The real-world AI we envision holds immense promise, offering opportunities for innovation and transformation. However, turning this promise into reality is far from easy. While many companies attempt to adopt AI, only 16% of global manufacturing organizations have achieved their AI-related targets. Bringing AI into the real world requires overcoming significant hurdles before it can truly drive business impact. So, what makes real-world AI so challenging? Here are the key reasons behind these difficulties.

The real world is complex: Far beyond Go’s 10¹⁷⁰ possible moves

Go is known for its immense complexity, with an estimated 10¹⁷⁰ possible outcomes. But the real world is far more complex than a Go board. AlphaGo was trained on high-quality, publicly available games played by world-class players, refining its performance through infinite repetitions. While its capabilities are assured within the confined space of a Go board against a single opponent, the real world introduces countless variables, and accessing all the high-quality data is fundamentally unattainable. 

Take the complexity of a manufacturing floor as an example. Variables like temperature, humidity, noise, equipment wear, and scheduling all interact, influencing machine performance and even the timing of part replacements. Additionally, variations in worker skills and changing conditions add to the dynamic nature of the production environment. This complexity is far beyond what AlphaGo was designed to handle. But what if there were an AI specifically built to tackle such challenges—trained on diverse variables and data in an environment that mirrors the intricacy of an industrial workplace? 

🔗 Connecting industrial sites in real time with AI digital twin 

The real world is unpredictable: Why AI struggles with new data 

Autonomous driving is often regarded as the pinnacle of real-world AI. Yet, despite hundreds of billions of dollars invested over the past decade, full Level 5 automation—where vehicles perform all driving tasks in any condition without human intervention—remains elusive. A major limitation of AI lies in its inability to perform reliably in unforeseen scenarios or when faced with completely new data outside its training. 

Even the best-performing AI models developed in a controlled lab environment can falter when deployed in the unpredictable real world. To ensure consistent performance, AI must be continuously updated with fresh data—a process known as continuous training (CT). However, relying on humans for this process is neither scalable nor practical. Automating continuous training is essential for enabling AI to adapt and thrive in unpredictable real-world environments.

🔗 Real-World AI: Runway, an AI platform enabling automated operational environments 

The challenges of accessing real-world data: ChatGPT vs. a 20-year veteran—Who knows the factory better?  

Who knows the factory better—ChatGPT or a 20-year veteran?

In industrial settings, ChatGPT is comparable to a senior at Stanford University. Imagine placing this highly capable student in a California factory. Could they perform well from day one? Likely not. They'd first need to figure out what to do—learning the names of the equipment, understanding the variables for optimal control, and identifying the right people to seek help from. 

By contrast, a veteran worker with 20 years of experience at the same factory could solve these problems in minutes. Years of hands-on knowledge and domain expertise enable them to quickly identify and solve problems, demonstrating the immense value of on-site experience. 

While ChatGPT can generate human-like text, images, and code, it cannot function effectively in industrial environments without access to industry-specific data. The challenge? Industry data is proprietary and tightly guarded, with companies rarely sharing it externally. Without this critical data, ChatGPT cannot deliver the level of performance required for such settings. This is why experience with industry-specific data is crucial. The better an LLM understands the complexity of industrial processes, their unique requirements, and the data collected from various equipment and facilities, the more effectively it can be fine-tuned. 

🔗 Why large language models are the future of manufacturing 

The real world needs 100%: AI that solves problems, not just provides suggestions 

In the digital world, general-purpose AI has made remarkable strides, from generating content like articles, images, and videos, to providing personalized recommendations. These systems don’t need a high degree of accuracy because their outputs can be partially referenced and adapted. Real-world AI, however, operates under entirely different conditions—it is highly specialized, tackles critical problems, and demands near 100% accuracy. 

For example, if an AI fails to detect a defect during quality inspection, defective products may reach customers, causing dissatisfaction, costly recalls, and damage to the company’s reputation. Errors on production lines can also lead to massive rework and significant financial losses. In the real world, AI must respond instantly to environmental changes. If it doesn’t analyze data in real-time to adjust machine settings like temperature and pressure, the production process may be disrupted, or product quality compromised. High accuracy isn’t optional—it’s essential.

🔗 Real-world AI: AI Use cases that solve problems on the industrial floor 

The real world is complex, uncontrollable, and unpredictable, with data that is difficult to access. It also demands extremely high accuracy. Despite these challenges, MakinaRocks is tackling intricate industrial problems with its compound AI system, bringing real-world AI to life. Our solutions have proven to achieve success rates more than four times higher than the global average for AI adoption. 

If you’re ready to explore how AI can transform your operations, let’s start the conversation. Share your challenges by clicking the banner below, and together, we’ll uncover the best way to bring real-world AI to your business.