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 offers a focused look at one of the most significant shifts in business: how artificial intelligence is transforming the way we work.. 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. Why Digital Ownership Matters More Than Ever with lilAgents

    10시간 전

    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.

    28분
  2. Fleetio on Why Customers Want Results, Not More Features

    10시간 전

    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.

    26분
  3. Single Player AI vs Multiplayer AI in the Workplace and Why It Matters

    6일 전

    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.

    24분
  4. How Thoughtly Is Turning AI Voice Into A Competitive Advantage

    6월 26일

    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?

    26분
  5. Why Travelport Believes The Real AI Opportunity Starts With People

    5월 31일

    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.

    28분
  6. How LaunchDarkly Is Helping Enterprises Control Shadow AI in DevOps

    5월 27일

    How LaunchDarkly Is Helping Enterprises Control Shadow AI in DevOps

    What happens when AI-generated code ships faster than humans can properly review it, and who takes the blame when something breaks? In this episode of AI at Work, I sit down with Cameron Etezadi, Chief Technology Officer at LaunchDarkly, to tackle one of the most uncomfortable questions facing modern software teams. As developers increasingly rely on AI coding assistants, copilots, and public LLMs to accelerate delivery, organizations are finding themselves caught between productivity gains and growing governance risks. Cameron explains why “Shadow AI” has become the modern evolution of Shadow IT, and why the stakes are far higher when AI-generated code is moving directly into production systems. We explore how engineering teams are balancing innovation with accountability, why runtime controls and kill switches are becoming essential in AI-native software development, and how organizations are struggling to maintain visibility into code generated by autonomous systems. Cameron also explains why he believes many companies are unknowingly exposing intellectual property, customer trust, and compliance obligations through careless AI use. The conversation also examines how the EU AI Act and Product Liability Directive could reshape software development globally. Cameron argues that organizations deploying AI-generated code are now effectively treated as manufacturers under emerging regulations, with accountability resting firmly on businesses shipping software, not the AI vendors creating the tools. From governance gaps and auditability concerns to token economics and developer productivity metrics, this discussion explores the operational realities behind the AI hype cycle. We also discuss why faster code does not automatically mean safer software, the hidden costs of AI-generated rework, and how some organizations are already spending more time fixing AI-assisted production issues than they expected. Cameron shares practical advice for boards, CISOs, and DevOps leaders on what questions they should be asking today before AI governance problems become tomorrow’s security incidents. If your organization is experimenting with AI-assisted development, this conversation offers a valuable reality check on where the risks are emerging, how the rules are changing, and why accountability still matters in an increasingly automated world.

    47분
  7. KPMG - Why AI ROI Depends More on Workforce Behavior Than Technology

    5월 20일

    KPMG - Why AI ROI Depends More on Workforce Behavior Than Technology

    Why are so many organizations investing millions into AI while still struggling to prove meaningful productivity gains? In this episode of AI at Work, I spoke with Rahsaan Shears, Principal and AIQ Program Lead at KPMG, about a major new study conducted alongside the McCombs School of Business at The University of Texas at Austin that analyzed 1.4 million real workplace AI interactions. What emerged from that research challenges many assumptions business leaders currently hold about AI adoption, productivity, and the future of work. One of the most surprising findings was that the most effective AI users were not necessarily the most technical employees, nor even the people using AI tools most frequently. Instead, the highest performers were what KPMG calls “sophisticated users,” employees who learned how to think with AI, challenge it, iterate with it, and use it as a reasoning partner rather than simply a faster search engine. Rahsaan explained how this distinction is forcing organizations to rethink how they measure AI success. Many businesses remain focused on surface-level adoption metrics like license counts, prompt volume, or chatbot usage. But those measurements often fail to capture whether AI is genuinely improving decision-making, productivity, creativity, or operational performance. The real challenge, according to Rahsaan, is that most organizations still lack a framework for understanding what meaningful AI-enabled work actually looks like. We also explored the growing behavioral capability gap emerging inside organizations. While some employees are rapidly learning how to integrate AI into their workflows in sophisticated ways, others remain stuck using these tools for basic task acceleration. Rahsaan shared why this gap has less to do with age or technical skill and far more to do with curiosity, ambition, critical thinking, and an employee’s willingness to rethink how work itself gets done. One of the strongest themes throughout our conversation was the idea that AI should not be treated as a technology rollout alone. Rahsaan argued that organizations succeeding with AI are redesigning culture, workflows, decision-making structures, and team dynamics at the same time they deploy new tools. He compared today’s AI systems to toddlers: incredibly capable compared to where they started, but still requiring guardrails, coaching, supervision, and careful integration into everyday work. For listeners interested in organizational transformation, this episode offers practical insight into how KPMG is building AI-first behaviors through peer-led champion networks, embedded learning models, AI coaching inside the flow of work, and safe environments where employees can experiment without fear of failure. Rahsaan shared why psychological safety, curiosity, and continuous learning are rapidly becoming core business skills in the AI economy. We also discussed why organizations that fail to create agency for employees may struggle to scale AI beyond pilot programs. According to Rahsaan, many existing business processes were designed around the limitations of human workers, limitations that no longer fully apply once digital teammates and agentic workflows enter the picture. Companies willing to question long-standing assumptions about work itself are beginning to separate themselves from the rest of the market. This conversation moves beyond AI hype and focuses on the human behaviors, organizational structures, and operational changes that will ultimately determine who wins and loses in the AI economy.

    30분
  8. LaunchLemonade Founder Cien Solon On Building The Canva For AI Agents

    4월 6일

    LaunchLemonade Founder Cien Solon On Building The Canva For AI Agents

    What happens when AI agent creation stops being the job of engineers and starts landing in the hands of the people who actually understand the business problem? In this episode of AI At Work, I sat down with Cien Solon, CEO and Founder of LaunchLemonade, to talk about why the next chapter of AI may have less to do with hype and more to do with practical problem-solving. Cien describes LaunchLemonade as the Canva for AI agents, and that immediately caught my attention because it gets to the heart of what so many businesses are looking for right now. They do not want more jargon. They want a way to build something useful, quickly, securely, and without needing a room full of developers to make it happen. What I found especially interesting in our conversation was Cien’s argument that the real barrier to AI is no longer cost or technical complexity. In her view, those obstacles have already fallen away. The bigger issue now is mindset. Too many organizations are still stuck in observation mode, watching from the sidelines, waiting for perfect tools and perfect certainty. Meanwhile, others are already building, testing, learning, and finding ways to turn AI agents into something that supports growth, fills skills gaps, and creates new revenue opportunities. We also talked about what return on investment actually looks like in the real world. That part matters because so many AI conversations still float around in theory. Cien makes the case that the people best placed to solve business problems are the ones living with them every day, not the engineers guessing from a distance. That is a powerful shift in thinking. Instead of waiting until there is budget to hire another person, businesses can now identify a gap, map out the workflow, and create an AI agent to help close it. There is also a bigger human story running through this episode. Cien shared examples of people who started out experimenting with prompts and basic no-code tools, then went on to build consulting businesses, launch products, sell courses, and reposition themselves in the market. One story that stood out was a university professor who used LaunchLemonade to learn, experiment, and eventually step into entrepreneurship full time. It is the kind of example that reminds us this technology is not only changing workflows, it is also changing careers and confidence. We also discuss the future of the no-code agent economy and where businesses need to focus next. Cien breaks people into a few camps, the observers, the operators, and the builders, and it makes for a memorable way of thinking about where each of us stands right now. Her message is clear. If you are still only watching, you risk falling behind. If you are building, the next challenge is no longer whether you can create something, but whether you can market it, sell it, and make it meaningful. By the end of this conversation, what stayed with me most was how accessible this all feels when someone explains it in plain English. This is not a conversation about futuristic abstractions. It is about people using AI to solve real business problems today, in ways that feel achievable rather than intimidating. So after listening, where do you see yourself in this new AI economy, observing, operating, or building, and what are you creating next?

    24분
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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 offers a focused look at one of the most significant shifts in business: how artificial intelligence is transforming the way we work.. 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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