Talking AI

How can high-reliability organizations transform their data from flat, 2D spreadsheets into a dynamic, 3D world of interconnected insights? In the Season 4 premiere of the Talking AI podcast, host Matt Paige dives deep with Jon Brewton, Founder & CEO at data², and Daniel Bukowski, CTO at data², to explore how leveraging knowledge graphs and AI is revolutionizing data transparency and explainability.Jon and Daniel share their journey from traditional analytics to building a modular, cloud-agnostic, zero-trust AI platform that empowers industries such as defense, intelligence, energy, finance, and healthcare. They break down the core concepts of graph databases versus relational systems, discuss how graphs provide the connective tissue needed to support AI-driven decision making, and reveal how integrating LLMs with structured graph data can overcome challenges like hallucinations and unreliable outputs.Learn how data² is transforming complex data into contextualized, three-dimensional insights that not only answer hard questions but also elevate the work of analysts and decision-makers. Whether you’re curious about how to improve data reliability or interested in the future of AI-augmented workflows, this episode is packed with practical insights and forward-thinking strategies.Key Moments:The data² Journey: From Flat Data to GraphsDemystifying Knowledge Graphs & Their ValueGraphs vs. Relational Databases: What’s the Difference?Integrating LLMs with Graph Data for Better AccuracyTransforming 2D Data into 3D InsightsReal-World Use Cases in High-Reliability IndustriesEmpowering Teams with Transparent, Explainable AIThe Future of AI-Driven AnalyticsKey Links:Data²Connect with Jon Brewton on LinkedInConnect with Daniel Bukowski on LinkedInMentioned in this episode:AI Opportunity FinderFeeling overwhelmed by all the AI noise out there? The AI Opportunity Finder from HatchWorks cuts through the hype and gives you a clear starting point. In less than 5 minutes, you’ll get tailored, high-impact AI use cases specific to your business—scored by ROI so you know exactly where to start. Whether you're looking to cut costs, automate tasks, or grow faster, this free tool gives you a personalized roadmap built for action. 👉 Try it now at https://hatchworks.com/ai-opportunity-finder/

Don't want to get behind an AI? You're in luck! Season four of the Talking AI podcast is almost here, and we're gearing up to bring you even more in-depth conversations with the pioneers shaping our future. This season, prepare to dive into the minds of AI startup founders, enterprise AI leaders, and some of the top voices in the field.What to Expect:Expert Insights: Hear from the innovators and early adopters who are redefining what’s possible with AI.Bonus Episodes: Enjoy special bonus content featuring our CTO, Omar Shanti, who will deep dive into specific topics, tools, and approaches that are revolutionizing the industry.Diverse Perspectives: Whether you’re an AI aficionado or just starting to explore the landscape, our lineup of guests will offer valuable insights tailored to every level of interest.While you wait for season four to drop, be sure to subscribe on your favorite podcast platform so you never miss an episode. Seriously, go do it!Key Links:Talking AIGet ready to talk some AI—Season 4 is coming soon!

How can organizations ensure that their data pipelines deliver consistent, trustworthy insights without frequent disruptions? In this episode of the Talking AI podcast, host Matt Paige co-host Omar Shanti, CTO of HatchWorks AI, speak with Andrew Jones, Principal Engineer at GoCardless, about the concept and practice of data contracts. Andrew explains what data contracts are, why they emerged, and how they improve data reliability and collaboration.The discussion explores how data contracts relate to data mesh and platform thinking, helping teams share responsibility for data while maintaining clear standards. Andrew and Omar share examples of data contracts addressing issues like schema changes, governance, and data quality.They also consider how data contracts support AI initiatives by ensuring that the data feeding into models is accurate and reliable. Practical steps are given, including starting with a clear problem and involving the right people.Andrew provides resources for deeper learning, including his book and online materials. Matt and Omar also mention an upcoming webinar on data contracts, inviting listeners to learn more and move toward more structured, dependable data practices.Key Moments:Introduction to data contracts and their purposeHighlighting the common reliability issues that data contracts can preventLinking data contracts to data mesh and other modern data architecturesExploring how data contracts help maintain governance and complianceDemonstrating how data contracts ensure quality for AI-driven applicationsDiscussing platform thinking and its role in scaling data contract implementationsOffering practical advice on how to begin using data contracts in your organizationKey Links:Connect with Andrew on LinkedInConnect with Omar on LinkedInMentioned in this episode:AI Opportunity FinderFeeling overwhelmed by all the AI noise out there? The AI Opportunity Finder from HatchWorks cuts through the hype and gives you a clear starting point. In less than 5 minutes, you’ll get tailored, high-impact AI use cases specific to your business—scored by ROI so you know exactly where to start. Whether you're looking to cut costs, automate tasks, or grow faster, this free tool gives you a personalized roadmap built for action. 👉 Try it now at https://hatchworks.com/ai-opportunity-finder/

How is the growing world of AI agents reshaping enterprise systems? In this episode of Talking AI, host Matt Paige is joined by Praveen Gunasekaran, Senior Director and Chief Architect at Visa, and Omar Shanti, CTO of HatchWorks, to explore the possibilities of agentic systems and their impact on software development.Together, they look at the power of agentic systems—technologies that can autonomously make decisions and execute changes in business processes without human intervention. This shift from deterministic to probabilistic systems opens up new opportunities for software developers to create more dynamic and responsive products.One of the key points in this episode is that agentic systems could help to democratize access to tech, enabling people with minimal coding experience to harness more advanced tools. But with that comes the question of governance. Omar talks about ensuring accountability and accuracy while preventing potential risks associated with AI agents. Curious about how AI agents are changing the landscape of automation and real-time systems? Tune in to discover the future of multi-agent systems, real-time processing, and AI-driven innovation with Praveen Gunasekaran and Omar Shanti!Key moments:How Praveen defines agentic systems and thinking How the agent model could lead to greater democratization of technologyWhy governance is essential for ensuring accuracy How measures like "AI judges" and breaking tasks into manageable parts could be the key How “an agent mesh” could become a new architecture for AI systemsAre APIs still the standard that the industry will follow?Praveen’s advice for making decisions on architecture and how agentic systems might change thatKey links: Connect with Praveen on LinkedInMentioned in this episode:AI Opportunity FinderFeeling overwhelmed by all the AI noise out there? The AI Opportunity Finder from HatchWorks cuts through the hype and gives you a clear starting point. In less than 5 minutes, you’ll get tailored, high-impact AI use cases specific to your business—scored by ROI so you know exactly where to start. Whether you're looking to cut costs, automate tasks, or grow faster, this free tool gives you a personalized roadmap built for action. 👉 Try it now at https://hatchworks.com/ai-opportunity-finder/

Could AI evolve from its position as a handy assistant to a fully-fledged teammate in the workplace? Patrick Lynch, PhD, AI Faculty Lead at Hult International Business School, and Omar Shanti, CTO of HatchWorks, join host Matt Paige to explore the AI industrial revolution we’re seeing and how human-AI collaboration could drastically affect the way teams collaborate. Patrick takes us through the "four faces of AI" - mechanical, analytical, intuitive, and empathetic - highlighting the progression of AI maturity and what it could mean for the future of work. This framework provides a valuable lens through which leaders can understand and leverage AI in the future.The episode also touches on cutting-edge AI applications, such as Hume AI's voice models, which can detect and respond to human emotions, opening up new possibilities for AI-human interaction in various fields, from customer service to therapy and education. Curious about how AI is becoming your new co-worker? Listen to Talking AI, where we explore how AI is turning into a real teammate at work. Subscribe on your favorite podcast platform and join us as we discuss how this technology is changing our future!Key moments:Why we’re seeing an AI industrial revolutionHow we can start thinking of AI as co-workers The four faces of AIA demonstration of Hume AIHow AI is starting to recognize emotionPotential use cases for AI co-workersThe challenge of coding charisma into AIHow adoption of AI could be affected by inaccurate resultsWhich cultures are best suited to using AIKey links: Visit Patrick’s websiteTEDx TalksConnect with Patrick on LinkedInConnect with Omar on LinkedInMentioned in this episode:AI Opportunity FinderFeeling overwhelmed by all the AI noise out there? The AI Opportunity Finder from HatchWorks cuts through the hype and gives you a clear starting point. In less than 5 minutes, you’ll get tailored, high-impact AI use cases specific to your business—scored by ROI so you know exactly where to start. Whether you're looking to cut costs, automate tasks, or grow faster, this free tool gives you a personalized roadmap built for action. 👉 Try it now at https://hatchworks.com/ai-opportunity-finder/

Change is often met with resistance, but Neil Goodrich, Chief Innovation Officer at Envista Forensics, challenges us to rethink our approach to innovation in this episode of Talking AI. Together with host Matt Paige, Neil explores where new AI tools can help to lead innovation and how small incremental changes can drive larger transformative projects. We look at how AI can boost efficiency at different stages of an innovation process, from creating faster prototypes to democratizing access to data insights across teams. Matt and Neil swap ideas on how AI could impact things like customer feedback, everyday work, job displacement, and education (for better or worse). A key takeaway point from this episode is the importance of adaptability – a skill that we will all need as tech like AI continues to evolve rapidly. Key moments:How AI can be involved in innovation projects big or small Neil provides the backstory to the DePaul innovation labsWill AI become an everyday tool like email is today?How AI could change different elements of an innovation processWhat excites and concerns Neil about the future of AIHow AI could impact jobs and educationKey links: Envista ForensicsConnect with Neil on LinkedInMentioned in this episode:AI Opportunity FinderFeeling overwhelmed by all the AI noise out there? The AI Opportunity Finder from HatchWorks cuts through the hype and gives you a clear starting point. In less than 5 minutes, you’ll get tailored, high-impact AI use cases specific to your business—scored by ROI so you know exactly where to start. Whether you're looking to cut costs, automate tasks, or grow faster, this free tool gives you a personalized roadmap built for action. 👉 Try it now at https://hatchworks.com/ai-opportunity-finder/

Generative AI may feel like the new kid on the block, but it's not as different from traditional machine learning as you might think.In this episode of Talking AI, we’re joined by Simba Khadder, Co-Founder & CEO of Featureform, and Omar Shanti, CTO of Hatchworks AI, to discuss how generative AI builds upon established ML principles and infrastructure. We explore the misconception that it exists as its own little island, when in reality, we see a clear continuity between classic ML and newer AI technologies.Simba and Omar break down the unique aspects of GenAI, such as prompting and large language models (LLMs), while also highlighting the shared foundations with traditional ML. They also touch on the evolution of retrieval augmented generation (RAG), the ML vs. GenAI lifecycle, and what MLOps teams should consider when it comes to driving value in a business.Want to stay ahead in the fast-moving world of AI? Tune in to Talking AI where we break down the latest trends and tools in Generative AI, MLOps, and more. Subscribe now, and don't miss out on practical insights from industry experts. Let’s transform your data into real business impact!Key moments:The similarities and differences between generative AI and traditional machine learningHow prompts differentiate LLMs from earlier transformer modelsThe challenges of implementing AI in production versus creating pilot projectsHow RAG has evolved and what it could mean for the future The importance of data as the core foundation for both traditional ML and generative AIHow feature engineering in ML relates to embeddings in LLMsHow an ML lifecycle differs from a GenAI oneKey links: Featureform Connect with Simba on LinkedInMentioned in this episode:AI Opportunity FinderFeeling overwhelmed by all the AI noise out there? The AI Opportunity Finder from HatchWorks cuts through the hype and gives you a clear starting point. In less than 5 minutes, you’ll get tailored, high-impact AI use cases specific to your business—scored by ROI so you know exactly where to start. Whether you're looking to cut costs, automate tasks, or grow faster, this free tool gives you a personalized roadmap built for action. 👉 Try it now at https://hatchworks.com/ai-opportunity-finder/

Does generative AI have a place in the highly regulated world of life sciences, and can it produce value without compromising patient safety?In this episode of the Talking AI podcast, host Matt Paige sits down with Matt Lewis, Global Chief AI Officer at Inizio Medical, to explore the delicate balance between innovation and risk in healthcare AI.Matt brings a wealth of experience from his role at Inizio Medical, a powerhouse in the life sciences industry that balances scientific knowledge with cutting-edge technology like AI to transform medical communication and content. He shares invaluable insights on how AI is transforming pharmaceutical research, biotech advancements, and medical device development. Matt reveals Inizio’s strategy for prioritizing AI use cases, explaining why there's no one-size-fits-all approach in such a complex field. He also discusses how AI can save time in creating patient summaries and why personalization and custom AI tools are needed to produce complex content. Key moments:Generative AI's potential in life sciences and the associated risksHow augmented intelligence can enhance decision-making and improve patient outcomesMatt’s dual role in improving internal operations and supporting clients in AI adoptionHow to identify and prioritize AI use cases unique to each organizationPractical applications of AI and how they can save time and improve understanding The limitations of off-the-shelf AI tools like ChatGPT and the need for tailored solutionsWhy personalization in AI is important to provide tailored content Key links: Inizio’s WebsiteConnect with Matt on LinkedInThe NeuronOpenToolsSuperhumanOne Useful ThingThere’s An AI For ThatRegulated industries white paper Conor Grennan (Chief AI Architect, NYU) and Matt Lewis co-authoredMentioned in this episode:AI Opportunity FinderFeeling overwhelmed by all the AI noise out there? The AI Opportunity Finder from HatchWorks cuts through the hype and gives you a clear starting point. In less than 5 minutes, you’ll get tailored, high-impact AI use cases specific to your business—scored by ROI so you know exactly where to start. Whether you're looking to cut costs, automate tasks, or grow faster, this free tool gives you a personalized roadmap built for action. 👉 Try it now at https://hatchworks.com/ai-opportunity-finder/

In this bonus episode of Talking AI, Omar Shanti, CTO of HatchWorks AI, breaks down how LangChain’s abstractions have both helped and hindered AI innovation.He explains why these tools, while useful for quick starts, can sometimes make simple tasks harder as projects scale. The conversation highlights where LangChain shines and where it falls short.Viewers will hear insights on why prompt engineering and tuning are more challenging than building agent tools. Omar shares how over-abstraction can cause issues when taking projects into production, leading many to rethink their toolkits.If you’re working with LangChain or similar abstraction tools, this episode gives you practical advice on avoiding common pitfalls and understanding when these abstractions might not serve your needs.Key Moments:Introduction to LangChain’s abstractionsWhat is abstraction?LangChain’s pros and consChallenges with LangChain in productionObservability and orchestrationHow much orchestration do you need?Final thoughts on using LangChainKey links: HatchWorks AIConnect with Omar on LinkedInMentioned in this episode:AI Opportunity FinderFeeling overwhelmed by all the AI noise out there? The AI Opportunity Finder from HatchWorks cuts through the hype and gives you a clear starting point. In less than 5 minutes, you’ll get tailored, high-impact AI use cases specific to your business—scored by ROI so you know exactly where to start. Whether you're looking to cut costs, automate tasks, or grow faster, this free tool gives you a personalized roadmap built for action. 👉 Try it now at https://hatchworks.com/ai-opportunity-finder/

Is AI-generated content really good enough to compete in the highly competitive world of search engine optimization? This was a question that Tim Hanson had early on in his AI journey, as many of us were experimenting with ChatGPT and other tools with varying results. His solution? Penfriend.ai – a tool that generates articles based on proven content strategies without the need for traditional prompting. As Co-Founder and Chief Creative Officer, Tim joins the Talking AI podcast to delve into the world of AI-powered content and how Penfriend avoids the dreaded “AI wrapper” label. We hear Tim’s journey of developing Penfriend, inspired by his background in SEO and experience constantly adapting to Google’s algorithm tweaks. He tells host Matt Paige about the top mistakes he’s seen marketers make when using AI, and how his solution is built to mimic the multiple complex decisions a human makes before writing an SEO article. Tim's approach demonstrates that successful AI integration goes far beyond simple prompts, involving complex workflows, proprietary data, and a nuanced understanding of the target audience. Key moments:Creating value with an AI solutionThe importance of effective prompting and not overloading AI with too many tasksComparison of human decision-making in blog creation vs. using a single AI promptPenfriend’s approach to using different AI models for specific tasksThe non-linear process of keyword research and title selection in PenfriendHow Penfriend uses different prompts for various blog sectionsDiscussion on how AI is affecting SEO practicesThe biggest challenges and lessons learned when creating PenfriendWhat Tim’s most excited about in the future of AI content creationKey links: PenfriendConnect with Tim on LinkedInMentioned in this episode:AI Opportunity FinderFeeling overwhelmed by all the AI noise out there? The AI Opportunity Finder from HatchWorks cuts through the hype and gives you a clear starting point. In less than 5 minutes, you’ll get tailored, high-impact AI use cases specific to your business—scored by ROI so you know exactly where to start. Whether you're looking to cut costs, automate tasks, or grow faster, this free tool gives you a personalized roadmap built for action. 👉 Try it now at https://hatchworks.com/ai-opportunity-finder/