From Data Chaos To Data Clarity: Lessons From LatentView Analytics

AI, Open Source, and the Security Challenges Few Leaders See Coming

What happens when AI can write software in seconds but lacks the context to understand whether the code it creates is built on secure foundations? In this episode of Business Tech Perspectives, I speak with Brian Fox, Co-Founder and CTO of Sonatype, about the growing pressure facing software teams as AI accelerates development while cyber threats continue to evolve. Brian brings a unique perspective from his work overseeing Maven Central and helping organizations understand the risks hidden inside modern software supply chains. Our conversation begins with a challenge that many organizations may not fully appreciate. While AI coding assistants are becoming increasingly capable, the information they rely on can already be months old. Brian explains why that matters when selecting open source dependencies and how outdated recommendations can leave security risks buried inside applications long after they are deployed. We also discuss the role open source software now plays in almost every application. Developers can build products faster than ever by using existing components, but that speed comes with responsibility. Brian shares why understanding what is inside your software has become a business issue as much as a technical one. Another major topic is the expected surge in vulnerability discoveries driven by new AI capabilities. Brian warns that both attackers and security researchers now have access to tools that can identify weaknesses at unprecedented speed. The result could be a flood of vulnerability reports that challenges maintainers, vendors, and security teams alike. The discussion also covers the changing state of vulnerability intelligence. With NIST narrowing its focus and public resources under strain, organizations may need to rethink how they gather information, assess risk, and prioritize remediation efforts. Throughout our conversation, Brian offers practical advice for leaders who want to prepare their organizations for what comes next. From understanding software bills of materials to improving patch management processes, he explains why preparation today could make all the difference tomorrow. Despite the challenges, Brian remains optimistic about the future. He believes AI will help developers create software faster and help the industry address years of accumulated security issues. The question is whether organizations can adapt quickly enough to keep pace with the changes already underway. How confident are you that your organization could identify, assess, and respond to a major software supply chain issue today, and are your teams prepared for the increase in vulnerabilities that AI may soon uncover?

How TWG AI Is Turning Enterprise AI Into Real Business Outcomes

What happens when the biggest barrier to AI success isn't the technology itself, but the way organizations are structured to adopt it? In this episode, I sit down with Milan Cooper, Head of Product at TWG AI, a company working alongside Palantir to help enterprises rebuild core business processes around AI. With previous leadership roles at JPMorgan Chase and Accenture, Milan brings a rare perspective from the intersection of AI, risk, governance, and large-scale transformation. Our conversation moves beyond chatbots, pilots, and proofs of concept to examine what it actually takes to make AI part of mission-critical operations. Milan explains why so many organizations remain stuck in what he calls "AI theater," measuring success through use cases rather than business value. He shares how TWG AI approaches enterprise adoption by focusing on entire value streams, helping organizations move from isolated experiments to AI-native operations that directly influence revenue, efficiency, and decision-making. We also discuss the growing challenge of AI concentration risk, why switching between AI models could become the equivalent of performing brain surgery on an enterprise, and how organizations can avoid locking themselves into a single provider. Milan offers insights from projects with companies including Guggenheim Investments, where AI is being embedded into investment workflows to increase deal throughput and remove operational bottlenecks. Along the way, we tackle governance, compliance, AI accountability, the future of SaaS, and why leadership conviction may be the single biggest factor determining whether an AI transformation succeeds or stalls. Milan also shares why trust remains the missing ingredient in enterprise AI adoption and what organizations need to do before employees are comfortable using AI with their most sensitive information. If you've ever wondered why some companies are turning AI into measurable business outcomes while others remain trapped in endless experimentation, this conversation offers a candid look at what separates the two. What do you think is holding back AI adoption in your organization, technology, culture, or leadership? Share your thoughts with me.

Veritone CEO on the Next Frontier of AI and Monetizing Multimodal Data

What if the most valuable AI asset your organization already owns is sitting untouched inside years of video, audio, and unstructured content? In this episode, I sit down with Sean King from Veritone to explore how organizations are transforming massive archives of content into searchable, licensable, and revenue-generating assets for the AI economy. As the executive leading Veritone’s commercial business, Sean works directly with major organizations, including the NCAA and ESPN, to help unlock value hidden inside decades of multimedia data. We discuss why the next phase of AI will be defined by multimodal data rather than text alone, and why businesses are dramatically underestimating the value locked inside their video, audio, and image archives. Sean explains how AI is turning what was once treated as a passive storage problem into an active business asset, making unstructured content searchable, contextual, and commercially valuable at scale. The conversation also looks at how organizations can modernize decades-old archives without becoming overwhelmed by the sheer volume of data involved. Sean shares how companies can approach AI transformation by first building scalable workflows for incoming content before tackling historical archives. From sports media and broadcasting to enterprise knowledge management, we explore how searchable multimedia data is creating entirely new opportunities for storytelling, fan engagement, licensing, and monetization. We also get into the growing debate around intellectual property and AI training data. Sean offers a thoughtful perspective on why trust, transparency, and rights-cleared content are becoming increasingly important as AI models evolve. He explains why sustainable AI ecosystems will depend on respecting creators, fairly compensating rights holders, and ensuring enterprises know exactly where their training data originates. The discussion then shifts toward the emerging “agentic web,” where AI systems move beyond research and begin handling tasks, workflows, and decisions autonomously. Sean argues that future competitive advantage will belong to organizations with access to the highest-quality licensed data, because the difference between an average AI agent and a truly effective one will come down to the quality, structure, and accessibility of the information they can use. We also talk about the human side of AI adoption. Sean shares why he believes AI should be viewed as a tool that amplifies human potential rather than replacing it, comparing today’s AI shift to the arrival of the internet itself. From personalized AI experiences to machine-driven workflows and new business models around licensed content, this conversation offers a fascinating look at how the AI economy is beginning to reshape media, enterprise technology, and digital experiences in real time.

The AI Visibility Gap: Why Enterprises Still Cannot Measure What They Are Using

How can businesses make smart AI bets when they cannot even see the full picture of what is already happening inside their own organization? In this episode of Business Tech Perspectives, I sit down with Russ Fradin, CEO of Larridin, for a conversation about one of the biggest blind spots in enterprise AI right now. While many leaders are focused on adoption, experimentation, and speed, Russ argues that a more fundamental issue is being overlooked. Companies are investing in AI at scale, but many still lack a clear view of which tools are being used, who is using them, and whether any of it is delivering measurable value. What made this conversation so timely for me was Russ’s perspective as someone who has lived through several major waves of technology change. From digital advertising and mobile to cloud and now AI, he has seen what happens when innovation moves faster than the systems designed to manage it. In this case, the challenge is what he calls the AI visibility gap, where tools are spreading across teams faster than IT, finance, and leadership can track. That creates questions around governance and cost, but it also raises a more practical business issue. If you do not know what is being used, how do you know what is working? We also get into why Russ believes experimentation is not the problem. In fact, he makes a strong case that organizations should be trying lots of tools right now. The issue is when those experiments happen without measurement, without accountability, and without a framework for understanding productivity and return on investment. I particularly liked his point that this is not about shutting innovation down. It is about building the right measurement, governance, and data foundations so businesses can experiment with confidence instead of chaos. Another part of the conversation that stayed with me was the idea of identifying the people inside an organization who are already becoming dramatically more productive with AI. Russ talks about how some employees are already figuring out what great looks like, while others are still staring at a blank prompt box unsure where to begin. That creates an opportunity for leaders to stop treating AI adoption as a vague aspiration and start turning real employee behavior into repeatable playbooks that can help the wider workforce improve. This episode is really about the gap between AI excitement and AI accountability. If AI is now moving into every corner of the enterprise, leaders need more than enthusiasm. They need visibility, they need measurement, and they need a way to connect spending with outcomes in real time. So as AI use continues to spread across your own business, do you actually know what is happening under the surface, and what do you think companies should be measuring first? Share your thoughts. The link Russ mentioned during the podcast can be found here: [https://www.linkedin.com/posts/rfradin_i-got-to-sit-down-with-rubriks-cio-ajay-activity-7439350276752482304-QEL8?utm_source=share&utm_medium=member_desktop&rcm=ACoAAAXdH7kBIKDS_VpiVi5prYY6_HafxFSS3p8]

Denodo CTO Alberto Pan On The Next Evolution Of Business Intelligence

What if enterprise AI could move beyond answering questions and start explaining why things are happening in your business? In this episode of Business Tech Perspectives, I sat down with Alberto Pan, Chief Technology Officer at Denodo, to explore how AI is shifting from surface-level responses to deeper, reasoning-driven insights. As organizations wrestle with fragmented data, governance challenges, and growing expectations around AI, this conversation gets to the heart of what meaningful progress actually looks like. At the center of our discussion is Denodo’s DeepQuery, an AI reasoning agent designed to perform complex, open-ended research across an organization’s data landscape. Alberto explains how it goes far beyond traditional approaches like retrieval augmented generation by creating research plans, analyzing patterns, and even refining its own process along the way. The result is not just faster answers, but a more complete understanding of what is really happening beneath the surface. We also unpack what this means for business intelligence teams. Rather than manually building dashboards and reports, analysts are stepping into a new role as guides, working alongside AI systems that can gather, analyze, and present insights in minutes. It raises an interesting question about how skills, roles, and expectations will evolve as these tools become more widely adopted. A big part of the conversation focuses on data itself. Alberto shares how Denodo’s logical data layer allows organizations to access and govern data across multiple systems in real time, without creating new silos. That foundation becomes even more important as AI adoption accelerates, especially when accuracy, context, and explainability are all under increasing scrutiny. We also touch on the growing importance of transparency in AI. With concerns around black box decision making continuing to rise, Alberto explains how DeepQuery provides full traceability, showing exactly how insights are generated and where the underlying data comes from. It is a practical step toward building trust in AI systems at scale. Looking ahead, this episode offers a clear view into how research-driven AI could reshape decision making across industries. From finance to healthcare, the ability to move from static reports to dynamic, AI-assisted investigation has the potential to change how organizations operate on a daily basis. So as AI becomes more embedded in business workflows, are you still asking your data what happened, or are you ready to understand why it happened and what to do next? Useful Links * Connect with Alberto Pan on LinkedIn [https://www.linkedin.com/in/alberto-pan-513813] * Learn more about Denodo [https://www.denodo.com/] * Stories of Your Life and Others book that Alberto shared for our Amazon list [https://www.amazon.co.uk/Stories-Your-Life-Others-Chiang/dp/1101972122] * O’Reilly’s The Rise of Logical Data Management book [https://www.denodo.com/en/document/e-book/the-rise-of-logical-data-management-oreilly] * Follow on LinkedIn [https://www.linkedin.com/company/denodo-technologies/], and Twitter [https://x.com/denodo]