Artificial intelligence continues to dominate conversations across the games industry, promising to transform everything from art pipelines to QA testing. But behind the hype, the reality on the ground appears far more measured. According to new insights from Keywords Studios, only a tiny fraction of AI tools currently available are actually ready for use in live production environments.
The results paint a sobering picture: out of roughly 500 tools evaluated, only around six were deemed genuinely fit for purpose in real-world development workflows. That finding was shared by Jon Gibson, Head of Transformation at Keywords Studios, who offered a candid assessment of where the industry stands today—and where it needs to go next.
AI’s “Chaos Phase” in Game Development
Despite rapid innovation and heavy investment, Gibson argues that the industry is still struggling to bridge the gap between experimentation and practical implementation.
“Everyone’s focusing on building better AI, and no one’s really focusing on how to use it in a live production environment,” Gibson told The Game Business. “AI feels like it’s in the chaos phase right now. And we need to move to the usable phase. How do we use AI in live production environments?”
This “chaos phase” reflects a broader pattern seen across emerging technologies. Tools are evolving quickly, capabilities are expanding, and new entrants are constantly appearing—but consistent, scalable use cases remain elusive.
For game developers working under tight deadlines and complex pipelines, that uncertainty creates friction. A tool that performs well in isolation or in a controlled demo environment doesn’t necessarily translate into something reliable enough for production.
Built for Cool, Not for Purpose
One of the core challenges, according to Gibson, is that many AI tools are being created without a clear understanding of the problems developers actually need to solve.
“A company will use a tool or build a tool without a specific use case and try and cram it into their production pipelines, rather than flipping that problem around and saying: ‘What are our pain points? What are we trying to solve?’ And then building a tool against that.”
This disconnect has led to a wave of solutions that look impressive on paper but fall short when integrated into real workflows. In game development, where processes are often deeply interconnected, even small inefficiencies can cascade into major issues.
As a result, studios are becoming more cautious. Instead of adopting AI tools wholesale, many are taking a more selective approach; testing extensively and only deploying solutions that demonstrate clear, repeatable value.
Rising Concern Among Developers
The gap between AI’s promise and its current capabilities is also having a noticeable impact on developer sentiment. According to Gibson, concern within the development community has been steadily increasing.
“That statistic of 52% of developers being concerned about the usage of AI, that’s gone up every year for the last three years. As AI tools and AI models and AI technology in general has become more prevalent, the lack of understanding and the concern has increased.”
This growing unease is not necessarily about AI itself, but about how it is being introduced and used. Uncertainty around job security, creative ownership, and the reliability of AI-generated outputs all contribute to a sense of caution.
For many developers, the question is not whether AI will play a role in the future of game development—it almost certainly will—but whether it can be integrated in a way that enhances, rather than disrupts, existing workflows and talent.
From Experimentation to Integration
Keywords Studios’ findings suggest that the industry is approaching a critical turning point. The initial wave of experimentation has generated valuable insights, but the next phase will require a shift in focus.
Rather than building more tools, the priority now is figuring out how to use the right tools effectively.
“Until we’re able to work out how to meaningfully build this technology into production pipelines in a way that is safe, in a way that adds value, in a way that complements the talent that already exists in this industry, and doesn’t threaten that talent, I think there’s always going to be that level of concern,” Gibson said.
This means developing not just better AI, but better frameworks for integration—clear guidelines, robust testing processes, and a deeper understanding of where AI can genuinely add value.
A More Measured Approach to AI Adoption
For Keywords Studios, the approach moving forward is one of careful evaluation and targeted deployment. The company continues to explore AI as part of its broader transformation strategy, but with a strong emphasis on tools that can prove their worth in live environments.
That means prioritising consistency, control, and measurable outcomes over novelty.
In practical terms, this could involve using AI to streamline specific tasks, such as localisation, testing, or asset generation, while ensuring that human oversight remains central to the process. It also means recognising that not every problem requires an AI solution.
What This Means for the Industry
The broader takeaway for the games industry is clear: AI is not a plug-and-play solution. Its potential is significant, but realising that potential requires a disciplined, use-case-driven approach.
Studios that succeed in this next phase are likely to be those that:
- Identify clear pain points before adopting new tools
- Test rigorously in real production conditions
- Integrate AI in ways that support, rather than replace, human talent
At the same time, developers and technology providers will need to work more closely together to ensure that tools are built with real-world needs in mind.
