Lessons for Ambitious Professionals in a Digital World
Andreas Kurz, Global Head of Digital Transformation, Alfagomma Group
Andreas Kurz, Global Head of Digital Transformation, Alfagomma Group
As Global Head of Digital Transformation, I lead ALFAGOMMA’s Digital Office, which is responsible for turning the group into a data-driven, cloud- and ai-enabled enterprise. My mandate is twofold: craft the global digital strategy and turn that vision into measurable business value across all our entities worldwide.
I steer five teams that cover the digital stack: 1. Infrastructure & Cloud – running our hybrid-cloud backbone, enabling data-driven services for the group.
2. Digital Marketing – driving brand visibility, lead generation and strategic projects.
3. Analytics, Reporting & Digital Finance – delivering a single source of truth for automated reporting and consolidation and predictive insights.
4. Software Engineering – building SaaS platforms and modernizing our ERP/API landscape with DevOps and micro-services.
5. Automation & AI – scaling Gen-AI agents that cut manual work and improve quality.
Beyond technology, I focus on governance, talent and culture: managing corporate master data processes, upskilling the existing workforce, enabling them for the AIdriven enterprise and driving agile, scrum-based working.
Opportunities and Challenges of Industry 4.0Opportunities we see in the following areas: 1. Increased Productivity—With labor and energy prices rising, connected sensors, AI-driven scheduling and autonomous production cells can unlock operational efficiency gains.
2. New revenue streams: Data-driven products turn oneoff sales into lifecycle service contracts, creating customer retention and additional revenue.
The Reality Check (and why it’s hard) 1. Brownfield heterogeneity – most groups, including ours, run a patchwork of ERPs and home-grown MES.
“Plug-and-play” Industry 4.0 is a myth; every interface is a tailor-made project.
2. Open-heart-surgery risk – Changing critical legacy systems to chase the latest platform can jeopardize uptime and compliance.
3. Talent and change fatigue: electricians suddenly asked to code, finance teams were flooded with new dashboards and cybersecurity teams stretched thin. The human runway lags behind the tech roadmap
Because of these constraints, we take an “indirectfunctions-first” approach:
• Digital backbone first: We standardise data and processes on cloud platforms, creating a clean data lake and modern API layer.
• Wrap, don’t replace – micro-services and event streaming sit on top of legacy ERPs, giving us real-time visibility without a risky big-bang swap-out.
• Quick-win automation will fund the journey— document AI for invoicing has already freed up thousands of hours and will finance further efforts.
I’m afraid to tell you nothing new. Successful (digital) transformation starts small but dreams big. We always keep a bold lighthouse vision in the background because people need to know where the journey ultimately leads. Day-to-day, we focus on smaller use cases that can be delivered in weeks. Those quick wins build confidence, free up cash and prove the vision isn’t PowerPoint only.
Real transformation isn't about flashy tech. It's about making progress real, fast and human. Start small, win trust and scale from there. When business teams co-own the change and see their needs reflected, transformation becomes momentum, not resistance
Second, successful programs have business stakeholders’ skin in the game. We can propose solutions and write the code, but the business functions must own and drive the initiatives. When the business sees its requirements being implemented, adoption becomes self-propelling. Push that same change into an environment without a champion and you’ll meet resistance that no mandate can beat
Finally, you need people in the business functions who understand and support your agenda. They help translate jargon, defuse objections and show their peers that digital isn’t an outside imposition. Luckily, we have those people in our organization and I'm very thankful for that.
Balancing Fast ROI with Long-Term GainsI see both objectives as complementary. In today’s AI-charged market, management rightly demands fast ROI. We lean into that pressure by running small projects that pay for themselves fast. Each win generates cash, especially political capital, which we use straight to drive more complex projects.
We started with data integration because you can’t scale any form of automation without clean, connected data. We attacked that first with a skeleton crew. For months, it felt like plumbing rather than transformation. But now, every new use case plugs into a structured, governed data backbone, making time to market for new solutions way faster.
For us, workforce reskilling runs parallel on two tracks. First, on-the-job exposure: When a colleague helps design a new solution, they learn the basics of the technology by osmosis. Second, self-directed learning: We reimburse formal programs, but our most successful upskilling has come from YouTube tutorials, Coursera degrees and— more recently—colleagues upskilling themselves through ChatGPT or Claude. It’s inexpensive and immediately practical.
Skills and Mindsets That Set You ApartI believe future leaders must speak two languages fluently: technology and leadership. It’s not enough to know ChatGPT. Understand how large-language models are trained, how APIs are exposed and how software is tested and deployed. A programming language, basic data structures and cloud services will soon be the price of admission, no matter your title.
Why? Traditional SaaS stack won't be needed once intelligent agents can execute custom workflows directly on your data. The real skill will be designing and orchestrating those agents, much as today’s leaders plan projects and delegate to people.
This brings us to the second language: human leadership. Everything we’ve learned about motivating teams, setting clear objectives and giving feedback now must extend to mixed crews of humans and AI agents. You’ll need to decide when a task goes to a colleague when it goes to an agent and how the two hands work back and forth. That’s far beyond clever prompt-engineering; a new management discipline is only starting to emerge.
Three practical tips:1. Build a maker habit. Ship small scripts, dashboards, or bots every month. Nothing accelerates learning like putting real code in front of real users.
2. Curate your learning feed. Supplement formal courses with YouTube deep dives, Coursera specials, opensource projects and—yes—ChatGPT as a study partner.
3. Seek cross-functional problems—volunteer for projects that force you to translate between finance, operations and IT. The most valuable people in this field are “bilingual” connectors who can explain GPU quotas to the CFO and margin pressure to the data scientist.
