NILG.AI

Generative AI is a powerful tool that many companies are rushing to incorporate into their operations. However, it’s crucial to understand the possible risks associated with this technology. In this article, we’ll discuss the top nine risks that could impact your business’s readiness for AI integration. Stay ahead of the curve, and make sure you’re prepared to deal with the potential problems that come with generative AI.

Risk 1: Data Governance

The first risk to look out for is data governance. Generative AI tools can potentially leak sensitive information. To avoid this problem, you need a clear data governance strategy and principles with the right access level to each data point. This will allow you to control who has access to what information and who can be exposed to each type of data.

Risk 2: Reliability of the Expected Output

Generative AI models perform well on quick tests. However, while they’re built to be accurate in general, they can fail sometimes. Thus, make sure you’re only using generative AI for processes that don’t require extreme reliability. Additionally, you should have fallback plans in case there’s a problem with the model.

Risk 3: Reliability of the Input

The quality of any AI model depends on the quality of the input data. In the case of generative AI, the input means both the training data and the context used as part of the prompt for these large language models (LLMs). So, you must ensure your input data is reliable and follows your company’s standard operating procedures, especially since the output is heavily dependent on this information. Additionally, make sure the input data is high quality and up-to-date so that your output will be consistent with what you expect.

Risk 4: Hallucinations

LLMs may seem eloquent, but they can’t differentiate between what’s true and what’s false. These models tend to answer even if they just need to guess, thus possibly creating misinformation. This process is called a hallucination and is a very common risk to watch out for in generative AI.

Risk 5: Knowledge Obsolescence

Let’s say you take a task performed by a human team and put it into generative AI. If the system shuts down and the generative AI can’t answer, you may have trouble recovering your work. Thus, instead of trusting this technology too much, make sure you document your work simultaneously so you can return to your previous stage if there’s an AI error.

Risk 6: Workflow Bottleneck

Suppose you’re automating a task using LLMs. You’d expect everything to work more smoothly and efficiently. However, you’ve just introduced a new bottleneck because your human team that handles this task can’t keep up with the new automated workflow. Thus, the generative AI doesn’t really bring much value in this case.

So, whenever you optimize one step into your workflow, make sure that you have elastic capacity in the rest of the processes so as to actually benefit from the optimization.

Risk 7: Control

You may be using generative AI to exponentially increase your company’s productivity. However, the AI could behave unpredictably or produce unintended results, leading to errors or adverse outcomes that could ruin your business. Therefore, make sure you have good quality control mechanisms to avoid catastrophic scenarios if these AIs start acting up.

Risk 8: Biases and Ethical Considerations

Be aware that generative AI models are trained with information from all over the Internet. They can embed all the biases and ethical problems that may be found on any page on the Internet.

Furthermore, companies that make these models try their best to avoid biases leaking into the models. However, the company’s values and considerations might still hold a bias, as they don’t align with yours as a person. You can’t help but be subjected to the company’s preferences. Thus, there may always be some level of bias in generative AI that can’t be avoided.

Risk 9: Legal and Compliance

Generative AI is a very recent technology that is causing a lot of hype. Since it’s relatively new, you can expect many regulatory changes in the near future regarding how these tools are designed. Due to an increase in regulations, you may be forced to rethink or even drop some of your applications. The models you use in your applications may even be at risk of being forbidden from the market.

Conclusion

While generative AI offers many benefits, it also poses many risks, ranging from data governance to legal and compliance challenges. You must be aware of all these problems and take steps to mitigate them.

If you’re considering embracing generative AI, we at NILG.AI can help align your value proposition with your tool and avoid all of these risks. Book a strategic meeting with us to learn how to use generative AI responsibly in your business and harness its full potential. You can also download our eBook for further guidance to help your business stay ahead in this rapidly evolving field.