Stop developing an obsolete AI strategy. Part 1: Project risk

Internal vs external risks

AI greatly amplifies the uncertainty that characterizes today’s business environment. Leaders must understand a critical distinction to manage AI risk effectively: the difference between project risks and enterprise risks. Project risk relates to the negative consequences that can arise from your own implementation of AI,; such as technical failures, integration challenges, user rejection, and ROI shortfalls. Enterprise risk emerges from what others are doing with AI,; such as the development of new models, competitor breakthroughs, industry disruptions, regulatory shifts, and fundamental changes in how value is created and captured in your sector. 

How to manage project risk: the portfolio lens

Managing AI project risk requires a fundamental shift in how you approach AI innovation. Treating AI initiatives in isolation often leads to their risks being treated as a series of disconnected ‘go/no-go’ decisions. This can stifle innovation because it separates the innovation process into a series of disconnected projects. Adopting portfolio-management principles that approach AI investments as a unified innovation pipeline enables you to balance risk and reward profiles across the entire portfolio.

This approach recognizes that some AI projects should be high-risk moonshots that could transform the business,; while others should be reliable workhorses that deliver steady added value with tightly circumscribed risk levels.  

It also enables you to calibrate the organization’s overall risk exposure while maintaining the innovation velocity necessary to compete in an AI-driven economy,; transforming risk from a constraint to be minimized into a strategic variable to be optimized.

A portfolio approach can also help set and manage risk levels across functions within the business, creating nuanced risk profiles that are both industry-specific and reflect your unique position. The key is that a portfolio-management approach allows these decisions to become conscious, strategic choices rather than accidental outcomes.

Key principles for implementing portfolio management

• Set explicit portfolio targets based on strategic context

Define your desired mix of risk levels across the portfolio. This mix should reflect your competitive position, industry dynamics, and organizational risk appetite. 

• Evaluate projects based on portfolio contribution

When reviewing AI initiatives, assess not only whether the project is worth pursuing based on an internal risk/reward calculation, but also how it affects your overall portfolio risk profile.  

• Create integrated governance systems that manage risk and innovation together

Replace separate risk and innovation review processes with unified portfolio reviews that consider both dimensions simultaneously.