Square Enix has told investors and the public it wants generative AI to handle roughly 70% of its quality assurance (QA) and debugging work by the end of 2027 — part of a broader three-year "Reboots and Awakens" plan to refocus the publisher on larger, multiplatform releases and greater operational efficiency.
Ambitious target, academic partner
The target appears in a progress report tied to the company's medium-term business plan, which says Square Enix has launched a joint research effort with the Matsuo–Iwasawa Laboratory at the University of Tokyo to pursue "Joint Development of Game QA Automation Technology Using Generative AI." The collaboration brings together researchers from the lab and engineers from Square Enix in a team of more than ten members aiming to "automate 70% of QA and debugging tasks in game development by the end of 2027," the company wrote.
Square Enix framed the initiative as an efficiency play: by automating repetitive and time-consuming QA tasks, the publisher hopes to speed up development cycles and "establish a competitive advantage in game development." The company has previously experimented with AI tools during development (including Azure OpenAI) and has said it will be "aggressive in applying AI." The progress report and related corporate materials are available from Square Enix's official investor pages.
Where this fits in Square Enix's strategy
The move toward AI-driven QA is one element of a larger strategic shift. Over the past year Square Enix has emphasized releasing major titles simultaneously across platforms (PlayStation, Xbox, Switch 2 and PC), prioritizing flagship IPs, and trimming lower-performing projects. The company has held internal AI-themed idea contests and signalled a push to raise the "quality over quantity" of its output, while increasing focus on mobile adaptations of major franchises alongside premium HD titles.
Square Enix's multiplatform approach has already shown some traction: the company reported a boost to back‑catalogue sales following its multiplat push. But it is also navigating uneven financials — in the quarter ending September 30, 2025, Square Enix recorded a 15% decline in net sales while operating income rose about 28.8% — underscoring why management is seeking new efficiencies.
Industry context and precedents
Automating QA is a long-standing aspiration in the games industry. Publishers and platform holders have discussed and piloted automated testing tools for years — in 2022 Microsoft executives publicly described a dream scenario of running vast numbers of AI-driven test instances in the cloud. More recently, other big companies have doubled down on AI partnerships: Electronic Arts announced a collaboration with Stability AI to develop generative models for game creation workflows, and several studios are integrating AI into design, art and testing pipelines.
Square Enix is not alone in aiming to use AI to reduce repetitive tasks, but the scale and timetable it has proposed are notable.
Reactions and concerns
The plan is likely to provoke mixed reactions across the industry and communities. Proponents argue that QA can involve repetitive, low‑satisfaction work that is well suited to automation; using AI could let human testers focus on nuanced playtesting, creative feedback and higher-value tasks. For studios, faster QA cycles could shorten development timelines and lower costs.
Critics warn automated QA presents risks. Human testers are often better at noticing unusual behaviour, writing clear bug reports and providing context that helps developers reproduce and fix issues. QA roles are also seen by many as an entry point into higher-skilled, better-paid studio positions; widespread automation could reduce career ladders and eliminate jobs. Some former employees in the industry have pointed to instances where AI tools were used to summarise playtester feedback or triage reports before human reviewers — a practice that has coincided with layoffs at certain studios.
There are also legal and cultural flashpoints. Earlier this year several Japanese publishers — including Square Enix — asked OpenAI to stop training a model identified as Sora 2 on their creative works, reflecting broader unease about how generative systems are trained and whether publishers' IP is used without consent.
Technical and practical questions
Key unknowns remain. It is unclear which types of QA tasks Square Enix expects to fully automate and which will remain human-led. Automated systems can excel at stress, regression and repeatable input testing, but they struggle to mimic human discovery, subjective judgement and emergent gameplay testing. The timeline to 2027 suggests a phased rollout: building models that can reliably detect crashes, reproduce certain bugs, and generate actionable reports would be an important early milestone, while replacing complex creative judgement would be more challenging.
The Matsuo–Iwasawa Laboratory, which has described ambitions to create a tech ecosystem akin to Silicon Valley, brings academic research capability to the project; whether that will accelerate robust production tools or primarily yield experimental prototypes is an open question.
What it means for players and workers
For players, improved QA could mean more polished launches and fewer game-breaking bugs at release. For developers and QA staff, the change could be disruptive: some roles may be eliminated or redefined, while new roles in AI oversight, tooling and higher-level testing may appear. Companies that pursue automation typically emphasise re-skilling and redeployment, but the effectiveness of those programs varies.
Square Enix's AI ambition sits at the crossroads of productivity, IP control and workplace change. How the company balances speed and cost savings with creative fidelity and workforce impacts will be watched closely across the industry.
Looking ahead
As Square Enix works with academic partners and rolls out internal AI projects, the publisher's progress against the 70% target will offer a concrete measurement of how far generative tools can penetrate game production in the near term. Whatever the outcome, the announcement underscores a broader trend: AI is moving from experimental assistance to strategic planning in big game studios, and companies are racing to convert that potential into competitive advantage — even as questions about jobs, training data and human judgment remain unsettled.