AI agents are reshaping how enterprises innovate, organize work, and experience disruption.

In the latest episode of “What’s the BUZZ?”, Andreas Welsch speaks with Christian Muehlroth, CEO of ITONICS, about how agentic AI will redefine innovation management and why many organizations remain structurally unprepared for it.

Here are the key insights from the conversation:

• AI’s foundations were created decades ago, but recent advances in computing, interfaces, and delivery models have turned it into a scalable innovation engine.
• Each technological wave builds on prior ones, accelerating change while large organizations slow down due to processes, politics, and legacy structures.
• Agents function as tireless digital interns with expert-level capabilities in narrow domains, amplifying the output of teams that already demonstrate initiative and creativity.
• Many place AI on top of legacy processes or rely too heavily on public LLMs, resulting in misaligned outputs and “AI tourism” instead of measurable impact.
• Clean enterprise data, secure deployment setups, and redesigned processes are essential to making agentic AI operational and strategically valuable.

Key takeaways:

1. Focus on real-value use cases: innovation begins with a business problem, not with experimenting for its own sake.
2. Prioritize structural readiness: clean data, redesigned workflows, and enterprise-grade AI infrastructure determine whether agentic systems deliver results.
3. Empower motivated teams: the highest return comes from equipping individuals who seek change with advanced tools that amplify their capacity, rather than attempting blanket adoption across the organization.

Leaders need to take disruption seriously, double down on strategic intelligence, empower the people who want change, and invest in data and platform foundations before scaling agents.

Is Agentic AI already disrupting businesses (or can we just not see it yet)?

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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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Agentic AI is pushing leaders to rethink roles, processes, and governance far beyond another automation wave.

In this episode, Andreas Welsch speaks with Danielle Gifford, PwC Managing Director of AI, about how organizations should prepare for agentic AI. Danielle draws on frontline experience with enterprise pilots and deployments to explain why agents require new infrastructure, clearer role boundaries, and fresh approaches to governance and workforce design.

Highlights from the conversation:

• Why agents are different from classic rule-based automation: they’re goal-driven, context-aware and can act with autonomy, which creates both opportunity and risk.
• Where companies (especially in Canada) are on the adoption curve: pilots and POCs are increasing, but full-scale deployments need better data, guardrails, and change planning.
• How leaders should approach agent projects: start with the business problem, map processes, and decide where human + agent collaboration delivers the highest value.
• Workforce design and the “digital coworker”: practical advice on defining role boundaries, delegation rules, and how to evaluate outcomes when humans and agents collaborate.
• Multi-agent orchestration and governance: how to prevent agents from converging on weak solutions and how to build review, control, and accountability into agent systems.

Key takeaways:

1. Business first: define the problem before choosing technology. Agents aren’t a silver bullet — they must solve a real, scoped pain point.
2. Move from experimentation to implementation: Canadian enterprises are ready to progress beyond proofs of concept and invest in production-ready agent solutions with proper controls.
3. Agents ≠ automation: treat agents as goal-based collaborators that need explicit boundaries, evaluation metrics, and workforce redesign.

If you lead teams, product strategy, or AI initiatives and want practical guidance for turning agent hype into measurable outcomes, this episode is for you. Listen now to get the full conversation and actionable next steps.

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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

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AI agents are arriving fast, but the winners will be the organizations that pair them with trusted data, clear processes and security — not those who chase scale alone.

In this episode, Andreas Welsch talks with analyst and diginomica co-founder Jon Reed about what’s actually next for agentic AI: where real value is emerging, why many pilots stall, and how to move from hype to measurable outcomes. They cut through the noise around model releases and scary headlines, and focus on practical pathways for enterprise adoption.

Highlights include:

• Why throwing scale at models isn’t a cure-all and how enterprises succeed by constraining problems and adding domain context
• The “last mile” problem: when out-of-the-box LLMs miss industry-specific language, and when to fine-tune or use smaller domain models
• Data readiness as a multi-year journey — and how to weave data cleanup into early AI use cases so you can get wins fast
• Real business examples (procurement long-tail, finance workflows, shop-floor QA) where agents and ML already drive measurable impact
• Security, trust and identity for agents: new threat vectors, authentication challenges and when agent-to-agent interactions create additional risk
• Practical tech signals to watch: RAG + tool-calling, MCP/A2A protocols, zero-copy data approaches, and when to favor internal wins over multi-vendor orchestration
• People and culture: empower middle managers and junior staff to experiment safely, avoid short-sighted headcount cuts, and build communities of practice
• Legal and fairness considerations, including why frameworks like the EU AI Act offer useful risk-drive guardrails

If you lead an AI initiative, build data platforms, or are responsible for secure automation, this conversation gives realistic next steps — from choosing the right model size and partners to hardening agent deployments and capturing the first ROI moments.

Listen to the episode to learn how to move past hype and design agent strategies that actually deliver business 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

More details:
https://www.intelligence-briefing.com
All episodes:
https://www.intelligence-briefing.com/podcast
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