Where Do We Actually Start

A Practical Introduction to AI Strategy

ℹ️ “I heard we’re building an AI team. What exactly are we supposed to do?”

“CEO told us to try something with AI, but no one explained where to begin.”

“We’re launching an internal AI lab. Isn’t that team handling everything for us?”

“I heard the deep learning group published a paper. It sounds impressive, but what does that mean for our game?”

“Our designers are using Stable Diffusion, but a completely different tool is used when it comes to making videos”

“Everyone keeps mentioning Agent AI, MCP, AGI… but what do these terms actually mean?”

These are the kinds of comments I frequently hear from game business stakeholders, especially game business planners, project managers, and operations leads.

Today, everyone is talking about AI. However, for many of us, it still feels vague.

What are we really expected to do?

How should we even begin to understand all this in the context of our actual work?

ℹ️ Different Names, Same Questions

Game companies in Korea began actively adopting AI technologies as early as 2011. NCSOFT established a dedicated “AI Division”, Krafton formed a “Deep Learning Division”, and in 2017, Nexon launched “Intelligence Labs” to research and implement machine learning and deep learning in its game systems.

Since then, various teams have emerged with different titles—AI Lab, AIML Team, Deep Learning Unit, and Generative AI Task Force, to name a few. These names vary across companies and projects, but from a business or operations perspective, it can often be difficult to understand what these teams do or how to work with them in practice.

To help bridge that gap, I’ve put together a simplified reference outlining what these teams typically focus on, along with the kinds of practical questions you can bring to the table when working with them:

What matters most isn’t the team’s name. What truly matters is its role.

At the core, every AI-related initiative starts with the same question:

“How can we use AI effectively in our company and in our games?”

ℹ️ AI, Machine Learning, Deep Learning, Generative AI… What’s the Difference?

For many people working in game business and operations, AI terminology can still feel unfamiliar or even overwhelming. To make it easier, I’ve organized the core definitions along with relevant in-game applications.

If it starts to feel more complex as you read, that’s completely fine. You don’t need to understand everything. In fact, just remembering the key characteristics of a few widely used models is often enough:

AI (Artificial Intelligence) is the broad concept that refers to machines performing tasks that typically require human intelligence. ML (Machine Learning) refers to systems that analyze data and make predictions based on patterns. Deep Learning/DL, a specialized branch of machine learning, is used to detect deeper and more complex patterns, often in areas like speech, images, or gameplay. Generative AI is designed to create new content such as text, images, and sound, based on patterns learned from existing data.

ℹ️ Common but Confusing AI Terms

You may frequently hear these terms during meetings. They often sound familiar in context but become difficult to explain when someone asks you to define them. That’s why I’ve put together a simplified explanation tailored to those working in game planning, publishing, or live service operations:

The most important thing to remember is that each of these AI models serves a different purpose. Not all AI is created equal, and not all models will suit the same tasks.

The real question isn’t which AI is best, its which AI is right for your game, your workflow, and your goals.

ℹ️ “I’m Not a Developer. Do I Really Need to Know This?”

This is one of the most common questions I get when talking to professionals in game business or publishing.

“I’m not a developer or a designer—do I really need to understand AI?” or “Isn’t this something for engineers or game planners to handle?”

Here’s the reality. As of 2024, one in four adults in Korea have used generative AI. Of those users, over 70% applied it to work-related tasks or everyday research. (Source: Korea Communications Commission / KISDI)

In other words, AI is no longer just for technical teams. It’s becoming a tool that everyone uses—just like email or spreadsheets once did.

✅ What You Need Isn’t Technical Knowledge — It’s Strategic Thinking

If you work in game operations or business strategy, your value doesn’t come from knowing how to build AI models. It comes from knowing how to ask the right questions. Ask yourself:

What routine tasks in our workflow could be handled more efficiently by AI?

Which parts of our service can be automated to improve the player’s experience?

Is there a way to use AI to identify and address issues before they affect the gameplay experience?

These are not technical questions. These are strategic prompts that help define your team’s AI adoption roadmap.

In the next post, we’ll move beyond terminology and look at practical examples. We’ll explore how game teams are already using AI—not to chase trends, but to solve real operational challenges.

AI for games is not just another tool. It represents a shift in how we approach and solve problems more efficiently.

※ Disclaimer: This content reflects the author’s personal views and includes only publicly available examples. It does not represent the official position of any company mentioned