As mentioned in our previous blog post, staying on top of technological disruptions must involve a clear plan for how to harness and manage artificial intelligence (AI). Looking back at the evolution of AI and its impact on business from 2020 to today, it’s amazing how drastically the landscape has changed. The release of advanced AI models like ChatGPT in 2022 has made a significant splash, and companies must continually reassess their strategies and the risks involved as AI continues to evolve.
Let’s examine the timeline of how these businesses and AI tools have adapted to each other and gather an idea of what’s to come in the years ahead for enterprise risk management (ERM).
Main Data Concerns of 2023
Throughout 2023, the primary concern among organizations and their customers revolved around data exposure. The concern stemmed from AI models consuming and utilizing every input to train themselves, sometimes inadvertently learning and revealing confidential information. High-profile cases emerged where AI models exposed proprietary data from one company to another, raising significant alarm bells about data privacy and security.
To combat this, companies began implementing strict measures. These included purchasing privately licensed models, which allowed employees to safely utilize AI without risking data exposure. Additionally, many organizations enforced stringent policies and provided training to ensure employees understood the importance of not sharing confidential information with AI systems. Though these efforts collectively reduced the risk, it wasn’t completely eliminated.
Data readiness was, and will remain, a crucial aspect of effectively and efficiently leveraging AI. Organizations in all industries need to ensure their data is in a format that AI can use seamlessly. Companies with disorganized or poorly maintained data will spend considerable time just getting their data ready. Those whose data has been properly prepared, however, can move swiftly to implementation, gaining a significant competitive advantage.
Emerging Risks in 2024: Advanced Cybersecurity Threats
As we move through 2024, the risk landscape is shifting.
The concern is no longer primarily about data exposure, but also advanced cybersecurity threats. AI has enabled the creation of more sophisticated phishing emails that have perfect grammar and spelling, thereby increasing the likelihood of successful attacks. Additionally, advanced AI-driven spoofing through both language and video is making it increasingly difficult for employees to separate genuine communications from fraudulent ones.
We’ve talked a few times about how AI is a wonderful tool, but can be utilized by bad actors, and that’s exactly what we’ve seen happen. An increased likelihood and impact of fraud and cybersecurity attacks can already be seen, and proper risk management is necessary to mitigate the potential effects of these events. This is by no means a reason to avoid AI altogether. Increased vigilance can prevent these bad actors from doing major harm to organizations, who will use the technology regardless of whether or not their targeted organization does.
The Growing Opportunity of Strategic AI Implementation and Utilization
If 2023 was the year of grappling with data exposure, 2024 is shaping up to be the year of seizing AI opportunities – or risking irrelevance. This evolution is reminiscent of the early days of the internet and social media when organizations initially resisted adoption due to fears of lost productivity and data breaches. However, just as organizations eventually recognized the immense opportunities of these technologies, they are now beginning to see the vast potential AI holds, provided they can use it correctly and mitigate its risks.
Organizations that continue to shy away from AI are at the greatest risk of falling behind, and the rapid pace of development means that companies not actively pursuing AI strategies will very quickly find themselves outpaced by competitors.
Leading businesses are now leveraging AI beyond basic applications. The greatest innovation comes from integrating AI deeply into business processes. This involves a combination of AI and robotics, significant automation, and more. The goal should be for AI to complement and enhance processes and resources, making operations more efficient and innovative. For example, an accounting firm could leverage AI to automate the generation of financial statements. By eliminating a significant portion of accountants’ manual processes, their main focus can be ensuring there are no errors, thus increasing their capacity to take on more clients. A firm that fails to adapt is not only one that risks being outpaced by more agile competitors, but may have already fallen behind.
The pace of AI development is unprecedented. Unlike the gradual ramp-up seen with the internet, AI’s integration into business practices has been swift and disruptive. Companies need flexibility in their strategies to adapt to unforeseen changes in AI capabilities and directions.
Key Use Cases for AI in 2024
- Content Generation: AI can assist in drafting risk statements, reports, and other documents, ensuring consistency and accuracy while saving time. By providing contextual prompts, practitioners can generate comprehensive risk statements that align with organizational standards.
- Private Trained Models: For tasks requiring high data security, private AI models can be used to analyze internal data without compromising confidentiality. This is particularly useful for risk assessments that require detailed insights from proprietary information.
- Data Correlation and Analysis: AI excels at sifting through large datasets to identify correlations and patterns. In ERM, this capability can be used to map internal controls to regulatory frameworks, identify gaps, and ensure compliance. This not only improves efficiency but also enhances the accuracy of risk assessments.
AI And ERM: Where The Conversation Stands Today
AI presents both significant risks and immense opportunities for businesses. From managing data exposure to combating advanced cybersecurity threats, from leveraging AI for strategic advantage to ensuring data readiness, organizations must navigate this complex landscape carefully. These same benefits must also be considered as risks, as there is a possibility that they put an organization in a vulnerable position. The pace of AI development demands agility and foresight, while those who are slow to adapt will likely find themselves at a competitive disadvantage. The future belongs to those who can effectively harness the power of AI to innovate and drive their business forward.