Launch Prep with AI

Automating Complexity, Reducing Risk

For a game business team, few moments are as tense as the days leading up to launch.

“Will the servers hold?”

“Will players churn?”

“How will we communicate if something goes wrong?”

These aren’t idle worries. Many games suffer major setbacks immediately after launch, such as unexpected server outages, early churn spikes, or slow incident responses.

But things are no longer the same. With AI and ML, repetitive and predictable operational tasks can be automated in advance, allowing business teams to focus on strategic decisions.

This guide introduces three critical workflows that can be automated before launch — explaining what can be automated, how it works, and which companies are already doing it.

ℹ️ What If AI Could Stress-Test Your Servers?

– Simulation Automation

One of the greatest fears at launch is the flood of players on day one. Will the servers hold? Will players disconnect in certain areas?

In the past, publishers relied on dozens of QA testers, hundreds of devices, or even company-wide test notices to gather perhaps a few hundred concurrent users. This was time-consuming and insufficient for simulating the millions of players that major launches attract.

Because large-scale concurrency testing is essential, publishers have long run stress tests prior to launch. One notable case was Riot Games’ global release of 《VALORANT》 in June 2020.

Ahead of launch, Riot developed a dedicated tool called ‘Harness’ to test server stability under millions of concurrent connections. Harness generated “virtual users” that could automatically log in, form parties, customize skins, enter queues, and use the shop.

Using AWS server infrastructure, Riot deployed Harness across hundreds of containers, optimizing it to simulate more than 10,000 users on a single test server. They scaled this to recreate a 2 million concurrent user load using a ‘mock server’ that replicated logins and match flow without running live gameplay.

Riot also built real-time monitoring and alert systems to detect overloads or failures and designed distributed data storage to prevent single points of failure. These preparations gave Riot confidence it could handle 2 million concurrent connections on launch day — and indeed, the launch was executed without major issues. (Source: Scalability and Load Testing for VALORANT, Riot Games, 2020)

Today, AI takes the role of these virtual users. By learning from real player behavior — such as where players move, when they click, and when they make purchases — AI creates thousands of simulated agents that log in and play simultaneously.

The core technology is Reinforcement Learning (RL). In RL, the AI learns optimal behavior by receiving rewards, just like a real player who’ earns rewards’ for completing a quest. In this context, a “reward” refers to a virtual scoring system that guides AI to realistically mimic player behavior or faithfully reproduce target scenarios. In other words, it tells the AI, “If you act this way, you’re doing it right.”

For example, in large-scale concurrent user simulations, reward structures are designed to encourage the AI to behave as actual players would. A few examples of reward design might include:

Reward structures should adapt to each genre, as players value different actions across games

Since players value different actions depending on the game genre, reward structures must be designed differently for each game

These AI-driven users don’t just connect; they walk, fight, visit shops, and even make purchases. This enables teams to detect bottlenecks or unexpected delays before launch. Many studios now adopt AI simulation models as a standard part of launch preparation.

AI agents simulate players, detecting server bottlenecks and errors before launch for smoother ops

ℹ️ What If You Could Predict Churn or Purchases Before They Happen? - Behavioral Prediction Automation

The questions don’t stop even after launch. Ahead of a content update or a new subscription package, business teams often ask the same questions:

“Will players actually like this?”

“Will the update drive them away?”

To answer these, AI now analyzes past player behavior to predict how users will respond in the future.

The key is to train AI with labeled data that connects past actions to their outcomes, such as whether a player churned or made a purchase. This approach is called Supervised Learning.

For example:

“Player saw a subscription pop-up → purchased within 3 days”

“Player completed the tutorial → churned within 1 day”

By learning from thousands of these action–outcome records, AI discovers patterns that can predict future behavior

In addition, a model frequently used today is LSTM (Long Short-Term Memory), a time-series based AI. LSTM is highly effective at capturing changes in player behavior over time and is especially valuable for churn prediction.

Once prediction is possible, operations change fundamentally:

Retention rewards can be targeted only at players at high risk of churn

Purchase prompts can be triggered at the exact moment a player is most likely to spend

This approach is already being applied by leading Korean game companies. For example, Kakao Games built a machine learning–based Lifetime Value (LTV) prediction system in partnership with AWS to support the long-term operations of its MMORPG 《Odin》. The system predicts each player’s LTV and, when a decline trend is detected for a particular user segment, it identifies them as at risk of churn and delivers tailored retention promotions.

《Odin》 generates roughly 300GB of log data per day, covering logins, leveling, purchases, community participation, and player profiles. Kakao Games processes this data automatically with AWS Glue, then uses Amazon SageMaker Pipelines to handle training, performance comparison, and model registration. Predicted LTV values are produced regularly via SageMaker Batch Transform, with models retrained periodically to improve accuracy. These predictions are directly integrated into operational strategy.

The entire workflow was built on AWS CDK (Cloud Development Kit), allowing easy reuse for future games. By combining a fully managed ML platform with an automated data pipeline, Kakao Games established a reliable AI prediction system suited for live operations. As a result, the system became a critical decision-making tool, enabling the operations team to fine-tune player engagement, reduce churn, and time strategic actions more effectively.

(Source: How Kakao Games automates lifetime value prediction from game data using Amazon SageMaker and AWS Glue, AWS Tech Blog, 2023)

ℹ️ When Problems Arise, AI Detects and Summarizes First

- Automated Anomaly Detection

One of the most frustrating situations right after launch is when players are experiencing issues, but the internal team remains unaware. For example, payment failure rates may suddenly spike in a specific region, or response latency may increase sharply without anyone noticing.

AI can detect such “abnormal states” quickly using Unsupervised Learning techniques. Unlike supervised learning, unsupervised learning does not require labeled data. Instead, AI learns normal patterns on its own and flags values that deviate as anomalies.

For instance:

If the average payment success rate is normally 98% but suddenly drops to 72% in the morning

Or if response times surge from 100ms to 500ms

AI automatically recognizes these as anomalies and alerts operators in real time.

Beyond detection, AI can now analyze logs and generate summary reports. This is where LLM-based generative AI models come into play.

For example, the system may produce a report such as:

“July 15, 10:00 AM – Database response delays detected in the Seoul region. API failure rate increased by 27%. Root cause: Connection pool overload. Recommended action: Expand RDS resources.”

With this, operators can respond quickly without having to manually review raw logs.

ℹ️ What If AI Could Write Patch Notes and Incident Responses?

– Automating Player-Facing Communication

Among the many repetitive tasks game operations teams handle, few consume as much time as drafting patch notes and incident response guides. Reading through Git commits or Jira issues and rewriting them into user-friendly updates takes hours and often leads to mistakes.

Generative AI can now take on this task. Large Language Models (LLMs) can scan thousands of development records and summarize them into clear, player-friendly notes. For example:

“Improved chat filter functionality”

“Fixed event duration error”

“New map added: Snow Canyon”

The output is automatically formatted into release notes, ready for distribution.

The same applies when service disruptions occur. By combining RAG (Retrieval-Augmented Generation) with generative AI, the system can reference internal manuals, real-time logs, and past incident histories to guide operators step-by-step. For instance:

“Step 1: Restart server → Step 2: Check DB connections → Step 3: Post user notification”

These transforms troubleshooting into an interactive guide or chatbot experience.

Many game companies are already experimenting with AI/ML technologies in anomaly detection and incident management. The focus is shifting from reactive troubleshooting to proactive early detection and fast response.

In 2024 and 2025, Nexon partnered with AWS on two experimental projects aimed at automating game operations.

In 2024, Nexon developed an “operations chatbot” powered by generative AI. Operators could simply ask, “How’s my server doing right now?” and instantly receive a unified view of multiple system statuses. Built on Amazon Bedrock, the chatbot integrated various back-office tools, enabling faster incident response.

The test results showed a 4x faster response time and about 40% reduction in operational costs. (*Source: “Taming LLM Agents,” NEXON, AWS for Games AI Roadshow, 2024)

In 2025, Nexon advanced further with an ML-based anomaly detection system. Instead of relying on operators to monitor dashboards, the system automatically analyzed server data and explained anomalies in plain language. For example:

“Combat server CPU usage has spiked, accompanied by increased disk activity.”

While effective, applying this in real-time to every game proved heavy and complex. Nexon adjusted its approach, transforming the system into an “early warning” platform. The models were tuned to detect both sudden spikes (e.g., login failures) and gradual declines (e.g., steady DAU drop).

The system also monitored external signals such as sudden increases in community complaints like “I can’t log in,” combining them into a unified anomaly report.

This new approach reduced false positives, improved alert speed, and lowered operator workload. Most importantly, it gave operators a single dashboard to monitor, detect, and act on incidents in real time—making AI not just a tool but a true extension of the operations team. (*Source: “Automating Monitoring with ML,” NEXON, AWS for Games AI Roadshow, 2025).

AI is evolving beyond a technical tool, laying the foundation to serve as the eyes and hands of the operations team.

♻️ Final Thoughts: AI Turns Complexity into Strategy

From summarizing patch notes to detecting anomalies and guiding incident responses, AI reduces repetitive tasks and mitigates operational risk. All of these technologies can be applied with data you already have, and they evolve from reducing manual work to enabling more strategic decision-making.

AI is a dependable partner that simplifies complexity and transforms repetition into strategy.

With launch preparations complete, now is the time to explore how AI and ML can power smarter live operations.