AI in video games: applications, challenges & industry examples

Artificial intelligence (AI) has been present in games for decades, with early AI models being rule-based, relying on simple “if-then” logic to simulate human decision-making. Today, advances in machine learning, including large language models like OpenAI’s ChatGPT, enable developers to create more varied gaming experiences and more dynamic character interactions than previous games could allow.

However, adopting AI is not straightforward. The technology can be costly to implement and complex to integrate with existing tools. It can also require specific technical skills that game developers lack. To overcome these challenges and maximize the value of AI, many companies turn to dedicated AI consulting services for guidance and support.

Core applications of AI technology in video games

In video games, artificial intelligence is used not only to support human-like decision-making and behaviors but also to automate repetitive, resource-intensive tasks that would otherwise require extensive manual effort. By automating these processes, studios can reduce development time, minimize human errors, and enable specialists to focus on creative and high-impact work that AI can’t replace.

Game design & QA

Game businesses apply AI to automate game development and quality assurance processes. AI-powered tools can perform the following tasks:

• Generate levels based on predefined design rules
• Create dialogue for non-player characters and scripts for voice actors
• Automate rigging and animation of 3D characters
• Produce textures and visual assets
• Simulate combat and gameplay scenarios
• Identify anomalies in builds during testing and notify developers about them

Player experience personalization

AI systems adapt game mechanics and content in real time based on gamers’ actions to sustain their engagement. Here is how AI models can personalize gameplay:

• Monitor player progress and determine when challenges, rewards, or story events should appear
• Provide recommendations based on the context, such as suggesting relevant items, to help players improve their performance and progress through levels
• Modify enemy behavior to maintain challenge levels

Game development & business insights

Game studios utilize AI to process vast amounts of data from live games, transforming it into actionable insights that inform game design, support monetization planning, and enhance areas such as marketing and fraud detection. Modern AI-powered analytics tools can:

• Segment the audience according to individuals’ behavior and preferences
• Predict churn risks by analyzing patterns in user engagement
• Forecast how players will respond to pricing changes or new game features
• Monitor transactions and player behavior to detect fraud, account takeovers, and potentially harmful interaction patterns, such as grooming

AI in video games: concerns & mitigation strategies

AI adoption in video game development is gaining momentum, yet studios encounter challenges such as high infrastructure costs, complex integration with existing toolchains, and concerns among both employees and players. Addressing these issues is essential for developers, designers, and publishers to keep projects on schedule, manage budgets, and preserve player trust.

High costs

Adopting AI in gaming entails significant expenses, including hardware investments, commercial software licensing, and continuous model maintenance. Additional costs arise from hiring experts capable of developing AI models and embedding them into core game mechanics.

Solutions:

• To reduce upfront infrastructure investment, use cloud AI services with consumption-based pricing
• Adopt small language models (SLMs) as an alternative to LLMs, as the former require less memory and processing power, making them ideal for resource-constrained environments, such as mobile apps
• Leverage open-source frameworks such as TensorFlow and PyTorch for developing ML models to minimize recurring licensing costs

Implementation complexity

Implementing AI in games can be difficult because the process often involves setting up specialized infrastructure, adapting workflows, and configuring and training the model to align its outputs with game logic. Additionally, integrating the model can be challenging as studios may rely on game engines and in-house tools that weren’t originally designed to support AI features, so they often lack native connectors and APIs to link modern AI functionality, models, and software.

Solutions:

• Choose pre-trained AI models that natively support gaming use cases instead of building the model from scratch
• Use middleware platforms that connect AI systems with your game engines, asset creation tools, animation software, testing systems, databases, and analytics tools, so neither models nor legacy systems need extensive code rewrites
• When creating the model from the ground up, employ AI development SDKs that provide tools such as debuggers, compilers, and libraries to simplify building or customizing models

Dependence on high-quality data

AI systems rely on the quality of the data they are trained on and operate with. Even the most advanced models can’t perform well if the data is inaccurate, incomplete, or biased, resulting in unreliable predictions and inconsistent gaming experiences.

Solutions:

• Choose data sources that are representative, reliable, and relevant to the game’s logic
• Implement a clear data management strategy that involves validating and cleaning incoming data and standardizing data formats to maintain model accuracy over time
• Continuously monitor data quality metrics to detect data drift and retrain models on new, appropriate datasets

Workforce resistance

Developers and artists can resist AI adoption due to concerns about job displacement and a loss of creative control.

Solutions:

• Involve developers and artists in defining how and where AI should be introduced to ensure it supports their workflows rather than replacing people
• Provide internal training and reskilling programs so staff can expand their expertise and confidently use AI-powered tools, making them less vulnerable to job displacement
• Introduce company policies that guarantee employees will retain their jobs after AI adoption

Mixed player perception

While some players welcome the use of AI in game worlds due to the personalization and game dynamics it offers, others remain skeptical about AI, criticizing it for producing low-quality visuals and generic dialogues, as well as fearing that AI systems will use their gameplay data to make a game harder.

Solutions:

• Set thresholds and quadrails within the game’s sensitive areas, like reward and progression systems, to make sure the AI operates in line with established boundaries
• Incorporate reporting and feedback tools into games so players can flag issues with the AI-generated experience
• Establish transparent data usage policies and obtain explicit player consent for collecting and utilizing data in AI algorithms, clearly communicating to players what data is being processed and why

Noteworthy real-world examples of AI in video games

Companies across the gaming industry use AI to handle different tasks. For example, AI powers procedural generation in Minecraft, pathfinding in Starcraft, and adaptive NPC systems in Skyrim, making it easier for developers to create game elements and the entire worlds that engage players. Below are more examples of both large publishers and smaller studios implementing the technology to create and manage virtual worlds and playthrough experiences.

Ubisoft

Ubisoft employs artificial intelligence across narrative design, production, and testing. At their Paris studio, writers utilize the NEO NPC prototype. This generative AI tool leverages Nvidia’s Audio2Face and Inworld’s large language models to generate character dialogue based on the backstory, personality, and style defined by the writers. The system improvises responses, while writers review and refine them to ensure consistency with the intended character.

Ubisoft’s La Forge team has also developed Ghostwriter, an in-house tool that drafts short NPC lines known as “barks”. Beyond writing, Ubisoft utilizes AI to test builds, predict and detect code bugs, and automate game balance checks, thereby performing repetitive tasks and enabling teams to focus on creative design.

Roblox

In late 2024, Roblox introduced Sentinel, an open-source AI system for content moderation that identifies subtle interaction patterns that can indicate child endangerment. Sentinel proactively flags cases before explicit abuse is reported.

Alongside safety, Roblox invests heavily in AI-driven game content creation tools. For example, their Cube 3D foundation model, trained on 3D data, generates objects and environments based on text prompts. Integrated into the Roblox AI solution, this capability allows developers to quickly prototype game assets and spaces.

For developers, Roblox provides complementary tools, such as Avatar Auto Setup for automating 3D character setup and animation and Texture Generator, which creates textures from text prompts. Besides these solutions, the company introduced Code Assist, a coding suggestion tool, and automatic chat translations, allowing players speaking different languages to interact seamlessly.

King

King’s designers rely on AI to draft and refine puzzle boards, accelerating game design and ensuring the player experience remains engaging without overwhelming users. At the same time, they also utilize AI-driven bots to simulate personas with various playing styles, testing and optimizing levels based on gamers’ needs. Looking ahead, the company plans to use AI to create more personalized and adaptive gameplay, automatically adjusting puzzle complexity based on player behavior.

Jam & Tea Studios

Jam & Tea Studios’ debut game relies on generative AI to support unpredictable interactions between players and NPCs. Instead of choosing from preset options, players can type their own responses or prompt the system to suggest dialogue that can be tailored to their preferences. In early playtests, NPCs even improvised unscripted activities, such as organizing a game of hide-and-seek.

While Jam & Tea uses AI to produce diverse dialogues in real-time, the company emphasizes that human input remains essential to designing characters, backstories, and tone, ensuring interactions feel meaningful. The studio is investing in additional projects to further test the capabilities of AI-driven game mechanics.

Conclusion

Game developers worldwide, from New York to London, are increasingly adopting AI to streamline procedural content generation, game testing, and data analytics, while also enabling more personalized experiences in both single-player and multiplayer settings. According to a recent survey, 41% of respondents now use AI for automated playtesting. Moreover, around 36% of game developers report utilizing generative AI tools this year.

AI is shaping the future of gaming by helping studios reduce repetitive work, manage development complexity, and improve player engagement. If you are assessing the benefits and risks of AI for your company, our team at Iflexion offers advisory and hands-on AI implementation support to ensure that the AI solution helps you reach your business goals and allows you to improve game designers’ efficiency and overall player experience.