In an exclusive interview with Regtechtimes, Ivana Nikolik, Board Member and CGO – Client Executive Partner at Atos and Founder & CEO of IN Global Advisory, shares her insights on AI adoption, digital transformation and the evolving role of business leadership. With nearly two decades of international experience, Ivana advises organizations on strategy, innovation and sustainable growth while championing diversity and female leadership in technology. As President of STEM Women in the Netherlands and G:100 Country Chair Netherlands for IT Services & Innovation, she brings a global perspective to the opportunities and challenges shaping the future of technology. In this conversation, she explores how organizations can overcome AI adoption challenges, implement responsible AI, drive meaningful innovation and build a more inclusive digital future.
1. You have moved between government, international business, executive leadership and the technology sector before building your current focus around Data & AI. Looking across those different environments, what has fundamentally changed about the way organizations make technology-driven decisions?
I started my career in government and diplomacy, and back then technology was mostly an IT topic. You bought a system, you implemented it, and the board heard about it only when something went wrong. Today, technology sits at the center of strategy. I see it every day because I work with both the private and public sector. For years I worked with Fortune 500 companies in telecom, finance, semiconductors, etc. Today, a significant part of my focus is on European institutions and highly regulated environments. In both worlds, the people at the table have changed. It’s no longer just the CIO, it’s the full board and the people who will actually use the solution. The other big change is the speed. The private sector learned this first, but I’m impressed by how much the public sector has accelerated too. They still move more carefully, and they should, because they serve citizens. But no one can afford to spend two years deciding anymore. The skill today is making good decisions quickly while leaving room to adapt as implementation progresses. At the same time, decisions are no longer driven only by innovation and business value. Security, digital sovereignty, regulatory compliance, resilience and trust have become equally important factors, especially across Europe.
2. AI has moved very quickly from experimentation to boardroom-level priority. From your conversations with senior executives and organizations across EMEA, what is the biggest gap you currently see between the AI ambitions companies communicate and what they are actually ready to implement?
The biggest gap is between the keynote slide and the data center. I’m often speaking on global stages myself, so I say that with a bit of humor. Almost every executive I meet has a compelling AI vision. Then you ask a few simple questions- Where is your data residing?, Who owns it?, Can you trust it?, How is it governed? That is often where the conversation becomes real.
In the public sector, and increasingly across highly regulated industries, like the financial sector and aviation, the questions about digital sovereignty, security, compliance and Responsible AI come up even faster, and rightly so. There’s also a people gap. Companies announce AI strategies without preparing their teams and then wonder why adoption is slow and why their transformation takes longer than expected. Ambition isn’t the problem. I’d rather work with an ambitious organization than a hesitant one. The problem is skipping the unglamorous groundwork because it doesn’t make headlines. Successful AI adoption depends just as much on governance, culture and trust as it does on the technology itself.
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3. Many organizations are investing in generative AI tools without first changing their underlying data, processes or operating models. In your view, what needs to happen before AI can move from a collection of pilots to a genuine business transformation programme?
First, every pilot needs a business owner. One of my favorite examples is an AI quotation assistant for a global aviation services company. They started with one very concrete problem: customers waiting up to 24 hours for a quote. Today, that process takes around 2 minutes, delivers approximately 99% accuracy and generates significant annual savings. It scaled because it was tied to a real process and a measurable business outcome. Second, fix the data behind the processes that matter most. You don’t have to fix every piece of data in the organization, but you need trusted data where business value is created. Third, be willing to redesign the process itself. AI on top of a broken process simply gives you a faster broken process. And finally, ownership and accountability. Someone senior must own the outcome, not just the technology. Transformation happens when AI appears in budgets, KPIs and operating models, not only in an innovation lab.
4. You work at the intersection of technology and business growth. How has AI changed the role of the technology leader or business leader when deciding which problems should be solved with AI and which problems should not be automated at all?
It has turned the conversation from “What can we automate?” into “What should we automate?” Those are very different questions and when I am advising my clients, I often challenge them to think about whether a decision creates real value for the organization and its employees, whether they can explain and defend the outcome if it’s challenged and what they might lose if there isn’t a human in the loop. Repetitive, high-volume work should absolutely be automated wherever possible. I’ve seen a telecom organization start with a hackathon idea and grow it into a conversational AI platform handling hundreds of thousands of customer interactions every month. That’s a great use of technology because it frees people to focus on more meaningful work.
At the same time, not every process should be automated simply because it can be. With the rise of Agentic AI, we’re moving from systems that generate recommendations to systems that can make decisions and take actions. That creates tremendous opportunities, but also new responsibilities. Leaders need to think carefully about transparency, explainability, accountability and risk. Personally, I believe AI should enhance human intelligence, not replace human judgment. It’s no longer about what AI can do, but what we should allow it to do. That’s where Responsible AI becomes critical, especially in areas involving people, trust, compliance and high-impact decisions.
5. AI adoption is increasingly becoming a leadership challenge rather than simply a technology challenge. What does effective AI leadership look like today, and what are the qualities you believe will distinguish successful leaders in an AI-driven workplace?
This is a topic very close to my heart. Some of my keynote presentations have focused on the future of leadership in an AI-driven world. In one of those sessions, I ran a simulation where human and AI agents competed in the same sales scenario. The AI was faster and more consistent. The humans were better at understanding context, reading emotions, negotiating, adapting to unexpected situations and building trust. That says a lot about leadership. You don’t have to be the most technical person in the room, but you do need to be curious, honest about what you don’t know and comfortable operating in uncertainty.
The leaders I see succeeding bring people along rather than pushing change onto them. They communicate openly because when leaders are vague about AI and jobs, fear fills the silence. AI leadership is also about balancing opportunity with responsibility. Employees have legitimate questions about privacy, job evolution, accountability and the role humans will play in increasingly automated environments. The strongest leaders are clear about their values and about what they will and won’t do with AI, even when there’s pressure to move faster. The technology is new. The trust, clarity and courage are not.
6. You have spoken publicly about AI and leadership, including with audiences focused on women in technology. Beyond improving representation, what structural changes do you think organizations need to make to ensure women have a meaningful role in shaping the AI systems, products and decisions that will influence the future workplace?
Women’s representation is the starting point, not the finish line. As President of STEM Women in the Netherlands and a long-term advocate for Women in Tech and Women in AI, I meet brilliant women who are present in the room but not always at the decision-making table. Organizations need to give women real ownership of AI products, budgets, innovation initiatives and strategy, not only supporting roles. And if the teams making the decisions and setting the rules aren’t diverse, the systems won’t be either.
We called one of our discussions on bias in technology “Equal by Design,” and I still believe that’s the right mindset. Fairness must be designed into systems from the beginning, not added later as an afterthought. We need more leaders willing to actively open doors and create opportunities for the next generation of women shaping AI. Equally important is creating environments where talented women can thrive, grow and lead throughout their careers. The more diverse the perspectives around the table, the more innovative, inclusive and impactful the technologies will become.
7. One of the biggest concerns around enterprise AI is that organizations may focus heavily on the model while underestimating the importance of data quality, security, human oversight and organizational culture. Which of these areas is most frequently underestimated in your experience?
Culture, without a doubt. Earlier this year I spoke to a mixed audience at a large multinational organization in Paris. Some participants were AI experts, while others had very little exposure to AI. Their biggest concern wasn’t the model itself. It was whether AI would collect personal information about employees and use it in ways they couldn’t control or understand. That conversation stayed with me. You can have clean data, a secure platform and an excellent model, and still fail because people don’t trust it or quietly work around it. Data quality and security are difficult challenges, but at least everyone agrees they’re important. Culture is the area people often assume will take care of itself, but it won’t. We need to involve employees early, communicate honestly about how roles will evolve and be transparent about how data is governed and protected. Trust is built through transparency, not through technology alone. In my experience, organizations that invest as much in change management and culture as they do in the technology itself are the ones that ultimately achieve successful AI adoption.
8. As AI becomes embedded into everyday business processes, how should organizations rethink the balance between human expertise and machine-driven decision-making? Where should human judgment remain non-negotiable?
Human judgment becomes non-negotiable wherever a decision has a meaningful impact on someone’s life, rights or livelihood. Hiring decisions, healthcare, legal outcomes, financial access and public services are all examples where AI can support decision-making, but humans must remain accountable for the final outcome.
Working with European institutions has strengthened this belief. In the public sector, every decision must be explainable to citizens, regulators, auditors and stakeholders. Personally, I think the private sector should follow the same principle. That’s why I’m a strong believer in XAI (Explainable AI), particularly in highly regulated industries. AI can identify patterns, process large volumes of information and help us make decisions faster, but speed should never come at the expense of accountability.
We also have to remember that even the most sophisticated AI systems can hallucinate facts, generate inaccurate conclusions or present information with a level of confidence that exceeds its accuracy. Human expertise remains critical in validating outcomes, challenging assumptions and understanding context. I’d add another category where human judgment remains non-negotiable: strategy and values. Where are we going? What risks are we willing to accept? What kind of organization do we want to become? Those decisions belong to people. The real danger isn’t AI making decisions. It’s people no longer questioning them because it’s easier not to. Human accountability should remain non-negotiable, regardless of how advanced the technology becomes.
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9. You have built a significant public profile alongside your executive career, including recognition as a Europe 40 Under 40 business leader and as a technology leader, Founder and CEO. How has becoming a visible voice in technology changed the way you approach leadership, networking and influence?
Over the years, I’ve realized that visibility itself is not the goal. What matters is the impact you can create through it. The biggest change is that I’ve become much more intentional about where I invest my time, energy and voice.
Most recently, I was recognized among the Top 50 most influential female CEOs and technology leaders in the Netherlands on LinkedIn. Alongside being named a 40Under40 Top Business Leader in Europe and receiving technology leadership recognitions, it was a tremendous honor. What matters most to me, however, is what comes afterwards. Younger women often approach me looking for advice on leadership, technology or career development. Business leaders are reaching out to me following a keynote presentation or panel discussion. Those interactions keep me grounded and remind me that visibility comes with responsibility, so my goal is always to provide real value and an authentic perspective, whether I’m speaking on stage, mentoring and empowering future leaders or leading strategic initiatives with clients. I enjoy helping people understand how technology can solve business challenges and real-world problems. To me, true influence is measured by the positive impact you leave behind and the opportunities you create for others.
10. Looking five years ahead, what do you think will separate organizations that genuinely transformed through AI from those that simply accumulated AI tools, and what should business leaders start doing differently today?
The organizations that genuinely transform will look fundamentally different on the inside, not just on their website. Their processes will be optimized and fully redesigned. Their people will work differently, meaning more productively. AI won’t be a separate initiative. It will be embedded into how they create value. The others will have plenty of licenses, impressive demonstrations and the same problems they had before. With Agentic AI, the gap will grow even faster because intelligent agents only deliver value when the data, governance, security and operational foundations underneath them are strong.
We’re rapidly moving toward a reality where AI agents collaborate with other agents, make decisions and execute actions with minimal human intervention. At the same time, cyber threats are becoming more sophisticated, with attackers increasingly leveraging AI themselves. Organizations that succeed will be those that invest in governance, cybersecurity, Responsible AI and digital sovereignty as aggressively as they invest in innovation. We’re also approaching an era where intelligent agents and humanoids become part of everyday business and personal environments, so the real question is how we design that future responsibly.
My advice to leaders is simple – focus on a few high-value areas and go deep instead of trying everything. Invest in your people as much as you invest in technology. Build trust, strengthen governance and get your data house in order. Transformation isn’t a project with an end date. It’s a capability. And the organizations building that capability today will be the ones leading tomorrow.