Operating Games with AI

Transforming Game Operation

From the moment a game officially launches, one question lingers in the minds of every game business team:

“Is our game running smoothly?”

After months or even years of planning, development, and marketing, this is the ‘start point’ where the game finally meets its players. Yet far from being the end of the journey, it is in fact the beginning of live service. And it is also the most fragile stage. Surges in traffic, bugs, unstable servers, unexpected player churn — problems spread quickly, and if the response lags even slightly, players turn away just as fast.

That is why the industry no longer waits to react.

Today, AI detects first, responds first, and enables real-time automation strategies that keep services stable.

In this article, I will share five real-world examples of how AI is being used to enhance the quality and reliability of game operations.

ℹ️ Testing in Advance, and Alerting Instantly When Failures Occur

Ahead of the launch of 《VALORANT》, Riot Games faced a daunting question:

“If millions log in at once, will the servers hold?”

Since no human team could possibly simulate such scale, they turned to AI. Trained on historical player patterns — logins, movement, combat, churn — AI agents automatically recreated scenarios of millions of players playing simultaneously, all within the cloud. (*Source: “Game Launches Prepared with AI”)

This simulation identified database bottlenecks before launch, allowing the team to optimize caching policies and regional distribution strategies. As a result, the game secured stability on day one.

Even after launch, AI continuously analyzes logs in real time. Whenever an incident arises, the system automatically guides operators by pinpointing “what is happening right now and which issue should be addressed first.” From decisions such as whether to restart a server, restrict a feature, or verify database connections, AI provides step-by- step guidance. The incident response time has been cut by more than half.

In this sense, AI has evolved from being a passive monitoring tool into an active “operations assistant” — interpreting live situations and proposing concrete actions.

ℹ️ Detecting Issues Before They Escalate, and Delivering Ready-Made Reports

Since 2024, Nexon has begun full-scale automation of game operations using AI, in partnership with AWS. The focus was on systems that could be directly applied to the daily work of live operations teams.

System presents intuitive traffic light signals per dashboard: green=normal, yellow=warning...

Its core workflow is as follows: Source: “Game Launches Prepared with AI”)

AI continuously learns from server and player metrics

Detects anomalies in real time (e.g., sudden traffic spikes on a server, rising churn among a user segment)

Analyzes root causes automatically and shares findings with the operations team

Generates regular reports automatically to support managerial decision-making

In the past, when abnormalities occurred, someone had to manually check logs, compile the findings into spreadsheets, and draft a report. Now, the entire process is completed automatically within one or two minutes.

The system also detects unusual player behavior or early signs of issue escalation in the community, alerting the team before problems spiral.

The results have been striking. Operational response speed improved more than fourfold, and annual operating costs dropped by over 40 percent.

For practitioners, the importance of this case is clear: AI is not just processing numbers. It acts as a collaborator — reading, interpreting, and summarizing operational data, then recommending actions.

ℹ️ What If AI and Robots Could Handle More Than Half of Customer Support?

Global game operations company PTW introduced an automated customer support platform called REACT.

At its core lies the combination of AI and RPA (Robotic Process Automation)

When a player submits a ticket, the AI chatbot classifies the issue, evaluates urgency, and either responds automatically or hands the task to RPA, which connects with internal systems to complete actions. Examples include account resets, compensation delivery, and in-game function checks — all handled without human intervention.

The system runs 24/7, supports 20 languages, and processes global customer requests on a scale.

Before REACT, only 10 percent of inquiries were automated. Today, 42 percent are resolved through AI and RPA.

Most notably, the average response time for urgent requests dropped from 22 hours to just 6.5 hours.

By automating repetitive tasks, live operations teams no longer spend valuable time on low-level support. Instead, they can focus on critical issues and strategy. In short, automation reshapes customer service into an efficient structure where people spend less time “handling tickets” and more time designing operations.

ℹ️ Delivering Adaptive Difficulty With the “AI Director”

At the heart of Valve’s 《Left 4 Dead》 series is immersion. But if a game is too easy, players get bored. Too difficult, and frustration sets in.

To solve this, Valve introduced the groundbreaking ‘AI Director,’ a real-time difficulty adjustment system.

This AI monitors a player’s skill, current health, weapons, and recent combat outcomes, then dynamically adjusts the experience. Enemy spawn locations, numbers, attack patterns, reward placement, and event pacing are all recalibrated in real time.

The result is that players feel “the game is reacting to me and my situation.”

Average playtime increased by 2.1x, and four-week retention jumped by 19 percentage points.

This system is more than static difficulty presets. It is a quality management strategy, automatically tailoring gameplay progression to each player’s condition, and delivering a far more engaging experience.

ℹ️ Talking to NPCs Like They Were Real People

NVIDIA’s ACE (Automated Character Engagement) technology introduces real-time generative AI into player–NPC interactions.

Traditionally, NPCs recycled pre-scripted dialogue. But with ACE, NPCs interpret a player’s intent and in-game context, then generate context-aware, emotionally nuanced responses on the spot.

For example, if a player asks, “Why should I take this quest?” the NPC can reply in context: “The village is in danger. We need your help.”

Conversations become fluid, personal, and immersive.

The impact has been measurable. NPC interaction time increased 2.3x, while acceptance rates for dialogue-driven quests rose 1.8x.

Through ACE, AI moves beyond static content creation. It understands context in real time and reacts meaningfully, raising both game quality and player satisfaction.

ℹ️ Yet… Human Judgment Still Matters

With all these cases, it might seem as though AI can automate everything. In reality, balance is key: automation by AI plus control by human judgment.

Nexon’s anomaly detection AI, for instance, issues alerts but leaves final decisions to operators. Riot’s incident response AI provides recommendation lists but never executes server actions directly. Even generative NPC AI operates strictly within frameworks such as lore consistency and dialogue guidelines.

AI reduces repetition and accelerates interpretation, but the final call rests with people.

♻️ Final Thoughts

Game quality is no longer safeguarded solely by the hands of operators. AI now plays a central role, detecting, interpreting, and recommending actions in real time to maintain stability and reliability.

Far from being just another technical tool, AI has become a practical partner in live operations, empowering practitioners to focus on judgment and strategy while automation handles the heavy lifting.

In this series, we have explored how AI enters every stage of the publishing process — from market trend analysis and contract review to game design, QA automation, marketing, launch, and live operations. We’ve examined how AI is applied in practice, with real industry cases.

And yet, one critical question remains:

“We’ve assigned the AI tasks to the experts, but I still can’t fully understand what the AI team is saying.”

This final question is where we will close the series. In the next and concluding article, we will focus on how game business professionals can effectively collaborate with AI teams.

For many, terms like models, training data, accuracy, error rates, or parameters may sound alien. But bridging this gap is what determines whether a game project enhanced by AI succeeds or fails.

The next chapter will lay out the fundamental concepts and practical methods for collaboration — even for those who will never directly build AI.

My hope is that it serves as a realistic guidebook for every game professional wondering what it truly means to “work with AI.”