Artificial intelligence is rapidly permeating everyday work — from supporting small tasks to enabling autonomous agent-driven actions. How do we navigate this pace of change, build a culture of innovation, and translate technology into real business value? Łukasz Bolikowski, Chief AI Officer at Comarch, shares his perspective on combining science and consulting experience, human–AI collaboration, and ambitious plans to shape future standards.
You combine an academic background with experience working with some of the world’s largest companies. How have these diverse experiences shaped your perspective on artificial intelligence and innovation? Are there lessons you carry into every new organization?
For the first 15 years of my career, I worked at the Interdisciplinary Centre for Mathematical and Computational Modelling at the University of Warsaw — essentially a supercomputing center. Collaborating with mathematicians, physicists, chemists, biologists, and sociologists taught me to view the world through the lens of mathematical models. Whether we were designing a new drug, forecasting weather, or modeling the evolution of galaxies, there was always a model helping us understand and predict complex phenomena.
I later brought that mindset to Boston Consulting Group, where I began applying mathematical modeling to business problems across industries and sectors. That experience taught me how to identify where the right model can create real value, select the appropriate approach, and implement it within specific time and budget constraints. This blend of scientific rigor and business pragmatism continues to shape how I approach digital transformation and AI adoption.
You moved from a supercomputing environment to global consulting. What cross-industry and cross-cultural experiences do you consider essential when introducing digital and AI transformation?
A critical element in every successful transformation I’ve led is close collaboration between technology and business experts. Technology without business alignment can produce elegant solutions that lack market value. Conversely, business initiatives without technological insight may become unrealistic or overlook opportunities enabled by modern tools. Real magic happens when technology and business work together.
With the rapid pace of AI innovation, how do you manage pressure and maintain a coherent vision while still delivering value?
The key is understanding what’s actually happening in the market and identifying what truly matters. I read AI newsletters daily — such as AlphaSignal, TLDR AI, and The Rundown AI — but it’s essential to filter out hype and avoid FOMO. Staying focused on the goal is what matters most.
For us, that goal is improving every employee’s effectiveness by providing tools that make their work faster, higher quality, and more satisfying. In that sense, collaboration between business and technology doesn’t just withstand the pressure of change — it transforms it into tangible organizational value.
Do you see AI primarily as a tool supporting people or as a partner in business decision-making?
The balance is shifting quickly. Not long ago, AI mostly played a supporting role — auto-completing code, for example. Today, we’re moving toward greater autonomy. There’s a growing number of tasks agents can perform reliably without constant human interaction. We’re heading toward a partnership model where AI actively supports decisions and actions rather than simply assisting with tasks.
What are the biggest non-technical challenges in adopting AI — those rooted in culture, habits, or ways of working?
The hardest part is changing how we think. We need to start viewing AI agents as coworkers — incredibly fast, obedient, and knowledgeable, but neither infallible nor mind readers. In many ways, each of us becomes a manager of AI team members. That means learning to communicate tasks clearly, providing context, allowing autonomy without micromanagement, and understanding limitations while offering constructive feedback.
What mindset and cultural shifts are necessary for AI to truly deliver organizational value?
Communication skills are becoming critical. AI can accomplish a great deal, but only when expectations are articulated clearly. Precision in communication allows organizations to unlock the full potential of AI agents while ensuring alignment with strategic goals.
Why should organizations involve employees — even non-programmers — in AI experimentation or hackathons?
Because everyone can benefit from AI. Many everyday tasks contain processes that could be faster and more effective with the right tools. We deploy solutions for non-engineers, such as NotebookLM or n8n, and provide training to help employees fully leverage them.
How can organizations stimulate creativity so experimentation translates into real value?
It’s important to carve out time to “play” with new technologies, even in busy schedules. This can be institutionalized through hackathons. Just as important is open conversation — about successes, failures, and unexpected results. The more channels exist for knowledge sharing, the more effectively innovation spreads.
Many talk about a “culture of innovation.” How can it be built in practice?
It inevitably involves risk — and managing that risk wisely. Ambitious ideas won’t always succeed, but even failure provides value by teaching lessons and pushing boundaries. In a “fail forward” mindset, setbacks become starting points rather than reasons to stop.
Practically, this means creating environments where employees feel safe sharing ideas and experimenting. Technology — including AI — supports this by enabling rapid prototyping, testing, and measurable learning. Innovation culture is ultimately a blend of courage, thoughtful risk management, and effective use of tools that help organizations learn in real time.
Will everyone become a “manager” of their own AI assistants?
With tools like Gemini or NotebookLM, every employee gains a powerful collaborator capable of answering complex questions in seconds. But the real skill lies in structuring work, issuing effective instructions, and managing capabilities intentionally. In practice, each of us becomes responsible for integrating AI into workflows to maximize productivity and quality.
What is your personal “moonshot” vision for AI?
My dream is that within 18–24 months, market pressure will push competitors to adopt an open agent protocol developed by Comarch engineers. I believe we have a real opportunity to set European — and possibly global — standards in selected sectors. It’s ambitious, but not impossible, and the vision itself is highly motivating.
Finally, what advice would you give to those just beginning their AI journey?
Read, watch, experiment, and talk. Ask your digital assistant how it can help you build AI skills. Subscribe to newsletters, blogs, and YouTube channels. Set a daily challenge — complete one task, personal or professional, using AI. Share what you learn with others. That’s how practical knowledge grows — and how you inspire those around you.