Scania accelerates AI adoption with ChatGPT Enterprise | Keryc
Scania, the storied maker of trucks and buses, is speeding up its adoption of artificial intelligence inside a global, highly technical organization. It doesn’t impose from the top; instead, it lets teams experiment and learn together, folding AI into their continuous improvement processes.
How Scania drives AI from within
Adoption began with a partnership with OpenAI and broad availability of ChatGPT Enterprise licenses for many teams. Rather than waiting for a central directive, Scania tapped into strong bottom-up demand: engineers and operational teams wanted to try new tools and see immediate results.
This wasn’t chaos: from day one they built practical governance. Legal and security acted as enablers, defining clear boundaries that allowed experimentation without paralyzing teams. The result? A rapid rollout that’s already delivering early gains in productivity and quality.
It's moving faster than we expected: both in speed and in quality.
Strong bottom-up push: teams pulled the change, and the company let them.
Cross-functional experimentation: use across engineering, operations, and improvement processes.
Early benefits: productivity, quality, and operational flow improvements.
AI embedded into lean and continuous improvement processes.
Team-based approach to build lasting capabilities.
Onboarding by teams, not by individuals
A key decision was to require training by teams. One person couldn’t learn it and leave; only whole teams were onboarded. That creates continuity, collective memory, and resilience: if someone leaves, the knowledge of how to use AI stays with the team.
That dynamic also makes it easier to share usage patterns, helpful prompt templates, and operational processes where AI provides real value. Think about how maintenance shifts or the creation of technical instructions change when the whole team uses the same tool and rules.
Practical lessons and what you can apply today
Scania offers a simple roadmap any technical organization can adapt:
Let the organization push: if there's demand, enable experimentation.
Put guardrails in place from the start: security and legal as enablers, not blockers.
Train teams, not just individuals: that way knowledge endures.
Insert AI into existing improvement systems: use cases will emerge there.
Prepare for speed: uptake can exceed your expectations; design processes that absorb it.
Does that sound familiar? In many workshops and plants I’ve visited, the first impact comes when operators and technicians use AI to solve concrete problems: faster diagnostics, better documentation, and less rework.
What's next for Scania and for the industry
Scania is already exploring agent capabilities, deeper integrations into workflows, and how AI can support its ambition for a more sustainable transport ecosystem. It’s not just about efficiency: it’s about wondering what role the company will play in an ecosystem where real-time information and decision-making matter.
If you work in a technical company, the lesson is clear: don’t wait for perfect top-down adoption. Enable, regulate what’s necessary, and let teams build the practice. That’s how AI stops being a promise and becomes part of the DNA of work.
