Vaisala is a global leader in weather, environmental, and industrial measurements. Based in Finland, the company develops innovative measurement instruments and software used in demanding environments around the world.
In a nutshell
Company
Vaisala is a global leader in weather, environmental, and industrial measurements, with more than 80 years of experience in innovation.
2,400+ employees globally.
28% of employees dedicated to R&D.
Challenge
Vaisala wanted to shift engineers' default daily behavior from passive learning to hands-on AI execution, freeing up time for tasks requiring human creativity.
The team aimed to overcome cross-team silos by sharing custom Claude Code skills, hooks, and configurations across the company.
Vaisala also wanted to standardize on Claude Code and Anthropic models (via direct API and AWS Bedrock).
Solution
We worked with Vaisala to run hands-on AI sprints based on real engineering challenges.
Teams identified friction in their daily work, developed testable AI hypotheses, built working Claude-powered agents, and demonstrated their solutions to senior leaders.
Teams created a roadmap for future scaling and development across the organization.
Results
Claude Code now Vaisala's go-to AI coding assistant.
~300 engineers using Claude Code in their daily work.
Hands-on experience building autonomous agents.
Framework now in place for sharing Claude Code configurations and skills across team borders.
A repeatable AI sprint model created for broader adoption.
A roadmap to expand the approach to other departments and product lines.
The challenge: Bringing AI into everyday engineering work
Following an Eficode-led git migration, Vaisala set out to build enterprise-wide AI capability, establishing Claude Code as its default AI coding assistant.
The objective was to fundamentally shift engineers' default habits —moving them from passive learning to using AI in their daily tasks. This would help Vaisala build the capability to integrate AI in its daily workflows and core products at scale.
Vaisala wanted to tackle the common challenges of sharing AI knowledge across team boundaries early on —ensuring custom Claude Code skills, hooks, and configurations built by one team could easily benefit others.
To power this transformation, Vaisala selected Claude Code as its main AI coding assistant. The team used Anthropic models to power its AI agents, connecting directly through the Anthropic API and via AWS Bedrock.
AI adoption becomes much more valuable when teams can connect the technology directly to their everyday work. The AI bootcamp sprints gave engineers a structured way to solve real challenges rather than theoretical examples.
The solution: AI sprints built around real engineering challenges
Together, we created a hands-on AI sprint program focused on challenges from Vaisala’s engineers’ daily work.
Before the first sprint, we interviewed key stakeholders to understand Vaisala’s goals and make sure the sessions focused on relevant business and engineering priorities.
Instead of working through synthetic exercises, engineers brought real challenges from their day-to-day work into the sessions. With guidance from our advisors, teams followed a practical process to:
Identify repetitive or time-consuming tasks in their daily work.
Turn those challenges into clear, testable hypotheses for AI-assisted solutions.
Build and test working agents using Claude Code and AWS Bedrock.
Share reusable Claude Code configurations, hooks, and skills across team borders to solve the common challenge of isolated learning.
Demonstrate their solutions to internal stakeholders and senior leaders.
This gave participants the opportunity to implement AI solutions directly in their everyday work environment.
I’m impressed with the return on investment that you brought us from the very first AI sprint iteration. We’re fascinated by the results and can’t wait to see more!
The result: Practical AI experience and a model for scaling
The hands-on format gave engineers practical experience using AI to address challenges from their own work.
During the engagement, participants developed and validated hypotheses for reducing engineering friction and built working autonomous agents. Presenting those solutions to leadership also helped make the potential of AI more tangible across the organization. After eight four-week sprints, ~300 engineers now use Claude Code in their daily work, having tested over 40 hypotheses.
Beyond immediate quick wins, the team also created a clear roadmap for future scaling and development across the broader organization, establishing a repeatable model for other functions and product lines.
The sprints showed what happens when engineers apply AI to their own work instead of watching demos. Our focus now is making this the normal way we work: sharing what teams build across Vaisala and extending the model to new functions and product lines.
- AI
- AWS
- DevOps
- Transformation
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