Generative AI may speed up routine tasks such as coding and boilerplate writing, but that does not guarantee a faster production process. Games-industry attendees described work shifting into policy updates, data protection, quality checks, rework, and HR and legal review. Their accounts, reported from a London HR Summit, are examples of how that extra work can arise—not proof that AI increases workloads across the industry.
Where studios see potential time savings
Attendees at the HR Summit in London on October 1, 2026, saw potential for generative AI to help with coding, boilerplate writing, and other routine tasks. The report did not measure time saved or establish that these gains were achieved consistently. A faster first draft or code snippet is only one part of a production task: the result still has to be checked, brought into the project, and maintained.
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That distinction matters especially when comparing routine work with creative or judgment-heavy tasks. One senior-level attendee said, “I haven’t seen AI create anything fun.” The remark is a personal assessment reported by Lewis Packwood for GamesIndustry.biz, not a finding that AI cannot contribute to creative work.
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Two reported production anecdotes show why output speed alone can be misleading. In one, AI-generated shader code did not meet the studio’s coding standards and the build had to be reverted. In another, AI-generated art with visible anatomical defects passed an initial check by someone outside the relevant department, then drew criticism from the community. These cases illustrate potential quality-control failures; they do not establish how often such failures happen.
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Count the whole workflow
To tell whether AI actually saves production time, compare the complete workflow with the previous method—not just how quickly an initial output appears. Include time spent on checking, correction, integration, and any rework. Also consider whether the studio has a documented process and a clear owner for judging the result against coding, art, and production standards. If those steps are missing, an apparent shortcut can create work for other team members.
Why acceptable-use rules need ongoing attention
Studios also face governance work as tools and risks change. One company leader described having to revisit guidelines shortly after writing them after learning about a prompt-injection concern: “I wrote a policy two weeks ago, and then I found out about some prompt injection yesterday that means I’ve got to go back and rewrite some of the guidelines.” That is one attendee’s experience, not evidence that studios generally revise policies on a particular schedule.
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Protect confidential material
A separate concern was employees entering NDA-covered information into personal AI accounts. One leader put it this way: “They could be going off to ChatGPT on their personal account and putting all sorts of NDA things in there. And I don’t see how you can physically stop people doing that.” The report does not assess the security of particular vendors or plans. Its practical point is organizational: studios need explicit boundaries for acceptable use, including how staff should handle confidential material and what to do when a task is unsuitable for an AI tool.
Rules alone may not settle the matter. Teams need workflows people can follow in practice, with responsibility assigned for reviewing and updating guidance as concerns emerge. An attendee discussing pilots observed that many faltered because they depended on individual know-how rather than documented procedures: “Most of them collapse, because [workflows are] driven by the tacit knowledge of individuals rather than formally documented operating procedures.”
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How AI-assisted grievances can add HR and legal work
The reported workload did not stop at production. Summit attendees described lengthy grievance documents written with AI assistance that included confusing or inaccurate material. HR and legal staff still had to read and respond to them. One attendee said, “And just because people are using AI or using LLMs to try and help these things, actually quite often it’s given me more to do.”
This is an account of a type of administrative burden, not a measured estimate of extra hours or case volume. It shows how generating more text can increase the effort required to understand a concern and respond carefully, even when the person submitting it used AI to prepare the document.
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What the summit accounts do—and do not—show
Packwood’s report describes a mixture of possible task-level acceleration and additional oversight, cleanup, governance, and staff-support work. It also characterizes many companies as dabbling in or piloting workflows, with some larger studios evaluating specific uses. It provides no representative adoption breakdown and no quantified productivity results, so the anecdotes should not be treated as a measure of prevalence or a proven industry-wide effect.
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The summit operated under the Chatham House Rule. Attendee quotations were published anonymously, without named identities or attributable job titles. The accounts are useful for identifying mechanisms studios may need to manage, but they cannot establish how common those experiences are across games companies.
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A practical way to judge an AI workflow
For a specific task, a studio can assess the full cost and risk with a short set of questions:
- Baseline: What routine task is AI helping with, and how is it done without AI?
- End-to-end time: How long does the result take after review, correction, integration, and any rework—not just generation?
- Data sensitivity: Could the workflow expose NDA-covered or other confidential material, and do the acceptable-use rules address it?
- Quality and ownership: Who checks the output against studio standards, and who is responsible for accepting or rejecting it?
- Repeatability: Is the process documented well enough that it does not depend on one person’s undocumented knowledge?
- Staff impact: Does the workflow create uncertainty about jobs or shift work to HR, legal, or colleagues who must support or review its use?
These questions do not guarantee a productivity gain. They help distinguish a useful shortcut from a tool that simply moves effort to another stage or team.
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