【Gamystery】EP22: Is Game Development Really Easier After Adopting AI?
- 彥澤 廖
- Mar 18
- 4 min read
Updated: Mar 19
During the Lunar New Year break, I took some time to research the voice-over industry, and I personally see strong potential in AI voice-related services( EP21: The Future of Sound—How AI Voice Generation Creates New Value for the Gaming Industry? ).
Not only are they suited for traditional applications, but in the future, more voice-controlled interfaces will rely on such services as a core technology.
That got me thinking—since I'm so optimistic about this field, why not dive deeper and explore whether implementing AI voice cloning services (hereafter referred to as AI voice) in game production is really as game-changing as people claim? After all, many small teams operate on tight budgets, and standing out in the increasingly competitive world of indie game development requires leveraging AI to enhance production quality.
Let's compare three different workflows:
Traditional pipeline
Basic AI voice integration
Advanced AI voice integration
Traditional Workflow
The traditional production pipeline relies heavily on live actors, where voice actors record voice-overs based on the script and context. From my observations, many teams tend to push voice acting to the later stages of development to minimize re-recording costs caused by ongoing script revisions.
Pros: Extremely high-quality potential
Cons: Expensive, high communication overhead, inflexible to changes
In game development, once the voice recordings are complete, they result in common
audio files (.wav, .mp3, etc.). These files must then be imported into the game engine and converted into engine assets. Finally, these assets are controlled by the project's dialogue system, which coordinates the display of text and triggers the corresponding audio playback during in-game performances.
It's easy to imagine that whenever the script is modified, this entire production cycle has to restart from left to right. Although the actual time required varies depending on project scale, the process often involves multiple roles, and even minor adjustments can result in over a week of additional work.
Basic AI Voice Integration Workflow
When a development team starts using AI voice services (e.g., ElevenLabs, Artlist, etc.), these services effectively replace traditional voice actors, shifting the role to virtual voice actors. However, the handoff still relies on conventional audio file formats.
Pros: Greater variety of voice actors, multilingual support, lower cost
Cons: Quality may vary, learning curve required
Let me elaborate on “quality may vary.” For example, Steam lists 12 common supported languages, while ElevenLabs can cover over 30 languages. However, there is a gap between "available" and "usable" depending on the language. This is primarily due to limitations in the platform's model technology and dataset quality. From my testing, ElevenLabs performs significantly better with Western languages compared to others. If you need voiceovers for less common languages, local vendors often deliver better results.
Compared to the traditional workflow, the "basic" AI voice pipeline trades performance quality for:
A broader variety of voices and languages
Lower cost
Reduced business coordination (e.g., less communication with voice-over agencies)
In theory, removing an external production step shortens the distance between script development and the final implementation in-game.
That's the theoretical view—but let's talk about hidden challenges:When it “appears” that the workflow has been shortened, teams may encounter:
False expectations — believing AI will make things faster (especially common among PMs).
Scope creep — “Since we're using AI, let's do multi-language voiceovers too.”
While production time is indeed reduced, it's often not as dramatic as expected. Here’s why:
Someone still has to operate the platform to generate audio files.
There's a learning curve (e.g., mastering proper sentence breaks, conveying emotions effectively).
The number of workflow stages remains the same as in traditional pipelines.
In short, a basic AI voice workflow merely transforms external tasks into internal management. In reality, you can't fully bypass the steps that were previously outsourced. Particularly when the script changes, generating new AI voice assets remains a tedious task—requiring sentence-by-sentence copying, configuration, and production.
From a friend's real-world project:For a 7,000-character Traditional Chinese script, the full process took around 10 hours, including voice generation, paragraph splitting, pacing adjustments (such as setting time gaps between lines), and asset setup.
Three major bottlenecks remain hard to optimize:
Repeatedly operating the AI platform
Setting up new assets
Updating engine resources to match the revised script
Advanced AI Voice Integration Workflow
So, is there really no solution to these challenges?
Before offering a solution, let's first consider what the ideal development workflow should look like.
As usual, everything begins with script changes. Once the updated text is pushed into the game engine, instead of following the basic workflow, this new approach leverages the already-updated engine assets (the text itself) to automatically trigger voice-over generation. This advanced pipeline allows us to eliminate two of the time-consuming steps mentioned earlier—we only need to update the text assets within the engine.
Pros: Diverse voice actors, multilingual support, lower cost, high synchronization between text and audio
Cons: Quality still varies depending on use case
In both the traditional and basic AI workflows, "text" and "voice-over" are treated as two separate production pipelines that are only merged later inside the system. This separation is often necessary when aiming for highly polished, professional performances, but it becomes a burden when working with AI voice solutions.
If the team is already open to using AI voiceovers, the real benefit lies in embracing the SaaS nature of these services by fully integrating them through APIs. By doing so, we can automate large portions of the workflow, saving manual labor and only intervening at specific points to fine-tune critical performances when necessary.
How to Implement an Advanced AI Voice Workflow?
In the spirit of experimentation and research, I’ve already completed the development of this advanced workflow—plus, I’ve included additional optimizations not covered in this article. The platform I used for this prototype is ElevenLabs, primarily because it’s a well-funded startup backed by a16z and offers a wider selection of voice actors.
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