From delivering personalized content and offerings to predicting IT issues, generative AI has become an increasingly crucial asset in the workplace. It has reached a point where 78 percent of Asia Pacific employees are more willing to delegate as much work as possible to AI to reduce their burden, according to Microsoft.
However, to ensure generative AI can deliver beneficial outcomes without compromising user privacy, leaders need to have a governance strategy in place to manage the technology’s security risks and external impacts. When designing their strategy, organizations should take into account the following key measures that can help them ensure more responsible AI usage. Some of these steps are relatively simple and should be done from the get-go, while others require more purposeful thinking.
Mastering AI starts with knowledge
Building employee knowledge is the most intuitive way to enhance AI governance. Through these programs, organizations can create an environment that favors product innovation while also reducing the likelihood of abuse or errors.
To achieve this, organizations should host AI and digital literacy training courses. These programs can bring C-suite leaders and developers together to learn about AI terminology and understand the limits of how and where generative AI can perform certain actions. Simultaneously, the courses should also prepare employees to communicate their AI technology in plain language. This skill is crucial in helping external parties like customers and board members understand what works and what doesn’t.
When creating training courses, leaders need to analyze the nature of their business and the people they interact with, as not all organizations face the same ethical challenges. For example, while there are no legal repercussions for universities sharing educational materials with their students, it is not the same case for banks when providing customers’ stock transactions to external parties. With the insights gained from the analysis, leaders can identify the specific dangers they may face when deploying AI applications.
Ensuring a safer AI experience
Once a common understanding of AI is established, things start to get harder. With the introduction of generative AI into the workplace, leaders need to be ready to counter prompt engineering attacks and model poisoning, which can affect the accuracy of insights and reinforce certain biases.
Creating risk mitigation practices and processes can help them tackle these threats head-on. However, leaders need to act deliberately so that the measures and capabilities they integrate do not get exploited by bad actors. For instance, feature stores and inference engines that enable AI models to adapt to changing contexts need to be protected against cyberattackers’ attempts to manipulate outputs.
Once companies have established control over their AI models, they should then move on to managing the technology’s impacts on the environment and society. Increased emissions that are fueled by growing computing and energy demands from generative AI can worsen the planet’s climate crisis.
Government bodies are taking the first step to mitigate climate-induced risks by putting generative AI usage under greater scrutiny. What this means for organizations is that they need to prepare themselves to comply with new and updated regulations. Leaders will need to outline governance and ethics practices that boost resilience, while mitigating the environmental impact on the planet.
With more employees across Southeast Asia adopting AI for their own benefit, achieving comprehensive governance is crucial to prevent bad actors from misguiding employees and needlessly wasting resources. This path is not always easy to achieve, and there are complicated steps involved. But those who stay the course will be able to achieve greater levels of productivity and discover new opportunities without exposing their operations to various threats.
