Initiatives

Board Tech Lab

The Board Tech Lab is an initiative that focuses on the role of technology and artificial intelligence in corporate governance. Board members today actively bear responsibility for technology-related decisions — regulatory, legal and reputational — often without full clarity about where algorithms make decisions or who is accountable when systems fail. The Board Tech Lab addresses precisely this gap: not with technical depth, but with a clear framework for informed oversight. Along six strategic domains, the initiative supports boards in asking the right questions, steering risks and owning accountability with confidence. It connects governance practice with current regulatory developments and makes technology oversight a core board responsibility.
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  • Anja Lagodny

    Lecturer, Swiss Board School

Themes
Strategy & Innovation for Boards | Risk & Compliance for Boards

Checklist for Board Members

Accountability stays with the board — whether an algorithm makes the call, and whether or not it runs through unsanctioned shadow-AI tools that no audit ever captured.

  • Do we have a complete AI inventory, including shadow AI?
  • Which decisions are one-way doors that require board sign-off?
  • Who notices when a model quietly degrades, before management reports it?

Crisis exposure doesn’t arise during the incident, but in the governance gap before it. Many companies can no longer shut down their AI agents without bringing operations to a halt.

  • Are AI agents so deeply integrated that an emergency shutdown would endanger operations — and is that reflected in the crisis plan?
  • Who communicates during an incident, and under which protocol?
  • Which lessons from earlier incidents have actually been implemented?

Data is a strategic asset — and decisions about AI-output ownership and training data bind the company long-term. Weaknesses in data governance amplify risks in AI, cyber and regulation simultaneously.

  • Who owns the outputs of our AI systems — we or the vendor?
  • How do we protect intellectual property when employees feed proprietary content into external models?
  • Who is accountable for data governance?

The EU AI Act applies from August 2026 to any EU market nexus — regardless of company domicile. Swiss companies without EU exposure carry the same risks with fewer formal obligations. This governance gap is a board responsibility, not a legal-department task.

  • Have we classified all AI systems by risk class — even where the EU AI Act does not formally apply?
  • Which governance framework are we using: EU AI Act, NIST RMF, ISO 42001 — or none?
  • How does the board actively document its oversight activity?

Transformation today is continuous — and becomes a risk when initiatives run side by side without portfolio logic. Many companies are stuck in “Pilot Purgatory”: many pilots, no pattern, no measurable value.

  • Do we have a deliberate Deploy–Reshape–Invent split — or are we collecting isolated pilots?
  • Are incentives aligned with transformation?
  • Does the leadership pipeline have the capacity to keep pace with the speed of transformation?

The choice between Azure AI, Google Cloud and AWS is not only an IT decision — it is a strategic one-way door with long-term cost lock-in. Platform dependencies shape resilience and freedom of action directly.

  • Which platform and architecture decisions require board sign-off?
  • Have we deliberately chosen between proprietary models and open source — and do we know the exit costs?
  • Where do we build internal AI capability, and where do we buy-in permanently?

Where to begin?

Not every domain is equally urgent. Three questions reveal where you stand:

  1. Which AI systems make decisions in our company today — and do we fully know?
  2. What would happen if our most critical system failed tomorrow — and who decides what?
  3. Which regulatory deadlines apply to us — and are they on the board’s radar?

Those who can answer these questions have a starting point. Those who can’t, now know where to start.

Further Readings

Data Strategy | Harvard Business Review

​Data Culture | McKinsey Quarterly

​Artificial Governance | Journal of Management and Governance

​Data Value | Directorship

​Data-driven Organization | Sloan Management Review

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