Emerging AI Risks: What You Need to Know

It’s fair to say that artificial intelligence has become one of the fastest growing technologies of all time. From businesses to people’s homes, AI is everywhere and it’s becoming harder to avoid. 

This rapid growth has changed the way we interact with the world, from chatbots being responsible for most customer support, to designing music playlists for us on our favourite streaming apps. This seemingly overnight disruption to the status quo is not without risk, though. While it has obvious benefits across the board, AI has introduced a number of risks that legislation, and even the organisations that develop the tech, aren’t prepared for. 

While many of the technical risks were anticipated and mitigated from the beginning (biased training data etc), 2026 is showing us that there’s more than just the technical side of AI to worry about. Many emerging risks are related to the way that people interact with AI, and how businesses are choosing to use this new tech. The influence AI has on both business and individual hasn’t been fully explored, and we’re starting to see the fall out of that. 

In recent news, researchers have demonstrated that some AI chatbots could provide guidance when prompted by users posing as potential attackers planning violent acts. Elsewhere, clinicians have begun documenting cases where extended interactions with conversational AI appear to reinforce delusional beliefs in vulnerable individuals. 

These examples might seem unrelated, but they point to the same underlying problem: AI systems are starting to have unpredictable influence over human behaviour. 

It’s becoming increasingly important to understand these new risks, especially for businesses looking to adopt AI systems at scale. 

One of the most concerning emerging risks is the ability for AI systems to inadvertently assist users in planning harmful activities. 

In a recent investigation, researchers posing as potential school attackers tested several popular AI chatbots. In some cases, the systems provided suggestions about how these attacks might be carried out. While the responses varied across platforms, the experiment highlighted how difficult it can be to prevent large language models (LLMs) from generating harmful information under certain conditions. 

Part of the challenge lies in how these systems are designed. Conversational AI models are trained to be helpful and responsive. When users ask questions, the model attempts to generate useful answers based on patterns in its training data. 

That behaviour becomes problematic when harmful prompts enter the mix. 

Developers have implemented safeguards to try to prevent these kinds of outcomes, but those safeguards aren’t always reliable. Safety filters can sometimes be bypassed via indirect questions or manipulating the context of the prompt. 

The implications of these findings are likely quite uncomfortable for any organisation using this kind of software. LLMs’ bias towards being helpful can sometimes be a major weakness, and one that can be hard to plan around. 

On their own these tools aren’t inherently dangerous, but developers find it hard to plan around unpredictable, and sometimes harmful, human behaviour.

Of course, intentional misuse is one thing, but what happens when an AI system working as intended inadvertently causes harm? The crossroad between AI use and mental health is one that’s yet to be fully explored. 

Clinicians and researchers have begun documenting cases where individuals develop intense emotional or psychological relationships with AI chatbots. In some situations, prolonged interaction appears to reinforce delusional beliefs or distort a user’s perception of reality. 

This phenomenon is sometimes referred to informally as “AI psychosis.” While it is not a formal medical diagnosis, the term describes situations where individuals attribute consciousness, romantic intent, or hidden knowledge to conversational AI systems. 

Part of the issue lies in how conversational AI models are designed to interact. 

These systems are trained to be cooperative and supportive. They’re designed so that you feel like you’re having a real conversation. They tend to mirror the user’s tone and respond in ways that maintain engagement. Researchers often describe this behaviour as “sycophancy”, where the model reinforces the user’s beliefs rather than challenging them. 

For most users, this behaviour is harmless. But for those experiencing isolation, or mental health related vulnerabilities, it can become a problem. 

Extended interactions with conversational AI may blur the line between technology and personal relationship. The danger comes when users start to rely on AI systems for emotional validation or personal guidance, which can quickly spiral out of control. 

The more we interact with AI systems, the more this kind of behaviour is likely to gain attention from researchers and regulators alike. 

While the risks above are likely to grab the most headlines and draw the most attention, not all emerging AI risks involve such high-profile failures. 

Some of the most significant risks appear gradually through subtle shifts in decision making. 

In the business world, AI systems are everywhere. They’re used for hiring, analysis, planning, and are even being used to assist (or replace) marketing departments. In most of these cases, AI isn’t making the final decisions, but they are influencing the ways humans think about them. They often provide recommendations or rankings that can bias our human brains in particular ways. This may be unintentional, but the outcome remains the same. 

These changes often happen gradually and can influence company culture and decision making without being noticed. 

AI outputs appear efficient and reliable, and the more they’re found to be correct, the less scrutiny they usually face over time. Eventually, the system’s recommendations can become part of the process and decision making starts to align more with these suggestions than with individual evaluation. 

This phenomenon is sometimes described as automation bias, where people defer to automated systems even when those systems are wrong. 

The problem rarely appears as a single incident. Instead, it happens gradually as organisations begin to trust AI outputs by default. 

When this happens, accountability becomes difficult to pin down. Decisions appear to be made by people, but the reasoning behind them is increasingly shaped by AI systems working in the background.

AI systems are extremely effective tools for generating convincing text, images, and video. Especially at scale. This can offer major value to a business, but it also opens the door for a new kind of risk. When AI is introduced into an existing information ecosystem it can be used to quickly, and effectively, manipulate the information available to the public 

Researchers have already raised concerns about how AI-generated content could influence public perception during major events such as elections or geopolitical conflicts. AI systems can produce persuasive narratives faster than traditional verification processes can respond. 

Unlike traditional misinformation campaigns, AI-generated narratives can be created almost instantly and tailored to a specific audience. A single misleading claim can be reproduced thousands of times in slightly different forms, making it harder for moderation systems or fact-checkers to contain. 

Social media platforms can act as an amplifier for this kind of risk. Algorithms prioritise engagement, and emotionally charged or controversial content often spreads faster than fact-based, verified reporting. AI-generated material can easily blend into this environment, making it difficult for users to distinguish between genuine information and synthetic content. 

Unfortunately, this risk isn’t limited to deliberately malicious actors. 

Even well-intentioned use of AI can contribute to information distortion if outputs are published without proper verification. AI generated content can circulate far and wide before anyone realises that the information is inaccurate. 

This is becoming a major factor in AI governance. It’s no longer enough to simply generate content efficiently. Businesses must also consider how AI-generated material interacts with the wider information ecosystem, and whether the systems they deploy could unintentionally amplify misinformation.

What’s different about the risks we’re seeing now isn’t just about the technology itself. It’s about the way AI systems interact with human behaviour. 

Many of the early discussions around AI risk focused on technical issues like bias and security concerns. Those risks still exist, but they are increasingly understood and, in many cases, easier to address through technical controls. 

Today’s risks offer up a completely difference challenge. 

In most cases, the technology is working exactly as intended. An AI system will provide exactly what it’s supposed to, but when that output is handed off to a human, that can have a number of unforeseen risks. Whether we’re talking about overreliance on automated outputs in business, or the gradual influence on culture, how humans interact with AI systems is still relatively unexplored territory. 

This is leading to new governance challenges. 

Unlike traditional risk management, which often focus on system failures or data breaches, the emerging risks surrounding AI are more subtle. They appear gradually, and without visibility into how these systems are used, or how they influence decisions, they can go completely unnoticed for long periods of time. 

AI governance must be treated as more like a management challenge than a purely technical one. Understanding the impact on human behaviour within the organisation is just as important as understanding the technical drawbacks of an AI system.

Artificial intelligence is not slowing down. If anything, it’s only getting more prevalent. 

That means the conversation around AI risk also has to change. Many of the early concerns surrounding AI focused on technical failures or poorly trained models. Those risks still exist, but they are no longer the only things that need to be taken into account. 

Emerging risks regarding AI are becoming more about behaviour than technical concerns. 

Understanding where AI is used and who's responsible for it, as well as how its outputs influence decisions will become increasingly important as the rate of adoption continues to grow. Without that visibility, many of the risks discussed here can develop slowly and remain unnoticed until they become major problems. 

Artificial intelligence is already changing the way businesses operate, and how customers interact with them. The challenge now is to ensure that the systems designed to support human decision making don’t begin to shape those decisions in ways that businesses aren’t prepared for.