When it comes to games and apps, traditional analytics are facing a major challenge. AI’s presence is growing and enlarging the entire BI and analytics industry. By 2033, it’s expected to grow from $27B in 2023 to $60B, demonstrating an impressive 10% growth YOY.
While traditional analytics tools are fighting for their place, AI-based solutions are taking over. With nearly half (49%) of developers claiming that Generative AI tools are being used in their workplace, it’s impossible to avoid these emerging technologies.
Yes, standard analytics tools can segment broad user groups and track predefined flows. But these platforms can’t keep up with the rapid evolution of personalized gaming and undefined user flows. AI agents can generate millions of unique gameplay permutations. As a result, the amount of data being collected will grow exponentially.
So developers may miss out on a much wider overview of user behavior. Unless these tools evolve, they may become obsolete in the AI gaming era. Let’s dive deeper.
The Rise of AI-Driven Personalization in Games
AI in game development has already revolutionized personalized user experiences. It enables games to adapt to each player’s preferences, generating unique experiences. AI agents can adjust various aspects of a game in real time.
This goes beyond just gameplay adjustments. Imagine a game tailored specifically for you. Every element feels uniquely designed to match your preferences, playstyle, and skills.
This could also impact the game’s storylines and in-game mechanics. And the possibilities that can sprout from these game aspects are potentially limitless.
AI-Driven Personalization in Games: An Example
Look at it this way: You and your friend are playing the same game. But your version features unique levels, characters, and challenges. And your friend’s version has completely different ones.
Let’s go one step further:
- In your version of the game, you’re fighting a big boss. The arena is designed to test your unique strengths and weaknesses.
- The game understands that you’re skilled at jumping to reach strategic platforms. So it adds more elevated points for your character to reach.
- Meanwhile, your friend is better at using the game’s weapons. So in their version, the game adds goons with more sophisticated weapons to assist the final boss.
Sure, this level of personalization is a game-changer for player engagement. But it also makes matters more complex than ever before.
It raises the question: How can traditional analytics tools keep up with these ever-changing, personalized technologies? More specifically, how can they track unpredicted events and deal with the potential exponential growth of data being collected as a result of this?
Why Traditional Analytics Won’t Cut It 2 Years From Now
The way I see it, it’s just a matter of time before AI completely phases out traditional analytics tools. Three notable reasons come to mind.
1 – They’re Not Detailed Enough
Most game analytics solutions segment users based on broad population groups. But personalizing games requires the tracking of millions of unique user-based flows.
In an AI-driven gaming landscape, tools need to monitor each player’s unique world. Each can be full of personalized characters, storylines, and dynamics.
Without these capabilities, developers don’t get a holistic overview of individual gameplay. Therefore, they can’t draw actionable insights from it.
2 – They’re Not Flexible Enough
Custom events and predefined flows are the backbone of traditional analytics platforms. The more prevalent AI-driven personalization becomes, the less prepared these tools will be. They need to work alongside dynamic AI agents that behave organically and spontaneously.
3 – They’re Not Up to Date
Yesterday’s analytics tools weren’t built to be particularly dynamic. Personalized games are exactly that, and are inherently unpredictable. Therefore, they require tools that can adapt to changing user behavior in real time.
To meet the new demands of this AI era, developers and analysts need next-gen AI analytics tools. This will help them interpret dynamic, personalized gameplay environments.
4 – A Technical Gap in Handling AI-Driven Data
The current database technologies out there can potentially store all the data that AI-personalized games generate. However, it will be practically impossible to extract within a reasonable amount of time and make even basic data aggregations or select queries.
And what about charts or data visualization? Do standard histograms or pie charts fit the variance and player distribution? The short answer is no.
Examples of Companies Innovating Game Personalization with AI
In today’s app analytics climate, you either seek to innovate or you stagnate. Thankfully, more and more companies are working on player-personalized AI agents. These agents aim to create tailored user experiences with their apps and games.
The result? Unlimited game flow permutations and options within a single game. The following companies are at the forefront of these innovations.
Company | Specialty | Use Cases |
Artificial Agency | Integrates generative decision-making into game mechanics, creating individualized, responsive environments. | – Teaching skills with in-game AI tutorials – Directs players to unexplored content |
Promethean AI | Trains custom AI models to accelerate the creation of expansive, personalized game worlds. | Generates environments in real-time based on player/developer input |
Unity ML-Agents | Turns games into environments for training intelligent, adaptive agents in Unity. | – Evaluating game design decisions – Controlling NPC behavior – Automated testing |
Nvidia (ACE) | Uses AI to create “digital humans.” | Real-time NPC interactions |
Jam & Tea Studios | Generates AI-driven, dynamic NPCs. | Created the game Retail Mage, in which AI fuels NPC interactions, dialogue, content, and mechanics |
Modl.AI | Creates realistic, adaptive NPCs. | – Automated game testing – Gains insights into player – NPC interactions |
Inworld AI | Produces photorealistic characters according to user’s specifications. | – Customizing character appearances – Generates NPC behavior and dialogue |
Ubisoft (Ghostwriter) | Uses AI to write NPC dialogue alongside video game writer. | Dialogue drafting process |
These companies demonstrate the growing need for analytics tools capable of managing complex, AI-driven personalization.
The Future of AI-Driven Analytics
If traditional analytics tools want to stay relevant, they’ll need to open up to change, and fast. I believe there are some significant innovations that these tools need to adapt to.
AI-Driven Actionable Insights
AI gives depth to the playing experience, making it more personalized. And future analytics tools can track and interpret millions of AI-generated permutations. Through these innovations, developers can better understand complex data with actionable insights.
Real-Time Data Retrieval
Underlying database technology should be updated to support quick data querying for large volumes. With fluid, AI-driven gameplay, analytics platforms must track player behavior in real time. Instead of only relying on data analysis in retrospect, developers must track user behavior as it unfolds.
Individual Gameplay Optimization
Previously, analytics tools would use player segmentation to present insights to optimize gameplay. But with AI, there is potential for future tools to zoom in on each player’s journey. This means that developers no longer need to depend on broad trends. Instead, they have the opportunity to optimize experiences for each player.
Embrace Change Today…Or Fall Behind Tomorrow
The rise of personalized AI agents is rendering traditional analytics tools obsolete. To remain competitive, they must adopt individualized profiling, real-time tracking, and AI-driven insights. Otherwise, they’re sure to fail in meeting this new era’s demands.
For analytics companies, you have a choice: Roll with the changes or fade into irrelevance. Whether you like it or not, AI will continue to spearhead game and app analytics. The potential is astronomical, so you might as well embrace it.

I have 20+ years of experience in hi-tech world, started my journey as software developer back in 1997. Down the road I Co-Founded and successfully exited 2 companies in SAAS/B2B domain, now started my new journey with friends @ Keewano. I’m passionate about data, AI and technology.