Interview: Svetlana Barsova on Generative AI – Practical Impact, Strategic Adoption & Future Vision
Insights from a Microsoft Germany leadership team member on the strategic integration, adoption journey, organizational impact, and visionary opportunities of Generative AI
Svetlana Barsova is Chief of Staff to the CEO at Microsoft Germany, with deep expertise in marketing and operations. A passionate AI explorer, she offers real-world insights on the strategic integration, adoption journey, organizational impact, and visionary opportunities of generative AI (GenAI). In this interview, she shares her perspective with business and innovation leaders navigating the future of technology and leadership.
This dynamic, powerful conversation is with someone who is truly making a difference— and from whom I had the pleasure to learn. We first met at an inspiring Promptathon in Munich, and her clarity of thought and vision stayed with me ever since.
Day-to-Day Impact and Use Cases
How has generative AI been integrated into your daily operations at Microsoft—both personally and organizationally? What GenAI tool or feature can you no longer live without?
I’ve been using GenAI for over a year, starting with ChatGPT for fun and then shifting to Copilot as part of our “customer zero” approach. This is a strategic initiative where Microsoft acts as the first customer for its products and services. This approach allows Microsoft to validate and improve its offerings before they are released to external customers. Personally, the thing I cannot live without as of now is drafting both in English and German, as I’m non-native in both languages, adjusting my emails and messages, making them more friendly and eloquent rather than direct and straightforward, as I naturally am. So, you might be surprised how diplomatic I am while writing compared with me speaking. This is what helps me a lot to adjust to the culture when I moved to Germany. Professionally, the catch-up features across emails, chats, meetings and unread documents are a game-changer for me as a Chief of Staff who has to be on top of everything and better support our CEO.
Can you share specific use cases where GenAI has created measurable value across different business functions?
Absolutely. In marketing, for example, we used to spend hours translating and editing C-level video podcasts. Now, GenAI handles translation, voiceover, and facial syncing — saving time and cost. More broadly, I spend far less time searching for data or drafting strategies. And we’re just scratching the surface as we move toward an agentic AI world.
What are some unexpected or “non-obvious” use cases where GenAI has driven innovation or efficiency gains?
I’m fascinated by its creative potential in filmmaking, gaming, and research. On the corporate side, GitHub Copilot is a standout, boosting developer productivity by 20–30%. One of the most inspiring and non-obvious GenAI use cases is our collaboration with WWF Germany and Accenture to combat ghost nets—abandoned fishing gear that silently devastates marine ecosystems. Through the GhostNetZero platform, we’re using AI to analyze high-resolution sonar data from the seabed, automatically identifying likely ghost net locations with over 90% accuracy. What once required divers and manual review is now a scalable, AI-driven process that transforms sonar scans, originally collected for shipping or offshore wind planning, into environmental intelligence. This is GenAI, not just accelerating productivity, but enabling environmental protection at a scale and speed previously unimaginable.
How do teams at Microsoft balance experimentation with GenAI and the need for operational stability and security?
Security is non-negotiable and is paramount at Microsoft, so there are no compromises. As you know, we utilize existing models, but with data sets that are either within a work environment, which is called Microsoft Graph. Feeding the graph helps to develop the outcome of work-related data and the web-based data, and you can choose what you would like to use now, but within a secured environment.
Adoption and Change Management
What were the key challenges in driving the adoption of GenAI tools internally, and how did you address them?
GenAI adoption isn’t just about tools—it’s about changing how we work. That means treating it as a leadership challenge and a change management challenge. I’m a fan of the ADKAR model, but whatever the method, it takes time, consistency, and role modelling. In our case, it took about six months to see real traction. The ADKAR change management model, developed by Prosci, stands for Awareness, Desire, Knowledge, Ability, and Reinforcement. It is a framework designed to guide individuals through the process of change by addressing these five key elements to ensure the successful adoption and implementation of changes.
How has the Chief of Staff function played a role in orchestrating the adoption of AI across teams?
It depends significantly on the organizational structure and the individual passion of the Chief of Staff. At Microsoft Germany, we had an Adoption Lead from the marketing team managing the AI influencers community across all organizations, so I played a supporting role and bridged the gap between the adoption v-team and our leadership team. However, in some cases, I observe that the Chief of Staff or the CEO’s Office plays a significant role in adoption as a transformation leader. It can be a truly inspirational change management project and experience for one, therefore I encourage my peers: if there is no Change or Adoption Lead, become one and make a difference.
What has worked best in terms of change management—training, incentives, leadership buy-in, or something else?
As I mentioned, we are “customer zero” at Microsoft, and we are eating our own “dog food,” meaning we receive all the new features and try them all before they go the market. I would say the following works the best:
Leadership buy-in and role-modelling. When a CEO speaks at a Town Hall and shares personal use cases of using Copilot, it helps a lot (but, of course, it must be authentic) and encourages employees to try it out.
Time investments. This requires a team approach and a significant investment of time in experiments, prompting, exchange, and discussion.
Healthy Competition. Always work well at Microsoft. We are competitive and always up for the challenge, not only within departments but also among countries.
Consistency. It’s not a one-time exercise – it’s a change in how we work, and it is constantly changing. Which means it’s not a one-time exercise; it’s daily micro-learning.
Passionate early adopters. If you have one on the management board, you are already lucky. But those people are the driving force behind all the above.
How do you handle resistance to AI adoption, especially among experienced leaders or knowledge workers?
We’re not at 100% yet. The tech evolves fast, and that’s intimidating. But once people experience their first “aha moment,” they’re hooked. And what really helped a lot is the native integration of Copilot into all our daily used tools, so the user experience is improved. We don’t need to copy and paste anything; we have the Copilot button available in Outlook, Teams, Word, Excel, PowerPoint, CRM, and more. It’s seamless.
What have you learned about digital upskilling in the age of GenAI?
It never stops. Upskilling isn’t a one-time course—it’s daily micro-learning. I think the big mistake is that we now think of upskilling as taking a course (or reading a book) and being ready. No, while we’re talking, new features are evolving and opportunities are coming up, and staying up to speed requires daily micro-learning. And I believe this will stay with us forever.
Vision for the Future of Work and Leadership
From your vantage point, how do you see the role of leadership evolving in organizations that deeply embed GenAI?
The key leadership role is to establish this vision for your company early enough and take the risk of change while maintaining performance. Our latest Work Trend Index confirms this urgency: 82% of leaders say 2025 is a pivotal year to rethink strategy and operations. We’re seeing the rise of ‘Frontier Firms’ — organizations built around intelligence on tap and hybrid teams of humans and AI agents. At these firms, 71% of employees say their company is thriving, compared to just 37% globally. And with every employee becoming an ‘Agent Boss,’ the leadership mandate is clear: empower your people, redesign workflows, and lead with intention into this new era.”
What does the future “AI-native enterprise” look like to you in terms of structure, capabilities, and culture?
Microsoft recently celebrated its 50th anniversary, prompting conversations with colleagues who began working at the company decades ago. It’s astounding to think that when they started, there was no Internet — no emails, no Teams, and certainly no AI tools. Reflecting on this, it’s fascinating to imagine how they managed their days in a world without the constant influx of emails that define our work lives today. On a more serious note, envisioning the future of organizations feels equally intriguing. As AI becomes an integral part of every process and industry, the distinctions we currently make between "AI" and "non-AI" processes may eventually disappear altogether.
How might GenAI reframe traditional leadership metrics or performance indicators?
I believe that the leadership metrics we are familiar with, such as revenue, P&L, and growth, will remain, but new ones will emerge, linked to the responsible use of AI, IP rights, and regulations governing how people interact with AI. It is also interesting to see if Revenue per Head will include differentiation between human heads and “AI heads”.
What advice would you give to other Chiefs of Staff or C-level leaders trying to lead through AI-driven change?
It’s not going away. Embrace it, lead the adoption, and upskill yourself. That’s how you stay ahead.
Rethinking Business Processes and Strategic Disruption
Have there been any business processes or models you’ve had to completely rethink due to GenAI?
Yes. We’ve embedded AI into everything. What began with productivity tools like Copilot has now expanded into every product line. This shift reflects a broader transformation in how Microsoft designs, delivers, and supports its solutions, with GenAI becoming a foundational capability rather than a feature add-on. That’s a comprehensive rethink in how solutions are designed, delivered, and supported.
Can you share a story where AI-enabled reengineering of a process led to significant disruption or advantage?
One of the most transformative examples I’ve seen was where the finance team reengineered core processes using AI. They automated contract reviews across 10,000+ documents, cutting review time by 50%. Journal entry anomaly detection became twice as fast, and audit briefings were streamlined with AI-generated summaries. Even executive expense checks were automated, reducing manual effort by 70%. These weren’t just efficiency gains — they fundamentally changed how the team worked, freeing up time for strategic decision-making and improving compliance. It’s a great example of how AI, when embedded thoughtfully, doesn’t just optimize — it elevates. (Source: Microsoft Modern Finance Journey with AI FY25)
How is Microsoft leveraging GenAI to create new value chains, not just optimize existing ones?
One example is our collaboration with Siemens on the Industrial Copilot. It’s helping engineers make faster decisions by combining real-time data and AI. Over 100 customers are already using it, and it’s creating a new digital ecosystem in industrial automation. We’re co-innovating with a lot of customers to build such entirely new ecosystems. Siemens is a great example, but we’re also doing this in healthcare, education, and sustainability — creating value chains that didn’t exist before.
Challenges, Risks, and Responsible Innovation
What are the most pressing challenges you see around GenAI deployment at scale —from governance, compliance, or culture?
Deploying GenAI at scale presents a multidimensional challenge that touches every part of the organization — from engineering and legal to communications and customer engagement. At Microsoft, we’ve recognized that responsible AI isn’t just a principle — it’s a system that must be operationalized across all internal and external projects.
To address this, we’ve implemented a global Responsible AI Standard that is mandatory for all AI development. This standard is enforced through the Responsible AI Lifecycle (RAIL), which includes governance checkpoints, risk assessments, and escalation paths for sensitive use cases. Every AI project must be reviewed for fairness, transparency, reliability, and safety. Sensitive or high-impact applications are routed through the Office of Responsible AI and, if needed, escalated to the Aether Committee: Aether stands for AI Ethics and Effects in Engineering and Research. It’s Microsoft’s internal advisory body on Responsible AI. Aether’s work ensures that AI systems are not only innovative but also fair, accountable, and aligned with societal values. And we also extend this expertise to support our customers, helping them navigate governance, compliance, and cultural transformation as they scale GenAI responsibly.
How does Microsoft think about responsible innovation and the balance between speed and safeguards?
At Microsoft, responsible innovation is not a trade-off—it’s a design principle. We believe that speed and safeguards must go hand in hand, and this philosophy is embedded in our company-wide commitment to the Secure Future Initiative (SFI). Launched in late 2023, SFI is our most ambitious cybersecurity engineering program to date. It is built on three foundational principles: Secure by Design, Secure by Default, and Secure Operations. These principles ensure that every product and service we build prioritizes security from the outset, with protection enabled by default and continuously monitored in operation. Every Microsoft employee has a Security Core Priority tied to their performance review. And it’s also included in the compensation of our Senior Leadership Team.
Where do you think organizations are most likely to go wrong with GenAI implementation?
One of the most underestimated pitfalls in GenAI implementation is the lack of investment in change management and workforce enablement.
One of the most underestimated pitfalls in GenAI implementation is the lack of investment in change management and workforce enablement. Many organizations start with promising pilots or proof-of-value (PoV) projects, but they struggle to scale because they haven’t prepared their people, processes, or leadership mindset for enterprise-wide transformation. Pilots are a great start, but to scale, you need to treat AI as a strategic asset. The most successful companies focus on four areas: empowering employees, transforming customer service, optimizing operations, and accelerating innovation.
In your opinion, what are the blind spots that most companies have when it comes to GenAI readiness?
Ownership and accountability are often unclear. Many also overlook data readiness, stakeholder alignment, and long-term sustainability. GenAI readiness isn’t just about launching - it’s about maintaining, evolving, and scaling responsibly
This conversation with Svetlana Barsova offers more than a glimpse into Microsoft’s AI transformation — it provides a playbook for leaders navigating the disruptive and transformative terrain of generative AI. Her grounded perspective, drawn from hands-on leadership, operational stewardship, and a deep appreciation for cultural and human nuance, reminds us that GenAI is not merely a technology. From rethinking strategy and structure to enabling environmental breakthroughs, Barsova exemplifies what it means to lead with clarity, courage, and conviction in an AI-native era. Her insights leave us with both inspiration and a challenge: to lead boldly, adapt continuously, and build organizations ready for the frontier ahead.