OpenAI reported it will earn $13 billion in revenue this year, just outside of the FORTUNE 500. Meanwhile, only 1 percent of C-suite leaders report that their organization has achieved a mature AI deployment, according to McKinsey. No wonder AI use has boomed within corporations – but the unsanctioned kind.
Enter the term “shadow AI.” Lurking in the shadows of most corporations are unapproved, unmonitored, and often insecure AI tools. MIT’s Project NANDA found that 93 percent of employees use unauthorized AI tools, a massive challenge for IT and business leaders alike.
The motivations are understandable: employees simply want to work smarter and faster. However, the consequences are serious. Without proper governance and data privacy, shadow AI introduces risk at every level of the business. Leadership teams and IT departments need to step back into the driver’s seat and define the parameters for how AI is used within their organizations.
The threat of shadow AI
Most employees do not realize the risk they are taking when they enter proprietary data into large language models. They do not know that secure company, client, or customer data may then be used to train those models. It is the digital equivalent of employees unknowingly leaving the back door open for anyone to come and take a look at their work.
This isn’t due to negligence from IT departments. Many IT teams have actively explored AI tool integration, but gaps persist because the tools employees are given do not meet their needs. They turn to external tools because of the value they can provide. Rather than just a security problem, shadow AI is a much larger usability issue.
If employees are turning to shadow AI, it is because those tools genuinely help them. They draft content, summarize research, and organize notes in ways that make employees’ jobs easier and more efficient.
This use sends two important signals to company leaders. First, their people are eager to use AI to do their jobs better. Second, the AI platforms sanctioned for company use are not up to the job.
There is a massive opportunity here to help both productivity while remaining the security and privacy of the company’s data. Operational AI brings innovation out of the shadows.
Operational AI bridges the gap
There is a solution to the security and privacy risks posed by shadow AI – and it comes in the form of reaching fully operational AI. This is when an AI platform becomes a built-in, secure, and standard part of daily operations of an organization. It is built into how teams work, with clear oversight, governance and security. Reaching operational AI means data is consistently turned into real-time insights that drive performance with persistence and continuity.
Operational AI differs from agentic AI, which operates autonomously and performs tasks with minimal human input. Instead, achieving the status of operational AI supports workers, and doesn’t replace them. Its use ultimately enhances productivity and results, and strengthens the human-AI partnership.
For AI to be truly operational, it needs to be accessible, governed and integrated. The AI tool meets employees where they are, and is tailored to their roles and their specific work. Its data usage and model training must be transparent and compliant with both internal policies and external regulations. Lastly, the system should connect seamlessly to existing workflows and tools, elevating how people work.
The story we have all heard is that AI will take our jobs. Reaching the status of operational AI flips that narrative. When companies adopt operational AI, they empower employees to develop new skills in working with AI, in turn creating more job security for those workers.
In an AI-driven economy, knowledge workers must manage their AI use and interaction. AI should be a resource for workers, not a replacement. Achieving operational AI in a workplace will set employees up for future success.
Successfully reaching operational AI
Reaching this state requires intentional coordination between IT and business leadership. Organizations can get there by using a few key strategies.
- Co-design AI adoption.
Involve your employees early. If they use shadow AI, they know what they need out of AI tools to best do their jobs. Run pilot phases and solicit candid feedback from employees at each stage. This is the only way to ensure your AI tools are truly operational – and give employees what they need.
- Establishguardrails.
Define what responsible AI use at your organization looks like, including clear boundaries for information given to the platform. This also includes guidelines for use. Should AI help employees with work like taking notes and summarizing reports, drafting social posts, and screening resumes, but not strategy documents? Set the guardrails, revisit them often, and stay open to innovative ideas and uses.
- Measure impact.
Track adoption rates, employee satisfaction, and business outcomes to understand how AI is improving productivity and decision-making. Continuous measurement promotes transparency and accountability, allowing leadership and IT departments to adjust strategies in real-time and ensure AI investments are delivering value across the organization.
When AI becomes fully operational within an organization, employees no longer have to choose between innovation and compliance. The organization gains both – secure data and empowered teams.
