- with Senior Company Executives, HR and Finance and Tax Executives
- with readers working within the Accounting & Consultancy and Construction & Engineering industries
Artificial Intelligence ("AI") is no longer a peripheral workplace tool. In 2026, it is expected to be embedded across core business operations, employee workflows and organisational decision making. This shift is reflected in recent analysis published by the IBM Institute for Business Value , which identifies artificial intelligence as a defining force shaping how organisations operate, manage talent and compete over the coming years.
IBM's analysis focuses on the strategic and operational consequences of this acceleration, including increased AI adoption across the workforce, changing employee expectations and growing pressure on organisations to demonstrate trust and accountability in the use of AI. What is often discussed less explicitly, however, are the legal consequences of these trends, and the risk management associated thereto. As AI becomes embedded in the workplace, organisations face a widening set of legal risks that extend beyond traditional compliance concerns and require a more structured and forward looking approach to reputation management, privacy, accountability, intellectual property and risk management.
From a legal perspective, the most significant shift is that AI driven workplace risk no longer arises at a single point of use. Instead, it flows across a connected chain that includes data sourcing and training, system design and deployment, employee interaction with AI tools, and the downstream consequences of AI influenced decisions. This risk chain cuts across technology, people, vendors, and governance structures, and it challenges the assumption that legal exposure can be managed solely by the organisations human resources or compliance teams.
At the top of this chain effect sits data. Workplace AI systems are heavily dependent on large volumes of information, often including employee personal data or data that can be used to infer sensitive information. Decisions made at the data stage have lasting legal consequences. If employee information is collected, reused, or repurposed without a lawful basis or clear purpose limitation, those deficiencies do not disappear once an AI tool is deployed. They follow the system into live use and can undermine the legality of every decision that relies on it.
Risk then shifts to how AI systems are designed and deployed within workplace processes. Choices about automation, human oversight, explainability, and integration into decision making are not purely technical decisions. They directly affect an organisation's ability to explain outcomes, defend challenges, and demonstrate procedural fairness when AI influences employment related decisions. While IBM emphasises trust as a business imperative, from a legal standpoint trust is inseparable from the ability to evidence control and accountability.
As AI becomes part of everyday work, employees increasingly rely on AI assisted outputs to perform their roles. This reliance introduces another layer of risk. Where employees are expected to act on AI recommendations, questions arise around training, delegation of authority, and whether individuals understand the limitations of the systems they are using. In practice, organisations remain accountable for outcomes, even where decisions are influenced by complex tools.
Legal exposure arises the moment where AI driven-decisions affect people. Hiring, performance management, remuneration, disciplinary action and termination decisions carry heightened risk when AI is involved. At this stage, employers must be able to explain how decisions were made, identify who was responsible and demonstrate that legal and regulatory obligations were met. AI does not displace accountability, it merely reshapes how accountability must be managed.
Privacy remains one of the most significant risk drivers in this context. As workplace AI becomes more sophisticated, the volume and sensitivity of employee data processed by organisations will increase. Privacy risk is not limited to regulatory enforcement. It affects employee trust, labour relations and reputational standing. Treating privacy as a downstream compliance exercise is increasingly untenable where AI systems operate at scale and influence core employment outcomes. Another critical risk management blind spot is the extent to which company and third-party intellectual property is utilised as part of the exploitation of AI systems, as well as questions as to intellectual property ownership.
IBM's focus on accountability and AI trust also reflects growing regulatory and societal scrutiny. In 2026, organisations are likely to face more fragmented regulatory requirements across data protection, cybersecurity, employment law and emerging AI governance frameworks. In this environment, reactive compliance will be insufficient. Organisations must embed legal risk management into every stage of procuring, governing and using AI.
Organisations who may be struggling with the adoption of AI in the workplace often find that the toughest questions are not the technical ones, but legal and governance related. Understanding where legal risk arises across the AI lifecycle, and how privacy, accountability and regulatory obligations apply in practice, requires a structured and informed approach. If you require assistance in assessing or managing the AI related risks your organisation may face, or is already facing, our Technology, Media, and Telecommunications (TMT) team regularly advises clients on AI governance, regulatory compliance and technology risk management. Contact the team below.
The content of this article is intended to provide a general guide to the subject matter. Specialist advice should be sought about your specific circumstances.
