Jakub “Kuba” Ruzicka, CPTO of retailtech SaaS company eyos, shares his vision for modern tech leadership. He discusses the shift from a growth-at-all-costs mindset to profitability, why engineers must now “go broad or go deep,” and the common pitfalls of AI adoption.
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Here’s a glimpse of what you’ll learn:
- Engineers Face a New Strategic Choice: “Go Broad or Go Deep.” As AI handles basic coding, tech professionals must either become a generalist with business acumen (broad) or a specialist in a non-automated niche (deep).
- The Bottleneck is Now Business Problems, Not Engineering Speed. AI makes building faster, but the new, urgent challenge for leaders is identifying the right business problems that will create real value.
- Use AI as a Precise Tool, Not a Catch-All Solution. A common mistake is thinking “AI solves everything.” The goal is to solve the problem efficiently, and often a simpler, non-AI solution is the most effective choice.
Please tell us about yourself and your role.
My name is Kuba, and I’m the Chief Product and Technology Officer at eyos, a global digital receipts, data-as-a-service, and retail media company bridging offline and online retail. My responsibilities cover everything you’d expect from a CTO, our strategy, budget, technical team, and our SaaS platform. More recently, I also took over our product function.
Over the last year, our main focus has been on modernising, streamlining, and automating our platform, as well as integrating AI, LLMs and multi-modal agents, especially for internal optimisation and improving back-office processes. My journey has taken me from being a technical leader, to a combined technical and product leader, and now to supporting AI adoption across the company.
The IT landscape evolves rapidly. What challenges are you currently facing?
To be sincere, the main challenge is achieving more with less. A few years ago, the usual solution to a problem was to add headcount and scale up fast with venture capital. Now, the focus is much more on sustaining profitability and building robust businesses, which I believe is a good thing.
The real challenge is organising ourselves as a team to use emerging tools strategically, focusing on the work that truly matters rather than routine tasks that should be automated, while maintaining momentum with fewer resources.
How do you think the IT sector will evolve in the next three to five years?
The democratisation of IT will accelerate. With AI and LLMs, even someone with no programming experience can now build websites or automate marketing. This is creating a major shift for engineers and other tech professionals.
I believe people now face two strategic career paths. They can either go much broader, augmenting technical skills with business, product, and financial knowledge to become a “one-person show”. Or they must go very deep, specialising in complex areas that are not yet automated, such as hardware or robotics. Both groups need to step up in information security.
The former must learn the fundamentals, while the latter must build security guardrails directly into their products.
How is the regulatory environment changing, and how are IT leaders navigating these shifts?
Regulations are expanding. It’s no longer just about GDPR in the EU, more and more countries are adopting their own specific data privacy and residency laws. This adds a layer of complexity to every international expansion.
A key challenge is the gap between abstract legal language and practical technical implementation. A regulation can’t tell you exactly what to do on a code level. I believe the legal and tech worlds need to be brought closer together, making it easier for companies to comply with regulations and know they’re doing the right thing, without wasting time and resources decoding regulations or, even worse, implementing measures that ultimately don’t benefit people.
How do you balance the need for innovation with the demand for stability and security?
With the modernisation of our platform, we’d like to reach a stage where rapid prototyping in a sandbox environment is possible. Creating a space where our team is free to experiment with new tools or use cases on non-production data. From there, holding sharing sessions to filter out the most promising ideas and decide which ones deserve to be promoted to production.
This approach enables rapid innovation. For example, a non-technical person can build a prototype with a no-code tool and test it internally. But once it’s ready to face real clients, that’s when a senior engineer must step in to rebuild it and ensure it is operationally stable, maintainable, and secure.
How do you approach mentoring and developing the next generation of leaders within your IT team?
As a university lecturer, this is an area close to my heart. For me, the key is to know each person’s personal career goals. If I understand them as professionals and as people, I can do a better job as a matchmaker.
I know our business goals, and I know what each individual wants to achieve, whether that’s mastering a specific technology, becoming a manager, or pivoting into a new role. When I can match a business problem with someone who is personally motivated to solve it, they almost always do a great job because they’re driven by both internal and external factors.
What’s the biggest IT-related mistake you see companies make?
Recently, I see two common mistakes. The first is thinking that AI solves everything. Companies often overestimate what AI is capable of, or they use a large, expensive LLM for a problem that a simple piece of code or an off-the-shelf solution could solve more efficiently and for less money.
The second mistake is hiding behind a lack of engineering resources or the need to boost engineering productivity, instead of admitting that it has always been about finding the right business problems to work on. With AI copilots, we can develop features faster than ever.
But the question is not just “Can we build this?”, it’s also “Should we build this?” and “Are we solving meaningful business problems that will actually solve customer needs and grow the company as a result?” To stay focused, you also need to filter out the things you will not do.
Reflecting on your experiences, what’s been a memorable insight from knowledge-sharing discussions?
You quickly figure out that some companies are better at one thing but have gaps in another, and your own company is the same. My takeaway is to share the areas where you shine so others can learn from you. For the areas where you have a gap, don’t be afraid to find a best practice from someone else and apply it.
By adopting a practice that works, you gain understanding and build the necessary experience. But the process doesn’t stop there, you need to tweak and adapt it to fit your company’s specific purpose and context, and eventually find your own unique approach.
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