Your employees aren’t waiting for you to create a centralized AI strategy. Shadow AI, the use of unsanctioned AI tools, puts your company’s data security at risk and prevents your organization from seeing the full benefits of AI adoption.
A 2025 survey found that 57% of employees still hide their AI use at work. If your employees are using shadow AI in the enterprise, it's because your organization is failing to keep up with the opportunities and demands of AI.
Creating and executing a documented AI strategy helps guide your enterprise toward the secure, optimized use of AI. Unfortunately, only 60% of companies have an AI strategy and less than 30% offer training.
What is shadow AI? How does shadow AI compare to shadow IT? How can your enterprise create an AI roadmap to mitigate Shadow AI risks while aligning AI initiatives with business objectives?
Risks Shadow AI Poses to Your Enterprise
In 2023, Samsung discovered that one of its engineers had uploaded sensitive internal source code to ChatGPT. The company quickly banned the use of generative AI tools enterprise-wide. This is just one shadow AI example that every enterprise is facing.
However, restricting employee’s use of AI won’t prevent shadow AI. While 39% of enterprises ban or limit the use of AI, half of them admit there is still unauthorized shadow AI use in the workplace.
Just like with shadow IT, shadow AI presents massive security risks. In a Capgemini survey, nearly every organization (97%) had encountered breaches or security issues related to generative AI use in the past year. This has contributed to a 15% YoY growth in data breaches caused by shadow data.
In addition to data breaches and security vulnerabilities, shadow artificial intelligence presents compliance risks, especially regarding data protection and privacy.
The reliance on shadow AI in the enterprise also means that employees may be using models that provide inaccurate information, don’t align with company messaging and values, or simply offer poor-quality outputs.
An enterprise-wide ban on artificial intelligence is not the answer either. Once Samsung responded to the security breach, they began developing in-house AI tools to provide their employees with the same capabilities without the risk. They have since created their own generative AI model called Gauss and integrated AI into their products through partnerships with Google and NVIDIA.
Is your organization ready for AI?
Advantages of Creating a Centralized AI Strategy
Creating a centralized AI strategy is key to limiting shadow AI risks and supporting your entire enterprise’s AI adoption in a secure, scalable, and productive way. Unfortunately, only 25% of organizations have a comprehensive generative AI roadmap in place.
Instead of individual employees experimenting with unsecured tools, teams gain approved platforms, clear usage policies, and trusted data sources. This not only reduces shadow AI risk but unlocks the real value of AI. Benefits include:
- Data Governance: A documented AI plan ensures your enterprise data can be used to train and fine-tune AI models securely. Instead of relying on generic, public data, AI can make your company data more valuable and actionable.
- Operational Efficiency: The operational efficiency gains of AI are also clear. According to a Google Cloud report from Q4 2024, 74% of enterprises are currently seeing ROI from their generative AI investments.
- Standardized Workflows: Integrate AI into operations with consistent, tested processes. This makes it easier to document enterprise AI workflows and implement them enterprise-wide.
- Trust and Visibility: Shadow AI prevents your organization from a full understanding of which tools are being used and for what purpose. Creating a centralized AI strategy gives you greater insight into your employee’s AI use to better understand where there is demand for AI tools and how you can best support them.
- AI Training: A McKinsey study found that 48% of employees believe that formal generative AI training from their organization would most increase their daily use of AI tools.
Does this mean you should build in-house AI models for every shadow AI use case you run across? Probably not. A hybrid AI model can blend centralized governance with decentralized enablement. This approach allows security and infrastructure to remain consistent while enabling departments to pursue AI opportunities that are aligned with their goals.
The time for individual experimentation is over. It’s time to approach AI as an enterprise-wide capability to reduce shadow AI and see the full benefits.
Building Your Enterprise AI Roadmap
Once you’ve acknowledged the need to address shadow AI in the enterprise, the next step is building a clear roadmap for enterprise AI readiness.
- AI Readiness Assessment: Evaluate your organization’s current AI usage, including shadow AI use cases, and assess your current data maturity, technical infrastructure, and business needs.
- Data Strategy: Your AI is only as good as the data it uses. Enterprise agentic or generative AI requires structured, accessible, and secure data. Invest in data pipelines, tagging, governance, and access controls.
- AI Skills Development: Provide training on AI best practices, tool usage, and shadow AI policies. Formal education reduces reliance on shadow AI and builds confidence in company-provided AI resources.
- AI Operations: To operationalize AI across your enterprise, you'll need scalable computing, secure storage, and integration-ready platforms.
- Change Management: People resist change, especially when it impacts workflows or job roles. Introduce AI change management strategies early. Communicate, involve key stakeholders, and start with pilot projects.
Currently, only 29% of employees feel fully supported in using generative AI at their company. Shadow AI is a symptom of bigger problems, including a lack of clarity, access, and alignment of AI tools. The more your teams are left to figure things out on their own, the greater the risk and the harder it will be to scale AI securely.
A centralized AI strategy gives your enterprise a foundation on which to build. It helps you address shadow AI risks, unlock AI-driven productivity, and ensure the tools your employees use are secure, consistent, and aligned with your business.
If you’re ready to take the first step in building your enterprise AI roadmap, sign up for Gigster’s AI Strategy Workshop. We’ll help you evaluate your current data maturity, AI integration capabilities, and infrastructure readiness. You’ll come away from the workshop with a clear path toward scalable, secure, and effective enterprise AI.
