What’s the BUZZ? — AI in Business

Your AI agent is about to be handed your credit card. Today, it recommends products. Soon it will complete the purchase and pay for it for you. Who is responsible when it buys the wrong thing, and will consumers trust it enough to let it?

In this episode of "What's the BUZZ?", host Andreas Welsch sits down with Michael Nevski, Consumer Payments Market Intelligence Expert, to work through what happens when AI agents start making financial transactions on our behalf. Michael draws on more than 15 years of consumer research in financial services and payments.

Key insights from this conversation include:

• Agentic commerce is coming, but it is mostly hypothetical today. Michael defines agentic AI as systems that act autonomously toward a goal, unlike generative AI, which produces outputs that still need a human to act. Payment networks have started preparing, but privacy, security, chargebacks, and disputes have to be solved before a true network is enabled.
• Consumers are excited and afraid at the same time. In Michael's research, 77% expect agentic commerce and generative AI to improve their lives, yet 83% have concerns about oversight and reliability. He calls it high hope and high fear, and says the fear comes from not understanding the black box behind the purchase.
• Early trust has a small price tag. Asked how much they would let an agent spend, people said no more than $ 10 to $ 20. Michael compares it to play money in Vegas, an amount you are ready to lose.
• Nobody agrees on who is responsible. Michael says the card issuer should still authorize the payment. When a transaction goes wrong, 39% of consumers point to the agentic AI service provider, 17% to the AI developer, 14% to themselves, and 6% to the payment networks. They want every party in the ecosystem to have a defined role.
• Data sharing depends on the task. Only 7% would share location data in general, but that jumps to 45% when it helps find a restaurant for a business lunch within an hour, and browsing data goes from 8% to 43%. Consumers will trade data for a specific outcome, but as Michael puts it, "I don't want you to be Big Brother."
• Preparing is everyone's job. Michael's list covers understanding early adopters such as Gen Z and millennials, merchants making product information readable for agents, issuers enabling tokenization, and working with regulators. Andreas adds that IT, finance, marketing, and supply chain all need to engage now.

What leaders can do now: make your product and service data clear and structured so an agent can read, compare, and verify it; give customers simple opt-in and opt-out controls over what an agent may do; review your legacy systems and work with partners and regulators on who is accountable when a transaction goes wrong.

Michael's line to write down: "Prove it to me that I can rely on that system."

If an agent made a purchase for your customers tomorrow and it went wrong, would they know who to turn to, and would they still trust you?

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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.

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Your next customer may never see your ad. More and more shoppers ask an AI assistant for the best option and take the answer it gives them, and agents are starting to recommend and buy on our behalf. So how do you advertise to an AI agent?

In this episode of "What's the BUZZ?", host Andreas Welsch sits down with Maarten Albarda, Marketing Executive and Consultant, to explore how marketing must change when AI agents shop, recommend, and buy for us. Maarten focuses on the "how" of marketing: the process, structure, agency ecosystems, and capabilities behind good marketing.

Key insights from this conversation include:

• The time to learn is now, but the payback is not guaranteed. What you learn today may not matter tomorrow. Maarten argues it is still far easier to learn now than to unlearn and relearn five years from now, and it makes you a better user of AI either way.
• Judge every AI change by its outcome. Maarten's test is that the result is at least the same as before, and if it is not better, it should be cheaper or faster. He hears a lot of AI anxiety from CMOs and CEOs and says the right pace is the one your organization can absorb, because "it's quite easy to break an organization with too much too soon."
• The savings have not reached advertisers yet. Agencies have always sold human time, and agents that plan and optimize media mean fewer people and fewer hours. Maarten says clients aren't seeing that in their agency contracts yet, and advertisers are starting to ask what outcome they are buying instead of paying for the process.
• Hourly-rate businesses change fastest. Maarten sees the same shift in legal, consulting, and financial advice. Services built on selling hours will feel the biggest impact over the next five years.
• AI search may be hurting traditional search more than reported. Each platform reports only on itself, so independent numbers are scarce. Maarten expects the impact is bigger than we are told, because most people see an AI-generated answer first, often on their phone, and may go straight to a retailer app.
• Find out how AI sees your brand. Ask the same question in GPT, Gemini and Claude and the answers and their sources differ. Maarten's first step is to buy a tool that shows which sources each model values for your category, then work out how to show up in that conversation.

What leaders can do now: link every AI initiative to a business purpose and measure a before and after, so you can show the outcome is the same or better; use a tool to find the sources GPT, Gemini and Claude rely on for your category, then plan how to show up in them; ask your agencies how AI savings are passed on, and move toward paying for outcomes.

Maarten's line to write down: "don't do AI for AI's sake."

If an agent chose between your brand and a competitor tomorrow, would you know which sources it is listening to?

Questions or suggestions? Send me a Text Message.

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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.

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AI adoption is often treated as a one-size-fits-all approach. But individual employees and users have individual needs and preferences that successful AI programs consider.

In the latest episode of "What's the BUZZ?", host Andreas Welsch sits down with Christina Fischer, principal AI consultant and founder of Human Pattern Works, to unpack why so many AI transformations quietly fail the people meant to carry them, and what an actually inclusive AI playbook looks like.

Here's what you'll walk away with:
1) The real reason most AI initiatives fail
Christina puts the number at around 70%, and by her account, the common thread isn't the technology. It's leaders running a business problem like a software rollout, skipping the work across process, culture, and compliance that decides whether any of it sticks.

2) Your edge cases are your early-warning system
The employee who needs clarity on objectives or flexibility in how they work is often the first to feel friction nobody else has noticed yet. Listen to them early, Christina argues, and you catch what's breaking before it costs you the rest of the team.

3) Trust beats surveillance, every time
Keystroke logging and return-to-office mandates measure the easy thing, not the valuable one. Christina expects the flexibility of the pandemic years to come back, because it's what lets companies keep the specialized people AI transformation actually depends on.

Actionable takeaways you can implement this week
- Score before you scale: run Christina's four-dimension audit,  process, work culture, compliance, technology, on your own team before you greenlight another AI pilot.
- Build one real feedback loop: swap a status update for an open conversation about what's actually working, and treat what you hear as signal, not noise.
- Check on your middle managers: ask the people absorbing pressure from both directions how they're doing, before they become your next regretted departure.

Who on your team is the canary in the coal mine right now, and are you actually listening to them?

Questions or suggestions? Send me a Text Message.

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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.

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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?

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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.

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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.

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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).

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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.

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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).

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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.

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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).

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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.

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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).

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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.

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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.

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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.

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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).

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