Shadow AI & Corporate AI Shadow AI refers to the use of artificial intelligence without official approval or oversight from management or IT departments. Essentially, it involves employees independently implementing AI to enhance their productivity or streamline processes without following proper protocols or receiving necessary permissions.
In today’s digital landscape, Artificial intelligence(AI) has become a cornerstone of innovation, efficiency, and competitive advantage for businesses across industries. However, alongside the rise of AI implementation within organizations, a concept known as “Shadow AI” has emerged, presenting both opportunities and challenges for corporate policies and governance.
Shadow AI refers to the unauthorized or unmanaged use of AI applications within an organization. It typically occurs when employees independently adopt AI tools or technologies without the knowledge or approval of the IT department or higher management. These initiatives might stem from a desire to streamline processes, increase productivity, or address specific business needs, but they often bypass established protocols for data security, compliance, and integration.
While it obviously raises some organizational concerns, shadow AI is not inherently a bad thing. In fact, it can provide an effective way for organizations to benefit from new technology and some of the increase in productivity and efficiency that AI offers. Further, it can allow individuals and teams to innovate and to come up with AI solutions that are specific to their tasks. Again, this can lead to more efficient and productive teams with better outcomes. Plus, it can improve employee morale and lead to more engaged employees.
At the same time, however, it raises a number of concerns. AI solutions that are outside of the control of IT are difficult to monitor and control. This makes it hard for organizations to ensure that proper security measures are in place and that technology is being appropriately used. Additionally, when AI solutions are siloed throughout an enterprise, it’s difficult to share data and information throughout the organization. While shadow AI is not necessarily a bad thing, it’s important to effectively manage AI to reduce some of the risks and concerns that are associated with shadow AI.
As AI becomes more accessible and spreads throughout more aspects of organizations, a key issue for enterprises in the coming years is how to properly address this new technology and, with it, the rise of shadow AI. What this means is that going forward, managing AI isn’t going to simply be about managing and expanding models. Instead, it’s also going to be about managing and preparing for the increase of shadow AI.
Managing the rise of shadow AI requires an organization-wide approach that includes a thorough governance strategy and well-defined policies. This is particularly important as AI begins to spread throughout all aspects of an organization, including marketing, research, legal, HR, and even recruiting. Given its complexities and its likely rise across enterprises, effective AI strategies are going to require increasingly complex procedures, policies, and governance.
The foundations of shadow IT policies are a good starting point for shadow AI policies. Yet, when developing your organization’s strategy, it’s important to appreciate the additional complexities that come with AI. IT simply involves technology, whereas AI is broader and will require some new approaches to apps, businesses, and even people. That said, starting with a strong shadow IT strategy is a good place to start.
In addition to developing effective AI strategies and policies, many organizations are working to shift their approach to AI. Rather than having siloed innovation or solutions that are specific to departments, consider shifting to a more centralized platform that can be deployed and used throughout your organization.
Broadly speaking, as AI becomes increasingly mainstream, it becomes more important to centralize this technology. Doing so will limit shadow AI while also allowing organizations to more effectively monitor, control, and deploy AI solutions. Further, creating a centralized platform will lead to having the necessary architecture and infrastructure to scale AI solutions. While this might seem like a big shift for many organizations, creating a more centralized solution can help your organization better utilize AI solutions while limiting the risks that often are associated with them.