Why AI Portfolio Leadership is Crucial for the Future of Our Business
As artificial intelligence (AI) continues to transform the insurance industry, companies face a growing challenge: how to strategically manage and scale AI initiatives across complex organizations. This is where the role of an AI Portfolio Leadbecomes not only relevant — but essential.
Building an AI Portfolio with Purpose
AI is no longer about isolated pilots or one-off innovations. To drive real business value, organizations must take a holistic view. As an AI Portfolio Lead, my mission is to build and steer an AI portfolio that is fully aligned with our company’s business strategy and digital transformation goals.
By establishing an AI inventory (which we are currently implementing in Collibra), we ensure transparency and structure:
👉 Which AI use cases exist?
👉 Where are synergies?
👉 How can we scale proven solutions across markets?
This systematic approach helps us avoid duplicated efforts, ensures regulatory compliance across different Market Units (such as Switzerland and the EU), and maximizes the return on AI investments.
Prioritization and Governance
Not all AI use cases deliver equal value. One of the most important aspects of my work is the evaluation and prioritization of AI initiatives:
- Which use cases offer the greatest business impact?
- What are the technological and regulatory risks?
- How mature and ready is the organization to adopt them?
Through this governance, we ensure that we focus our resources on those AI projects that create real business outcomes — whether that’s operational efficiency, better customer experience, or even new revenue streams.
Trust is the foundation of every successful organization.
Fredmund Malik, 2000
Navigating Compliance and Trust
In the European insurance industry, compliance is non-negotiable — especially with the introduction of the EU AI Actand updates to data privacy regulations such as DSGVO and Swiss Data Protection Law.
A key part of my responsibility is ensuring that all AI use cases meet these regulatory standards and can be trusted by customers, employees, and regulators alike. Without this trust, even the most advanced AI will not succeed.
Building the Foundation: Trust, Adoption, and Scalability
In the insurance industry, trust is everything. It is the foundation on which customers choose to take out an insurance policy — trusting that the company will be there when it matters most.
As we bring AI into more processes and customer interactions, this core value must be upheld. AI Trust means that our AI systems must be:
✅ transparent (customers and regulators understand what AI does and why),
✅ fair (free from bias and discrimination),
✅ robust (perform reliably even in edge cases),
✅ explainable (decisions and outcomes are understandable to humans), and
✅ aligned with legal and ethical standards (such as the EU AI Act and local regulations).
Without trust in AI, adoption will stall — internally and externally.
Driving AI Adoption
Building trustworthy AI is the starting point. The next step is ensuring that AI adoption happens meaningfully within the organization:
✅ By fostering AI literacy and understanding across business units,
✅ By embedding AI into core processes rather than treating it as a side project,
✅ By involving stakeholders early, including compliance, legal, and business leaders, to ensure buy-in and alignment.
AI adoption is not just about deploying technology — it is about changing how we work, supported by AI.
Scaling AI for Business Impact
Finally, once trust and adoption are in place, we focus on scaling AI use cases:
✅ Identifying successful pilots that can be scaled across markets or product lines,
✅ Building AI platforms and shared capabilities to avoid reinventing the wheel for each project,
✅ Creating feedback loops to continuously improve AI models as they scale,
✅ Ensuring scalability respects regulatory differences across jurisdictions.
In this way, AI becomes not just a collection of isolated experiments — but an integrated, trusted enabler of enterprise-wide transformation.
Measuring Success and Scaling Value
Finally, success must be measurable. We define KPIs for each AI use case — from accuracy and efficiency gains to business impact — and continuously monitor progress.
More importantly, we identify scaling potential: AI that works well in one unit or country can often be adapted and expanded elsewhere, accelerating overall digital transformation.
In Conclusion
AI has immense potential to reshape how we operate and serve our customers. But without strong portfolio leadership, that potential risks becoming fragmented, siloed, and ungoverned.
As AI Portfolio Lead, my job is to ensure that we drive AI forward — strategically, responsibly, and with measurable business value. In this way, we turn AI from an experimental technology into a trusted, scalable enabler of our company’s future success.
