiveness. Further, Shanmugam explains the benefits of using large language models (LLMs) and retrieval-augmented generation (RAG) for tailored customer experiences, emphasizing a future where AI-driven goals will be pivotal in business strategies. The discussion wraps up with advice for other product and marketing executives to embrace AI for enhancing customer experience and operational efficiency.

In this episode of AI Realized, Tim Crawford, founder and CIO of Avoa, joins the show to unpack the realities of enterprise AI deployment. Tim dives into the rise of AI agent sprawl, clears up common misconceptions between chatbots and AI agents, and underscores the critical role of governance frameworks in managing AI at scale. He explores the cultural, technical, and strategic shifts required for organizations to adopt agentic AI effectively—and shares why the next 1–3 years will be pivotal in shaping how humans and AI agents collaborate. From orchestration layers to digital agents, this conversation offers a forward-looking roadmap for enterprise leaders navigating the agentic era.00:00 Introduction to AI Realized Podcast00:47 Meet Tim Crawford: Influential CIO and AI Expert01:53 The Rise of AI Agents04:20 AI Agent Sprawl: Challenges and Solutions08:17 Governance Frameworks for AI Agents10:33 Misconceptions About AI Agents13:49 Future of Human-AI Agent Interaction18:22 Conclusion and Future Events

 In this episode of AI Realized, we dive into the future of AI-powered enterprise operations with Matthew Swanson, CEO of Motion Enterprises. Swanson shares how his company is redefining customer lifecycle management by deploying AI agents as conversational interfaces, replacing the need for traditional, click-heavy software. These agents sit on top of platforms like Salesforce, automating workflows and delivering real business outcomes—measured and monetized through a performance-based model. Tune in to hear Swanson’s take on where AI agents are heading next and how enterprise leaders can start integrating them into their operations today. 00:00 Introduction to AI Realized Podcast01:00 Meet Matthew Swanson of Motion Enterprises01:38 The Role of Agents in Customer Lifecycle Management02:37 Transforming User Interfaces with Conversational Agents04:07 Integrating Agents with Existing Systems07:01 Training and Implementing Agents09:31 Outcome-Based Business Models11:31 Challenges and Opportunities with Agents12:23 Key Takeaways and Conclusion

In this episode of AI Realized, hosts Christina Ellwood and David Yakovich interview Kris Bondi, CEO of Mimoto. The discussion revolves around the challenges and opportunities in AI-driven security. Kris explains how AI can exacerbate security issues by enabling bad actors to innovate and automate attacks. He also highlights the benefits of AI in security, such as anomaly detection and advanced pattern matching. They discuss the use of AI for internal security measures, including red and blue team simulations, and the identification of deep fakes. Kris shares insights into Mi Moto's unique approach to security, which uses AI and machine learning to create a 'digital double' for more precise identification and anomaly detection. The episode concludes with advice for executives on innovating their security posture and ensuring adaptive, real-time security measures.00:00 Introduction to AI Realized Podcast01:14 AI and Security Challenges02:52 Benefits of AI in Security08:12 Deep Fakes and Security Measures13:52 Inspiration Behind Mimoto19:01 Advice for Enterprise Security20:25 Conclusion and Thank You

In this episode of AI Realized, hosts Christina Ellwood and David Yakovich delve into the evolving intersection of data science and AI within enterprises. Joining them is Domenic RDA, VP of Marketing at Plotly, who discusses the transformative impact of AI on data science workflows and operational efficiency. Domenic highlights exciting developments in generative AI, the convergence of traditional and generative AI teams, and the use of open-source tools like Python and Plotly's Dash for creating flexible, custom data applications. He shares real-world examples from industries like financial services and pharmaceuticals, illustrating how companies are driving ROI through AI-enhanced data products and clinical trial operations. The conversation also explores the future of AI, the commoditization of large language models, and the growing importance of local AI for maintaining data privacy and security.00:00 Introduction to AI Realized Podcast01:16 Guest Introduction: Dominic RDA from Plotly01:42 AI and Data Science Integration03:28 Ensuring Trust in AI Results03:58 Generative AI in Data Science Workflows05:04 Plotly's Role in Data and AI10:37 Case Studies: Financial Services and Pharma24:49 The Future of AI in Enterprises29:23 Resources and Final Thoughts

In this episode of AI Realized, host Christina Ellwood talks with Randy Friedman, Chief Commercial Officer at Cognizer AI, about how AI is now being used to build AI. They explore the evolution from traditional machine learning models to agentic AI workflows, where AI agents autonomously create specialized models. The discussion covers AI’s impact on contract intelligence, cross-organizational collaboration, and the need for decentralized data governance. Learn how enterprises can leverage AI to optimize operations, enhance decision-making, and unlock new efficiencies in the data-driven economy.00:00 Introduction to AI Realized Podcast01:16 Guest Introduction: Randy Friedman from Cognizer AI01:36 Using AI to Build AI: Cognizer's Approach03:07 Challenges and Opportunities in AI Deployment04:58 Future of AI in Cross-Organizational Data Use07:44 Agentic Workflows and Real-Time Optimization12:00 New Data Infrastructure and Security Paradigms18:35 Business Needs Driving Technological Innovation24:37 Final Thoughts and Takeaways

In this episode, Maher Hanafi, VP of Engineering at BetterWorks, discusses the critical role of trust in AI adoption. He highlights the importance of gaining internal stakeholders' and customers' trust by understanding AI concepts and risks, creating transparent AI privacy policies, and implementing responsible AI frameworks. Maher addresses challenges specific to enterprise and HR technology, detailing the necessity of adapting to rapid AI advancements while maintaining compliance and governance. He also explores the future potential of agent-based AI systems and offers advice for executives on effectively deploying AI technologies, emphasizing the need for education, cross-disciplinary collaboration, and focusing on impactful implementations.Chapters:00:00 Introduction to AI Realized Podcast01:20 Guest Introduction: Maher Hanafi from BetterWorks01:30 Building Trust in AI06:03 Challenges in AI Adoption10:52 The Role of AI Agents17:12 Guidance for Executives on AI Deployment21:24 Final Thoughts and Conclusion

Steve Jones, Executive Vice President of Data Driven Business and Generative AI at Capgemini, discusses the challenges and opportunities of deploying AI in enterprises. He explains that moving from proof-of-concept to production is a significant hurdle, with the focus needed on operational maturity. Jones warns that while agentic systems can enhance processes, they require stringent governance to avoid risks. He highlights that AI should be perceived as a tool within the business, not just an IT backend, and predicts a substantial increase in production-level AI deployments by 2025. Additionally, Jones talks about potential use cases for agents, emphasizing the importance of decomposing problems to manage risks effectively. He recommends utilizing both traditional AI and LLMs where appropriate and stresses the need for continuous learning and adaptation.

In this episode of AI Realized, Allan McLennan, founder and chief executive of PADEM Media Group, discusses the impact of generative AI on the media and entertainment industry. He highlights efficiency improvements, potential economic impacts, and challenges such as layoffs and legal implications. Mclennan touches on the integration of AI in metadata management, which is critical for content authenticity and security. He also describes the collaborative effort at the IBC accelerator to identify and manage fake content using AI. Moreover, Mclennan forecasts advancements in personalized advertising through AI, allowing for more engaging and less intrusive ads. He stresses the importance of staying informed about AI developments and recommends resources like Paul Beyer's GAI Insights and Substack for those in the media and entertainment sector.Chapters:00:00 Introduction to AI Realized Podcast01:18 Guest Introduction: Allan Mclennan01:45 Impact of Generative AI on Media06:27 Metadata and Content Security08:50 Identifying Fake Content14:14 Personalization and Intelligent Advertising19:18 Staying Updated with AI Innovations20:37 Conclusion and Final Thoughts

Kenn So, Director of Corporate Development and Strategy at Smartsheet, discusses AI adoption trends and strategies. He highlights the importance of unique acquisition structures like licensing and acquisitions (L&A) to acquire talent and technology. Kenn talks about using AI tools to enhance productivity, especially among software engineers, and the need for companies to explore multiple areas for AI application. He advises enterprise executives to establish AI committees with diverse representation to champion AI adoption and manage risks. Executives should actively use AI tools to understand their benefits and model their use for others. Kenn also recommends learning from workshops, AI-focused law firms, and other executives to stay updated on the risks and benefits of AI deployment.00:00 Introduction to AI Realized Podcast01:03 Meet Ken: Director of Corporate Development at Smartsheet01:14 Ken's Journey into AI and His Newsletter02:17 Trends in AI Acquisitions04:29 AI at Smartsheet: Tools and Strategies06:16 Advice for Enterprise Executives on AI Adoption08:46 Resources for Learning and Risk Management12:29 Final Thoughts and Takeaways

Chris Butler, staff product operations manager at GitHub, discusses his role in enhancing product management effectiveness and driving innovation. He distinguishes between product managers, who focus on delivering innovation, and product operations managers, who streamline processes and ensure effective team dynamics. Chris introduces his unique approach as a 'chaotic good product manager,' advocating for challenging established norms to foster innovation and resilience. He highlights the importance of recognizing different stages of tech adoption—explore, expand, and extract—and tailoring strategies accordingly. Furthermore, Chris emphasizes the significance of leveraging AI as a thought partner in decision-making and process optimization. He envisions a future where tools like language models assist in cross-functional collaboration within development teams, promoting better understanding and efficiency. He urges organizations to create safe environments for experimenting with AI technologies, addressing the need for supportive IT, HR, and legal frameworks.00:00 Introduction to AI Realized Podcast01:04 Meet Chris Butler from GitHub01:18 Understanding Product and Product Operations Management03:18 The Concept of Chaotic Good Product Management04:43 Navigating Uncertainty in AI and Product Development08:36 Exploring the Future of AI in Product Management14:20 Leveraging LLMs for Better Decision Making19:44 The Future of AI for Development Teams at GitHub21:24 Final Thoughts and Takeaways

In this episode of AI Realized, Christina Ellwood interviews Sean White, CEO of Inflection AI, discussing the augmentation of human experience through AI and its implications for enterprises. Sean emphasizes AI as a tool to enhance human capabilities and compares AI's evolution to the early Internet era. He discusses Inflection AI’s competitive advantages, such as its large-scale data models and commitment to transparency and user control, including licensing options for enterprises. He highlights use cases in healthcare, regulatory compliance, and enterprise operations. The conversation also explores ethical considerations around AI and wearable technology, the importance of privacy, and the role of federated learning. Sean concludes by encouraging creative thinking about AI's potential and collaboration across the field.00:00 Introduction to AI Realized Podcast01:00 Meet Sean White, CEO of Inflection AI01:24 Augmenting Human Experiences with AI03:32 Inflection AI's Competitive Edge06:01 Differentiating from Other LLM Companies15:42 Ethical Considerations in AI and AR19:29 Resources and Final Thoughts

In this episode of AI Realized, Isar Meitis, CEO of Multiplai, outlines crucial steps for successfully implementing AI in organizations. He emphasizes the importance of continuous education and staying updated with AI capabilities, using tools like Google's Notebook LM. Isar recommends forming an AI committee with representatives from different departments and leadership to guide implementation, define rules, and ensure data privacy. The committee should encourage AI experimentation within set guidelines, define the proper tools, manage training, and create efficient processes. Long-term strategies involve conducting strategic assessments, skills gap analysis, and identifying low-hanging fruits for immediate benefits. To drive adoption and enthusiasm, leadership should lead by example, celebrate small wins, and foster human relationships to differentiate in a competitive AI-driven world. Finally, he advises not to fear AI and highlights the importance of hands-on experimentation and learning.00:00 Introduction to AI Realized Podcast01:05 Meet the Guest: Isar Meitis, CEO of Multiply01:16 Blueprint for Successful AI Implementation03:48 The Role and Structure of an AI Committee05:47 Defining Rules and Continuous Education11:25 Strategic and Tactical Assessments14:01 Driving AI Adoption and Creating Excitement16:52 Differentiating in an AI-Driven World19:05 Final Thoughts and Takeaways

In this podcast episode of AI Realized, Ivan Lee, founder and CEO of Datasaur, discusses his company's mission to democratize access to natural language processing (NLP). Datasaur introduced a data labeling platform used by major organizations like Netflix and the FBI and launched LLM Labs to facilitate the customization of large language models (LLMs). Ivan elaborates on the importance of evaluating AI models in production, focusing on cost, latency, and quality, and introduces the concept of prompt unit testing to ensure consistent model performance. He highlights the need for data scientists to understand business-side ROI and notes the significant unit costs associated with LLMs. Ivan explores the potential for cost reduction driven by innovations like OpenAI's GPT 4o Mini and open-source models like Lama. He also considers the implications of running LLMs on-device for industries with strict data privacy needs. Lastly, Ivan advises enterprise executives to start small, piloting AI solutions to demonstrate their effectiveness, and emphasizes the future of a multi-model AI solution landscape within organizations.00:00 Introduction to AI Realized Podcast01:04 Meet Ivan Lee, CEO of Datasaur01:18 Datasaur's Mission and Products02:31 Ensuring Quality in AI Models03:51 Calculating ROI for AI Use Cases06:41 Cost Reduction in AI07:47 Future of AI Deployment09:46 Advice for Enterprise Executives12:22 Key Takeaways and Conclusion

In this episode of AI Realized, Christina Ellwood interviews Matt Maccaux, the head of customer engineering at Google Cloud, to discuss the challenges and practical advice for AI adoption in enterprises and digital native companies. The conversation covers the ethical use of data, focusing on reducing bias and using synthetic data. Maccaux emphasizes the importance of understanding and improving existing data sets and discusses the conditions under which synthetic data is useful. He highlights barriers to AI deployment, such as lack of executive sign-off and budget constraints, and offers advice for enterprises to start with productivity use cases to build a business case. For digital natives, he notes their faster pace due to less technical debt but acknowledges budget constraints. Finally, Maccaux advises executives to engage with peers across different industries and to learn from both traditional enterprises and digital natives to balance innovation with practical challenges.00:00 Introduction to AI Realized Podcast01:19 Ethical Use of Data in AI03:46 Synthetic Data and Bias in AI06:43 Challenges in AI Deployment15:15 Guidance for Enterprise Executives16:49 Learning and Networking Resources18:19 Final Thoughts and Takeaways

The Next Wave of Enterprise AI: Local Inference and Data Privacy with Knapsack's Mark HeynenMark Heynen is the Co-Founder and Chief Product Officer of Knapsack, where he's building private AI automations for enterprise use. A serial entrepreneur with over 20 years of experience, Mark has consistently focused on expanding technology access across markets. His journey includes key roles at Google's early maps initiative, Facebook's mobile efforts, and co-founding PayJoy, a fintech company he helped expand to 20 countries. Prior to Knapsack, he served as VP of Partnerships at the Stellar Development Foundation, working on extending financial services through digital currencies.Episode Highlights:[01:13] Origins and Market Need for Private AI[06:52] Enterprise AI: From Large to Small[10:15] Data Security Risks in Enterprise AI[12:57] Hidden Costs of Enterprise AI Adoption[14:40] How Instant Private Automations Work Today[17:55] Local AI Benefits and Implementation Strategy[19:43] Getting Started with Private Enterprise AI[19:56] Balancing Innovation and Compliance in AIEpisode Links:  Knapsack: https://www.knapsack.ai/ Mark Heynen’s LinkedIn: https://www.linkedin.com/in/markheynen/ Mark Heynen’s Twitter: http://x.com/markheynen

Unintended Data Sharing: The Hidden Cost of Your AI Strategy with Paul Baier of GAI InsightsPaul Baier is the CEO and Co-Founder of GAI Insights, a leading industry analyst firm specializing in enterprise Generative AI. With over 20 years of experience in software leadership, Paul is passionate about helping companies achieve ROI through the use of AI technologies.As an Executive Fellow at Harvard Business School, Paul brings academic insight to practical business applications. He is also the organizer of AI Blueprint for MA, a volunteer initiative supporting AI talent development in Massachusetts.Episode Highlights:00:00 - AI Adoption Urgency for Enterprises01:24 - "Owning Intelligence" in the AI Age04:11 - Steps to Own Organizational Intelligence07:03 - Getting Started with AI Implementation09:31 - Structuring Organizations for AI Integration11:46 - Board's Role in AI Leadership14:42 - AI's Potential Beyond Process Optimization15:40 - Advice for Continuous AI Learning18:35 - Reorienting Mindsets for AI FutureEpisode Links:  GAI Insights Website: https://gaiinsights.com/ Own Your Own Intelligence: https://gaiinsights.com/own-your-own-intelligence MA AI Blueprint: https://ai-blueprint-ma.com/ Paul Baier’s LinkedIn: linkedin.com/in/paulbaier  Paul Baier’s Twitter: https://twitter.com/PaulBaier