Understanding the Evolution and Risks of AI Systems’ Development
October, 2025 | Roman Koles, Practice Architect, Data and AI
Artificial intelligence stands as one of the most transformative technological advancements, enabling machines to perform tasks that traditionally require human intelligence.
At its core, AI involves simulating human cognition in machines programmed to think and learn. This encompasses a broad range of capabilities, from simple problem-solving and decision-making to complex functions like natural language processing (NLP) and visual perception.
Enterprise AI applications span industries such as healthcare, finance, transportation and entertainment. In healthcare, for example, AI algorithms analyse medical images to detect diseases with high accuracy, while in finance, AI-driven analytics predict market trends and manage risks more effectively.
In this article, we’ll explore the history and evolution of AI, as well as common risks and errors that users and enterprises encounter with this rapidly growing technology.
The Evolution From Early AI to Artificial General Intelligence
AI systems’ development began in the 1950s when experts seriously explored the concept of thinking and learning machines. Pioneers like Alan Turing proposed the idea of a “universal machine” capable of performing any computational task.
The 1956 Dartmouth Conference is often cited as AI’s birthplace, where leading scientists discussed creating machines that could simulate human intelligence.
Early researchers focused on symbolic AI, creating algorithms that manipulated symbols to solve problems. Initial successes included programmes playing chess and proving mathematical theorems, demonstrating machines’ potential for intelligent tasks.
The 1970s and 1980s marked the “AI winter,” when funding and interest waned due to unmet expectations and technological limitations. However, the late 20th and early 21st centuries witnessed an AI resurgence, driven by advances in computing power, the availability of large data sets and machine learning breakthroughs.
The introduction of neural networks and deep learning in the 21st century revolutionised AI, enabling machines to learn from data in ways that mimic human cognitive processes.
Today, AI has become integrated into daily life through virtual assistants, recommendation systems, autonomous vehicles and advanced robotics. The ongoing evolution of AI continues to push the boundaries of machine capabilities, promising a future where intelligent systems play even more significant societal roles.
The final logical level of AI enhancement is artificial general intelligence (AGI), which represents the next evolutionary leap in artificial intelligence – systems capable of matching and ultimately surpassing human cognitive abilities across all domains.
AGI would possess human-level reasoning, creativity and problem-solving skills while potentially operating at superhuman speeds and scales.
This transformative technology promises unprecedented opportunities for scientific breakthroughs, economic growth and solutions to humanity’s greatest challenges.
AI Risks and Common Errors
Developing AI systems requires awareness of inherent risks and common errors. One significant risk of AI is algorithmic bias.
Since AI systems learn from historical data, they can inadvertently perpetuate existing biases, leading to discriminatory outcomes affecting certain groups unfairly. For instance, facial recognition technologies show higher error rates for people with darker skin tones, highlighting the need for careful data curation and algorithmic transparency.
Another common AI risk is overfitting, where models perform exceptionally well on training data but fail to generalise to new, unseen data.
This occurs when models become too complex and “memorise” training data rather than learning underlying patterns. Overfitting leads to poor real-world performance, where data may not match the training set.
Additionally, insufficient testing and validation can result in AI systems lacking robustness to handle edge cases or unexpected inputs, potentially causing catastrophic failures in critical applications like healthcare or autonomous vehicles.
Large language models (LLMs), also known as generative AI models, can produce “hallucinations” – outputs not grounded in reality or input data. These hallucinations manifest as fabricated facts or nonsensical sentences.
Understanding and mitigating hallucinations is crucial for enhancing AI system reliability and trustworthiness. When generative AI models hallucinate, they can cause misinformation, confusion and potentially harmful consequences, especially in accuracy-critical applications like finance and legal services.
Another significant AI risk is possible misalignment between human values and AGI objectives in the future. Despite rigorous programming and ethical guidelines, AGI systems might interpret goals in ways diverging from human expectations, leading to unintended consequences.
For instance, an AGI system designed to maximise human happiness might implement counterproductive or harmful solutions if its understanding of happiness doesn’t align with complex human emotions and societal norms.
Be Aware and Avoid Potential AI Errors
As we advance toward artificial general intelligence, acknowledging potential errors becomes imperative. Having a clear understanding of AI’s risks enables your enterprise to be forward-thinking and stay ahead of potential issues that could derail your AI systems and even your business.
In the next article in this series, we’ll explore the FRIENDS acronym for best practices in enterprise AI implementation and development so leaders can help set their organisations on the right path to make the most of AI and human cooperation.