“Use AI as a sparring partner, as a colleague, as a peer… ask it to take another perspective, take something you’re weak in, and have a dialog.” — Nikolaos Kaintantzis

In this episode of SPCs Unleashed, the crew tackles a pressing question: how should leaders navigate AI? Stephan Neck frames the challenge well. Leadership has always been about vision, adaptation, and stewardship, but the cockpit has changed. Today’s leaders face an environment of real-time coordination, predictive analytics, and autonomous systems.

Mark Richards, Ali Hajou, and Nikolaos (Niko) Kaintantzis share experiences and practical lessons. Their message is clear: the fundamentals of leadership—vision, empowerment, and clarity—remain constant, but AI raises the stakes. The speed of execution and the responsibility to guide ethical adoption make leadership choices more consequential than ever.

1. Provide clarity on AI use Unclear policies leave teams guessing or hiding their AI usage. Leaders must set explicit expectations. As Niko put it: “One responsibility of a leader is care for this clarity, it’s okay to use AI, it’s okay to use it this way.” Without clarity, trust and consistency suffer.

2. Use AI to free leadership time AI should not replace judgment, it should reduce waste. Mark reframed it this way: “Learning AI in a fashion that helps you to buy time back in your life… is a wonderful thing.” Leaders who experiment with AI themselves discover ways to reduce low-value tasks and invest more time in strategy and people.

3. Double down on the human elements Certain responsibilities remain out of AI’s reach: vision, empathy, and persuasion. Mark reminded us: “I don’t think an AI can create a clear vision, put the right people on the bus, or turn them into a high performing team.” Ali added that energizing people requires presence and authenticity. Leaders should protect and prioritize these domains.

4. Create space for experimentation AI adoption spreads through curiosity, not mandates. Niko summarized: “You don’t have to seduce them, just create curiosity. If you are a person who is curious, you will end up with AI anyway.” Leaders accelerate adoption by opening capacity for experiments, reducing friction, and celebrating small wins.

• Treat AI as a sparring partner to sharpen your leadership thinking.
• Provide clarity and boundaries to guide responsible AI use.
• Buy back leadership time rather than offloading core duties.
• Protect the human strengths that technology cannot replace.
• Encourage curiosity and create safe spaces for experimentation.

Navigating AI is less about mastering every tool and more about modeling curiosity, setting direction, and creating conditions for exploration. Leaders who use AI as a sparring partner while protecting the irreplaceable human aspects of leadership will build organizations that move faster, adapt better, and remain deeply human.

“What the heck am I doing here? I’m just automating a shitty process with AI… it should be differently, it should bring me new ideas.” — Nikolaos Kaintantzis

In this episode of SPCs Unleashed, the hosts contrast the sluggish pace of traditional enterprises with the urgency and adaptability of what they call “extreme AI organizations.” The discussion moves through vivid metaphors of camels and eagles, stories from client work, and reflections on why most enterprise AI initiatives fail. At its core, the episode emphasizes a fundamental choice: will organizations bolt AI onto existing systems, or embed it deeply into the way they operate?

Mark Richards reflects on years of working with banks, insurers, and telcos — enterprises where patience is the coach’s most important skill. He contrasts this with small, AI-driven startups achieving more change in three months than a bank might in two years. Stephan Neck draws on analogies from cycling and Formula One, portraying extreme AI organizations as systems with real-time coordination, predictive analytics, and autonomous responses. Nikolaos Kaintantzis highlights the exponential speed of AI advancement, reminding us that excitement and fear walk together: miss the news for a week, and you risk falling behind.

1. Bake AI in, don’t bolt it on. Enterprises often rush to automate existing processes with AI, only to accelerate flawed work. True transformation comes when AI is designed into workflows from the start, creating entirely new ways of working rather than replicating old ones.

2. Treat data as a first-class citizen. Extreme AI organizations treat data as a living nervous system — continuous, autonomous, and central to decision-making. Clean, structured, and accessible data creates a reinforcing loop where the payoff for stewardship comes quickly.

3. Collapse planning horizons. Enterprises tied to 18-month or even quarterly cycles are instantly outdated in the world of AI. The pace of change demands lightweight, experiment-driven planning with rapid feedback and adjustment.

4. Build culture before capability. AI fluency is not just a tooling issue. Extreme AI organizations cultivate a mindset where employees regularly ask, “How could AI have helped me work smarter?” This culture of reflection and experimentation is more important than any single tool.

5. Keep humans in the loop — for judgment, not effort. The human role shifts from heavy lifting to guiding direction, evaluating options, and applying ethical oversight. Energy is conserved for judgment calls, while AI agents handle more of the execution load.

Enterprises may survive as camels, built for endurance in their chosen deserts, but the organizations that want to soar will need to transform into eagles. Strapping wings on a camel isn’t a strategy — it’s a spectacle. The path forward lies in embedding AI into the very DNA of the organization: data as fuel, culture as the engine, and humans providing the judgment that keeps the flight safe, ethical, and purposeful.

“Learning AI isn’t just about acquiring a new skill… it’s about unlocking the power to fundamentally reshape how our organizations work.” – Stephan Neck

In this episode of SPCs Unleashed, the hosts — Stephan, Mark, and Niko — share their personal AI learning journeys and reflect on what it means for practitioners and leaders to engage with this fast-evolving space.

They emphasize that learning AI isn’t only about technical skills — it’s a shift in mindset. Curiosity, humility, and experimentation are essential. From late-night “AI holes” to backlog strategies for learning, the discussion highlights both the excitement and overwhelm of navigating an exponential learning curve. The hosts also explore how to structure an AI learning roadmap with projects, fundamentals, and experiments. The episode closes with reflections on non-determinism in AI: its creative spark, its risks, and the reminder that “AI won’t replace you, but someone who masters AI will.”

1. Anchor AI learning in real problems. Mark emphasized: “Have a problem you’re trying to solve… so that every time you go and learn something, you’re learning it so you can achieve that thing better.”

2. Treat AI as a sparring partner, not a servant. Niko showed how ChatGPT improved his writing in both German and English — not by doing the work for him, but by challenging him to refine and think differently.

3. Use a backlog to manage your AI learning journey. The hosts compared learning AI to managing a portfolio — prioritization, focus, and backlog management are key to avoiding overwhelm.

4. Don’t get stuck on hype or deep math too early. Both Niko and Mark stressed that experimentation and practical application matter more in the early stages than diving into theory or chasing hype cycles.

5. Practice humility and collaboration. Stephan underlined that acknowledging blind spots and working with peers who bring complementary strengths is critical for sustainable growth.

The AI learning journey is less about chasing the latest tools and more about reshaping how we think, collaborate, and experiment. For practitioners, leaders, and change agents, the real challenge is balancing curiosity with focus, hype with fundamentals, and individual learning with collective growth. As the hosts remind us, mastery doesn’t come from endlessly consuming content — it comes from applying AI thoughtfully, with humility, intent, and a willingness to learn in public.

By treating AI as a partner and structuring your learning with intent, you not only future-proof your skills but also strengthen your impact as a leader in the age of AI.