Published on February 12, 2024, 8:11 am

Businesses are increasingly encountering challenges related to problematic outputs, explainability, privacy issues, and regulatory concerns in the field of Generative AI, according to recent research. While Generative AI holds the promise of speeding up enterprise operations, companies must first pilot solutions, establish metrics of success, and adapt protocols to address potential risks.

However, some businesses are hesitant to fully embrace generative AI due to a lack of clarity on guidelines. Many enterprises are waiting for more mature regulatory frameworks and clarity on the relevance of foundation models to specific industry verticals or corporate functions before implementing generative AI applications into production. This cautious approach stems from a desire to understand the potential risks and benefits more comprehensively.

Nevertheless, business leaders also feel the pressure to adopt generative AI quickly in order to stay competitive. Early adopters have begun exploring both internal and external use cases for generative AI. For example, software design, development, and testing represent internal use cases that can greatly speed up code production. However, deploying these solutions may also introduce the risk of faulty, plagiarized or insecure code.

In fact, according to a survey conducted by Snyk, over half of organizations report security issues with AI-generated code sometimes or frequently. To address these concerns and minimize risks associated with generative AI adoption, Forrester researchers recommend that enterprises set standards for evaluating generative AI in vendor solutions. They also advise updating existing AI strategies with guardrails and establishing governance guidelines for free versions of generative AI tools.

It is important for businesses to stay informed about advancements and trends in Generative AI. Subscribing to newsletters like CIO Dive’s daily newsletter can provide valuable insights from industry experts who cover top news and analysis in this field.

In addition to the challenges posed by Generative AI itself, there is also evidence of a disconnect between how workers want to collaborate and the tools available to them. Microsoft research suggests that there is a mismatch between workers’ preferences and the tools they have access to. This presents an additional area for businesses to address as they navigate volatile market conditions and make decisions about technology implementation.

In conclusion, while Generative AI offers the potential for accelerated enterprise operations, businesses must carefully assess and mitigate associated risks. By piloting solutions, setting clear metrics of success, and adapting protocols, companies can harness the benefits of generative AI while minimizing potential drawbacks. Staying up-to-date with industry news and analysis through newsletters like CIO Dive’s can further inform decision-making in this evolving field.

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