Business Technology Perspectives

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]

How Procurement And AI Are Transforming Spend, Risk, And Compliance

What if one of the most influential functions in your business is also the one you understand the least? In this episode of Business Tech Perspectives, I sat down with Anders Lillevik, Founder and CEO of Focal Point, to unpack the hidden complexity of procurement and why it has quietly become one of the most critical levers in modern enterprise strategy. With more than two decades of experience leading procurement at organizations like Fannie Mae and QBE Insurance, Anders brings a rare perspective shaped by real-world scale, regulatory pressure, and the shifting expectations placed on global businesses. Our conversation explores how procurement has evolved from a cost-saving function into something far more expansive. Today, it sits at the center of spend, risk, compliance, supplier relationships, and ESG accountability. Yet despite that growing responsibility, many teams are still relying on spreadsheets, email chains, and disconnected systems that create inefficiencies and expose organizations to unnecessary risk. Anders explains how this fragmented approach slows decision-making, increases manual effort, and often leaves leadership without a clear view of what is really happening across their supplier ecosystem. We also get into the role AI is beginning to play in bringing structure and visibility to procurement. Rather than replacing people, Anders shares how automation can remove repetitive tasks, validate supplier data in real time, and streamline processes that once took hours into seconds. But he is equally clear on where expectations are running ahead of reality. The real value, he argues, comes from applying AI to repeatable, auditable workflows rather than chasing novelty or treating it like a conversational tool. One of the most interesting parts of the discussion centers on orchestration. Anders describes how Focal Point is designed to sit across existing systems, connecting data, workflows, and stakeholders into a single, unified view. Instead of forcing organizations into disruptive rip-and-replace transformations, this approach allows companies to start small, prove value quickly, and scale change without breaking what already works. It is a pragmatic take on digital transformation that feels grounded in how enterprises actually operate. Looking ahead, Anders paints a picture of procurement as a strategic capability rather than a back-office function. The organizations that get this right will not only manage cost and risk more effectively, they will also unlock new sources of innovation, improve supplier collaboration, and even influence working capital in ways many leaders overlook. So how should businesses rethink procurement in a world shaped by AI, rising compliance demands, and increasing operational complexity, and what opportunities are being missed by those who still treat it as an afterthought?

From Data Chaos To Data Clarity: Lessons From LatentView Analytics

What happens when companies rush into AI without fixing the fundamentals that actually make data useful? In this episode of Business Tech Perspectives, I sit down with Rajan Sethuraman, CEO of LatentView Analytics, which is a global data engineering and analytics company that helps businesses excel in the digital world by harnessing the power of data. I learn more about their refreshingly pragmatic approach to AI adoption that many organizations are overlooking. Rajan brings a rare blend of leadership experience to the table. Before becoming CEO, he spent more than two decades at Accenture, including a role leading talent and people strategy. He later joined LatentView, eventually guiding the company through its IPO and its first acquisition while expanding its work with more than 50 Fortune 500 clients. Our conversation begins with an idea Rajan describes as AI minimalism. At a time when many executives feel pressure to experiment with every new generative AI capability, Rajan argues that the real challenge is not adopting more technology. Instead, organizations need to simplify their data ecosystems and create trusted foundations before scaling AI initiatives. Without that clarity, companies often end up with multiple data pipelines, conflicting metrics, and competing versions of the truth. We also talk about the hidden friction inside many AI projects. Rajan explains that technology is rarely the real barrier. Culture and clarity often determine whether a transformation succeeds. If organizations cannot agree on how key metrics are defined or where their source of truth lives, even the most advanced AI models will struggle to deliver meaningful results. Rajan also shares what he has learned working with global enterprises across industries such as financial services, retail, healthcare, and technology. From governance and data lineage to embedding analytics into everyday decision making, he outlines the patterns that separate organizations that claim to be data driven from those that actually operate that way. One of the most valuable moments in the conversation comes when Rajan offers practical advice for CEOs under pressure to accelerate AI adoption. His recommendation is surprisingly simple. Start by defining the metrics that matter most to the business. Then work backwards from those metrics to identify the data, systems, and decision processes that influence them. Only after that foundation exists should organizations decide which AI capabilities to deploy. We also explore how LatentView is helping enterprises apply emerging technologies such as generative and agentic AI to improve efficiency, effectiveness, and speed across business operations. Rajan explains why partnerships, experimentation, and ecosystem collaboration are becoming essential as the AI landscape evolves. If you are trying to cut through the noise surrounding AI and focus on what actually drives measurable outcomes, this episode offers a thoughtful and practical perspective. Are organizations moving too fast in the race to adopt AI, and could a simpler, more disciplined approach actually create stronger results?

How MediaMint is Turning AI Into Measurable Growth

What does it actually take to turn AI from an experiment into measurable growth? In this episode, I caught up with Jason Riback, President of MediaMint, to unpack what “agentic growth services” really mean in practice. MediaMint works with leading organizations across media, entertainment, retail, and technology to scale front-office operations across marketing, sales, media, and data. But this conversation was less about buzzwords and more about execution. Jason shared his journey from engineering at the University of Michigan to McKinsey, and then into the heart of the San Francisco startup ecosystem. That blend of operational rigor and startup agility clearly shapes how he thinks about growth today. For him, AI is only valuable when it produces definable improvements in real workflows. That means fewer manual handoffs, fewer errors, faster cycle times, and better output quality. Otherwise, it is just another system sitting on the shelf. We spent time breaking down the gap between data-driven marketing in theory and decision-making in reality. Reporting is always retrospective, Jason reminded me. The real challenge is using insights in near real time to influence spend allocation, targeting, and optimization before a campaign ends. That requires governance, clean data, and clear accountability. Without those foundations, organizations risk operational exposure and opaque decision logic that no one can confidently explain. One of the most thoughtful parts of our discussion centered on human oversight. Jason was clear that while AI can technically retrain models and adjust guardrails on its own, handing over full autonomy creates a black box problem. Enterprises need the right governance layer, where recommended changes are reviewed and approved against clear outcomes. Automation should feel invisible within the workflow, not like another dashboard demanding attention. We also explored MediaMint’s Intelligent Assistant platform, MIA. What stood out to me was the pragmatic approach. Rather than offering a one-size-fits-all tool, MediaMint customizes AI agents around each client’s tech stack, data connectors, and workflow steps. That flexibility is essential because no two marketing organizations operate the same way. The goal is applied agentic execution embedded into daily workloads, not theoretical AI capability. Finally, we turned to the people side of transformation. As automation becomes embedded in front-office operations, roles will inevitably shift. Jason believes teams will move away from repetitive execution and toward managing, interpreting, and optimizing AI-driven processes. That shift demands AI literacy, cross-functional alignment between marketing, tech, and finance, and shared agreement on what good looks like. Accountability does not disappear simply because an agent executes a step. If you are wrestling with how to apply AI inside marketing and revenue operations without creating new risks or unnecessary complexity, this episode offers a grounded perspective. It is a conversation about discipline, governance, and measurable outcomes, not hype. What would change in your organization if automation genuinely reduced cycle times by 50 percent while improving quality and transparency at the same time?