Sigrid R, Global Chief AI Officer, EFG Private Bank, 0
In a thought-provoking interaction with Global Woman Leader Magazine, Sigrid shares her views on transformative role of AI in banking, its real-world applications, team innovation, and leadership strategies. She emphasizes how technology, when aligned with purpose, can redefine the future of financial services.
To know more about Sigrid’s insights on AI and leadership, read the full article below.
How do you see AI transforming the banking industry in the next five years? What excites you most about its potential impact?
In the next five years, AI will enable innovative banks to deliver dynamic, predictive, and context-aware services tailored to each client’s goals, risk tolerance, and life stage, beyond improving efficiencies in operations. This can be achieved through a “predictive wealth engine” powered by multiple autonomous and coordinated agents that can integrate vast datasets—financial histories, market trends, behavioral patterns, real-time global events etc—to deliver hyper-personalized solutions to each client.
What excites me most is the potential for a bank to be a “holistic wealth partner” to every client at every stage of his/her life–evolving from reactive, impersonal services to understanding and anticipating each individual’s needs to provide predictive, life-integrated wealth orchestration. This includes being a proactive financial steward, addressing needs like retirement, healthcare, inheritance or philanthropy by proactively recommending appropriate adjustments to optimise investment portfolios, tax plans, estate structures etc.
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Can you share examples of use cases where AI insights significantly influenced a major business decision in your industry?
Private Banks, as highly regulated entities, face complex multi-jurisdictional rules across local, regional, and cross-border operations, compounded by clients' diverse citizenship, residency, and business locations. This demands significant time and resources for compliance, making AI automation in private banking—especially for AML, KYC, and transaction monitoring—one of the most obvious use cases to streamline processes and free up resources.
Besides compliance, AI transforms private banking across three pillars. In Portfolio Management, AI analyzes vast datasets in real-time to identify investment opportunities and risks, dynamically rebalances portfolios based on market conditions, client risk tolerance, and life-stage changes, and provides customised financial forecasts to enable proactive client advice. Client Advisory features 24/7 multilingual virtual agents delivering hyper-personalized, culturally sensitive recommendations tailored to client preferences and global market volatility. Operations automate repetitive back-office tasks like reconciliation and reporting, optimizes workflows by predicting bottlenecks, and offers instant client support to enhance efficiency and satisfaction.
What challenges have you faced while building cross-functional AI teams, and how have you inspired them to innovate consistently?
Building cross-functional AI teams requires commitment and collaboration. Technical experts, business stakeholders and risk/compliance teams speak different languages and their incentives are often misaligned. Engineers tend to prioritize model accuracy, the front line focuses on user experience and conversion, while minimising risks is important to risk/compliance teams. This often leads to miscommunication and delays.
To inspire consistent innovation, it is important to start by educating the top management on AI fundamentals and the reality that not every initiative will succeed. Like any funnel, most AI project start with 50 ideas, where 20 get prioritized and 10 are productionized. In addition, sprints should involve business stakeholders from day one to ensure buy-in and alignment across all stakeholders. Finally, risk and compliance teams should be incentivized to enable AI use cases, not just implement controls.
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Data literacy is often a hurdle, how have you cultivated a culture where everyone, not just technical teams, understands and uses AI effectively?
Achieving successful AI adoption requires a multi-faceted strategy focused on organizational preparedness. First, it is essential to invest in skill development and training to build AI competencies across the organization, ensuring employees have access to resources that allow them to leverage new tools effectively. Second, it is important to cultivate a cultural readiness for change, encouraging employees to embrace AI's potential benefits rather than fear job displacement. Finally, cross-functional collaboration is essential; AI specialists must work closely with Risk, InfoSec, IT, Legal, and Compliance to ensure all necessary governance and expertise are successfully onboarded.
What strategies or approaches have helped you navigate the rapid changes in AI, and how do you pass that adaptability on to your teams?
Navigating the rapidly evolving AI landscape requires a blend of continuous learning and flexible frameworks; I stay ahead by dedicating time weekly to exploring emerging research, attending conferences, and experimenting with new tools in low-stakes projects.
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Our team works in Agile mode with short sprints where teams can work on prototypes as well as full scale projects and learn from failures quickly. Pair programming is used to bridge experience gaps, promoting knowledge transfer, while rigorous code reviews ensure quality. This approach builds adaptable, resilient teams who are ready for the next technological shift.
What advice would you give aspiring leaders aiming to combine AI expertise with business strategy successfully?
Start with the end goal in mind. The key to success is not just in knowing the technology, but in translating AI's power into tangible business value. Therefore, leaders must always align technological innovation with organizational goals to ensure that every AI initiative directly addresses a core business problem or growth opportunity. This strategic alignment turns AI enabled innovation into sustainable competitive advantages that drive long-term growth and measurable results.
Building cross-functional AI teams requires commitment and collaboration. Technical experts, business stakeholders and risk/compliance teams speak different languages and their incentives are often misaligned. Engineers tend to prioritize model accuracy, the front line focuses on user experience and conversion, while minimising risks is important to risk/compliance teams. This often leads to miscommunication and delays.
Start with the end goal in mind. The key to success is not just in knowing the technology, but in translating AI's power into tangible business value
To inspire consistent innovation, it is important to start by educating the top management on AI fundamentals and the reality that not every initiative will succeed. Like any funnel, most AI project start with 50 ideas, where 20 get prioritized and 10 are productionized. In addition, sprints should involve business stakeholders from day one to ensure buy-in and alignment across all stakeholders. Finally, risk and compliance teams should be incentivized to enable AI use cases, not just implement controls.
Also Read: 5 Pioneers of Japanese Automotive Industry You Should Know About
Data literacy is often a hurdle, how have you cultivated a culture where everyone, not just technical teams, understands and uses AI effectively?
Achieving successful AI adoption requires a multi-faceted strategy focused on organizational preparedness. First, it is essential to invest in skill development and training to build AI competencies across the organization, ensuring employees have access to resources that allow them to leverage new tools effectively. Second, it is important to cultivate a cultural readiness for change, encouraging employees to embrace AI's potential benefits rather than fear job displacement. Finally, cross-functional collaboration is essential; AI specialists must work closely with Risk, InfoSec, IT, Legal, and Compliance to ensure all necessary governance and expertise are successfully onboarded.
What strategies or approaches have helped you navigate the rapid changes in AI, and how do you pass that adaptability on to your teams?
Navigating the rapidly evolving AI landscape requires a blend of continuous learning and flexible frameworks; I stay ahead by dedicating time weekly to exploring emerging research, attending conferences, and experimenting with new tools in low-stakes projects.
Also Read: Trend Watch: Asian Startup & Tech Leaders' Expectations for 2026
Our team works in Agile mode with short sprints where teams can work on prototypes as well as full scale projects and learn from failures quickly. Pair programming is used to bridge experience gaps, promoting knowledge transfer, while rigorous code reviews ensure quality. This approach builds adaptable, resilient teams who are ready for the next technological shift.
What advice would you give aspiring leaders aiming to combine AI expertise with business strategy successfully?
Start with the end goal in mind. The key to success is not just in knowing the technology, but in translating AI's power into tangible business value. Therefore, leaders must always align technological innovation with organizational goals to ensure that every AI initiative directly addresses a core business problem or growth opportunity. This strategic alignment turns AI enabled innovation into sustainable competitive advantages that drive long-term growth and measurable results.