In this episode, Markus welcomes Dominik Wittenbeck, Group CTO of SNP Group, to explore the company’s multi-decade journey from an SAP consulting service to a global automation software provider. Dominik shares insights into the challenges and inflection points that shaped SNP’s evolution, highlighting how they tackled SAP’s complexity, embraced automation, and empowered both internal teams and external partners with flexible, modular tools.

Listeners will learn how SNP scaled its platform by focusing on repeatable patterns, reducing project risk, and enabling non-technical users through intuitive design. Dominik also reflects on how generative AI is influencing the next chapter of transformation by accelerating onboarding, reducing manual tasks, and surfacing new opportunities across the enterprise landscape.

Whether you're leading a digital transformation, modernizing legacy systems, or exploring GenAI’s enterprise use cases, this conversation offers actionable guidance and hard-won lessons from a leader who's lived the journey.

00:00 Episode Start

02:50 How SNP Group accelerates time to value

07:15 Moving from consultation to transformation

16:00 Automation is inevitable

18:05 What GenAI unlocks for all enterprises

21:40 The importance of human guidance

25:45 Why democratizing tool sets should be your highest priority

29:45 Reflections from Dominik's career

33:10 Don't boil the ocean when automating

38:20 Thinking about automation differently

41:10 Final thoughts

• Culture determines automation success: Without a culture that embraces experimentation and failure, even the best tools will stall. Leaders must encourage learning, iteration, and low-friction change.
• Let the field drive platform evolution: SNP’s most successful tools were shaped not by top-down requirements but by consultants building on real-world problems. Innovation thrives where freedom and feedback loops exist.
• Process ownership must be distributed: A single team or department can’t scale automation alone. SNP’s evolution proves that shared ownership enables faster problem-solving and continuous refinement.

“In a dream of mine that hasn't come true yet, you're sitting there in a workshop with a customer, they tell you requirements verbally, you note them down, you take the transcript of basically what you have, and it automatically reflects in the software. With agentic behavior and function calling, this is actually quite possible, and it can bring the learning curve down quite a lot.”

“You will become the best engineer in automation if you are a subject matter expert. And what we’ve seen is that when we give our consultants the tools and the freedom to experiment, they come up with practical solutions we in R&D would’ve never imagined. That’s why democratizing toolsets should be your highest priority.”

“From a culture perspective, you need to build a company where failing and learning is an integrated part of the process, it’s not a flaw. If your thinking is always ‘when will this be delivered’ or ‘when will it be done,’ you miss the chance to find new opportunity. The best improvements come from failures you’ve actually made.”

In this episode, Markus sits down with Michael Lebron, Head of Digital Applications and Shared Services at Canon, to discuss how Canon is integrating Generative AI into its operations through a cross-functional committee focused on intelligent automation. Drawing from his experience leading enterprise architecture and innovation initiatives, Michael shares how the committee evolved from addressing AI-related risks to enabling productivity, creativity, and scale across departments. From upskilling employees and securing executive buy-in to deploying AI agents for process automation, this episode provides a practical blueprint for enterprise leaders looking to operationalize AI in a secure, scalable, and value-driven way.

00:00 Episode Start

02:40 Canon's Gen AI Committee

06:25 Moving past a fear mindset

11:55 A spotlight on technologists

14:45 AI Integrated

22:20 The massive unlock with unstructured data

28:45 Analyzing the ROI of AI implementation

32:10 How these tools are democratizing knowledge across organizations

39:35 Is fear holding us back?

43:25 Michael's advice

• Executive alignment is non-negotiable: Success with GenAI starts at the top. Canon’s committee model shows that broad adoption and cultural change only happen when executive leadership actively champions the vision.
• Democratization drives enterprise scale: GenAI lowers the barrier to technical innovation, enabling non-technical staff to ideate, experiment, and contribute. This shift is redefining what it means to be “digital ready” in the enterprise.
• Education eliminates fear: Resistance to AI often stems from a lack of understanding. Canon's emphasis on hands-on training, internal advocacy, and use-case transparency helps mitigate fear and build enterprise readiness.

"When we started the AI committee, it wasn’t just to control risks, it was to unlock productivity and creativity while protecting the company. We built policies not to suppress, but to encourage innovation safely. That balance of governance and enablement has been critical to our success."

"Generative AI is removing barriers to innovation. You no longer need to be a seasoned developer to build real applications or automate business tasks. It’s democratizing expertise, allowing people with curiosity, not just technical skill, to drive transformation."

"This isn’t just about deploying tools. It’s a shift in DNA, in how we work and think. From executive strategy down to individual workflows, every function at Canon is now evaluating how AI can enhance efficiency and customer experience. It’s a complete cultural evolution."

In this episode, Markus sits down with John Miller, VP of Consumer and Retail Solutions at AT&T, to explore how one of the world’s largest telecom companies is using AI agents and LLMs to enhance service delivery and modernize operations. John explains how his team deployed the very first GPT-style interface implemented across AT&T and how it is empowering employees throughout the organization.

He also shares AT&T’s strategy for evolving from traditional workflow logic to asynchronous, agent-driven interactions that better mirror customer behavior. From managing petabytes of unstructured data to wrapping legacy mainframes with AI interfaces, John provides actionable insights for any enterprise looking to embrace generative AI.

00:00 Episode Start

03:00 A broad overview of how AT&T is leveraging AI

06:15 The best solution for scale

09:30 Reshaping workflows to better serve customers

15:20 How to design your org's AI agents

20:55 Different models for different tasks

22:50 Empathizing with your customers

29:35 Tackling legacy systems one step at a time

35:55 The data problem

44:15 Advice for others beginning their journey

• Legacy systems don’t have to be a blocker: AT&T is proving that even decades-old mainframes can be modernized by “wrapping” them with AI agents. This approach enables gradual transformation without disrupting end users or retraining staff.
• Perfect data isn’t a prerequisite: Instead of waiting to clean every dataset, AT&T uses AI to surface and correct data quality issues in real-time. This lets them move fast while improving data fidelity through use.
• Empathy matters in AI adoption: User trust and satisfaction rise when AI interactions mimic natural human communication. AT&T saw measurable improvements by designing agents that pause, clarify, and respond with warmth.
• AI success is iterative, not linear: AT&T's most effective agents didn’t launch fully formed. They evolved through constant prompt tuning, testing, and feedback. Treat agents like teammates who learn and improve over time.

“For companies trying to wait until they have perfect data, you’ll never have perfect data. So it’s better to just start. Even with imperfect data, you can begin to see meaningful trends and value, especially when your AI tools are designed to adapt and refine based on real-time feedback.”

“We’re wrapping legacy systems like a python [by] slowly squeezing out functionality until they’re obsolete. This approach lets us modernize without disruption, giving employees a seamless experience while we replace backend systems incrementally. Over time, those old systems just quietly phase out.”

“AI lets you break the traditional workflow logic. Now we meet customers wherever they are in their journey, instead of forcing them through a rigid sequence. It’s a shift from thinking about linear steps to enabling outcomes through flexible, asynchronous interactions.”