Business Technology Perspectives

Episodes

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  Who Authorized the AI Agent? Digital Identity With Pion 18.10.2026 25m

  How can a business personalize a digital experience without collecting unnecessary customer data, and who is responsible when an AI agent acts on a shopper’s behalf?In this episode of Business Tech Perspectives, I speak with Sapphire Samiullah, Product Lead at Pion, about why digital identity is moving from a specialist check at registration or checkout into the infrastructure behind customer trust, loyalty and fraud prevention. Pion supports verified offers across 10 consumer groups in over 100 countries, including students, graduates and workers in eligible professions.Sapphire explains how a reusable verification status can reduce repeated data collection. A customer who has already proved eligibility can enter a phone number on a participating brand’s site, allowing Pion to return a simple verified status. The brand receives the result it needs, while the customer avoids sharing the same supporting information again. She also describes why adding SMS-based multi-factor authentication made students feel safer rather than creating the expected friction.We discuss the measures that connect identity systems to business results. Registration and verification conversion rates reveal whether people can complete the process. Method, channel and market comparisons expose weak points. Pion also tracks activation, defined here as a verified user issuing a first discount code within 24 hours, to connect successful verification with customer action.The conversation then turns to agentic shopping. Existing fraud controls often assume a human is behind each request. An AI agent introduces another test: is it authorized to make this purchase for this person at this moment? Sapphire discusses the need for standards around delegated authority, consent and agent credentials as automated purchasing becomes more common.Privacy and inclusion remain central. Pion asks for the minimum information required, tells users why it needs each field and encourages people to redact irrelevant details from submitted documents. Multiple verification routes, including human review, can also prevent automated systems from excluding apprentices or others without standard records.Can companies build identity systems that recognize both human customers and authorized AI agents while preserving consent, inclusion and trust?
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  Measuring the True Cost of Enterprise AI With Apptio 04.10.2026 21m

  What does artificial intelligence really cost when an experiment becomes a production service used across an enterprise? Token consumption is easy to see, but Greg Holmes argues that it may represent only a small part of the final bill.In this episode of Business Tech Perspectives, I speak with Greg, EMEA Field CTO at Apptio, an IBM company, about AI economics, technology business management, and the growing challenge of connecting infrastructure spending to business value. Greg works with CIOs, CFOs, and technology leaders across EMEA, where conversations that once centered on cloud cost and FinOps are increasingly focused on a simple question: is AI worth what the organization is spending?Greg describes how teams can move surprisingly far through an AI pilot before finance sees the full consumption pattern. The token bill may then reveal a larger operating model involving data storage, data movement, cloud infrastructure, governance software, security, and the people who built and maintain the service. In examples discussed during the interview, he says direct AI costs can account for around 20 percent of the total. That figure should remain attributed because every workload and enterprise will have a different cost structure.The measurement problem becomes harder when businesses ask for a single return-on-investment formula. Greg argues that each initiative needs a clear unit of work. A service desk system might be measured by tickets resolved, an insurance system by claims processed, and a document service by files completed accurately. Leaders can then compare the value of that completed work with its full production cost rather than treating token usage as the result.We also discuss agentic AI, where autonomous activity can consume resources continuously and unpredictably. Greg recommends guardrails such as a spending ceiling per agent, automatic checks when consumption rises, and model tiering. A cheaper model may complete the straightforward 80 percent of tasks successfully, leaving an expensive frontier model for work that needs greater capability. This approach gives engineers room to test ideas while limiting the risk of applying the costliest system to every request.Another concern is behavior. Greg shares examples of organizations that encouraged employees to prove they were using AI by consuming their full token allocation. Engineers responded to the metric and built tools that used tokens because appearing low on the usage chart looked bad. The organization received the behavior it rewarded, even when that behavior had little connection to a useful business result.That lesson matters for the relationship between finance and engineering. Finance wants predictability, while technologists need room to test new ideas. Technology business management and FinOps can give both sides a shared language based on services, unit costs, and outcomes. Greg argues that visibility creates the basis for trust, allowing a team to discuss whether a service is worth funding rather than fighting over an unexplained bill.We also examine the belief that AI will inevitably become cheaper. Models and computing methods may become more efficient, but growing demand, data center construction, energy constraints, and the creation of additional workloads could keep total spending high. Greg’s advice is to make a production service financially worthwhile from its first day rather than depending on future price reductions to rescue a weak business case.Is your organization counting AI tokens, or can it explain the full cost and the business result produced by every AI service? Listen to the episode and share how your finance and technology teams are measuring AI value today.
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  From Transactions to Interactions in Banking With CSI 23.09.2026 26m

  What if your bank could recognize that you needed help before you had to ask for it?In this episode of Business Technology Perspectives, I speak with Michel Jacobs, Chief Strategy Officer at CSI, about how artificial intelligence and customer intelligence are changing the relationship between financial institutions and the people they serve.Community banks and regional financial institutions have traditionally competed through personal relationships rather than scale. But digital banking, fintech competition, and changing consumer expectations are putting that advantage under pressure. Michel argues that the future of banking will increasingly be defined by interactions rather than transactions.That means understanding why a customer is contacting the bank, what may be happening in their life, and which service could provide value at that moment. It also means preparing for interactions initiated through APIs, open banking services, and AI agents, rather than assuming every customer journey begins with a person opening a banking app.Michel explains how CSI’s Customer Intelligence Suite combines transaction information, card activity, merchant category data, and digital behavior to identify signals and changing patterns. According to Michel, these signals can help a financial institution understand when a customer’s circumstances may have changed instead of relying on the demographic category assigned when the account was opened.This creates opportunities for relevant financial support, but it also introduces serious questions about privacy, consent, accuracy, and customer trust. Inferring that somebody has changed jobs, bought a home, become a parent, or encountered financial difficulty can be useful when the response genuinely helps. The same capability can feel intrusive when it produces poorly timed sales offers or conclusions the customer cannot question.We discuss how banks can balance personalization with regulatory responsibilities and why younger consumers are less likely to remain loyal when a provider offers little practical value. As Michel puts it, the era of offering everybody the same account and throwing in a free beach ball has probably run its course.Customer retention is another major part of our conversation. Michel explains how declining digital activity, money leaving an account, late payments, or changes in income can reveal that a relationship is weakening. Used responsibly, this information could allow a bank to offer assistance before missed payments or financial strain become harder to address.For community banks, the answer is unlikely to be copying every product offered by the largest global institutions. Michel believes they should decide where they can provide distinctive value, then use technology and data to support that position. CSI’s stated aim is to give smaller financial institutions access to customer analytics capabilities that would otherwise require considerable internal investment.Can AI help banks become useful partners in their customers’ lives without crossing the line from personalization into intrusion? Listen to the conversation and share your thoughts with me.
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  Closing the Technology Confidence Gap With Babble 12.09.2026 30m

  Why do 88 percent of UK SMB leaders see technology as important to business performance while fewer than half feel highly confident about adopting it effectively?In this episode of Business Tech Perspectives, I speak with Mark Weait, Chief Revenue Officer at Babble, about the company’s inaugural Technology Performance Index. The research draws on responses from 1,000 UK SMB leaders and C-suite executives and examines a growing technology confidence gap between recognizing what digital tools could achieve and knowing how to identify, implement, and expand them successfully.Mark describes a pattern many businesses will recognize. A security incident prompts an urgent software purchase. A board request to “do something with AI” leads to a batch of licenses. A new cloud application arrives without a clear business problem, owner, or adoption plan. The company gains another tool, but employees may lack the training, processes, or time needed to turn it into a result.His analogy is simple: a business can buy a Ferrari, but that does not mean its people know how to drive it. Technology adoption depends on clarity about the desired outcome, the skills needed to deliver it, and the connections between systems, people, security, and data. Mark argues that treating every program as an IT responsibility can overlook the management support and operational change required across the wider business.The Babble research groups SMBs into Tech Vanguards, Emerging Adopters, and Tech Bystanders. According to figures discussed in the interview, 84 percent of Tech Vanguards report strong processes for technology-led change, compared with 3 percent of Tech Bystanders. The research also reports that 57 percent of Tech Vanguards are receiving measurable results from AI and automation, compared with 21 percent of Tech Bystanders.Those figures suggest that confidence may build through experience. Companies that begin with a defined problem, equip their people, and measure a deployment can use that success to support the next decision. Those buying technology through fear of missing out risk adding cost, security exposure, and another disconnected application to an already complicated environment.Mark does not believe smaller businesses have missed their opportunity with AI. He says many companies remain near the beginning, which means an SMB can still catch up without attempting to apply AI everywhere. His recommended approach is to assess readiness, train employees, confirm that networks and data controls are suitable, identify a process where AI can produce a worthwhile result, and expand gradually from there. He also distinguishes AI from established automation, noting that some business problems can still be solved without a generative system.We also discuss the difference between cyber confidence and cyber resilience. Mark compares it with believing you are a good boxer until you are punched in the face. Security covers identity, email, endpoints, cloud services, and AI, so no single purchase finishes the job. He recommends regular assessments and independent validation to find gaps that a familiar provider or internal team may no longer notice. A company can decide to accept a risk, but that decision should be informed rather than accidental.For SMB leaders working with limited budgets, Mark suggests reviewing the current technology estate before requesting new money. Duplicate products, unused licenses, old contracts, and disconnected systems can absorb funds that could support better adoption elsewhere. The larger question is whether technology confidence has become a boardroom issue because it affects revenue, customer experience, resilience, and reputation as much as IT performance.Which of Babble’s three groups best describes your organization today, and what would help it move forward with greater confidence? Listen to the episode and share your thoughts.
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  Moving AI Pilots Into Production With QualityAI 20.08.2026 42m

  What evidence would persuade your board that a successful AI pilot is ready to become part of everyday business operations?In this episode of Business Technology Perspectives, I speak with Andrew Duncan, CEO of QualityAI, formerly Qualitest. Andrew previously served as CEO of Infosys Consulting and has spent over 30 years advising global organizations across technology and business change.Andrew believes the AI era differs from previous technology cycles because adoption is moving faster than many organizations can govern it. Fear of missing out is also creating pressure for companies to appear further along than they really are.This creates a growing gap between AI activity and operational value. A company may have numerous pilots, prototypes, and employee experiments without possessing enough evidence to deploy any of them across the business.Andrew describes the enterprise AI conversation as moving from “can we build it?” toward “can we trust it?” Answering that question requires companies to assess three areas. Does the technology work? Is it producing the expected business outcome? Will it continue behaving within agreed boundaries as its data, model, users, and environment change?We discuss the questions boards should ask before approving AI at scale. What business result is the company pursuing? What level of risk is acceptable? Who becomes accountable when the system makes an unexpected decision? Has it been tested under real operating conditions? What evidence demonstrates that people can rely on it?Andrew explains why a successful pilot should never be confused with production readiness. Controlled tests cannot fully represent real data, human behavior, connected applications, operational processes, or downstream consequences. A system that performs well in isolation may behave very differently once it reaches customers and employees.This is why Andrew advocates continuous AI assurance. Testing cannot end when an application enters production. Organizations must monitor whether the system continues functioning technically, produces the intended result, and remains within defined guardrails.Our conversation also addresses AI washing and the growing tendency to measure progress through the number of pilots or features launched. Andrew suggests asking a much simpler question: what has materially improved because AI is involved?Useful measures could include productivity, revenue, operating costs, cycle times, customer experience, adoption, and the consistency of results. If those outcomes remain unchanged, a collection of impressive demonstrations may amount to activity without meaningful business progress.Could continuous assurance give businesses the confidence to scale AI faster, or will many companies remain trapped between promising pilots and production risk? Listen to the episode and share your thoughts with me.
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  How Larridin and Hunton Andrews Kurth Assess Enterprise AI Risk 02.08.2026 28m

  Could your company produce a complete inventory of every AI tool, model, agent and embedded feature operating across the business today?In this episode of Business Tech Perspectives, I speak with Russ Fradin, founder and CEO of Larridin, and Michael Levine, partner at Hunton Andrews Kurth, about the enterprise AI visibility gap and the growing legal, financial and insurance consequences for companies that cannot identify their AI systems.Russ has experienced several major periods of technology adoption across digital advertising, mobile and workforce communication. He launched Larridin after recognizing that standard measurement systems could not account for the speed and variety of AI tools entering businesses. The company helps enterprises understand which AI systems employees use, what they cost, which models they rely on and where adoption is taking place.The scale of the problem can be surprising. Russ describes an early customer that expected to find approximately 70 AI tools operating across the company but discovered 220. Most were benign, including five separate AI meeting assistants, but the discovery revealed unnecessary spending and fragmented adoption.Another customer found an AI agent built by a former employee that continued running at a cost of $1,800 each month. Nobody remaining at the company understood what the agent did. The example shows why an enterprise AI inventory concerns business spending and accountability alongside security and compliance.Mike explains how embedded AI can expose a company to legal action without leaders realizing that AI was involved. He discusses a class action concerning an HR screening tool that allegedly used AI-assisted lie detection without the required disclosure.We also examine the growing patchwork of US AI regulation. Mike discusses the Texas Responsible Artificial Intelligence Governance Act and the Colorado AI Act, along with the difficulties faced by businesses operating across several states. A web-based company may need to comply with a state law because it serves residents there, even when the company is based elsewhere.Board accountability is another focus. Russ says boards increasingly want to know which tools and models are being used, what employees use them for, what data agreements apply and how much the company is spending. Mike adds that inaccurate statements about AI capabilities can create directors and officers exposure through shareholder litigation.Insurance adds further complexity. Mike explains why traditional cyber coverage may not apply to many AI claims because they do not involve a data breach or loss of protected information. Insurers are beginning to introduce AI-related exclusions across several forms of commercial coverage, making the wording and definition of AI increasingly important during renewal.Russ and Mike close with practical guidance for companies building a verified AI inventory. IT, finance, legal, HR, operations, risk teams and board members must contribute because no single department has a complete view of AI use across the business.Could your leadership team identify every AI system, its owner, its cost, the information it touches and the insurance protection available if something goes wrong? Please share your thoughts with me.
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  Why Investing in AI Without Investing in People Is a Costly Business Mistake 13.07.2026 30m

  Why do companies spend millions on AI and digital transformation while overlooking the people expected to make those investments successful?In this episode of Business Technology Perspectives, I speak with Elena Varo, founder of Elevatio Tech & Culture, about why the biggest barriers to technology adoption are often human rather than technical, what leaders misunderstand about organizational change, and why companies need to invest in people as seriously as they invest in technology.Elena brings more than a decade of experience across the technology industry, including work with Microsoft, Red Hat, CyberArk, and WSO2. Having worked across Spain, Ireland, and the UK, she has seen how leadership, workplace culture, communication, and organizational structures influence whether technology investments produce meaningful business outcomes.A major theme throughout our conversation is the gap between buying technology and preparing a company to use it effectively. Elena argues that many businesses begin with the wrong question. They focus on which AI platform, cybersecurity product, or cloud provider to purchase before identifying the problem they need to solve and understanding whether employees have the skills, training, information, and support required to adopt new technology.We discuss why AI is creating a leadership challenge that may be harder to solve than the technology itself. Employees want answers to practical questions about training, skills, changing roles, and expectations. When companies fail to communicate clearly or listen to their workforce, uncertainty grows, experienced employees leave, and technology adoption becomes harder.Talent retention is another major part of the conversation. The technology industry frequently talks about skills shortages and recruiting new employees while paying far less attention to why talented people leave. Elena explains why salary alone rarely solves problems caused by unclear responsibilities, poor communication, limited career development, internal competition, and workplaces where employees do not feel safe speaking openly.We also examine the relationship between employee well-being and long-term business performance. Constant technology change, economic uncertainty, demanding targets, and pressure to do more with fewer resources affect employees and managers alike. Companies that want people to collaborate, share knowledge, question assumptions, and develop new ideas need to create working environments where people feel supported and heard.The conversation also addresses diversity in technology and why attracting more women and people from different backgrounds is only part of the challenge. Companies need to improve retention, promotion processes, sponsorship, access to leadership positions, and opportunities for people with different experiences to influence decisions.Elena also shares why she founded Elevatio Tech & Culture and her plans for a technology congress in Córdoba that brings business leaders, technologists, universities, public institutions, and other participants together to discuss technology adoption, organizational culture, talent, leadership, and employee well-being.For CEOs, CIOs, HR leaders, technology executives, and anyone responsible for AI adoption or digital transformation, this conversation provides practical lessons about preparing employees for change, retaining experienced talent, improving communication, supporting psychological safety, and creating workplaces where technology investments can produce better outcomes.Elena’s advice to business leaders is clear: invest in people as seriously as you invest in technology. Technology can be purchased, but building a workplace where people trust their leaders, develop new skills, share knowledge, and want to contribute requires sustained attention.
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  AI, Open Source, and the Security Challenges Few Leaders See Coming 08.06.2026 35m

  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?
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  How TWG AI Is Turning Enterprise AI Into Real Business Outcomes 31.05.2026 31m

  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.
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  Veritone on the Next Frontier of AI and Monetizing Multimodal Data 27.05.2026 24m

  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.
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  The AI Visibility Gap: Why Enterprises Still Cannot Measure What They Are Using 09.04.2026 29m

  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:
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  Denodo CTO Alberto Pan On The Next Evolution Of Business Intelligence 06.04.2026 21m

  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 LinksConnect with Alberto Pan on LinkedInLearn more about DenodoStories of Your Life and Others book that Alberto shared for our Amazon listO’Reilly’s The Rise of Logical Data Management bookFollow on LinkedIn, and Twitter
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  How Procurement And AI Are Transforming Spend, Risk, And Compliance 27.03.2026 26m

  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?
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  From Data Chaos To Data Clarity: Lessons From LatentView Analytics 15.03.2026 24m

  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?
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  How MediaMint is Turning AI Into Measurable Growth 26.02.2026 21m

  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?
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  James Benham on Why Insurance Is One of the Most Interesting Tech Problems 28.01.2026 38m

  What does it really take to build profitable technology companies without outside funding, and why does that mindset matter even more as AI reshapes every industry?In this episode of Business Tech Perspectives, I sat down with James Benham, a lifelong technologist, serial founder, and unapologetic bootstrapper who has spent more than two decades building enterprise software businesses on his own terms. Broadcasting from Texas, with stories that stretch from Louisiana to Argentina to the UK, James brings a rare mix of candor, humor, and hard-earned perspective on what it means to survive and grow in technology when there is no safety net.James shares his journey from writing code as a teenager to running an early dial-up internet service provider, before going on to co-found JBKnowledge and later launching Terra, a modern core system transforming workers’ compensation and insurance operations. We talk openly about why he chose bootstrapping over venture capital, how that decision shaped his leadership style, and why cash discipline still separates companies that endure from those that quietly disappear.The conversation also explores why insurance, often dismissed as dull from the outside, becomes endlessly fascinating once you understand how deeply it touches everyday life. James explains how risk, data, and claims connect everything from football matches to flight safety, and how working inside the industry fundamentally changes how you see the world around you. It is a reminder that some of the most meaningful innovation happens in places that do not shout for attention.We spend time unpacking lessons from his book, Be Your Own VC, including why survival matters more than growth headlines and how many founders underestimate the emotional toll of building companies over decades. James does not shy away from discussing the hard days, the moments of doubt, or the reality that technology leaders are always one misstep away from trouble.As expected, AI enters the discussion, not as a buzzword but as a genuine shift in how fast software can be built and how quickly businesses can fall behind. James offers a clear-eyed view on why speed to market, trust, and execution now matter more than ever, especially in regulated industries like insurance where legacy processes still dominate.We close on a deeply human note, talking about creativity, music, flying, and the role of art in staying grounded while leading global teams. It is a reminder that the best business leaders rarely draw their energy from work alone.So as automation accelerates and building software becomes easier than ever, how do you design a career and a company that can still last for decades, and what would you do differently if you truly had to bet on yourself?Useful links James Benham’s personal siteJBKnowledge: Terra Workers’ Compensation Platform: Be Your Own VC book: InsureTech Geek Podcast: James Benham on LinkedIn:Thanks to our sponsors, Alcor, for supporting the show.
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  Bluehost CEO Sachin Puri on Building an AI-Ready Web for the Next Generation of Founders 21.12.2025 32m

  What if the biggest barrier holding entrepreneurs back was never a lack of ideas, but the friction created by tools, platforms, and infrastructure that were never designed for speed?In this episode of Business Technology Perspectives, I sit down with Sachin Puri, CEO of Bluehost, to explore how AI-native platforms are reshaping what is possible for entrepreneurs and small businesses. Sachin brings a long-term view from the heart of the web, sharing how the shift toward AI-assisted creation is lowering barriers, accelerating experimentation, and enabling a new wave of solo-scale founders to build brands, storefronts, and full customer journeys in days rather than months.Our conversation looks beyond AI hype and focuses on where real value is showing up. Sachin explains how customer expectations are evolving in an AI-driven marketplace, where instant performance, personalization, and seamless experiences are no longer differentiators but baseline expectations. We also discuss why infrastructure alone is no longer the advantage it once was, and why speed of innovation, adaptability, and integrated experiences now determine which businesses scale and which struggle to keep up.We also dig into an important milestone for an open, AI-ready web, with Sachin sharing why collaboration across the ecosystem matters. From Bluehost’s work with Yoast on brand insights and generative engine optimization to broader partnerships with companies like Microsoft, this episode highlights how the next phase of the web will be shaped by platforms that help entrepreneurs get discovered not just by search engines, but by AI systems as well.Sachin offers practical insight into what happens when success arrives faster than expected, why virality can become a crisis without the right foundations, and why hosting has quietly evolved into a strategic performance layer rather than a background utility. As we look toward 2026, we explore the rise of intelligent, living websites, the return of owned web real estate, and why the future belongs to builders who move quickly while staying grounded in reliability, security, and trust.If AI is giving entrepreneurs access to capabilities once reserved for the biggest players, how will you use that advantage to build something that lasts?Useful LinksConnect with Sachin PuriLearn more about Bluehost AI featuresTech Talks Network is sponsored by Denodo
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  Why Construction Leaders Are Turning to Reality Intelligence 19.11.2025 35m

  How do you transform an industry that most people still assume runs on clipboards, manual checks, and fragmented updates? And what happens when reality capture and AI finally combine to give construction teams a clear and measurable picture of progress on every site? In this episode of Business Tech Perspectives, I sit down with Chaitanya NK, the co-founder and CEO of Track3D, to explore how reality intelligence is reshaping the way major projects are built and managed.NK shares how three founders with no construction background stepped into one of the most complex environments in the world and uncovered a problem that had been hiding in plain sight. Field data was inconsistent, incomplete, and nearly impossible to verify at scale. The result was delays, rework, and avoidable cost overruns. Track3D tackles this head-on by unifying drones, 360 cameras, scanners, mobile devices, and even robots into one clear view of what has been installed, when it happened, and whether it meets the plan. Through practical examples, NK explains how automated measurement brings immediate clarity, how deviations are caught early before they snowball, and how objective data changes coordination across trades, schedules, and budgets.This is a conversation for anyone interested in the real business value of AI in physical environments. NK talks openly about trust, data governance, security, and the balance between automated insight and human judgment. He also shares his vision for 2026, where AI agents act as partners to schedulers, coordinators, and field teams. If you want to understand how technology is reshaping one of the world’s largest and most demanding industries, you will find real insight here.Listeners who want to learn more about Track3D or connect with the team can visit track3d.ai or reach out on LinkedIn at Track3D. Where would you like to explore this conversation further in your own work?The Tech Talks Network is Sponsored by NordLayer:Get the exclusive Black Friday offer: 28% off NordLayer yearly plans with the coupon code: techdaily-28. Valid until December 10th, 2025. Try it risk-free with a 14-day money-back guarantee.
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  The Future of Endpoint Security: Insights from IGEL’s Darren Fields 10.11.2025 19m

  What does it take to lead one of the fastest growing regions in enterprise security while spending your year hopping between Sydney, Tokyo, Singapore, and London? At IGEL’s Now & Next event in Frankfurt, I caught up with Darren Fields, Senior Vice President for UK and Asia Pacific, to find out.From rebuilding the partner ecosystem across continents to forging new alliances with industry giants like Lenovo, Palo Alto Networks, and CrowdStrike, Darren has been at the center of IGEL’s global expansion. He shared how regional differences shape technology adoption, why prevention first security is resonating with customers everywhere, and how business continuity has shifted from being a safety net to a core design principle for modern IT.In our conversation, Darren explained how the endpoint has become the new control point for everything from zero trust to AI driven automation, and why so many organizations are rethinking old assumptions about bring your own strategies. He also opened up about the mindset shift he’s witnessing among IT leaders, one that moves from reacting to threats toward proactively preventing them.We also discussed how cultural differences influence the pace of digital transformation, with regions like Japan preferring careful evaluation, while Australia and New Zealand have embraced rapid innovation. Darren’s global perspective highlighted how security maturity looks different around the world, yet the ultimate goal remains the same: empowering organizations to build trust, resilience, and operational continuity.As we approach another year defined by AI, cybersecurity, and digital resilience, Darren’s reflections offer a rare window into how global teams are turning complexity into simplicity and scaling trust in every region. What does a truly prevention first future look like, and how can enterprises across the world prepare for it?Useful LinksConnect with Darren Fields, on LinkedInLearn more about IGELFollow on LinkedIn, Twitter and YouTubeTech Talks Network is Sponsored by NordLayer:Get the exclusive Black Friday offer: 28% off NordLayer yearly plans with the coupon code: techdaily-28. Valid until December 10th, 2025. Try it risk-free with a 14-day money-back guarantee.
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  How Mural Is Transforming Sales with AI and Human Insight 29.10.2025 37m

  Sales enablement has come a long way from slide decks and one-size-fits-all training. In this episode of Business Technology Perspectives, I’m joined by Bill Dwoinen, Chief Revenue Officer at Mural, to explore how AI and visual collaboration are changing the way teams sell, learn, and align around outcomes.Bill brings a wealth of experience from Salesforce and LinkedIn, but what makes this conversation special is how openly he shares the lessons Mural has learned from its own transformation. Once known purely as a visual collaboration tool, Mural is now evolving into a platform that helps organizations accelerate deal velocity, strengthen customer retention, and align teams through what he calls “collaborative selling.” For Bill, the goal is simple: help people spend less time managing tools and more time solving real customer problems.He talks about the growing disconnect between sales strategy and enablement, explaining that many teams today have lost touch with foundational skills such as discovery and negotiation. The solution, he says, lies in a three-part framework of training, enablement, and execution, each powered by technology and reinforced through timing and alignment with the business strategy. This isn’t about piling on new software but understanding what problem each tool actually solves and how it supports human performance.We also explore how AI is reshaping the sales process in ways that are both practical and profound. From generating account insights in minutes to surfacing hidden “Trojan horse” contacts, AI is helping sales teams work smarter without replacing the human element. Bill shares how simple AI prompts have turned Mural’s own teams into experts on their target accounts, driving consistency, efficiency, and confidence across the field.Our discussion also touches on the cultural side of enablement, breaking down silos, moving back to more synchronous communication, and bringing inclusivity into decision-making. As Bill puts it, great enablement mirrors great product design: if you build it without the user’s feedback, it doesn’t matter how good it looks.By the end of this episode, it’s clear that Mural’s next chapter isn’t about whiteboards or sticky notes. It’s about empowering teams to think together, act faster, and use AI to bring clarity to the chaos of modern sales.Listen now to hear how Bill and his team are redefining enablement in an AI-driven world and why sometimes, the smartest sales strategy starts with asking better questions.