AI can be your rocket booster or your critical point of failure.
In modern workflows, AI has moved from experimental to essential. If the model or the service you depend on stalls, or if bad actors weaponize AI to probe and disrupt your infrastructure, your operations freeze.
Forward-thinking founders and business leaders are starting to factor this into their plans.
Here are three practical steps I suggest:
- Trace every AI touch-point – Follow the customer journey from first click to final fulfilment and mark where each AI model, API, or script steps in.
- Stress-test the timeline – Ask, “What if this AI service disappears for an hour, a day, a week?” Then weigh the impact of each gap.
- Build a Plan B (and C) – Line up alternative models or providers, document a manual fallback, and rehearse the switch-over. Outage day is not the time to improvise.
Big companies are already treating AI this way. Over 75 per cent of the S&P 500 rewrote their risk statements last year to cover AI threats; 193 of them now flag deep-fake fraud and malicious code as material risks. GE HealthCare has even warned that limited IP rights to inspect external models could hamper oversight.
Technology choices matter too. Generative AI is powerful, but not every problem needs a large language model. Hallucinations, bias, and unclear IP can introduce more risk than benefit if the fit is poor.
The lesson: treat AI like any other critical infrastructure.
Build redundancy, clarify licences, and test your escape routes.
For further reading, many AI providers publish whitepapers and bestpractice guides, and industry groups such as the Coalition for Secure AI (CoSAI) also offer useful guidance.
Is your AI risk plan ready?
#GenAI #DigitalTransformation #AIRisks
