Artificial intelligence (“AI”) tends to be spoken about in terms of risk creation – from hallucinations in the results to concerns of data misuse – but is there another side of the story? While this is a complicated and multifaceted issue, AI, when used responsibly and with the guidance of experts, can provide significant benefits to risk mitigation in crisis management.
With predictive analytics, AI can analyse vast amounts of data to forecast potential risks before they materialise, allowing organizations to take proactive measures much earlier and more effectively than they have traditionally been able to do. Machine learning algorithms can amplify automated compliance monitoring by identifying unusual patterns and flagging potentially fraudulent or problematic activities in real-time, lessening the impact of fraud and other events going undetected for months or years.
Despite this potential, the use of AI in crisis mitigation so far remains untapped. In Turbulent Waters, Trusted Anchors: The General Counsel’s Evolving Role in Navigating Crises, an Economist Impact report sponsored by FTI Consulting, less than half of respondents said they are deploying AI and machine learning-based solutions for comprehensive impact analysis of crisis events. Why is this?
The Power of People
Many organisations lack the necessary expertise to implement and manage AI solutions effectively. In fact, utilising technology solutions was cited as one of the top five challenges preventing crisis preparation by 30% of the survey’s respondents. Combined with the array of concerns surrounding AI, hesitation to adopt it due to these perceived risks and lack of awareness of its capabilities, and it is not surprising that there is a stalemate. However, there are solutions.
Investing in upskilling current employees through training programs and certifications in AI and data science is an important foundational step. Additionally, engaging with AI specialists and data scientists can establish a corporation’s AI readiness, while tackling resourcing issues and bridging the skills gap.
Organisations can start with small-scale pilot projects to demonstrate the value and feasibility of AI solutions. Through these demonstrated wins, a culture of cautious experimentation can be attained, allowing the organisation to incrementally build clear examples and case studies showing the return on investment and benefits of AI for risk mitigation. Through all of this, regulatory compliance should remain at the forefront to uphold trust and nurture confidence with key stakeholders.
Another option, of course, is to wait. Wait for others to take the lead. But at that point, innovation becomes a game of catch-up, while the unique opportunities to develop specific, fit-for-purpose use cases and be in an industry-leading position are lost
“Many organisations lack the necessary expertise to implement and manage AI solutions effectively.”
More Data, More Problems
It is no surprise that when it comes to AI systems, garbage in equals garbage out. Outputs are only as good as their prompts, and the prompts will only be effective when applied to high-quality and relevant data. But what if that data is not readily available?
It is essential to develop a comprehensive data strategy to ensure data is collected, cleaned and stored properly. From there, advanced data analytics techniques can be used to improve data quality and extract valuable insights. Consider forming strong partnerships with other organisations to access additional data sources.
Building this foundation puts an organisation in a strong position, not only to mitigate risks but also to unlock new efficiencies and insights to tap into the potential of AI.
Turning Past Lessons into Future Resilience
Outside of preparation and prevention AI can have a powerful impact when it comes to incorporating lessons learned from previous crises. Machine learning algorithms can help identify the underlying causes of a crisis by analysing patterns and correlations in data and events, unearthing insights that would otherwise have remained hidden. AI-driven simulations can model different scenarios and outcomes, and when trained on previous crises they can bring a level of realism and personalisation, helping equip responders more effectively and improve preparedness for future crises.
More resilient infrastructure can be designed in light of predictions from AI about the impact of future crises – and the more you know, the more you can do. With 70% of surveyed organisations not stress-testing their business functions against the effects of past crises, AI can help bridge this gap and help the past to inform the future.
AI is not magic. AI cannot predict the future. But when it comes to crisis management, it can be a vital tool to guide you through the storm and enhance your crisis management plan.
Wendy King, Craig Earnshaw and Nick Hourigan at FTI Consulting
