At Twenty44, we’ve seen it repeatedly: you don’t maximize the value of AI by outrunning risk, you do it by understanding it.
Risk isn’t the brake; it’s the steering. Ignore it, and velocity just takes you faster in the wrong direction. PwC found that 80% of executives already recognise AI risk as central to value realisation, yet few are mapping it with intent.
The challenge isn’t speed. It’s control.
Executive Risk Mapping
Real AI risk management isn’t a compliance exercise. It’s a business discipline that spans four core dimensions.
Technical and Performance – If the model doesn’t perform, everything else falls over.
- Drift: Models degrade as data shifts.
- Vulnerability: Security lapses expose IP and customer data.
- Scalability: What works in testing can fail in production.
Ethical and Societal – The moment your system impacts people, the stakes rise.
- Bias: Unintended discrimination can destroy trust and reputation.
- Opacity: If you can’t explain it, you can’t defend it.
- Misuse: AI that fuels misinformation erodes brand and credibility.
Regulatory and Compliance – AI law is evolving fast, and ignorance won’t protect balance sheets.
- Non-compliance: EU AI Act and sector rules carry steep penalties.
- Contractual gaps: Misaligned vendor terms or data controls.
- Litigation: Bias and privacy breaches invite enforcement.
Commercial and Strategic – The costliest risk is investing in the wrong thing.
- Value erosion: ROI fails when adoption lags or alignment slips.
- Capability gap: Lacking the skills to govern or maintain systems.
- Vendor lock-in: Overdependence limits flexibility and margin.
