What’s the BUZZ? — AI in Business

Your next customer might not be a person. It might be an agent that searches, compares, and negotiates on someone else's behalf. So what happens to advertising, to pricing, and to trust when that becomes normal? In this episode of "What's the BUZZ?", host Andreas Welsch sits down with Michael Carroll, Strategic Advisor and Fellow at LNS Research, to work through what actually changes when the customer is an AI agent. Michael spends his time on advisory boards and AI research, focused on what this technology should do for people, not only on what it can do. Key insights from this conversation include: The web was built for clicks. Agents are built for intent. Michael frames the shift as moving from a click-based architecture and economy to one organized around what you want. Today you do most of the orchestration yourself to get it. Reasoning at the edge flips that: the agent handles the orchestration, and the effort left to you is working out why you want what you want. Ads do not disappear. They change target. Michael expects ads to keep running on TV and on your phone, but their job changes. Instead of persuading you to buy, they work to bias you into telling your agent what your preference is. The emotional pull advertising relies on does not land on an agent, so it gets aimed one step earlier, at you. Intermediation will go autonomous. Mediation needs permission. Michael splits organizational work into intermediation, the moving and translating of information, and mediation, changing something from one state to another in a way that requires approval. The first is largely reversible and a strong candidate for autonomy. The second carries responsibility, and a machine never bears responsibility. Causal reasoning is the trust architecture, not explainability. His verdict on the alternative is blunt: "Explainable AI is an abomination. It's an invitation to re-litigation." A causal chain of reasoning, he argues, is auditable and defensible, and it is not the same thing as deterministic. Whoever closes the triad holds the influence. The reasoning agent at the edge, the platform you view the world through, and the architecture of trust. Close all three and you have something controllable, which raises the question of who is positioned to do it. The collapse of distance points back at the individual. Michael cites Stanley Milgram's six degrees of separation from the 1960s and LinkedIn's own figure of roughly three today, and argues agents take it to one. His read is that this pulls people closer to everything rather than further apart. What leaders can do now: make your business legible to agents and not only to people, with structure, schema, and clear answers to the questions an agent will ask on a buyer's behalf; sort your processes into intermediation and mediation, treating the first as candidates for autonomy and the second as needing a permission and audit trail before an agent goes near them; and work out, for a vendor agent and a customer agent transacting with each other, what each side is authorized to do, what information it is acting on, and whether it can be swayed. Michael's line to write down: "If you can't trust it, it won't happen." If your customer's agent evaluated you today, what would it find? Questions or suggestions? Send me a Text Message. Support the show *********** Disclaimer: Views are the participants’ own and do not represent those of any participant’s past, present, or future employers. Participation in this event is independent of any potential business relationship (past, present, or future) between the participants or between their employers. Level up your AI Leadership game with the AI Leadership Handbook ( https://www.aileadershiphandbook.com ) and shape the next generation of AI-ready teams with The HUMAN Agentic AI Edge ( https://www.humanagenticaiedge.com ). More details: https://www.intelligence-briefing.com All episodes: https://www.intelligence-briefing.com/podcast Get a weekly thought-provoking post in your inbox: https://www.intelligence-briefing.com/newsletter

Most organizations are sprinkling agentic AI on top of a legacy, heterogeneous stack — and calling it a strategy. That works until the low-hanging fruit runs out, because it rests on a flawed assumption: that the business will keep operating exactly as it always has, only faster. In this episode, host Andreas Welsch sits down with Grant Ecker, Chief Architect at Ecolab and founder of the Chief Architect Network — a 682-member nonprofit collaborative of Fortune 500 architecture executives. Drawing on architecture leadership roles at Danaher, Walgreens Boots Alliance, Medtronic, Lowe's, and General Mills, Grant lays out what it actually takes to make an enterprise architecture — and an architecture organization — ready for agentic AI. Key insights from this conversation include: Start with capability-based planning, not use cases. Decompose your strategy into the capabilities you have to be great at, map the gaps, and build a heat map of transformation opportunities. Some will be exciting (product innovation); some won't (order management). Both determine whether the strategy lands. Earn the seat at the table through IT first. Architects get invited into strategy conversations because IT leadership trusts them — not in spite of it. Go to the business first and you risk undermining the partners who own that relationship, and losing the invitation entirely. Move from T-shaped to pie-shaped. Technical depth alone no longer sustains an architecture career. Add a second point of depth in business understanding and communication, and become the connector and strategist for your part of the ecosystem. Rethink "if it ain't broke, don't fix it." If something is fixed and sitting in the middle of a transformation, that's precisely the moment to challenge it — when a business outcome justifies the disruption. The prize is refactoring decisions so agents can make them with the right guardrails, governance, trust, and permissions. Treat AI FinOps as a budgeting discipline, not an afterthought. Meter consumption by department, forecast token spend before the sticker shock arrives, and tie the next wave of investment to demonstrated ROI. Consumption is not a proxy for value. Centralize, then federate. Pilot and learn centrally with safe off-the-shelf tools, then embed capabilities into the organization as maturity grows — the same curve cloud followed, and the one quantum will follow next. Peer communities beat published best practice. By the time an approach is written up, it's six to nine months old. A trusted relationship with a peer in a non-competing industry is the fastest way to stay ahead of a market moving this quickly. Whether you're a chief architect building the agentic reference architecture, a CIO deciding who holds the keys to AI adoption, or a technology leader working to be in the room where strategy gets set, this conversation offers a grounded view of the organizational work that makes agentic AI possible. Tune in now to learn how to prepare your architecture — and your architects — for a shift Grant frames as an age-like change on par with the printing press and the internet. Questions or suggestions? Send me a Text Message. Support the show *********** Disclaimer: Views are the participants’ own and do not represent those of any participant’s past, present, or future employers. Participation in this event is independent of any potential business relationship (past, present, or future) between the participants or between their employers. Level up your AI Leadership game with the AI Leadership Handbook ( https://www.aileadershiphandbook.com ) and shape the next generation of AI-ready teams with The HUMAN Agentic AI Edge ( https://www.humanagenticaiedge.com ). More details: https://www.intelligence-briefing.com All episodes: https://www.intelligence-briefing.com/podcast Get a weekly thought-provoking post in your inbox: https://www.intelligence-briefing.com/newsletter

AI agents are everywhere in the headlines, but successful agentic AI is about much more than deploying the latest technology. The organizations that succeed will be the ones that build the right foundation across data, culture, and strategy. In this episode of “What’s the BUZZ?”, Andreas Welsch welcomes Sebastian Wernicke, author of Data Inspired , to explore what it takes to turn AI ambition into business outcomes. You will learn why data alone is not enough, why organizations need to become data-inspired rather than simply data-driven, and how leaders can create the culture and strategy required for AI agents to deliver real impact: The three critical success factors for agentic AI: data, culture, and strategy How data-inspired organizations use information to drive innovation and competitive advantage Why culture and technology conversations must happen together How AI agents will reshape organizational decision-making What leaders should do today to prepare their organizations for an AI-powered future Whether you are exploring AI agents, building an AI strategy, or leading organizational change, this conversation provides practical guidance for moving beyond AI hype and creating meaningful outcomes. Questions or suggestions? Send me a Text Message. Support the show *********** Disclaimer: Views are the participants’ own and do not represent those of any participant’s past, present, or future employers. Participation in this event is independent of any potential business relationship (past, present, or future) between the participants or between their employers. Level up your AI Leadership game with the AI Leadership Handbook ( https://www.aileadershiphandbook.com ) and shape the next generation of AI-ready teams with The HUMAN Agentic AI Edge ( https://www.humanagenticaiedge.com ). More details: https://www.intelligence-briefing.com All episodes: https://www.intelligence-briefing.com/podcast Get a weekly thought-provoking post in your inbox: https://www.intelligence-briefing.com/newsletter

When your AI agents start transacting on your behalf, who’s protecting your interests — and how do they prove it? In this episode, host Andreas Welsch sits down with Nitin Badjatia, enterprise software veteran and board member of Customer Commons, to explore one of the most consequential and least-discussed gaps in agentic AI adoption: the absence of enforceable, machine-readable contracts between autonomous agents and the commercial web. Together, they unpack why the digital commerce infrastructure we’ve built on surveillance and click-through consent is fundamentally incompatible with a world where agents act, negotiate, and commit on our behalf — and what the emerging IEEE 7012 “My Terms” standard proposes to do about it. Discover the strategic and governance foundations that responsible agentic commerce requires: Recognize that consent is not a contract — and build your agentic governance accordingly. The terms of service paradigm grants vendors expansive rights users never meaningfully agreed to. As agents multiply, that exposure compounds. IEEE 7012 proposes human-readable, machine-executable contract templates as the foundation for agent-to-agent and agent-to-vendor interaction. Model your digital interactions on physical-world norms. You wouldn’t share your financial strategy with an uncredentialed stranger, and you wouldn’t accept lifetime tracking as the price of entry at a grocery store. Yet that’s the standard your agents are operating within today. Bridging that gap is both an ethical imperative and a governance requirement. Understand the five contract tiers the My Terms standard introduces — from simple transactional exchanges (no data retained beyond the transaction) to more complex arrangements — and identify which tier is appropriate for each agent interaction in your commercial stack. Build toward a neutral, nonprofit contract infrastructure, analogous to how Creative Commons standardized licensing for creative works. The goal is a library of pre-negotiated, legally coherent templates that agents can carry into transactions — giving both parties a verifiable, auditable “legal receipt” for what was agreed. Treat data quality as a governance outcome. A surveilled customer shares guarded context; a customer operating under a mutual contractual arrangement shares richer, more accurate data willingly. Organizations that adopt contract-first agentic models early will generate a higher-fidelity customer signal — not just reduced legal exposure. Whether you’re a Chief Digital Officer designing your agentic commerce strategy, a privacy or legal leader trying to get ahead of regulatory exposure, or an AI architect determining what rights your deployed agents should carry, this episode reframes the agentic opportunity through the lens that matters most: who is actually accountable when the agent acts? Tune in now to understand why the infrastructure of trust — not the sophistication of the model — will determine which organizations can scale agentic commerce with confidence. Questions or suggestions? Send me a Text Message. Support the show *********** Disclaimer: Views are the participants’ own and do not represent those of any participant’s past, present, or future employers. Participation in this event is independent of any potential business relationship (past, present, or future) between the participants or between their employers. Level up your AI Leadership game with the AI Leadership Handbook ( https://www.aileadershiphandbook.com ) and shape the next generation of AI-ready teams with The HUMAN Agentic AI Edge ( https://www.humanagenticaiedge.com ). More details: https://www.intelligence-briefing.com All episodes: https://www.intelligence-briefing.com/podcast Get a weekly thought-provoking post in your inbox: https://www.intelligence-briefing.com/newsletter

How fast is AI really moving, and what does that mean for your business strategy? In this episode, host Andreas Welsch reconnects with Doug Shannon, an intelligent automation and generative AI leader, to explore the accelerating pace of AI innovation and what it means for organizations navigating this transformation. Together, they discuss why the speed of change has become almost impossible to predict, how companies should approach the build-versus-buy decision, and why your competitive advantage lies not in technology, but in what your organization does best. Key insights from this conversation include: Understanding scaled intelligence: AI isn't just getting faster because of better tools—it's because we're deploying intelligence at scale, which compounds the pace of innovation exponentially. This means your eight-week roadmap may already be outdated. The build-versus-buy paradox: While buying off-the-shelf solutions offers speed and vendor expertise, staying agnostic to specific models and platforms protects you from lock-in. Leverage what you need, when you need it, without becoming beholden to any single provider. Governance meets velocity: The solution isn't to choose between speed and safety—it's to create internal sandboxes and centers of intelligence where teams can experiment safely. Enable your people to build, but within guardrails that keep your organization secure and your data protected. The human element remains critical: Don't fire people to cut costs; instead, empower them with AI tools to become 10X more productive. Context and institutional knowledge walk out the door when you lose experienced team members, and that's a cost you can't easily recover. Orchestration is the future: Single agents are yesterday's news. Multi-agent systems and orchestration—where AI coordinates across different specialized agents—represent the next evolution. This is where enterprises will find their competitive edge. Small and medium-sized companies have an unexpected advantage: Without legacy systems, legacy data, and legacy processes, they can move faster than large enterprises. The real question isn't whether to adopt AI, but how quickly you can. Whether you're a business leader grappling with AI strategy, an IT professional managing governance, or a team member wondering how AI will change your role, this episode offers practical perspectives on navigating the fastest-moving technology shift in modern business. Tune in now to discover how to harness the momentum of AI innovation without losing sight of what makes your organization truly unique. Questions or suggestions? Send me a Text Message. Support the show *********** Disclaimer: Views are the participants’ own and do not represent those of any participant’s past, present, or future employers. Participation in this event is independent of any potential business relationship (past, present, or future) between the participants or between their employers. Level up your AI Leadership game with the AI Leadership Handbook ( https://www.aileadershiphandbook.com ) and shape the next generation of AI-ready teams with The HUMAN Agentic AI Edge ( https://www.humanagenticaiedge.com ). More details: https://www.intelligence-briefing.com All episodes: https://www.intelligence-briefing.com/podcast Get a weekly thought-provoking post in your inbox: https://www.intelligence-briefing.com/newsletter

What if the real competitive advantage in AI isn't about having the biggest models, but about building systems that can be trusted, audited, and governed at scale? In this episode, host Andreas Welsch explores the convergence of AI, cybersecurity, and quantum computing with Joseph Ng, Chief Strategy Officer at GeneGenius and author of "The Hybrid Mind: The Human-AI Convergence." Together, they challenge the prevailing narrative around the AI race and reveal why most organizations are solving the wrong problem. Joseph shares critical insights on why companies must shift from treating AI as a tool deployment challenge to redesigning their entire decision-making architecture: Capability is scaling faster than control. Organizations are deploying AI systems without understanding how they behave, how they're exposed, or how they can be influenced—creating exponential risk that compounds across interconnected agents and workflows. The real differentiation won't come from model size or compute power. It will come from organizations that can build systems where intelligence, oversight, and human authority are embedded into the architecture from day one—what Joseph calls Cognitive AI and Native Architecture (CANA). Quantum computing isn't a distant threat. The "harvest now, decrypt later" approach means sensitive data collected today could be compromised once quantum becomes viable, making cryptographic hardening and governance redesign urgent priorities for leaders. For mid-sized organizations without large AI centers of excellence, Joseph recommends a phased, modular approach: audit your current systems, identify breaking points, and integrate AI incrementally while building governance into execution—not policy documents. Whether you're a business leader navigating the AI landscape or a technology executive preparing for what's next, this conversation cuts through the noise to reveal what actually matters: building institutions that can operate intelligence responsibly, visibly, and at scale. Tune in now to discover how to move beyond AI hype and build the governance-first systems that will define competitive advantage in the years ahead. Questions or suggestions? Send me a Text Message. Support the show *********** Disclaimer: Views are the participants’ own and do not represent those of any participant’s past, present, or future employers. Participation in this event is independent of any potential business relationship (past, present, or future) between the participants or between their employers. Level up your AI Leadership game with the AI Leadership Handbook ( https://www.aileadershiphandbook.com ) and shape the next generation of AI-ready teams with The HUMAN Agentic AI Edge ( https://www.humanagenticaiedge.com ). More details: https://www.intelligence-briefing.com All episodes: https://www.intelligence-briefing.com/podcast Get a weekly thought-provoking post in your inbox: https://www.intelligence-briefing.com/newsletter

What happens when AI moves faster than your ability to govern it safely? In this episode, host Andreas Welsch sits down with Reid Blackman, founder and CEO of Virtue and author of "The Ethical Nightmare Challenge," to explore the critical ethical risks that emerge as AI evolves from narrow systems to generative and agentic solutions. Reid brings a philosopher's perspective to the business challenges of AI deployment, discussing how organizations can avoid the reputational, regulatory, and legal pitfalls that come with increasingly autonomous systems. Discover why traditional approaches to responsible AI governance are breaking down and what you need to do instead: Understand the escalating complexity of AI risk as systems become more autonomous and interconnected. Cascading failures, emergent risks, and the loss of meaningful human oversight create a perfect storm of potential disasters that move at unprecedented speed and scale. Recognize that the standard top-down, policy-driven approach to AI ethics is fundamentally broken. Enterprise-wide policies take years to implement while technology leaps ahead, leaving organizations perpetually chasing yesterday's problems with tomorrow's tools. Shift from abstract values to concrete nightmare scenarios. By identifying organizationally relevant ethical nightmares—discriminatory outcomes at scale, hallucinated reports, unforeseen system failures—you create actionable strategies that everyone across your organization can understand and collaborate on. Prioritize rapid, scalable governance solutions that move at the pace of AI innovation. Whether you adopt Reid's Ethical Nightmare Challenge framework or another approach, your risk management must be nimble enough to keep pace with deployment, not slow it down. Whether you're a business leader deploying AI systems, a risk officer concerned about governance, or a technologist grappling with ethical complexity, this conversation reveals why facing AI's challenges head-on is the only path to capturing its genuine opportunity. Don't miss this essential discussion on turning AI hype into responsible, sustainable business outcomes. Tune in now to learn how to navigate the ethical minefield of modern AI deployment. Questions or suggestions? Send me a Text Message. Support the show *********** Disclaimer: Views are the participants’ own and do not represent those of any participant’s past, present, or future employers. Participation in this event is independent of any potential business relationship (past, present, or future) between the participants or between their employers. Level up your AI Leadership game with the AI Leadership Handbook ( https://www.aileadershiphandbook.com ) and shape the next generation of AI-ready teams with The HUMAN Agentic AI Edge ( https://www.humanagenticaiedge.com ). More details: https://www.intelligence-briefing.com All episodes: https://www.intelligence-briefing.com/podcast Get a weekly thought-provoking post in your inbox: https://www.intelligence-briefing.com/newsletter

What happens when you deploy multi-agent systems into your HR operations—and how do you ensure they elevate your workforce rather than replace it? In this episode, host Andreas Welsch sits down with Kris Saling, Senior Data Science Leader working on AI integration for personnel management at scale, to explore the critical foundations needed for successful multi-agent deployments in human resources. Together, they discuss how to identify where agents truly add value, the importance of governance without stifling innovation, and why domain expertise matters as much as technical capability. Discover the strategic framework that transforms agent implementation from a technology exercise into a business outcome: Use the eliminate, simplify, automate, elevate framework to determine which tasks genuinely benefit from intelligent automation versus simple RPA solutions. Not every workflow needs a sophisticated multi-agent system—sometimes the best solution is far simpler. Build governance structures that encourage citizen development while maintaining visibility into what agents are doing, who built them, and when they were last validated. Think of it as traffic laws that keep innovation flowing safely, not bureaucratic red tape. Shift HR's role from transactional processing to full-spectrum talent management. Create a "Waze model" for your workforce where employees can see their skills, available opportunities, and career pathways as automation evolves their current roles. Prioritize domain knowledge alongside technical training. As automation removes the foundational "toil" that traditionally teaches new employees how systems work, you must intentionally preserve that learning pathway. Recognize that AI will transform jobs, not eliminate them—if you keep elevating your human workforce into the work only humans can do. The future belongs to organizations that master this balance. Whether you're an HR leader navigating AI integration, a business executive building multi-agent systems, or a technologist curious about enterprise-scale deployment, this episode offers practical insights and a refreshing perspective on how to turn AI hype into sustainable workforce outcomes. Tune in now to discover how to build multi-agent systems that strengthen your organization's most valuable asset—your people. Questions or suggestions? Send me a Text Message. Support the show *********** Disclaimer: Views are the participants’ own and do not represent those of any participant’s past, present, or future employers. Participation in this event is independent of any potential business relationship (past, present, or future) between the participants or between their employers. Level up your AI Leadership game with the AI Leadership Handbook ( https://www.aileadershiphandbook.com ) and shape the next generation of AI-ready teams with The HUMAN Agentic AI Edge ( https://www.humanagenticaiedge.com ). More details: https://www.intelligence-briefing.com All episodes: https://www.intelligence-briefing.com/podcast Get a weekly thought-provoking post in your inbox: https://www.intelligence-briefing.com/newsletter

AI is moving quickly from experimentation to deployment, but what does it actually take to operationalize AI successfully? In this episode of “What’s the BUZZ?”, host Andreas Welsch speaks with Kristen Kehrer about the operational realities behind deploying AI, LLMs, and agentic systems in enterprise environments. Three key insights stand out: Production AI requires more than a successful demo Many organizations underestimate the complexity of moving AI systems into production. Reliable deployment requires monitoring, governance, iteration, and collaboration across business and technical teams. Clean data and knowledge bases remain essential Even advanced AI systems depend on high-quality documentation and structured information. Weak knowledge bases often lead to unreliable outputs and poor user experiences. LLMOps introduces a new operational layer Managing prompts, retrieval pipelines, evaluations, and interaction quality has become critical as organizations scale customer-facing AI systems and AI agents. A practical reminder: successful AI adoption is not about deploying the newest model first. It is about building reliable systems, strong operational processes, and the right collaboration between people and technology. Listen to the full episode for a grounded perspective on what it takes to operationalize AI at scale. Questions or suggestions? Send me a Text Message. Support the show *********** Disclaimer: Views are the participants’ own and do not represent those of any participant’s past, present, or future employers. Participation in this event is independent of any potential business relationship (past, present, or future) between the participants or between their employers. Level up your AI Leadership game with the AI Leadership Handbook ( https://www.aileadershiphandbook.com ) and shape the next generation of AI-ready teams with The HUMAN Agentic AI Edge ( https://www.humanagenticaiedge.com ). More details: https://www.intelligence-briefing.com All episodes: https://www.intelligence-briefing.com/podcast Get a weekly thought-provoking post in your inbox: https://www.intelligence-briefing.com/newsletter

AI is getting a lot of attention, but where does it actually create measurable impact? In this episode of “What’s the BUZZ?”, host Andreas Welsch speaks with Ariana Smetana, CEO of AccelIQ Digital, about how finance teams can move from experimentation to real outcomes using AI in document processing and reporting. Three key insights stand out: - Start with the bottleneck, not the technology Many finance teams still rely on manual spreadsheets to assemble and validate data. The real opportunity lies in addressing these operational constraints and enabling faster, more proactive decision-making. - Balance probabilistic AI with deterministic accuracy In finance, “almost right” is not acceptable. A layered approach of combining human validation, deterministic calculations, and AI-driven summarization ensures both speed and trust. - Keep humans in control to build trust and adoption AI should augment, not replace, domain expertise. Embedding human oversight across the process is critical to ensuring accuracy, security, and confidence in outputs. A practical reminder: successful AI adoption is not about doing everything or about doing something flashy. It is about solving the right problem, in the right way, with the right level of control. Listen to the full episode for a grounded perspective on applying AI where precision truly matters. Questions or suggestions? Send me a Text Message. Support the show *********** Disclaimer: Views are the participants’ own and do not represent those of any participant’s past, present, or future employers. Participation in this event is independent of any potential business relationship (past, present, or future) between the participants or between their employers. Level up your AI Leadership game with the AI Leadership Handbook ( https://www.aileadershiphandbook.com ) and shape the next generation of AI-ready teams with The HUMAN Agentic AI Edge ( https://www.humanagenticaiedge.com ). More details: https://www.intelligence-briefing.com All episodes: https://www.intelligence-briefing.com/podcast Get a weekly thought-provoking post in your inbox: https://www.intelligence-briefing.com/newsletter

What if you could get your executive's feedback before your big presentation to them (with zero effort)? Agentic AI makes it possible. In the latest episode of “What’s the BUZZ?”, host Andreas Welsch sits down with Elise Neel, SVP of Global Strategy and Strategic Partnerships at Panasonic, to explore what it really takes for executives to lead in an era of Agentic AI. This conversation goes beyond the hype and gets into a far more important question: How does leadership evolve when intelligence is no longer scarce? Catch the BUZZ: - Why the best leaders are the best orchestrators - How Agentic AI reshapes operating models in addition to productivity - Why executive leaders need hands-on experience beyond theoretical understanding - How sparring agents scale executives’ time and feedback Leaders looking to move from AI curiosity to real organizational impact find examples and inspiration in this episode to evolve their own leadership approach. Questions or suggestions? Send me a Text Message. Support the show *********** Disclaimer: Views are the participants’ own and do not represent those of any participant’s past, present, or future employers. Participation in this event is independent of any potential business relationship (past, present, or future) between the participants or between their employers. Level up your AI Leadership game with the AI Leadership Handbook ( https://www.aileadershiphandbook.com ) and shape the next generation of AI-ready teams with The HUMAN Agentic AI Edge ( https://www.humanagenticaiedge.com ). More details: https://www.intelligence-briefing.com All episodes: https://www.intelligence-briefing.com/podcast Get a weekly thought-provoking post in your inbox: https://www.intelligence-briefing.com/newsletter

What does it actually take to build AI governance that enables innovation instead of slowing it down? In this episode of “What’s the BUZZ?”, host Andreas Welsch sits down with Walter Haydock, CEO of StackAware, to break down how leaders can move from reactive AI usage to structured, scalable governance without killing momentum. Together, they discuss why most organizations get AI risk wrong, where governance efforts typically fail, and how to design a program that balances speed, security, and business value. He shares practical insights on defining risk appetite, simplifying policies, and avoiding the extremes that derail AI adoption. Highlights you’ll get from the conversation: Why most companies fall into two traps—“ban AI” or “anything goes”—and how to find the middle ground. The three risks every AI leader must address: data confidentiality, IP ownership, and reputation. What ISO 42001 actually provides and how it helps operationalize AI governance at any scale. The only four ways to handle risk—and why AI doesn’t change these fundamentals. How to define risk appetite in real business terms to guide faster, better decisions. Why overly complex data classification policies fail—and what to do instead. The #1 mistake organizations make when implementing governance programs: unrealistic timelines. If you want a clear, practical approach to managing AI risk while still moving fast, this episode delivers actionable guidance you can apply immediately. Listen now to learn how to turn AI governance from a bottleneck into a competitive advantage. Questions or suggestions? Send me a Text Message. Support the show *********** Disclaimer: Views are the participants’ own and do not represent those of any participant’s past, present, or future employers. Participation in this event is independent of any potential business relationship (past, present, or future) between the participants or between their employers. Level up your AI Leadership game with the AI Leadership Handbook ( https://www.aileadershiphandbook.com ) and shape the next generation of AI-ready teams with The HUMAN Agentic AI Edge ( https://www.humanagenticaiedge.com ). More details: https://www.intelligence-briefing.com All episodes: https://www.intelligence-briefing.com/podcast Get a weekly thought-provoking post in your inbox: https://www.intelligence-briefing.com/newsletter

What do you do when AI takes the junior roles, and attention becomes your most valuable resource? Steven Puri and host Andreas Welsch map a practical path from distraction and short-term thinking to sustainable high performance. In this episode, Steven, a former studio exec, serial founder, and the mind behind a focus app, explains why entry-level jobs are changing, where real human value is rising, and how individuals and teams can design work around attention, not just task lists. He shares concrete techniques for getting into flow, beating the "cold start" procrastination loop, and using AI as a force multiplier rather than a replacement. Highlights you’ll get from the conversation: Why the bottom rungs of traditional career ladders are evaporating and what that means for talent development. The new premium on deep work: what humans still do better than LLMs and how to protect that time. Practical habits to find your best creative windows (chronotype + simple tracking exercise). A productivity hack that actually works: hide everything but your top 3 tasks to defeat paralysis. How to use AI tools to prototype, learn, and ship faster — and why that can accelerate career growth. Leadership blind spots: the danger of short-term cost cuts and why planning for multi-year development still matters. If you want actionable ways to reclaim your attention, structure your day for meaningful output, and turn AI into an enabler of skill growth, this episode is full of concrete, repeatable tactics. Listen now to learn how to turn AI hype into habits and outcomes that actually move your work and career forward. Questions or suggestions? Send me a Text Message. Support the show *********** Disclaimer: Views are the participants’ own and do not represent those of any participant’s past, present, or future employers. Participation in this event is independent of any potential business relationship (past, present, or future) between the participants or between their employers. Level up your AI Leadership game with the AI Leadership Handbook ( https://www.aileadershiphandbook.com ) and shape the next generation of AI-ready teams with The HUMAN Agentic AI Edge ( https://www.humanagenticaiedge.com ). More details: https://www.intelligence-briefing.com All episodes: https://www.intelligence-briefing.com/podcast Get a weekly thought-provoking post in your inbox: https://www.intelligence-briefing.com/newsletter

The uncomfortable trust for leaders is this: AI is changing how leadership works, or is it? In the latest episode of the “What’s the BUZZ?” podcast, host Andreas Welsch sits down with Sadie St. Lawrence, founder of Human Machine Collaboration Institute and author of Becoming an AI Orchestrator, to discuss what it really takes to lead in the age of AI. Sadie introduces a powerful idea: the future of work belongs to AI orchestrators. They are leaders who know how to guide AI systems the way a conductor leads a symphony. Here are a few key insights from our conversation: - The shift from doing to orchestrating Work is moving from execution to coordination. Instead of completing every task ourselves, professionals will increasingly guide AI systems—asking the right questions, refining outputs, and turning rough drafts into real business value. - Managers and individual contributors must evolve Managers often know how to delegate—but may not be using AI themselves. Individual contributors may use AI—but lack experience delegating work. The future requires both groups to develop leadership-level thinking, even without a formal leadership title. - AI success starts with systems thinking Many organizations want AI outcomes without the right foundations. Leaders need to understand their data, tech stack, and workflows so that AI can support real business strategy rather than becoming another disconnected tool. - AI is an opportunity for everyone to lead You don’t need to be a technical expert to start. The most important step is simple: get your hands on the keyboard and start experimenting. That’s how leaders begin to see what’s possible. If you want to understand how your role and your organization must evolve in the AI era, this conversation is for you. Questions or suggestions? Send me a Text Message. Support the show *********** Disclaimer: Views are the participants’ own and do not represent those of any participant’s past, present, or future employers. Participation in this event is independent of any potential business relationship (past, present, or future) between the participants or between their employers. Level up your AI Leadership game with the AI Leadership Handbook ( https://www.aileadershiphandbook.com ) and shape the next generation of AI-ready teams with The HUMAN Agentic AI Edge ( https://www.humanagenticaiedge.com ). More details: https://www.intelligence-briefing.com All episodes: https://www.intelligence-briefing.com/podcast Get a weekly thought-provoking post in your inbox: https://www.intelligence-briefing.com/newsletter

Stop chasing the rainbow—this episode shows how to turn AI pilots into repeatable programs that actually deliver business value. Host Andreas Welsch talks with Ivo Strohhammer about the hard work behind scaling AI adoption: moving from experiments to production, building a community that learns together, and helping small and medium businesses avoid the same pitfalls large enterprises faced. Ivo shares hands-on approaches from his work at Siemens and his new local ecosystem: how to enable people, provide secure playgrounds, and balance fast experimentation with the governance and standards needed to scale. Highlights from the conversation: Why employees are your most powerful lever: democratize access, offer secure tools, and create tiered learning paths so people can progress from curious user to local AI champion. How to balance speed and structure: let teams experiment but create standards to avoid reinventing the wheel; use short, adaptive planning cycles and measure impact early. The difference between everyday AI vs. process AI vs. new AI—and why rethinking processes often produces far larger gains than just layering models on existing workflows. Practical ways to help SMEs: open local labs, shared trainings, and a three-stage approach (Awareness → Ability → Application) so smaller orgs can punch above their weight without huge budgets. Three quick takeaways: Put people first—train, enable, and give secure spaces to experiment. Find the sweet spot between experimentation and standardization—pilot widely, scale selectively. Stay agile—test fast, keep what works, fail fast, and move on. If you want a practical playbook for making AI stick—whether you lead a global program or run a local SME—this episode is full of examples and actionable advice. Tune in now to hear the full conversation and start turning your AI pilots into lasting programs. Questions or suggestions? Send me a Text Message. Support the show *********** Disclaimer: Views are the participants’ own and do not represent those of any participant’s past, present, or future employers. Participation in this event is independent of any potential business relationship (past, present, or future) between the participants or between their employers. Level up your AI Leadership game with the AI Leadership Handbook ( https://www.aileadershiphandbook.com ) and shape the next generation of AI-ready teams with The HUMAN Agentic AI Edge ( https://www.humanagenticaiedge.com ). More details: https://www.intelligence-briefing.com All episodes: https://www.intelligence-briefing.com/podcast Get a weekly thought-provoking post in your inbox: https://www.intelligence-briefing.com/newsletter

Stop building the same capabilities over and over when everyone builds agents. Standardize and reuse common features across your business. In this episode of “What’s the BUZZ?”, Andreas Welsch sits down with Samantha McConnell to discuss how large enterprises can build reusable AI agents that create real business value. The conversation moves beyond vendor claims to examine how organizations operationalize agentic AI, manage rapid innovation cycles, and balance empowerment with governance. Samantha shares how Cox approaches AI through centralized hubs, agent registries, and differentiated governance models for individual productivity agents versus enterprise-scale solutions. The discussion also highlights why adoption is critical, and why many AI agents will have much shorter lifecycles than traditional software products. Catch the BUZZ: Preventing reinvention through AI hubs and agent registries Governing enterprise AI agents without slowing innovation Managing the lifecycle of rapidly evolving AI agents Measuring adoption and business impact, not just usage Connecting agent initiatives to clear business success metrics Using a land-and-expand approach to scale agentic AI responsibly Key Takeaways: Balance innovation and control by tailoring governance to agent scale and risk Design for faster time-to-value and shorter solution lifespans Define outcome-based success metrics before deploying AI agents A practical episode for leaders focused on turning agentic AI from experimentation into repeatable, enterprise-ready impact. Questions or suggestions? Send me a Text Message. Support the show *********** Disclaimer: Views are the participants’ own and do not represent those of any participant’s past, present, or future employers. Participation in this event is independent of any potential business relationship (past, present, or future) between the participants or between their employers. Level up your AI Leadership game with the AI Leadership Handbook ( https://www.aileadershiphandbook.com ) and shape the next generation of AI-ready teams with The HUMAN Agentic AI Edge ( https://www.humanagenticaiedge.com ). More details: https://www.intelligence-briefing.com All episodes: https://www.intelligence-briefing.com/podcast Get a weekly thought-provoking post in your inbox: https://www.intelligence-briefing.com/newsletter

Imagine shrinking a one-hour code review to under ten minutes—and using that same agentic approach to boost sales, reduce fraud, and make branch and call‑center staff far more productive. In this episode, Andreas Welsch interviews Mo Jamous, CIO at U.S. Bank, who has taken agentic AI from experiments into real production at a major financial institution. Mo walks through what worked, what surprised him, and the practical guardrails banks (and other regulated companies) need to adopt agents safely and effectively. Episode highlights: A clear three‑bucket strategy: persona‑driven productivity, revenue/growth use cases, and operational excellence (fraud, security, DevOps, resilience). A concrete win: an agentic code‑review tool built in weeks that reduced review time from ~1 hour to <10 minutes and scaled to hundreds of thousands of reviews per year. How to instrument agents for measurement: attach metadata to agents, count executions, and map successful runs to dollar or productivity impact so you can report ROI. People, process, platform: upskill teams with hackathons and brown‑bags, put a governance council (risk, security, compliance) in place, and build an orchestration/registry layer to track many agent implementations. Common pitfalls: getting stuck on “one tool” decisions, underestimating change management and adoption, and failing to bake monitoring and guardrails into deployments. Practical starting advice: pick high‑value, low‑complexity pilots (e.g., developer or call‑center assistants), measure outcomes from day one, and scale using an observability dashboard rather than betting on a single vendor. Who should listen: business and tech leaders who want actionable guidance for moving beyond demos and into production-ready agentic AI that creates measurable business outcomes. Want step‑by‑step lessons from an operator who’s done it? Listen to the full episode now to learn how to turn agent AI hype into real business value. Questions or suggestions? Send me a Text Message. Support the show *********** Disclaimer: Views are the participants’ own and do not represent those of any participant’s past, present, or future employers. Participation in this event is independent of any potential business relationship (past, present, or future) between the participants or between their employers. Level up your AI Leadership game with the AI Leadership Handbook ( https://www.aileadershiphandbook.com ) and shape the next generation of AI-ready teams with The HUMAN Agentic AI Edge ( https://www.humanagenticaiedge.com ). More details: https://www.intelligence-briefing.com All episodes: https://www.intelligence-briefing.com/podcast Get a weekly thought-provoking post in your inbox: https://www.intelligence-briefing.com/newsletter

Stop chasing flashy multi‑agent demos. The big gains in enterprise AI are coming from focused, context‑driven systems, not agents in a room. In this year‑end conversation host Andreas Welsch and analyst Jon Reed cut through the noise to explain where AI is failing in the wild and where it's producing measurable business value. Jon lays out the vendor‑customer gap, the real risks of agentic experiments, and the practical architectures that are working today: compound systems, context engineering, RAG/knowledge graphs, evaluation and observability, and right‑time data layers. What you’ll learn: Why multi‑agent orchestration rarely works at scale today and the narrow exception where it does How vendors are ahead of buyers, and how leaders should close the gap with clear communication and upskilling The difference between treating AI as a worker vs. a tool, and why that choice matters for people and projects Practical, enterprise‑ready wins: document intelligence, procurement RFP automation, AP/AR, hyper‑personalization, and focused assistants Why explainability, audit trails, and granular autonomy toggles are essential for trust and compliance How to approach AI readiness: clean data, metadata/annotation, and composing smaller specialized models into reliable workflows If you build or buy AI in the enterprise, this episode is full of real examples and honest advice on where to invest, what to avoid, and how to design systems that produce results now, while preparing for broader scale. Tune in to hear the full conversation and get actionable guidance for turning AI hype into business outcomes. Questions or suggestions? Send me a Text Message. Support the show *********** Disclaimer: Views are the participants’ own and do not represent those of any participant’s past, present, or future employers. Participation in this event is independent of any potential business relationship (past, present, or future) between the participants or between their employers. Level up your AI Leadership game with the AI Leadership Handbook ( https://www.aileadershiphandbook.com ) and shape the next generation of AI-ready teams with The HUMAN Agentic AI Edge ( https://www.humanagenticaiedge.com ). More details: https://www.intelligence-briefing.com All episodes: https://www.intelligence-briefing.com/podcast Get a weekly thought-provoking post in your inbox: https://www.intelligence-briefing.com/newsletter

Can you trust an AI agent to act in line with your values — and who’s responsible when it doesn’t? In this episode, Andreas Welsch talks with AI ethics consultant Rebecca Bultsma about the pitfalls of rushing AI agents into business workflows and practical steps leaders should take before handing autonomy to software. Rebecca draws on her early ChatGPT experiments and academic work in data & AI ethics to explain why generative AI raises fresh ethical risks and how organizations can reduce harm. What you’ll learn: Why generative AI and agents amplify old AI ethics problems (bias, hidden assumptions, and Western-centric worldviews). Why you should build internal understanding first: experiment with low-stakes, traceable use cases before deploying public agents. The importance of audit trails, explainability, and oversight to trace decisions and assign accountability when things go wrong. Practical red flags: agents that transact autonomously, weak logging, and complacency about vendor claims. A legal reality check: new laws (like California’s chatbot rules) are emerging and could increase liability for organizations that deploy chatbots or agents prematurely. The top takeaways: Learn by experimenting personally and internally in your organization to discover where agents fail. Start small with low-stakes, narrowly scoped tasks you can monitor and audit. Don’t rush; rather, observe others' failures, train your people, and build governance before going public. If you’re a leader evaluating agents or responsible for AI governance, this episode gives clear, actionable advice for keeping your organization out of the headlines for the wrong reasons. Tune in to hear the whole conversation and learn how to turn AI hype into safer business outcomes. Questions or suggestions? Send me a Text Message. Support the show *********** Disclaimer: Views are the participants’ own and do not represent those of any participant’s past, present, or future employers. Participation in this event is independent of any potential business relationship (past, present, or future) between the participants or between their employers. Level up your AI Leadership game with the AI Leadership Handbook ( https://www.aileadershiphandbook.com ) and shape the next generation of AI-ready teams with The HUMAN Agentic AI Edge ( https://www.humanagenticaiedge.com ). More details: https://www.intelligence-briefing.com All episodes: https://www.intelligence-briefing.com/podcast Get a weekly thought-provoking post in your inbox: https://www.intelligence-briefing.com/newsletter

What actually changes when AI agents become part of your workforce — and which human skills still matter most? In this episode, host Andreas Welsch talks with HR and talent-intelligence veteran Todd Raphael about the practical realities of bringing agentic AI into organizations. They move beyond proofs-of-concept to ask the tough questions: How do agents fit into daily workflows, what invisible human contributions should you protect, and how should HR and IT collaborate to redesign roles, org charts, and the employee lifecycle? Listen for concrete thinking and strategic framing, including: The hidden value humans bring: Why many critical contributions (trusted relationships, customer touchpoints, institutional memory) don’t appear on job descriptions — and what that means when you automate tasks. Rethinking structure and advancement: How flatter org models and new measures of impact (knowledge, networks, influence) may change who gets promoted and how leadership is defined. HR’s seat at the table: Why HR is uniquely positioned to plan holistically for hire-to-retire changes, from skills-based hiring to internal marketplaces, reskilling, and retention when agents handle more tasks. You’ll also hear examples and practical prompts for leaders: identify the intangible work that must remain human, map tasks vs. relationships before automating, and start workforce planning that considers people and agents together. If you’re an HR leader, people manager, or technology decision-maker trying to turn agent hype into durable business outcomes, this episode gives you a playbook to start redesigning work the right way. Tune in now to learn how to protect human advantage and build an effective human+agent workforce. Questions or suggestions? Send me a Text Message. Support the show *********** Disclaimer: Views are the participants’ own and do not represent those of any participant’s past, present, or future employers. Participation in this event is independent of any potential business relationship (past, present, or future) between the participants or between their employers. Level up your AI Leadership game with the AI Leadership Handbook ( https://www.aileadershiphandbook.com ) and shape the next generation of AI-ready teams with The HUMAN Agentic AI Edge ( https://www.humanagenticaiedge.com ). More details: https://www.intelligence-briefing.com All episodes: https://www.intelligence-briefing.com/podcast Get a weekly thought-provoking post in your inbox: https://www.intelligence-briefing.com/newsletter