CCAIO™ Services – Resiliency
Artificial Intelligence
As AI continues to grow rapidly, one of its commonly discussed use cases is its application in improving business resilience:
Resilience AI — The goal of Resilience AI is to create systems to combat unprecedented scenarios to avoid both minor and catastrophic system failures. AI can improve business resiliency by maintaining functionality during situations such as hardware failures, cyberattacks, and environmental changes. AI improves current resiliency methods by improving efficiency and automation to reduce the need for manual labor.
AI in Business Resiliency Framework — The fast growth of AI will push firms to implement AI as part of their business continuity framework. Incorporating AI in business resiliency frameworks can reduce repetitive tasks and promote consistency, creating more efficient processes. While AI can be extremely beneficial, it will be necessary to ensure a balance between automation and the level of human involvement to prevent algorithm bias, unintended consequences, programming errors, reputational aspects, and more.
AI and Predictive Analysis — AI can provide predictive analysis using algorithms and machine-learning capabilities to process large amounts of data. Modern predictive analysis tools shift data analytics from a small team of data scientists exploring hypothesis, to tools that can be used by both data analytics experts and regular business users in day-to-day business. In light of ongoing economic uncertainty, having tools to adapt quickly allows organizations to remain resilient. Incorporating AI into predictive analysis can help organizations navigate the pace of change and respond quickly.
Cybersecurity
Firms must manage cyber risks, security, and resiliency as technology continues to improve and threats evolve:
Cybersecurity AI — Businesses are utilizing AI to combat cyberattacks. They see AI increasing process efficiency, reducing operational costs, and resolving issues related to scaling. While AI can provide businesses with more automation by analyzing vast amounts of data in real-time to find threats, it requires human expertise to be the most efficient. Creating a framework to follow and measure success is imperative to the success of implementing AI into cybersecurity.