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Tatyana Sorokina is the Executive Director, Head of Advanced Quantitative Sciences Transformation at Novartis, where she leads enterprise AI strategy for the International commercial organization. She oversees cross-functional teams delivering data-driven solutions such as customer segmentation and omnichannel engagement, embedding intelligence into commercial decisions to drive impact, adoption and measurable business outcomes.
The Roadblocks to Productizing AI at Scale
The hardest part isn’t building the algorithm— it’s embedding it into business workflows. Scaling AI requires a solid product architecture, clean data pipelines, user-centric design and seamless integration with both upstream and downstream systems. A key challenge in international markets is balancing standardization with localization, determining what can scale globally versus what must be tailored to specific commercial contexts. Change management is another hurdle. AI often disrupts legacy workflows and decision-making processes, so building trust and adoption is critical. At Novartis, we’ve addressed this by involving local activation teams early and prioritizing use cases with strong commercial sponsorship and clear, measurable KPIs. Our goal is to embed intelligence into commercial decision making to drive measurable impact. We operate with a product mindset, agile approach to development and maintain a laser focus on adoption and business outcomes.
The Core Ingredients of a High-Impact AI Team
I look for people who can turn ambiguity into action, those who blend technical rigor with business acumen. The most effective team members are curious, pragmatic and highly collaborative. They are equally comfortable engaging with both technical and commercial leaders. Beyond skills, mindset is key. Building enterprise AI products is a marathon, not a sprint. We value resilience, accountability and the agility to pivot when needed. I also invest in cross-functional fluency. Roles like analytics translators are essential in bridging business needs with technical execution.
Our goal is to embed intelligence into commercial decision making to drive measurable impact.
The Expanding Role of Enterprise Product Leaders in Advanced AI Development
AI product leaders are evolving from capability enablers to outcome owners. As generative and agentic AI technologies mature, the emphasis will shift from experimentation to governance, orchestration and scalability. Leaders must be adept at translating advanced AI capabilities into tangible business value. Managing AI will increasingly resemble managing a portfolio, knowing when to invest, scale, pivot, or retire solutions. Success will be measured not just by adoption but by sustained, measurable impact. The role itself is becoming more interdisciplinary, requiring a blend of data science, change management, product strategy and deep domain expertise, all brought together into a cohesive, value-driven function.
Final Thoughts to Inspire Action
AI is no longer a future bet - it’s a present-day differentiator. But impact at scale doesn’t come from hype. It comes from focus, rigor and execution. Treat AI not as a one-time project, but as a living product within a broader ecosystem. While AI is a powerful tool for driving behavioral change, it’s not a solution to every problem. Success lies in building cross-functional coalitions early and grounding solutions in real business needs. Don’t chase the most complex models—focus on what will actually be used. And always consider the full lifecycle: data acquisition, model governance, user onboarding and feedback loops. Without scalable adoption, there can be no scalable impact.
