Vaisala is a global leader in measurement instruments and intelligence for climate action . With over 90 years of innovation and expertise, they employ a team of close to 2,500 experts committed to taking every measure for the planet. Vaisala series A shares are listed on the Nasdaq Helsinki stock exchange.
In a nutshell
Company
Vaisala is a global leader in weather, environmental, and industrial measurements, with 90 years of experience in innovation.
2,400+ employees globally.
23% of employees dedicated to R&D.
Challenge
Vaisala wanted to turn engineers' interest in AI into hands-on daily use, 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.
Solution
We worked with Vaisala to run hands-on AI experimentation 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.
Vaisala created a roadmap for future scaling and development across the organization.
Results
Claude Code adopted as the primary AI coding assistant across participating teams.
40+ practical AI hypotheses developed and tested.
Almost 300 engineers with hands-on experience building AI agents.
Framework now in place for sharing Claude Code configurations and skills across team boundaries.
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
Vaisala set out to build enterprise-wide AI capability, starting with a targeted trial.
The objective was to turn engineers' interest in AI into daily practice — moving from learning about AI to using it in everyday engineering tasks. This would help Vaisala build the capability to integrate AI into its daily workflows 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 chose Claude Code as the primary AI coding assistant for the program. Teams built their AI agents on Anthropic models, accessed through Vaisala's approved enterprise platforms.
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 experiment with real challenges rather than theoretical examples
The solution: AI sprints built around real engineering challenges
Vaisala's Applied AI team and Eficode co-designed a hands-on AI experimentation approach focused on challenges from 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 boundaries to solve the common challenge of isolated learning.
Demonstrate their solutions to internal stakeholders and senior leaders.
This gave participants the opportunity to experiment with AI in a setting directly connected to their own work.
I’m impressed with the value we saw from the very first AI sprint iteration. We’re excited 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 tested hypotheses for reducing engineering friction and built working AI agents. Presenting those solutions to leadership also helped make the potential of AI more tangible across the organization. Over eight four-week sprints, almost 300 engineers created and tested more than 40 hypotheses.
Beyond immediate quick wins, Vaisala’s Applied AI 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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