In its Top 10 Strategic Technology Trends for 2020, Gartner discusses Shadow AI as a key issue that organizations are going to have to contend with in the coming years. Gartner defines a strategic technology trend as “one with substantial disruptive potential that is beginning to break out of an emerging state into broader impact and use, or which is rapidly growing with a high degree of volatility reaching tipping points over the next five years.”
Per that definition, identified trends have the potential to significantly impact organizations and, thus, should be proactively addressed by enterprises. In its report, Gartner predicts that by 2022, 30% of organizations that use AI for decision-making will have to address shadow AI as the biggest risk to effective decisions. To be prepared for this, organizations should develop AI policies and strategies that protect against the risks of shadow AI, thus allowing organizations to enjoy some of the technology’s benefits.
Understanding Shadow AI
Over recent decades, shadow IT has become a well-known and well-addressed issue for organizations of all sizes. Shadow IT broadly refers to employees using apps or infrastructure that are outside of the control of the organization’s IT department. As this has become increasingly prevalent, organizations have put policies in place, often known as BYOD – Bring Your Own Device – to allow employees to use this technology while also allowing the organization to have some control over it. Specifically, it’s important that organizations have some control over security, access, reliability, backups, and privacy of devices and technology that employees are using.
Much like other areas of technology, the increased deployment of AI will lead to an increase in the shadow AI environment. Shadow AI simply refers to AI solutions that are not officially known or under the control of the IT department. In addition to specifically addressing shadow AI, in its report, Gartner notes that the democratization of technology will be a key trend in 2020. The democratization of technology means making technology more accessible without the need for extensive training, expertise, or expense. This is a positive trend in many ways, yet a natural result of democratization is shadow AI. As more people have access to AI, there will be more AI usage and solutions that are outside of the control of IT organizations.
Additionally, many organizations lack a unified approach to AI solutions. Instead, they have individual teams or units working to develop and implement AI. This can lead to disconnected solutions that are essentially siloed from IT and other departments and that can contribute to the rise of shadow AI.
Like shadow IT, there are pros and cons to the development of shadow AI. On the one hand, it can be an effective way to bring about innovation and to allow individuals and teams to develop innovative solutions. With it, however, come concerns over security, communication, monitoring, deployment, and scalability.
Benefits and Concerns of Shadow AI
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.
