As organizations look to move beyond isolated AI pilots and achieve measurable business outcomes, the challenge is shifting from experimenting with individual AI tools to embedding AI securely and effectively across everyday operations.

While AI technology continues to evolve rapidly, many organizations are still trying to layer AI onto systems and processes that were not designed for it. Operationalizing AI is not simply about deploying more models, but about creating the conditions for AI to deliver value across the enterprise.

According to the 2025 Cisco AI Readiness Index, only 33% of organizations have a formal plan to guide employees through AI adoption. Drawing on its own experience, Cisco identifies three key principles for operationalizing AI at scale: trusted enterprise data, a secure platform and AI-native workflows.

Three principles for operationalizing AI at scale:

1. Build AI on trusted enterprise data. AI is only as good as the data it can access. Business data sits across applications, warehouses, documents and legacy systems, and even the strongest model will not deliver useful answers without secure access to the right information and the context behind it. This means bringing enterprise data together responsibly, connecting AI to the applications where information already lives, and building the semantic understanding needed for AI to reason across the business. Trusted data is what allows employees to trust the answers AI gives them.

2. Give employees a secure alternative to shadow AI. When generative AI first emerged, employees across most organizations began experimenting with consumer AI tools immediately. Without a secure alternative, shadow AI becomes the default. Cisco’s own approach was to offer a better option rather than try to stop the behaviour, building an internal, secure, responsibly governed, model-agnostic AI platform. The platform was built on three principles: secure enough for employees to work confidently with enterprise data and aligned with Cisco’s Responsible AI Principles; compelling enough to give access to the right model for the right task; and extensible enough for teams to build and share prompts, projects, connectors and agents.

3. Redesign the workflow, not just the task. Organizations often take a ten-step process and use AI to improve each step. The better approach is to rethink the experience from the beginning, becoming AI-native. Across Cisco, more than 21,000 engineers use AI coding tools, with engineers saving an average of six hours per week and employees across the broader business saving an average of five hours.

Beyond time savings, AI can help reduce friction by enabling employees to spend less time searching for information and moving between systems and more time solving problems, making decisions and creating value.

As AI moves beyond answering questions to completing work, trusted data, secure access, enterprise context and clear governance become increasingly important. The goal is not maximum autonomy, but the right level of autonomy for the right task.

Organizations that create the most value from AI will not necessarily be those that adopt it first, but those that operate it most effectively.