GenAI: Navigating the Risks That Come with Change

For enterprises, commercial use of AI is still in its early stages, and it’s a case of risk and reward, weighing up both and investigating the best way forward. Of course, there’s much to gain from the use of AI. Already, companies are providing better customer service, parsing complex information through natural language inputs, and generally making workflows faster. But with this technology comes a range of security risks, which include, but are not limited to, hallucination, the loss of personal data, weaknesses in model architecture, and bias.

The reason generative AI can potentially be so risky for enterprises is that we are only scratching the surface of what’s possible, and we don’t know exactly where the tech will take us. Despite the record-breaking uptake of ChatGPT when it first became publicly available, we still don’t have a killer enterprise generative AI app that has gained the same popularity among business users. And we don’t know where that game changing app will come from. We’re in unchartered territory, and we don’t quite know where we’ll end up.

As leaders are grappling with the complexities that come with this change, it is important to explore the risks and regulatory challenges imposed by generative AI. This will aim to illuminate the path forward for decision-makers who are shaping policies and implementing practices that guide the chart a course toward a future where innovation and responsible use will coexist.

Whether it’s large language models or other generative systems, understanding the nuances is paramount for organizations to succeed in this era marked by transformative changes.

Risks and opportunities of generative AI

Infrastructure supporting generative AI for mission-critical applications is crucial. Envisioning transformative use cases, such as ultra-personalized healthcare, suggests groundbreaking possibilities. For example, the ability to prescribe medication tailored to an individual's needs based on generative AI represents a potential breakthrough in the healthcare sector.

Despite these promising opportunities, it's essential to acknowledge the challenges associated with generative AI. The lack of explainability in its models is a significant hurdle, which has brought about ongoing efforts to provide traceability and audit trails. This is one of the reasons we emphasize that AI models cannot run without a human training the models. This is particularly important for addressing concerns related to transparency and accountability.

Harmonizing diverse data silos, especially in the context of data from varied sources, poses a significant challenge. Data harmonization across different towers is essential for the effective deployment of generative AI applications. In sectors like financial services, where data sensitivity is paramount, a strong focus on data privacy is maintained to ensure responsible and ethical use of data.

Mitigating the risks: a strategic approach

To address the challenges and risks associated with generative AI, organizations are proactively forming AI steering committees and leadership councils. These cross-functional teams, involving product leaders, CIOs, CISOs, and legal teams, play a pivotal role in guiding the seamless integration of AI.

A cautious approach to AI integration is adopted, emphasizing a crawl-walk-run strategy. Organizations prioritize the incorporation of compliance, governance, security, and responsible AI practices right from the outset. This approach ensures a sturdy foundation and minimizes potential pitfalls as AI becomes an integral part of organizational processes.

In the words of one of the panelists at the Hitachi Vantara Exchange event in New York, organizations need to "experiment aggressively and implement thoughtfully." This wisdom underscores the importance of balancing innovation and meticulous implementation of generative AI.

As businesses navigate the complexities of AI adoption, it is essential to leverage the opportunities presented by generative AI while prudently managing associated risks. The future holds immense potential for transformative advancements, and organizations that navigate this landscape thoughtfully are poised to reap the benefits of responsible AI utilization.