Wise

Background

Wise wanted to explore how generative AI could be introduced into everyday internal operations in a practical, controlled, and value-driven way. The objective was not simply to experiment with AI, but to identify real tasks where employees could save time, reduce repetitive effort, and improve the speed and consistency of their work.

Across technical and operational teams, many day-to-day activities involve recurring analysis, documentation, troubleshooting, drafting, coordination, and process execution. These activities are necessary, but they can consume significant employee time when performed manually. Wise therefore looked for an AI platform that could support employees directly in their workflows while also creating a foundation for future automation initiatives.

A further objective was to introduce AI in a way that could be adopted by different teams rather than limiting usage to a single technical function. This meant providing a shared environment where employees could use AI assistance for approved business use cases and where technical teams could benefit from more specialized coding capabilities.

Proposed Solution

Wise adopted Claude for Teams and introduced Claude Code as part of its internal AI enablement initiative. Claude for Teams provides employees with a shared environment for using Claude in daily work, while Claude Code gives technical teams a more focused way to use Claude for software development, troubleshooting, code understanding, and implementation-related tasks.

The solution is centered on practical internal use cases. Employees can use Claude to assist with analysis, documentation, drafting, summarization, problem solving, and other knowledge-work activities. Technical teams can use Claude Code to better understand existing code, investigate issues, accelerate development tasks, review implementations, and reduce the time required to move from an initial idea or problem to a workable solution.

Wise is also using Claude as an enabler for workflow automation. Repetitive internal processes can be assessed to determine where AI can assist with information handling, decision support, content generation, or task execution. By combining direct employee productivity use cases with workflow automation, Wise is building a broader AI-enabled operating model rather than treating Claude as a standalone tool.

The rollout approach emphasizes gradual adoption, practical value, and continued identification of suitable use cases. This allows teams to learn where Claude provides the greatest benefit and creates a path for expanding usage as internal confidence and experience grow.

Metrics for Success

With approximately 10 Claude seats deployed across the organization, Wise is measuring success primarily through time saved, reduced manual effort, faster execution, and the growing use of AI across day-to-day technical and operational activities. The following indicators provide a practical view of the expected productivity impact of the initiative.

Conclusion

By introducing Claude for Teams and Claude Code, Wise has created a practical foundation for integrating generative AI into everyday internal work. The initiative supports both technical and non-technical employees, helping them reduce repetitive effort, accelerate common tasks, and use AI as an assistant within real operational workflows.

The value of the initiative extends beyond individual productivity. By identifying repeatable processes that can be enhanced or automated with Claude, Wise is creating opportunities to improve efficiency at the workflow level and to make AI part of how work is executed across the organization.

As usage expands, Wise can continue identifying new high-value use cases, refining internal practices, and measuring the impact of AI on productivity and execution speed. This provides a scalable path toward broader AI adoption while keeping the focus on practical business outcomes and employee enablement.

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