In the rapidly evolving world of artificial intelligence, leaders across industries are grappling with the implications of generative AI on business strategy, governance, and workforce transformation. Maria Zelkovskaya, a visionary leader with extensive experience in digital transformation, AI governance, and corporate innovation, is at the forefront of this dialogue. With a career spanning top-tier consumer goods giants like Danone and AB InBev and advisory roles across leading firms and boards, Maria brings a rare combination of strategic foresight and hands-on expertise.

Speaking at BOI—Board of Innovation, Maria illuminated the complex challenges and opportunities AI presents to leadership structures, corporate decision-making, and the future of work. She advocated for fundamentally rethinking business models, urging organizations to embrace AI as a tool and as a transformative force reshaping industries at their core.

In this exclusive Q&A, Maria shares her insights on how companies can successfully integrate AI, what ethical challenges lie ahead, and why leaders must actively shape the AI-driven future.

As an expert in transformation governance, how do you see generative AI reshaping executive decision-making and leadership structures in large organizations?

For the time being, the good sign that I see is that it has finally reached the leadership table. It is no longer something leaders can deprioritise. However, there is still a long way to go till we can say genAI has reshaped decision-making and leadership structures in large organisations. The reason is that incumbent organisations (vs digital-by-design ones) have too much inertia in embracing something unnatural to their core business model. Huge ones. Even if they are in the fast-moving consumer goods (FMCG) sector, nowadays, most of the boards and leaders I work with as part of my corporate and non-corporate (advisory and independent board member) engagement realise that there is a value behind it and this is where everyone in all the industries is moving, and the faster you get there the more competitive advantage it will give, and the better career this leader can make. However, what exactly needs to be done, or how AI differs from GenAI in business applications, is not always clear to them. To become data-driven, incumbent organisations need to revisit and rethink their data foundations: data governance, data availability, data capabilities, footprint of solutions, etc. And this is quite a tedious, costly, and lengthy process. It is hard to lead something you don’t fully grasp and are unsure if you will succeed, let alone make a career out of it.

As for the impact on leadership structures, I observe the struggle of CDOs (Chief Data Officers) to get out of the CIO umbrella and prove to businesses that they are business functions. They hardly succeed because businesses do not fully understand how data is a business problem, and CDOs do not tackle the business. I envision that only when we have roles that encompass both data and organisation/business implications within one domain (say, it could be Chief Data People Officer – reach out to me if you want to know more of how this can look like from job description and business impact perspective), we will be able to bring AI to the board level. From the supervisory board level, I see another trend – if before financial + MD experience was the essential requirement for the board service, nowadays, AI expertise has become tremendously important. So, to avoid making a hard choice between them (as not so many people on the market have all three capability sets), boards have started to extend the number of seats to include an AI transformation perspective into governance.

You have led large-scale digital and data transformations. What are the key success factors when integrating generative AI into global business operations?

Key success factors in all the transformations are:

(a) how you onboard people into that. And I mean both C-level and top-management and shop-floor employees, as approaches, tactics and governance would be very different.

(b) understanding the technology and its limitations. Like with the art of prompting – if it is about asking the right questions to get the correct answer – we need to elevate this skill first in ourselves as humans. And how many of us can boast we can ask good questions in real life? We function based on assumptions, context, biases, overthinking, etc. GenAI does not have it. So we must think it through, make it conscious, and then curate all the questions. Sometimes, it feels like it requires us to go back to ancient skills of rhetoric/polemic art, philosophy and logic. Are we prepared for that at the scale of the company?

(c) from a broader perspective, AI transformation has data as its basis. So, to become AI enabled, we need to ensure that data governance is in place—not somewhere centrally, but for all business data, from the moment it enters the organisation to when and how it is amended. It must be thought through and designed; the business (not IT or some central data COE) needs to own it.

The key to success is bridging AI and people in all their multidimensional perspectives.

Your work focuses on innovation and organizational development. How do you see the role of AI evolving in fostering a more human-centric and creative work environment?

So far, I still see it as a promise only. But I do believe that in the end, those who would manage to get through the “coming wave” of AI-silicon shoring (when you redesign your process to be fully outsourced not to near/offshore locations, but to AI, with only a few people – highly skilled individuals – left where it would still be needed) will have a chance to benefit from AI at scale. For the rest, people will face a large-scale need to reinvent themselves, finding true purpose and new work meaning to remain relevant to the future. It will not be very easy, but it will also allow you to get into a more human-centric and creative environment. Why am I sceptical about other promises? So far, when AI implementation is scattered across organisations, it has resulted in highly skilled employees acting as a copy-paste interface between various not-talking-to-each-other AI solutions. On top of the poorly managed data, the load of non-added value and demotivating activity is too big. The challenge is much more significant than simply implementing yet another tool. We as organizations need to revisit and rethink our business models, operation models, working methods, and organization charts to support them. However, small-scale and more agile start-ups and scale-ups can benefit from AI much quicker. But this can't easily be translated into large-scale corporate environments. That’s why we might see even a change in the competition footprint, both from business and human resource perspectives, forcing large-scale corporates to double down on AI transformation efforts.

Given your board governance expertise, what are organisations' most prominent challenges in ensuring responsible and ethical AI deployment?

In a nutshell, these are the same as for the rest of the company: people (in the broad sense from having someone, but better the majority, understanding AI & its implications, way to govern it, risks & opportunities, etc, but also having readiness across the board to embrace it and lead/invest into multi-year transformation) and data (how to fix the “data problem”- its accuracy, governance, availability, consistency across the organization – which is the foundation of whatever you want to do in AI-area).

As a thought leader and public speaker, what advice would you give to leaders navigating the rapid evolution of AI and its impact on workforce transformation?

Don’t try to outwait it. Or wait for someone to come and explain it to you or transform your organisation. Leaders need to figure out how AI changes the underlying foundations of society, the economy and business. The speed of change is too massive. If leaders want to be relevant to the future, they must educate themselves and see how to get on top of this agenda. The time of small trials and experiments is over. Companies need to figure out how to scale from it. (Spoiler: there is no framework yet for it.) To make a fundamental shift to become data-driven.

Alternatively, get yourself a pool of advisors/advisory boards to help fill in the knowledge gap and have an independent perspective on what is possible and what risks and opportunities exist. AI is no longer (and has never been) an IT domain or technological topic. Data and AI are the fundamentals, the basics of our future, which is already here, just not evenly distributed.

Maria Zelkovskaya’s insights underscore a stark reality: AI is no longer a distant frontier—it is already redefining industries, organizations, and careers. However, as she points out, the journey to AI maturity is not just about adopting new technologies but fundamentally restructuring how businesses think about data, decision-making, and human-AI collaboration. From the role of generative AI in governance to the growing importance of AI expertise at the board level, Maria highlights the urgent need for leaders to actively educate themselves, rethink business models, and take ownership of AI-driven transformation.

Success will be achieved by organizations that embrace AI as an enabler of strategic advantage, operational efficiency, and human-centric innovation—not just another technological add-on. Maria’s call to action is clear: waiting is not an option. The future of AI is now, and leadership must rise to meet it.

Building Creative Machines

Interview: Maria Zelkovskaya - Transforming…