Enterprise Wide Search

In this episode, co-hosts Emma McGrattan and Ole Olesen-Bagneux sit down with Ryan Duffy, U.S. Chief Data & Analytics Officer at BMO (Bank of Montreal), to explore what it takes to turn AI innovation into meaningful business outcomes in a highly regulated industry.Drawing on his experience leading data and analytics in financial services, Ryan shares a practical perspective on the evolving responsibilities of data leaders as organizations navigate AI transformation. For him, the Chief Data Officer role goes beyond managing data and enforcing governance: it's about helping the business make better decisions. As AI adoption accelerates, he argues that transformation ultimately depends on people, not just technology.Together, they explore:Why data leaders need to focus on better business decisions rather than data and technology for their own sakeHow to balance AI innovation with governance, regulatory requirements, and enterprise-wide consistencyWhy AI transformation is fundamentally a people transformation, not simply a technology rolloutWhy adopting the latest AI technology isn't always the best way to solve a business problemWhy AI should sharpen human thinking and judgment rather than replace themThey also discuss the challenges of balancing centralized governance with federated execution, the risks of chasing new technologies without clear business objectives, and how AI is reshaping the skills and responsibilities of the next generation of data professionals.🎧 Tune in for a conversation on the realities of leading AI transformation in banking, making technology work for the business, and keeping people at the center of change.Hosted on Ausha. See ausha.co/privacy-policy for more information.

In this episode, co-hosts Emma McGrattan and Ole Olesen-Bagneux sit down with Dia Adams, Chief Data & AI Officer, former Principal Data Consultant at the White House, and Board Chair of The AI Table, to explore how AI policy is evolving in the United States and what it means for enterprises.Drawing on her government experience and work with Fortune 500 AI executives, Dia brings a unique perspective to a technology evolving faster than the policies meant to govern it. As organizations accelerate AI adoption and regulators struggle to keep pace, she argues that effective governance requires a better understanding of how AI is actually being implemented across industries.Together, they explore:Why AI is creating an unprecedented moment for US policymakersThe challenges of navigating a fragmented regulatory landscape without a comprehensive national AI frameworkWhy enterprise practitioners need a stronger voice in shaping responsible AI policiesHow AI is reshaping the responsibilities of data professionals and the leadership role of Chief Data OfficersWhat the next generation of proactive, agentic AI could mean for enterprises and societyThey also discuss The AI Table's role in connecting enterprise experience with policymaking, the tension between innovation and regulation, and why Dia remains a pragmatic optimist about AI's future.🎧 Tune in for a conversation on the realities of US AI policymaking, the responsibilities of enterprise data leaders, and how to balance innovation, accountability, and responsible AI adoption.Hosted on Ausha. See ausha.co/privacy-policy for more information.

In this episode, Ole Olesen-Bagneux sits down with Vanessa Andersson, Senior Data Engineer and founder of Data & AI Stockholm, to explore what the rapid evolution of AI looks like from the perspective of a practitioner working directly with it.With a background that spans marketing, management, programming, and data engineering, Vanessa brings a grounded perspective to an industry moving at extraordinary speed. As organizations move beyond their first AI experiments, she sees the conversation shifting from what AI can do to harder questions around reliability, governance, responsible use, and how the roles of data professionals themselves are changing.Together, they explore:Why organizations are moving from asking “can we build it?” to “can we make it useful and reliable?”What the rise of AI agents means for data engineers and other data professionalsWhy predictions about AI replacing entire professions may overlook how work actually evolvesHow organizations can embrace AI without ignoring bias, privacy, security, and responsible useWhy practitioners need spaces to discuss real-world challenges without the sales pitchThey also discuss Stockholm's growing data and AI ecosystem, why sharing challenges can be as valuable as showcasing successes, and what it takes to protect a practitioner community's independence as it grows.🎧 Tune in for a conversation on how data work is changing in practice and the value of communities where practitioners can learn from one another.Hosted on Ausha. See ausha.co/privacy-policy for more information.

In this episode, co-hosts Emma McGrattan and Ole Olesen-Bagneux sit down with Ben Mathew, General Manager of Business Intelligence at Actian and former GM of Jaspersoft, to explore how business intelligence is evolving in the age of AI.With more than 20 years in enterprise software and two decades of experience in the BI space, Ben looks back at how business intelligence evolved from static reporting to analytics. And why, despite decades of investment, the promise of truly self-service BI has remained largely unfulfilled.Together, they explore:How BI evolved from hindsight to insight, and increasingly, foresightWhy self-service BI has struggled to deliver on its original promiseHow natural language and AI could finally make analytics accessible to a much broader audienceWhy probabilistic AI cannot replace the deterministic outputs enterprises still needHow governance, trust, and auditability will shape the next generation of business intelligenceBen also shares the story of Jaspersoft, from its open-source beginnings 25 years ago to its recent acquisition by Actian, and explains why he believes AI won't make BI smaller: it will make it more foundational.🎧 Tune in for a conversation on the evolution of business intelligence, the impact of AI on analytics, and why the future may be less about replacing BI than finally delivering on its original promise.Hosted on Ausha. See ausha.co/privacy-policy for more information.

In this episode, co-hosts Emma McGrattan and Ole Olesen-Bagneux sit down with Mariana Hebborn, Global Head of HR AI & Data Governance at Merck, to explore what it really takes to move AI from experimentation into the enterprise.With a background in computational linguistics and semantics, Mariana has spent her career at the intersection of data, technology, and business. Today, she is applying that experience to one of the most sensitive and consequential areas of enterprise data: HR, where, as she puts it, the data describes someone.Together, they explore:Why enterprise AI requires more than access to powerful modelsThe difference between everyday AI and enterprise AI (and why adoption looks very different for each)How Merck is using AI agents to simplify complex HR processesWhy data quality, governance, privacy, and security need to evolve alongside AIHow organizations can give employees room to experiment with AI without losing controlWhy transparency can help organizations move faster with sensitive dataThey also discuss why AI is becoming a business capability rather than simply a technology, the challenge of governing a growing ecosystem of employee-created agents, and why understanding the value of enterprise data remains as important as ever.🎧 Tune in for a practitioner-led conversation on moving beyond AI experimentation and building enterprise AI that people can actually use, trust, and govern.Hosted on Ausha. See ausha.co/privacy-policy for more information.

In this episode, Ole Olesen-Bagneux sits down with Lulit Tesfaye, Joseph Hilger, and Zach Wahl from Enterprise Knowledge to explore why the future of enterprise AI depends on connecting knowledge, data, and semantics.Drawing from their new book, Bridging Knowledge, Data, and AI, they explain why organizations can no longer treat structured data, unstructured content, taxonomies, ontologies, glossaries, and knowledge graphs as separate disciplines. As AI pushes enterprises to make better use of context, meaning, and organizational knowledge, the need for a true semantic layer becomes more urgent than ever.Together, they explore:Why Enterprise Knowledge was created, and how its mission has evolvedWhat a semantic layer really is, beyond vendor diagrams and buzzwordsWhy knowledge assets include much more than structured dataHow taxonomies, ontologies, business glossaries, and knowledge graphs work togetherWhy AI is forcing data, IT, and knowledge teams to collaborate in new waysThe risks of competing semantic layer initiatives inside the same organization🎧 Tune in for a conversation on semantics, enterprise knowledge, AI readiness, and why the next phase of data work depends on bridging the silos that organizations have built for decades.Hosted on Ausha. See ausha.co/privacy-policy for more information.

In this episode, co-hosts Emma McGrattan and Ole Olesen-Bagneux sit down with Xavier Gumara Rigol, author of Data as a Product Driver and Head of Data at Manychat, to explore what happens when data moves beyond reporting and becomes part of how product teams make decisions.Drawing on more than 15 years in data, engineering, and platform leadership, Xavier explains why treating data as a product is only part of the story. The bigger challenge is helping product organizations become truly data-informed, from idea validation and experimentation to ownership, prioritization, and lifecycle management.Together, they explore:Why Data as a Product Driver is not just another book about data productsWhat data teams can learn from product management, and what product teams need to understand about dataWhy centralized data teams often become bottlenecks as organizations growHow embedding analysts and data scientists into product teams changes decision-makingWhat good looks like when product teams use data for discovery, experimentation, and outcomesWhy product mindset matters when deciding what data products to build, maintain, or retire🎧 Tune in for a conversation on data, product thinking, and how organizations can move from building data assets to using data as a driver of better product decisions.Hosted on Ausha. See ausha.co/privacy-policy for more information.

In this episode, co-hosts Emma McGrattan and Ole Olesen-Bagneux sit down with Samuel Bitrus, Data Quality and Governance Manager at Lufthansa Group, to explore the evolving role of data catalogs in large, complex enterprises.Drawing from his work across governance, metadata, and trust, Samuel explains why data catalogs are no longer just inventories of data assets. They are becoming critical context layers that help both humans and AI systems understand what data means, where it comes from, who owns it, and whether it can be trusted.Together, they explore:What a data catalog really is, beyond simple asset discoveryWhy trust, context, and accountability matter as much as findabilityHow AI is changing the role of catalogs for both human and non-human usersWhy large enterprises often end up with multiple catalogs, and why that is not necessarily a failureThe idea of a “catalog of catalogs” as a pragmatic way to connect fragmented metadata ecosystems🎧 Tune in for a conversation on metadata, governance, enterprise complexity, and why the future of data catalogs may be less about one perfect system and more about making many systems work together.Hosted on Ausha. See ausha.co/privacy-policy for more information.

In this episode, Ole Olesen-Bagneux sits down with Sune Selsbæk-Reitz, Data & AI Strategist at Demant and author of Promptism, for a conversation on how generative AI is reshaping the way humans interact with knowledge, meaning, and decision-making.Drawing from his background in data strategy, philosophy, history, and writing, Sune explores why the real challenge of AI may not be hallucinations or technical limitations, but the growing tendency to mistake fluency for understanding.Together, they discuss:Why prompting represents more than a technical skill, and may signal a broader cultural shiftHow large language models are changing our relationship with knowledge and truthWhy “smooth” AI interfaces can weaken critical thinking and human judgmentThe role of friction, uncertainty, and hesitation in responsible AI designWhat enterprise leaders must rethink about governance, accountability, and decision-making in the age of generative AIWhy humanities-inspired thinking may become increasingly important in AI-driven organizationsThe conversation also explores the origins of Sune’s book Promptism, the emerging concept of “ontological design,” and how enterprises can remain thoughtful and responsible while adopting increasingly fluent AI systems.🎧 Tune in for a philosophical and practical discussion on AI, meaning, human judgment, and what we risk losing when machines start sounding like they understand us.Hosted on Ausha. See ausha.co/privacy-policy for more information.

In this episode, co-hosts Emma McGrattan and Ole Olesen-Bagneux sit down with Carsten Bange, founder and CEO of BARC, one of Europe’s leading analyst firms focused on data, analytics, and AI.With more than 25 years advising enterprises on data strategy, analytics platforms, and organizational transformation, Carsten shares an independent analyst perspective on why so many data and AI initiatives still struggle to deliver business value, despite massive investments in technology.Together, they explore:Why data culture has become one of the most important success factors in enterprise AIThe gap between technology investments and actual business adoptionWhy curiosity, leadership, and organizational change matter more than everHow enterprises are moving from AI hype toward rebuilding their data foundationsWhat BARC’s latest research reveals about the relationship between data products and AI successThey also discuss the growing realization that the biggest barriers to AI transformation are often not technical, but human.🎧 Tune in for a conversation on enterprise AI, data culture, analyst independence, and why successful transformation requires much more than new technology.Hosted on Ausha. See ausha.co/privacy-policy for more information.