· Connectors · 3 min read
Business Continuity in AI: Executive Summary
Recent AI industry disruptions highlight critical strategic imperatives for boards:
- Ensure robust business continuity planning for AI systems
- Design AI implementations for flexibility and change
- Implement backup strategies and avoid vendor lock-in
- Consider developing in-house AI capabilities for long-term competitiveness
Board implications include comprehensive AI risk management, strategic technology decisions, and long-term positioning in an AI-driven business environment.
Recent events in the AI industry, such as OpenAI’s service interruptions and leadership changes, underscore the need for corporate boards to take a proactive stance on AI strategy and business continuity . This summary outlines key considerations for boards as they navigate the complex and rapidly evolving AI landscape.
Key Strategies for AI Business Continuity and Strategic Positioning
Design for Change and Flexibility
- Prioritize AI systems and software that support model interchangeability
- Prepare for rapid technological advancements and changing market dynamics
Board considerations: How flexible are our current AI implementations? What is our strategy for adapting to technological changes in the AI landscape?
Implement Robust Backup Strategies
- Develop and maintain backup plans for critical AI vendors and models
- Regularly assess and test alternative AI solutions for business-critical functions
- Consider potential cost savings and performance improvements from diversification
Board considerations: What is our backup plan if our primary AI vendor becomes unavailable? How often do we review and update these contingency plans?
Avoid Embedding Lock-in
- Be cautious of dependencies on proprietary embedding models, especially for large-scale document management systems
- Prioritize open or transferable embedding solutions to maintain flexibility
- Consider the long-term implications and potential costs of changing embedding models
Board considerations: Are we creating any technological dependencies that could be costly or difficult to change in the future? How are we ensuring long-term flexibility in our AI infrastructure?
Develop In-house AI Capabilities
- Consider investing in proprietary AI models and processes as a long-term competitive strategy and way to minimize external business continuity risks
- Build internal expertise to reduce dependence on external vendors
- Position the organization to compete effectively as AI becomes more ubiquitous
Board considerations: What is our long-term strategy for AI capabilities? Should we be investing in developing proprietary AI assets?
Strategic Implications for the Board
Risk Management Oversight: Ensure that AI-related risks, including vendor dependency and service interruption, are adequately addressed in the organization’s risk management framework.
Vendor Diversification Strategy: Encourage management to diversify AI vendors and solutions to mitigate single-point-of-failure risks (though keep in mind that this may introduce new risks, so establish a balance that aligns with your risk profile).
Technology Investment Decisions: Consider not just current capabilities but also long-term flexibility and business continuity implications when making AI technology investments.
Competitive Positioning: Consider how AI capabilities, including potentially developing proprietary models, factor into the organization’s long-term competitive strategy.
Talent and Skill Development: Ensure the organization has the right talent to manage AI systems and potentially develop in-house capabilities.
In the short-run, boards should ensure that their organizations have considered how to effectively address business continuity with respect to third-party AI models and tools; in the long-run, boards should develop and support the strategic decisions regarding internal AI development.