Many companies are experimenting with AI, but relatively few are ready to scale it into reliable business operations.
In this session, Miika Soininen, Chief Digital Officer at Ponsse, shares how the company is moving from individual proofs of concept toward a governed, production-ready AI capability built on trusted data, shared platforms, and in-house competence.
Through practical examples and honest lessons from experiments that both succeeded and failed, the session explores how Ponsse is building its data platform, AI landing zone, and governance model while keeping business value and operational resilience at the center.
The examples include making information in technical manuals easier to find and use, supporting requirements specification with AI, and enabling access to enterprise data through natural language.
Key Takeaways
- Build the data asset before scaling AI.
AI readiness requires trusted, well-governed, accessible, and business-relevant data. - Create common foundations for experimentation and production.
A shared data platform and AI landing zone make it easier to develop, govern, and scale solutions. - Build critical competence in-house.
Real capabilities emerge through experimentation, trial and error, and retaining the lessons learned from proofs of concept. - Move deliberately from proofs of concept to production.
A technically successful experiment is only the beginning. Production requires ownership, governance, integration, security, and continuous development. - Keep business value at the center.
AI initiatives should deliver measurable customer value, operational efficiency, better decision-making, or increased business resilience. - Treat failures as part of capability building.
Not every experiment should reach production. Failed initiatives can still clarify requirements, expose weaknesses in the foundations, and strengthen organizational competence.
