5 Reasons Why Gaming Studios Are Investing in Team Training as the World Goes Crazy Over AI and Neural Networks

Despite the increasing popularity of AI and the hopes and fears associated with it, investments in training specialists continue. Magdalena Stryevski, the COO, and Elena Ryakkonen, a manager at WN Media Group, explained why this is happening in their column for App2Top.

Artificial intelligence has burst into IT (and consequently, into game development) with full force. Today, Claude writes code, Nano Banana creates art, and AI assistants automate routine tasks.

To date, many companies have not only learned to work with neural networks but also to truly optimize processes with their help. There are numerous examples of this.

For instance, last year, in one of their columns, the studio Game Gears claimed they accelerated the development of games by four times using AI. Moreover, they managed to speed up the creation of 2D and 3D content by 10-30 times.

An equally impressive case was shared by ZiMAD. In an article about working with Stable Diffusion, their artist wrote: "I have a task to prepare 50 icons. Creating them manually could take at least a week. With the neural network, it would likely take no more than two days."

Today, it’s quite challenging to find anyone who doesn’t try to optimize their processes and production to work with AI. Hence the question: why do we see a growing interest from companies in training personnel against this backdrop? For reference, according to a study by the Higher School of Business at the National Research University Higher School of Economics, over 61% of Russian companies plan to increase expenditures on employee training in 2026.

Why spend budgets on training people when an AI subscription only costs $20 a month (and is free if you have Stable Diffusion on your local device)?

This question is the focus of this material.

1. Training is cheaper than fixing mistakes

After waves of optimizations over the past few years, studios work with smaller teams but with the same or greater volumes of tasks. Every layoff and mistake has become costlier — there isn’t an excess of hands or time as a buffer.

For instance, if a company loses a top-tier developer today, the team will need at least a month, or even two to three months, to find a new one (according to the recruitment agency WN Talents). In the wake of a layoff, tasks will also need to be redistributed among the remaining team members, which, of course, can lead to their burnout and even departure.

Add another three months for the new employee to adapt, along with the risk of them leaving in the first weeks (statistics show 20% of employees quit within the first 45 days). All in all, that results in four months of lost profits.

Or consider another example: a team hires a new marketer. There isn’t time to understand the product, platforms, formats, analytics — resulting in several tens of thousands of dollars easily wasted just during adaptation.

Or take a real case from WN Academy. It’s exceedingly illustrative.

Young studio owners approached us. They had business experience but not in the gaming industry. They wanted to establish processes and achieve specific results in game development. We offered them a program that included topics such as market analysis, pipeline creation, and team role distribution. In short, it provided the basics, adjusted to their situation. We proposed a fully outlined business roadmap. They declined the program, believing that their previous experience would suffice to navigate the market.

Four months later, they returned. It turned out that due to a lack of expertise, the company lost much more than they would have spent on training.

The conclusion: training isn’t an expense; it’s insurance against costly mishaps. In an era of small teams, a single serious mistake can cost more than a year of training for an entire department.

2. AI doesn’t replace an employee; it multiplies their capabilities

ChatGPT can indeed draft a system architecture in five minutes. The real question is: who on your team will notice that this architecture doesn’t account for client-server state synchronization? Who will see that the proposed solution falls apart with a thousand simultaneous players?

Midjourney can create 50 character sketches in an hour. But who will decide which of them suits the target audience? Who will check if the silhouette reads well on a mobile screen? Who will understand that resolving these issues before production is mandatory?

Previously, a game designer had to create mechanics. Now, AI can suggest 100 options in a minute — but it is the game designer who must determine which will work for your audience and how they will affect retention and monetization.

The problem isn’t that AI performs poorly; it’s that it only works as well as the knowledge of the team interacting with it.

AI is an amplifier. It makes a strong developer faster and more efficient. But it turns a mediocre specialist into… a mediocre specialist with ChatGPT, quickly completing tasks that someone else will have to spend time correcting later.

This is why companies invest in training fundamental skills: architecture, optimization, balance, monetization, and art production. These skills determine whether an AI-generated draft becomes a working product.

3. Accelerating adaptation to new changes

The gaming industry is changing at a breakneck pace. What worked a year ago is already outdated today. For example, in July 2026, Google Play Games Level Up is set to become active, and in Europe, investigations regarding the potential cancellation of ATT are in full swing. Both could change the rules for platform operations.

Constantly monitoring new information, extracting the essentials, and implementing only what is necessary are skills that need to be taught. Otherwise, people feel overwhelmed, a sense of "I'm not keeping up" drains energy, and productivity slows, leading to more errors.

Often in WN Academy training programs, we conduct "overview" sessions. An industry expert gathers all the current trends in one place and analyzes them. They explain the real application cases and what isn’t worth the time. Most importantly, they demonstrate how to apply the trend to the current tasks of a specific project. It provides stability, confidence, a new to-do list in the tracker, focus, and a framework that helps employees understand how to handle the constant flow of new information in the future.

Training participants note that they no longer feel constant anxiety about missing something, nor do they procrastinate because they clearly understand what to do and what to focus on. It goes without saying that the increase in task completion speed, engagement level, and fewer mistakes is something we frequently hear in feedback about training some time after it.

4. Addressing the "bottleneck" problem

A "bottleneck" is a person on whom the entire team's work depends. Without them, the process halts or significantly slows down.

Why do such individuals emerge:

• It's historically developed — the person has been with the company the longest, built the system from scratch, and knows all the nuances and workarounds.
• They are genuinely the strongest specialist — simply more knowledgeable about the topic, naturally attracting tasks.
• There’s no time to teach others — there's always a rush, always deadlines, "we'll teach later" turns into "we’ll never teach."
• They have specific knowledge — worked with a particular publisher for three years, knows all their quirks and requirements. Or they're the only one who understands how monetization is set up specifically in your game.
• There’s an unspoken agreement — "this is Vasya's area; don't go there."

Such an individual works and everything seems fine. Until they fall ill, go on vacation, or suddenly resign.

The company loses not only years of expertise but also faces downtime. The team scrambles to learn the topic this person was handling. They fumble, not fast enough, and with mistakes. The project slows, deadlines are missed, and nerves are stretched thin.

Systematic team training allows for proactive measures. When everyone possesses critical competencies, at least at a basic level, the team becomes interchangeable. If someone leaves, others can step in. If someone is on vacation, the work continues unimpeded.

This doesn’t mean everyone must have the same in-depth knowledge. But critical knowledge should be distributed among at least two or three people to prevent the departure of one from turning into a catastrophe.

5. Increasing key project metrics

Training the team directly impacts metrics that determine a game's success and the studio's income:

1. Retention: A team that recently completed training and reviewed their project with an experienced expert identifies weaknesses and growth points, better understanding why players leave and how to prevent it. The result is more precise gaming mechanics adjustments, informed A/B testing, and clear steps to boost retention.
2. Monetization: A team member identifies where exactly conversion is lost: on the initial purchase, subsequent ones, or subscriptions, and knows how to address it, optimizing offers to increase ARPU.
3. Speed to market: A team familiar with modern development pipelines and adept with new tools (including AI) spends less time on recurring tasks. This means the studio releases updates faster, responds more rapidly to player feedback, and tests hypotheses more swiftly.
4. LTV of a player: Team members who delved into game economy and metrics during training have a toolbox to ensure players remain engaged for months, generating regular income rather than one-time payments.

According to WN Academy, training a single employee costs around 50,000 to 200,000 rubles.

If training a producer for 150,000 rubles increases ARPU by 15%, it pays off in the first month after implementing monetization changes.

Training isn’t abstract "people development." It specifically influences metrics, development speed, and team stability. Each of these indicators directly impacts the studio's revenue.

Conclusion

In the gaming industry, it’s not the biggest studios winning now, but the fastest — those who can adapt to market changes and work faster than competitors.

AI has provided a significant boost, but its effectiveness fully hinges on the team's competencies. AI covers 80% of the work, but the remaining 20% is what differentiates a functioning product from a beautiful but non-functional result.

However, it’s important to understand: not all training is created equal. Standard business programs or generic IT courses do not address game development needs. They don’t grasp the specifics.

Seeing how many studios face this issue, at WN Academy, we build programs around the real tasks of the company that contacts us. We take into account all the nuances of game development. We invite industry veterans as instructors — specialists who continue to develop games and view the subject through the lens of their experience and expertise. It’s both consulting and training. But the price is for the latter.

The program format involves minimal theory and focuses on solving specific team tasks. For example, within one of the programs, participants go through the full cycle of creating a game event — from concept to economy balancing — in six sessions. Essentially, participants work on a real event, apply tools in practice, experiment, and post-program can launch a ready event into production.

In an era where AI changes the game rules, markets realign, the old no longer works, and the new is still unclear, investing in training is no longer optional. It’s the way to remain competitive when adaptation speed counts more than the studio's size.

Learn more about WN Academy programs at this link.