97% of studio leaders have used AI in some form during development, with 94% having used AI in at least one of the nine production stages.
This data comes from a new AI adoption report by consultancy firm Vivrato, based on 101 interviews with studio leaders in Europe and North America conducted between April and September 2026.
The interviews addressed six types of AI: generative AI, rules-based automation, fine-tuned or custom-trained models, classical machine learning, retrieval-augmented systems, and agentic systems.
Vivrato clarified that a “studio’s view of generative AI isn’t treated as its view of AI overall.”
Among the nine production stages, AI is most commonly used in code and engineering (engine, tools, gameplay code) at 86%, followed by general productivity tasks (such as documentation, email, meetings, and planning) at 83%, and concept and pre-production (ideation and brainstorming) at 79%.
Design and prototyping (mechanics, systems, features) use is 68%, while art, assets, and animation (characters, environments, motion capture) are at 60%.
More than half of studios use AI for QA and testing, as well as for narrative and world building. Analytics and player data, along with LiveOps and monetisation, have the lowest AI adoption rates.
Over half of respondents rate the integration of AI into the full production pipeline at three or lower out of ten.
European studios report deeper AI adoption, with an average self-rated pipeline integration depth of 4.2 out of 10 (including the UK), compared to 3.5 out of 10 in North America.
European studios use AI more than their North American counterparts in code and engineering, design and prototyping, art, assets and animation, and analytics and player data.
“The interesting question isn’t around which game studios are using AI anymore,” said Vivrato founder and CEO Adam Boyes. “Most of the leaders we spoke with declared that they have adopted some form of AI within their development pipelines.”
“The focus is now where they’re using it, what is changing, what’s actually helping, and where teams are still running into challenges.”
Boyes added: “We’ve noticed this year, public discourse and definitions about AI in games usually defaults to generative AI: chatbots, image generation, and synthetic voices.”
“However, we know that studios use AI in a variety of ways, from machine learning that predicts player churn to rules-based automation in testing, with very different results – that makes now the right moment to set a clear baseline.”
Earlier this week, Sheridans partner Anna Poulter-Jones provided an overview of the current laws surrounding AI in game development.