Gaudenz Koeppel is Chief Data and Analytics Officer T&S at Axpo, where he leads the end-to-end data and analytics agenda across one of Europe’s most complex energy trading environments.
He began his career as an electrical engineer before spending around a decade in short-term power trading centred on hydro assets. Working in a system where decisions are time-critical, uncertainty is constant and reliability is non-negotiable fundamentally shaped his leadership perspective. Optimising the dispatch and trading of a national hydro portfolio, where power is consumed instantly and outages are unacceptable, taught him that progress depends on trust, resilience and repeatability rather than theoretical perfection.
An early turning point was shifting trading decisions from experience-based judgement towards model-supported decision-making. The goal was not perfect forecasts, but signals that people could rely on under pressure. A second inflection came with the creation of an Advanced Analytics Centre of Excellence across the trading business. Not being a data scientist himself, Gaudenz focused on enabling specialists, hiring for potential, delegating deeply and aligning teams around clear outcomes. Building a data and analytics community that now spans around 300 of Axpo’s 2,000 colleagues reinforced his belief that listening at scale is a core leadership skill, particularly in decentralised organisations.
In 2024, Gaudenz became Chief Data and Analytics Officer, expanding his remit from advanced analytics to the full data value chain, including platforms, governance and adoption. Alongside his executive role, he teaches power markets at ETH Zurich, grounding his leadership in fundamentals while driving change at scale.
As a data and AI leader, which traits and skills do you think matter most, and which of those have been most influential for you in your current position?
“Effective data and AI leadership isn’t a technical role in disguise. It’s a leadership role that requires technology literacy: you need to understand what modern platforms, governance tooling, and AI methods can and cannot do because your strategic options, risk posture, and the skill development you foster in the organisation all depend on it. But the differentiator is not writing the code; it’s turning technical possibility into business capability.
“The traits that matter most are: business judgment, anchoring work in real decisions and value; influence without authority, aligning stakeholders across silos; systems thinking, platform, governance, adoption, and operating model as one system; and trust-building, delegating to specialists while holding the line on outcomes and standards.
“I also value a rare combination: being conceptual and pragmatic at the same time. You need a clear target state, but you can’t wait for perfect information. The most effective leaders make no-regret moves and implement what creates optionality, test assumptions, learn fast, and pivot deliberately.
“In my organisation, the most influential skills have been collaboration, listening at scale, and empowerment. In a decentralised setup, a big central team becomes a bottleneck; so, I build mechanisms that scale alignment (community, council, clear guardrails) and enable teams to move safely and deliver outcomes.
Reflecting on your career, what is one non-traditional piece of advice (outside of technical skills) you would give to an aspiring data or AI leader aiming for the C-suite?
“The non-traditional advice I’d give is lead as if your job is to make yourself unnecessary. Repeat relentlessly what needs to become true for the company, not what your function wants to build. If data and AI only ‘work’ because the CDAO is pushing, it’s not a capability yet; it’s a dependency.
“In practice, that means designing for ownership and empowerment. Put decisions where the value is created, make responsibilities explicit, and build simple guardrails that let teams move safely without waiting for a central gatekeeper. Spend as much time shaping incentives, language, and trust as you do on platforms and models. Your best achievements will look boring on an org chart: teams adopting standards voluntarily, reusing assets without being told, and leaders demanding evidence-based decisions as a habit.
“If you do it right, outcomes won’t depend on a central leader’s push; they’ll emerge from distributed ownership and repeatable practices.”