Like the 19th century pioneers who ventured west in search of wealth and prosperity, many manufacturers are embarking on bold AI journeys of their own. There is no doubt that the past 12 months have seen a shift in tack, with tentative conversations around AI “ifs” giving way to “hows.” Experiments turned to expectations, ambitions to realizations, possibilities to demonstrations.
However, while boldly venturing into the unknown, there are still many unanswered questions regarding the implementation of AI in manufacturing. However, this does not mean that the sector is blindly developing technology without due process or diligence. Key considerations include expected ROI, optimal applications for AI to bring about change, employee buy-in, and integration with legacy systems.
Important points
- The adoption of AI in manufacturing has moved from experimentation to real-world implementation. Companies no longer want if AI should be utilized, but how To deploy it effectively and bring tangible results.
- There is a huge gap between the use and governance of AI. While 93% of organizations are using AI, only 7% have the right governance framework in place, creating significant risks due to regulations such as: EU AI law It takes effect.
- Ungoverned AI can amplify existing organizational weaknesses. Issues such as poor data quality, weak processes, and compliance gaps can be exacerbated when augmented through AI systems.
- Key risks of AI include bias, privacy violations, misinformation, and over-reliance. AI can produce inaccurate or biased output, mishandle sensitive data, reduce critical human thinking, and even pose legal risks regarding intellectual property.
- A strong governance framework is essential to using AI safely and effectively. Organizations have clear oversight, regular audits, ethical guidelines and structured standards (e.g. ISO42001) Manage risk, ensure compliance, and build trust.
FAQ
- What does “ungoverned AI” mean?
- Why is AI governance important in manufacturing?
- What are the main risks of using AI without proper controls?
- how EU AI law Will it impact your business?
- How can organizations implement effective AI governance?
These are definitely important issues that need to be part of the conversation. However, the rapid acceleration of technology and its capabilities has meant that technology has outpaced its own governance. The rules, processes, and decision-making structures used to oversee and guide how AI is managed, controlled, and held accountable.
The most important provisions of the EU AI law are due to come into force later this year, and new data shows that while 93% of organizations are already using AI, only 7% have a governance framework in place, leaving a significant gap as regulatory oversight increases.
The AI Act is the European regulation on artificial intelligence (AI) and the first comprehensive regulation on AI by a major regulator. The law assigns applications of AI to three risk categories. First, applications and systems that create unacceptable risk will be banned, such as government-run social scoring of the type used in China. Second, certain legal requirements apply to high-risk applications, such as resume scanning tools that rank job applicants. Finally, applications that are not explicitly prohibited or listed as high risk remain largely unregulated.
Kirsty Wakefield, information security sector manager at ISO certification body ISOQAR, warned that many organizations are effectively operating “ungoverned AI”, where tools are deployed without clear oversight, accountability or risk management.
“It continues to surprise us, and while many organizations are racing to deploy and collaborate with it, in 2026 we remain hopeful but cautious. Its suboptimal, but increasingly human-like intelligence and its power to transform traditional linear technologies into limitless opportunities raises an important question: How far can we go before it is beyond our control?” she commented.
AI is now being developed and deployed in nearly every sector, with manufacturing organizations leveraging everything from customer service chatbots to automation tools and analytical models to streamline processes, open new revenue avenues, and gain competitive advantage.
Of course, it is also important that AI is developed and deployed safely, ethically, and in line with business priorities and broader societal values. Kirsty added that while AI could pose new problems on its own, it was also likely to amplify existing weaknesses within organizations. Issues such as operational weaknesses such as inconsistent data management, compliance and regulatory gaps, and mismatched workflows can quickly escalate into systemic problems that are much more difficult to resolve.
Additionally, “With the right governance in place, companies can conduct regular audits as part of their AI compliance process to identify potential risks early and address them before implementing new AI technologies.”
“Organizations have rarely looked to software and tools that make them question who is really in control of whom. AI and machine learning tools have endless capabilities that require a deep understanding of their intended purpose, capabilities, and functionality.
“Machine learning tools, in particular, continue to automatically adapt and learn without requiring direct programming or human intervention. Without an organization’s clear understanding of how these systems work, AI tools can become unpredictable, and even when used intentionally, companies risk deploying tools that can lead to errors, bias, data breaches, and even ethical violations.”
Unlike humans, AI tools lack context and awareness of both truth and ethics. Additionally, since it is a non-sentient system, it does not have the ability to distinguish between correct and incorrect information, or between accurate and inaccurate information. In particular, generative AI models (such as chatbots and text/image generators) can generate content based on illusory facts, false reports, or misleading statements. appear Trustworthy. In some cases, uncontrolled tools can be misused to manipulate information and intentionally create misinformation, false claims, and scenarios.
Concerns about these risks are already influencing regulatory responses, including provisions in EU artificial intelligence law that restrict certain high-risk AI systems. Without governance, small mistakes can quickly lead to the spread of misinformation, leading to reputational damage, compliance violations, and an overall decline in public and customer trust. Kirsty picks up the story.
Privacy and data protection
Unmanaged AI can pose significant privacy risks, especially when dealing with sensitive data. AI technology can process, reason about, and classify sensitive personal data at a scale and depth that traditional systems cannot. This includes the ability to infer sensitive attributes and store and analyze biometric information.
Without clear governance, large datasets can be collected, processed, or retained without a clear legal basis. Worse, if mismanaged, this data can be unintentionally shared across departments or to vendors or third parties.
Possession of highly sensitive information without proper governance and safeguards in place can quickly lead to a catastrophic data breach, leakage, or cybersecurity incident that can be irreversible.
Prejudice and unethical classification
Another widely discussed risk of AI when collecting sensitive information and data is algorithmic bias. AI systems learn patterns from the data they are trained on, so if that data reflects inequality, incomplete information, or social bias, AI models can reproduce this and even amplify those patterns when making decisions.
Even when protected characteristics such as gender and ethnicity are removed, they can still be interpreted through proxy indicators such as postal code, educational background, language patterns, and purchasing behavior. Biased results can quickly impact the various systems used for recruitment, financing, and risk assessment, and can unintentionally disadvantage certain groups.
Bias may not be immediately noticeable and may emerge gradually as the system continues to learn from new data or interact with users over time. This requires regular auditing, testing, and monitoring to ensure that inequitable outcomes do not persist or escalate.
Excessive dependence and de-skilling
While many of us appreciate the opportunities that artificial intelligence provides in the workplace by cutting down on mundane and time-consuming tasks, it also increases the risk of over-reliance on tools. Throughout history, technology has regularly replaced skills and jobs, but AI tools can lead to a decline in critical thinking, decision-making abilities, and professional judgment within the workforce.
To mitigate this risk, organizations may consider implementing clear AI usage policies based on team functionality. This will ensure that AI tools are used to support human capabilities rather than completely replace them.
Intellectual property and copyright
As you know, generative AI tools can generate new content such as text, images, audio and video, and computer code. These models are trained on billions of pages of data and information from any topic. Because much of this material is copyrighted or privately owned data, disputes have arisen over whether the processing and output of the generated material violates privacy, intellectual property, and copyright laws.
Whether it’s marketing content, software code, or product development, AI creates content that can be considered derivative of copyrighted material, potentially leading to lawsuits, fines, or removal. Companies therefore need clear policies, oversight, and legal review processes to avoid liability for accidental infringements.
environmental risk
Artificial intelligence requires significant computing power and extensive data storage, which creates a huge server footprint. AI training alone is extremely energy-intensive, and running AI tools can spike data center energy consumption and cooling.
Without governance and strict oversight, companies may not be able to understand, track, and report on their AI energy usage. This can undermine sustainability efforts and make it difficult to track progress towards a company’s ESG and net-zero goals.
Therefore, organizations need clear goals and governance standards from the start to prioritize energy-efficient models within their workflows and incorporate the use of AI into their sustainability goals and reporting.
conclusion
As AI adoption increases across all industries, the potential benefits are immense. However, these benefits are partially offset by the risks associated with poorly managed AI systems. As this analysis shows, ungoverned AI poses significant and multifaceted risks, and the consequences of deploying artificial intelligence without robust governance are real, tangible, and potentially severe.
Implementing AI responsibly and ethically requires the right tools, oversight, and standards. Based on a structured framework like ISO 42001, organizations should focus on identifying risks before they escalate and ensuring compliance with legal, ethical and moral obligations for successful business implementation.
