AI at Work

What does AI really mean for the modern workplace, and are we ready for what comes next? AI at Work is a podcast from the Tech Talks Network, the home of conversations that showcase the voices at the heart of enterprise technology. You may know me from Tech Talks Daily, where we explore a different area of innovation in every episode. This show takes a focused look at one of the biggest shifts in business: how artificial intelligence is transforming the way we work. From intelligent automation to agentic AI and from the promise of workplace efficiency to the risks of unintended consequences, we aim to provide a grounded and accessible perspective on how AI is shaping the future of work. If you’re using AI in your business or thinking about how to get started, this podcast is your chance to learn from the people already doing it.

  1. 13 hr ago

    Measuring AI ROI Through Expertise Compounding With Kantata

    How do you know whether AI is making your company smarter rather than simply filling dashboards with impressive activity? In this episode of AI at Work, I speak with Michael Speranza, CEO of Kantata, about why familiar productivity metrics may be giving business leaders an incomplete picture of AI ROI. Companies can measure time saved, tasks completed, and documents generated, but those figures say little about whether AI is improving commercial decisions, creating revenue, or producing better client outcomes. Michael introduces the idea of the expertise compounding rate. This measures how effectively a company captures, synthesizes, shares, and builds upon the knowledge created through its projects and people. For professional services firms, that knowledge can include client conversations, previous deliverables, staffing decisions, financial performance, project outcomes, and relationships between colleagues. We discuss how AI can connect that information through a business specific knowledge graph. A team beginning a new project could identify similar work, locate colleagues with relevant experience, understand previous outcomes, and make better staffing or pricing decisions. Institutional knowledge that previously sat inside documents, meeting transcripts, or an employee’s memory can become available at the point of decision. Michael also shares an example of a services company using AI to change its project economics. By reducing delivery costs, the firm could offer projects at prices that created a viable business case for clients who previously would have postponed the work. That suggests AI ROI could be measured through sales conversion, opportunity close times, revenue growth, and the ability to expand without adding headcount at the same rate. Kantata frames the wider market around a revealing paradox. AI adoption across professional services reportedly increased by 40 percent last year, while executive confidence in real time visibility declined and revenue growth slowed to roughly half the industry’s historical benchmark. Greater adoption alone clearly does not guarantee stronger results. Michael argues that efficiency has become the price of admission. The commercial advantage comes from making each project more informed, predictable, and valuable than the one before it. We consider what leaders should measure, how human expertise and AI resources may influence future pricing models, and why clients care far more about outcomes than invisible automation behind the scenes. If every project created knowledge that improved the next one, how would that change the way your company measures AI ROI? Listen to the conversation and share your thoughts with me.

    Measuring AI ROI Through Expertise Compounding With Kantata
  2. 18 Jul

    What Omnissa Learned From a 1000% Rise in Workplace AI Apps

    What should IT leaders do when employees adopt AI tools faster than their organization can evaluate or approve them? In this episode of AI at Work, I speak with Hemant Sahani, Vice President of Product Management for Workspace ONE at Omnissa, about the rapid growth of unsanctioned AI applications across the digital workplace. Omnissa’s State of Digital Workspace 2026 research found that workplace use of AI assistant applications grew by nearly 1000% during 2025. Hemant describes this period as AI’s iPhone moment, with employees choosing the tools that help them work faster instead of waiting for an official corporate rollout. We discuss why blocking every unapproved application can leave IT blind to what employees need. Hemant explains how observability can reveal where people are finding value, why approved tools may be falling short and which applications deserve a proper security, legal and procurement review. Our conversation also examines Omnissa’s vision for the autonomous workspace. Hemant imagines an environment that can configure, secure and repair itself while identifying digital experience problems before employees need to raise a support ticket. We also consider how AI is changing the responsibilities of enterprise IT. As device management, security and employee experience converge, IT teams increasingly need data skills, commercial awareness and closer relationships with HR, finance, security and legal teams. Could shadow AI become a valuable source of workforce intelligence, and how should organizations balance employee freedom with their responsibility to protect company and customer data? Please share your thoughts with me.

    What Omnissa Learned From a 1000% Rise in Workplace AI Apps
  3. 9 Jul

    Why Tomorrow's Leaders Still Need Today's Entry-Level Jobs with ICIMS

    Is artificial intelligence really eliminating entry-level jobs, or is something much bigger happening beneath the surface? As businesses race to improve productivity and invest in AI, many graduates and early-career professionals are wondering whether the first rung of the career ladder is quietly disappearing. In this episode of AI at Work, I welcome Trent Cotton, Head of Talent Insights at iCIMS, for a data-driven conversation about how AI is changing hiring, workforce development, and the future of careers. Drawing on decades of HR experience and the latest workforce research, Trent separates headlines from reality and explains why the story is far more complex than many people assume. We begin by examining one of the biggest concerns surrounding AI. Is the technology actually replacing entry-level jobs? Trent argues that the evidence tells a more nuanced story. Rather than AI directly removing roles, many organizations are redirecting investment toward AI infrastructure while failing to rethink how entry-level positions create long-term value. The result is a hiring market where junior candidates increasingly feel employers expect mid-level experience before offering someone their first opportunity. Our conversation explores why that should concern every business leader. Entry-level employees don't simply fill today's vacancies. They become tomorrow's managers, specialists, and senior leaders. If organizations weaken that pipeline, they risk creating a leadership gap that may not become obvious for years. We also discuss how AI presents an opportunity rather than simply a challenge. Instead of replacing early-career employees, Trent believes organizations should use AI to reduce repetitive work, accelerate learning, and shorten the time it takes for new hires to become productive contributors. That requires rethinking learning and development, coaching, and career progression instead of simply automating existing processes. Another fascinating part of our discussion focuses on where technology talent is actually going. While many headlines concentrate on layoffs across large technology companies, Trent explains why skilled professionals are increasingly finding opportunities in healthcare, manufacturing, and other industries that are embracing AI to solve longstanding workforce shortages and operational challenges. We also examine the skills that are becoming increasingly valuable regardless of how AI develops. Critical thinking, communication, sound judgment, and the ability to orchestrate people, processes, and technology remain difficult to automate. These capabilities, combined with technical literacy and continuous learning, are becoming the qualities that employers value most. One of the biggest surprises from the conversation comes from changing attitudes among younger job seekers. Where previous generations often resisted assessments during the hiring process, many Gen Z candidates are now actively asking for opportunities to demonstrate their abilities through practical exercises rather than relying solely on a resume. As AI makes resumes easier to generate, proving genuine capability is becoming far more valuable than simply listing experience. We also discuss responsible AI in recruitment and why governance cannot become an afterthought. Trent explains why organizations need clear policies, transparency, and accountability before introducing AI into hiring decisions if they hope to maintain trust with candidates and employees alike. Is AI really closing the door on the next generation of workers, or is it giving businesses an opportunity to completely rethink how talent is developed? And as hiring continues to change, are we placing enough value on the human skills that technology still cannot replicate? I'd love to hear your thoughts after listening.

    Why Tomorrow's Leaders Still Need Today's Entry-Level Jobs with ICIMS
  4. 5 Jul

    Why Digital Ownership Matters More Than Ever with lilAgents

    What if your business doesn't actually own its website, customer data, or digital marketing infrastructure? It's an uncomfortable question, but one that many founders never ask until they try to switch providers and discover just how difficult it is to leave. In this episode of AI at Work, I welcome David V. Kimball, Co-Founder and CEO of lilAgents, for a conversation that challenges many of the assumptions businesses have made over the last decade about websites, software subscriptions, AI, and digital ownership. David argues that convenience often comes at a hidden cost, with businesses gradually handing control of their most valuable digital assets to platforms that make it increasingly difficult to move elsewhere. We begin by exploring how so many organizations found themselves locked into ecosystems that seemed like the simplest option at the time. Website builders, ecommerce platforms, marketing suites, hosting providers, and CRM systems all promise convenience, yet many businesses only discover the downside when prices increase, features disappear, or they attempt to migrate to something better. The conversation then turns to artificial intelligence and where it is genuinely making a difference today. Rather than focusing on AI chatbots that have been added to almost every product, David explains why AI agents are becoming far more interesting. These systems can perform real work, connect different applications, automate repetitive processes, and solve practical business problems while people focus on higher-value work. One example that stood out involved a Shopify store with thousands of products that had accumulated years of inconsistent metadata. Using AI agents connected directly to Shopify's APIs, David was able to automate work that would have taken weeks by hand, helping improve search visibility and delivering measurable growth in organic revenue. It serves as a practical reminder that AI delivers the greatest value when solving real operational challenges rather than simply generating content. We also spend time discussing the hidden costs many businesses overlook. From paying for CRM contacts that no longer engage to running websites on platforms with far more functionality than they actually need, David explains why simplifying technology stacks can often reduce costs while improving flexibility at the same time. The objective isn't simply spending less. It's building systems that businesses genuinely own and can adapt as their needs change. Another theme running throughout our discussion is portability. Whether we're talking about websites, marketing platforms, AI models, or business data, David believes organizations should avoid becoming dependent on any single vendor. As AI continues to develop, he argues that businesses should think carefully about building modular systems that make it easy to change providers instead of finding themselves trapped by the next generation of platform lock-in. This episode offers a refreshing perspective on AI by moving beyond the hype and focusing on practical outcomes. It also raises an important question about the future of digital business. Are companies investing in technology they truly control, or are they simply renting increasingly expensive pieces of someone else's platform? How much of your digital business do you genuinely own today? And if one of your technology providers disappeared tomorrow, how easily could you move somewhere else? I'd love to hear your thoughts after listening.

    Why Digital Ownership Matters More Than Ever with lilAgents
  5. 5 Jul

    Fleetio on Why Customers Want Results, Not More Features

    What if the next competitive advantage in business isn't working faster with AI, but making better decisions because of it? As organizations rush to become AI-native, many conversations still focus on productivity, automation, and shipping work more quickly. But is speed really the outcome that matters most? In this episode of AI at Work, I welcome Jorge Valdivia, Chief Technology Officer at Fleetio, for a thoughtful discussion about what AI is actually changing inside modern organizations. Rather than adding another voice to the growing hype around artificial intelligence, Jorge offers a refreshingly practical perspective on why the future belongs to businesses that combine trusted expertise with intelligent technology. We begin by exploring how enterprise software has evolved over the past decade. For years, success meant becoming the system of record, collecting information in one central place and serving as the trusted source of truth. Today, however, customers expect something more. They want software that helps them produce measurable business outcomes, save money, improve operations, and clearly demonstrate return on investment. That shift naturally leads us into one of the most interesting parts of our conversation. Jorge challenges the common belief that AI automatically turns average performers into exceptional ones. Instead, he argues that the people gaining the greatest advantage from AI were already deeply curious about their customers, understood their industry, and knew how to solve meaningful problems. AI doesn't replace those qualities. It amplifies them. Throughout our discussion we examine what separates productive work from valuable work. While AI can certainly automate repetitive tasks and reduce time spent on administration, Jorge believes its greatest contribution comes from helping teams make better decisions. By bringing together customer feedback, product information, engineering data, and business context, AI becomes another source of insight that helps organizations identify the right opportunities instead of simply executing more tasks. We also discuss what it really means to become an AI-native leader. Rather than chasing every new tool or trend, Jorge explains why successful leaders focus on understanding where AI genuinely creates value for customers. That often means balancing experimentation with discipline, embracing automation where it removes friction, while keeping people responsible for the strategic decisions that still depend on judgment, context, and experience. One example that stood out involved Fleetio's own product development process. Faced with defining its long-term AI vision, the team used AI to synthesize customer conversations, product feedback, engineering insights, and design concepts into a shared understanding that had previously taken months of discussion without resolution. The technology didn't replace human thinking. It accelerated collective understanding so better decisions could be made. As our conversation draws to a close, Jorge shares advice for anyone building products or developing their career in an AI-powered workplace. Learning to use AI tools is rapidly becoming an expected part of the job, but lasting success still depends on becoming a trusted expert who understands customers, business problems, and the context behind every decision. Is the biggest opportunity with AI really about doing more work? Or is it about making smarter decisions that create better outcomes for customers, employees, and the business itself? I'd love to hear where you stand after listening.

    Fleetio on Why Customers Want Results, Not More Features
  6. 29 Jun

    Single Player AI vs Multiplayer AI in the Workplace and Why It Matters

    What if the biggest obstacle to AI success isn't the technology at all, but the way your business actually works? In this episode of AI at Work, I sit down with Justin Watt, CEO and Co-founder of Switchboard, to discuss why so many AI initiatives disappoint and what organizations should focus on before adding another AI tool to the mix. Justin has spent his career helping growing businesses replace disconnected spreadsheets, manual handoffs, and fragmented workflows with systems that are designed to support the way people really work. During our conversation, Justin explains why many organizations are trying to build an AI-first business on top of processes that were never designed for automation. Rather than chasing the latest technology, he argues that leaders should first understand how work actually moves across their organization, identify unnecessary complexity, and remove friction before introducing AI. One of my favourite moments in our discussion is Justin's comparison between "single player AI" and "multiplayer AI." While many employees are already seeing personal productivity gains from tools such as ChatGPT and Copilot, the real opportunity comes when AI works across departments, connecting sales, operations, finance, legal, and customer teams instead of remaining isolated in individual chat windows. We also discuss why spreadsheets continue to dominate business operations decades after their introduction, how companies can move beyond them without disrupting the business, and why operational workflows should be treated like products that are continuously improved rather than collections of disconnected fixes. Justin also shares practical lessons from working with organizations that believed they had an AI problem, only to discover the real issue was broken processes. From legal teams overwhelmed by poor sales handoffs to businesses relying on undocumented workflows held together by spreadsheets and institutional knowledge, he offers a grounded perspective on where AI genuinely creates value and where better operational design delivers faster results. If you're leading digital transformation, responsible for operations, or trying to move AI from experimentation into everyday business value, this conversation offers practical advice that can be applied immediately. How well does your organization really understand its own workflows before asking AI to improve them? I'd love to hear your thoughts after listening.

    Single Player AI vs Multiplayer AI in the Workplace and Why It Matters
  7. 26 Jun

    How Thoughtly Is Turning AI Voice Into A Competitive Advantage

    What does it take for AI agents to move beyond impressive demonstrations and become part of the working day? In this episode of AI at Work, I speak with Will Del Principe from Thoughtly about what happens when AI voice agents are deployed into live customer and revenue operations. While many organizations are still evaluating where AI fits, Thoughtly is already helping businesses automate conversations, qualify leads, and manage customer interactions at a scale that would have been impossible just a few years ago. Will explains why the first breakthrough for AI voice isn't replacing complex human conversations. Instead, it is handling high-intent follow-up, where customers are already expecting a call and want fast, accurate answers. We also discuss why being open about using AI often increases trust, how even a fraction of a second in response time can determine whether a conversation feels natural, and why building conversational AI is far more technically demanding than many people appreciate. The conversation also highlights customer success stories, including Nomad, where Thoughtly's AI agents quickly grew to managing 20,000 tenant calls each day and 13,000 outbound sales calls every month. Rather than replacing employees, the technology allowed existing sales teams to focus on closing deals while AI handled repetitive outreach, qualification, and scheduling. We also discuss why businesses should experiment with AI before competitors gain an advantage, how AI agents are developing long-term memory across multiple communication channels, and why learning to work alongside AI is becoming an important skill for professionals at every stage of their careers. If AI can remove repetitive work while helping people spend more time on the tasks that matter most, where could it make the biggest difference in your organization? After listening, I'd love to hear your thoughts. How do you see AI changing the way you work over the next few years?

    How Thoughtly Is Turning AI Voice Into A Competitive Advantage
  8. 31 May

    Why Travelport Believes The Real AI Opportunity Starts With People

    What if the biggest AI challenge facing organizations has nothing to do with technology at all? In this episode of AI at Work, I sit down with Lee Senderov, Chief Transformation Officer at Travelport, to discuss why AI should be viewed as a workforce transformation rather than a technology project, and why many organizations are still framing the opportunity in entirely the wrong way. While many businesses continue to focus on AI pilots, innovation labs, and isolated technical use cases, Lee argues that the real opportunity lies in empowering every employee. Drawing on Travelport's own AI journey, she shares how teams across the organization are using AI to eliminate repetitive work, create time for higher-value thinking, and solve problems that would never make it onto a traditional technology roadmap. We explore the practical framework Travelport has developed to drive adoption, covering capability building, creating the right operating environment, and fostering a culture that encourages employees to openly share ideas and AI-powered innovations. Lee explains why successful AI adoption requires far more than deploying tools, and how organizations can create an environment where experimentation becomes part of everyday work. The conversation also looks at the future of hiring, talent, and workplace culture. Lee predicts that AI proficiency will soon become as commonplace as email skills, shifting hiring conversations away from whether someone uses AI and toward how they use it to improve outcomes. At the same time, she warns against both ignoring AI and becoming overly dependent on it, arguing that the most successful employees will combine AI capabilities with human judgment, creativity, and critical thinking. We also discuss how AI is transforming the travel industry itself. From changing the way travelers search and book trips to supporting travel professionals during disruptions and complex itineraries, Lee explains how AI and human expertise are increasingly working together to create better customer experiences. Looking ahead, Lee believes the organizations that thrive will be those that build cultures capable of adapting quickly to whatever comes next. AI may be today's disruption, but the larger challenge is creating a workforce ready to embrace continuous change. Is your organization treating AI as another software tool, or is it rethinking how work itself gets done? Share your thoughts with me.

    Why Travelport Believes The Real AI Opportunity Starts With People

About

What does AI really mean for the modern workplace, and are we ready for what comes next? AI at Work is a podcast from the Tech Talks Network, the home of conversations that showcase the voices at the heart of enterprise technology. You may know me from Tech Talks Daily, where we explore a different area of innovation in every episode. This show takes a focused look at one of the biggest shifts in business: how artificial intelligence is transforming the way we work. From intelligent automation to agentic AI and from the promise of workplace efficiency to the risks of unintended consequences, we aim to provide a grounded and accessible perspective on how AI is shaping the future of work. If you’re using AI in your business or thinking about how to get started, this podcast is your chance to learn from the people already doing it.

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