Lessons in shadow AI management from shadow IT

The lesson gleaned from the era of shadow IT is clear: individuals are driven to excel in their work, yet their pursuit of efficiency and speed may sometimes disregard organizational protocols.

Enter the AI era, where IT infrastructure must align with the rapid pace and efficiency demands of all personnel within the company.

In recent times, the swift integration of generative AI technology in the workplace mirrors the earlier trend of shadow IT, where employees sidestepped official channels to utilize cloud-based solutions. Despite increasing restrictions on AI tool usage in corporate environments, employees persist in their adoption, whether authorized or not. Recent data underscores this trend: a recent study by Adobe reveals that over half of individuals in the US are already employing generative AI tools. However, a significant 45% of enterprises still lack a formal policy governing the use of GenAI. This phenomenon of shadow AI, characterized by the unauthorized utilization of generative AI beyond IT oversight, introduces new complexities and risks, emphasizing the urgent need for businesses to adapt promptly and judiciously.

Safeguarding your organization during the shadow AI era

In light of the risks involved, organizations must adopt strategic measures to mitigate potential harm. These steps include:

Formulating your AI strategy and governance

Given the rapid uptake of GenAI in workplaces, it's evident that organizations should have already established a formal AI policy. However, if not done yet, the next best time is now.

Start by implementing centralized management and oversight of AI tools to exert better control over their usage and access. This involves establishing clear policies and supporting security measures. Decide which tools will be accessible to team members and clearly define appropriate usage. Ensure that these guidelines are easily accessible and well understood.

Prioritizing your use cases

Many organizations are in the process of evaluating various use cases to determine which ones to endorse or develop internally. With potentially numerous use cases to consider, it's essential to group them according to common objectives and prioritize based on their potential impact on the organization's overarching goals. Keep team members informed about progress on these use cases regularly so they can share in the excitement as tools evolve.

Integrate AI with your data

Recognizing the sensitivity of certain data and limiting its exposure is paramount. Organizations should categorize data and clearly outline which types should not be processed by either public or privately hosted AI solutions. For particularly sensitive data, consider utilizing on-premises AI solutions to keep the data within the organizational perimeter.

Instead of transmitting data to cloud-based AI services, explore options to deploy capabilities directly where the data is stored. This approach reduces the risk of data exposure and facilitates easier governance. Additionally, it fosters user compliance, as employees are more inclined to trust and utilize systems that are securely established and aligned with organizational protocols.

Invest in your team

Investing in training and education is vital to ensure team members use GenAI tools responsibly and effectively. Surprisingly, many enterprises have ample room for improvement in this area. According to a survey by the Boston Consulting Group of C-suite executives, only 6% of companies have trained over a quarter of their workforce on GenAI tools.

Start with the fundamentals: educate team members on the ethical and responsible use of approved GenAI tools, and enlighten them about associated risks. Subsequently, progress to helping them master these tools to enhance their work performance.

Encouragingly, there's a significant thirst for knowledge. A recent Dell survey revealed that 86% of respondents expressed a desire for training either for themselves or their teams on using GenAI. This indicates that skills workshops and other training initiatives may be eagerly embraced by teams. Thus, the emergence of shadow AI within your organization could present an opportunity to harness enthusiasm and drive innovation.

Emerging from the shadows

Similar to its predecessor, shadow IT, shadow AI poses governance challenges, albeit on a larger scale. Fortunately, the lessons learned from dealing with shadow IT can guide us in overcoming these obstacles. By adopting a thoughtful approach to AI adoption and governance, prioritizing use cases, integrating AI with data, and investing in team members, organizations can navigate the era of shadow AI while harnessing the transformative potential of AI technologies. The objective is clear: securely and effectively integrate AI into business processes, ensuring that innovation drives growth without compromising security.

For organizations seeking to streamline AI implementation and unlock a wide array of enterprise use cases, the Dell AI Factory with NVIDIA offers a solution to propel AI business initiatives forward.