We’re currently at the start of the artificial intelligence age, rapidly reshaping the landscape of all professional services. Financial advisers, in particular, are grappling with the implications of AI. According to research from Unbiased, advisers see AI as both the greatest challenge to their profession and one of its biggest opportunities, even though most have yet to meaningfully adopt it. Despite this hesitation, more than half of respondents (56%) believe AI could support their business in the future, revealing an industry caught between fear and optimism.
As AI adoption accelerates and expectations rise, advisers face complex questions about compliance, liability, data use, intellectual property, and client trust. Navigating these issues requires more than technical understanding, it demands legal insight, advice and proactive risk management. For firms and advisers looking to utilise AI competitively but also responsibly, enlisting specialist legal advice early is paramount.
The UK’s current regulatory landscape for AI
The government has opted for a principle-based regulatory approach rather than rushing in with detailed, prescriptive legislation. The UK framework is founded on five core principles which govern the creation and use of AI systems:
- safety
- security and robustness,
- transparency and availability
- fairness and accountability
- contestability and redress.
Unlike the EU which has passed an AI Act, these principles are not yet codified into a standalone AI law but instead are being implemented through existing regulatory bodies such as the Financial Conduct Authority, applying existing English legal principles.
One risk for financial advisers lies in complacency: without specific AI legal obligations, firms may mistake this principle-based regime for regulatory leniency. In fact, targeted legislation is likely in future, particularly for highly capable general-purpose AI systems and the protection of intellectual property. In addition, because there is no formal statutory definition of “AI” in the UK, different regulators may interpret AI concepts such as ‘adaptivity’ and ‘autonomy’ in sector-specific ways, with different regulators reaching different conclusions as to what counts as ‘high risk’. This could lead to uncertainty for firms trying to comply with the key principles.
For advisers, this means AI governance must begin now. Senior management cannot treat AI as a purely IT matter but instead, they must own AI risk, build AI-risk registers, document decision-making, and prepare for regulatory scrutiny. Contracts with AI vendors will have to reflect not only commercial issues but also evolving regulatory priorities.
Intellectual property and AI generated advice – who owns what?
Provided that the AI system’s licence is drafted correctly, legal issues are unlikely to occur for financial advisers who use AI systems for portfolio analysis, client reports, or marketing materials generated by AI.
However, beware the licence terms of some AI providers who may claim ownership of any output generated by their systems, or reserve rights to reuse user-inputs for future model training. For financial advisers that feed in proprietary client data or strategic models, this presents a serious ‘trade secret’ risk to their own or their client’s data and models. Unless carefully drafted, contracts may inadvertently give the AI vendor rights in the firm’s or client’s data and models.
From a practical standpoint, advisers must insist on explicit provisions in their AI vendor agreements to ensure that they (or their clients) own all the AI generated outputs and data (including any IP rights in them) and that these outputs may not be used by the AI vendor without express permission. It will also be crucial to have the right to extract or migrate data and models if the advisers switch platforms, to avoid losing access to valuable proprietary insights.
Liability, accountability, and risk
Although an AI system produces outputs, in most adviser/client relationships the human adviser (if a sole trader) or the advisory firm, will still be ultimately legally responsible for any recommendations or communications given to the client based on the AI system. In the financial services world, the FCA may use individual accountability under the Senior Managers and Certification Regime (SM&CR) as a way of monitoring the use of AI by financial service organisations.
A key risk is that AI systems could generate plausible but incorrect information, as well as classic “hallucinations”. Therefore, without human review, these errors may be passed on in client communications or in strategic advice. As such, the adviser must ensure they’re overseeing and vetting AI outputs, validating them against internal standards and retaining the right to override and correct the AI system’s outputs where necessary.
What are AI hallucinations?
A hallucination occurs when an AI system generates output data that contains false or misleading information presented as fact; data that seems plausible but is incorrect or nonsensical.
Another risk area that requires consideration is bias. AI models are only as good as the quality of their training data. If these models have embedded biases, for example, in risk-profiling or in credit scoring where someone is seeking a loan, then an adviser could face regulatory scrutiny under the fairness and accountability principles, as well as existing consumer protection and anti-discrimination laws. Accordingly, contracts with AI vendors need to include obligations around bias mitigation, testing, and reporting so that advisers can show that they have done all they can to ensure the system they use is free from bias (as well as having a remedy against the developer if things go wrong later).
In this regulated sector, maintaining an audit trail is required. Documentation should record how the AI was developed and used, how the decisions were made, who reviewed them, and whether any override took place. This will support both internal governance and external regulatory review of the organisation.
Advisers can mitigate their potential liability to clients in relation to the use of AI systems by ensuring that their client terms have:
- a strong limitation of liability clause with the correct exclusions and caps.
- clear wording that, like all software, the AI system is not error-free and that its use should be subject to human checks.
- clear wording that the adviser/firm is not liable for any decisions/actions taken by the client as a result of use of the AI system.
- a term that clarifies that the client is responsible for the quality of any data it provides or inputs into the AI system.
Compliance transformed – opportunities and obligations
AI is transforming compliance in financial services in profound ways. According to survey data from Moody’s, 96% of risk and compliance professionals believe their roles will be impacted by AI, but they do not expect to be replaced. AI is already being used across wealth management, banking, and professional services to strengthen fraud detection, streamline KYC checks, and to make compliance functions more predictive rather than reactive.
While this transformation brings tremendous opportunity for financial advisers, it also brings new obligations. Firms must build governance frameworks to oversee AI systems, including training for compliance staff, controls around model use, and internal software tools to monitor outputs. Many organisations acknowledge this and are establishing oversight and training programmes to ensure that internal expertise catches up with technological change in AI.
Advisory firms must embrace this shift to build trust. If an adviser is going to rely on AI to inform compliance-sensitive decisions, clients will want assurance that human judgement remains involved in the development or use of AI systems and that a competent person can override AI results when necessary.
Negotiating with AI vendors
From a legal standpoint, negotiating with AI providers requires developed contract skills and attention to detail. Many off the shelf AI tools are not designed for regulated financial advice, so a firm considering vendor relationships needs to think carefully about liability, data rights and IP rights.
First, the contract should address how the AI was created and vendors should be willing to warrant that their system will comply with a defined specification or description. In addition, there should be clarity that any training data used was used lawfully without infringing any third-party IP rights.
Secondly, there are questions around the allocation of risk if the model produces flawed advice or mistakes – who bears the liability? The contract should state that the model with comply with an objective specification or description, and the vendor should warrant that the AI will not infringe third-party intellectual property rights and as far as possible indemnities should also cover data breach, misuse of client data and regulatory non-compliance.
Thirdly, data-use and confidentiality need to be tightly controlled. Many advisory firms deal with sensitive client information and proprietary strategies. A vendor contract should specify how input data will be used and the contract should prohibit the vendor from using client data without explicit permission.
Lastly, In order to comply with the core regulatory principles of safety and transparency, firms should require vendors to supply audit logs, decision-traceability, and documentation which explains how the AI model was developed and how it arrived at specific outputs. Without those, firms may struggle to demonstrate regulatory compliance or to defend their advice if later challenged by clients or the FCA due to the “black box” nature of many complex AI systems which makes it hard to explain how AI-driven decisions are reached.
The need for ‘humans in the loop’
One of the fundamental tensions in deploying AI in financial advice lies in retaining trust. Client relationships are deeply personal, especially when it comes to financial matters. Clients rely on human judgement, experience, empathy, and accountability. Integrating AI must therefore be handled in a way that reinforces, rather than erodes that trust.
Advisers must communicate effectively the use of AI to clients: is the AI a support tool, or is it a decision-maker? Do clients know which parts of the process are automated and which are human driven? Such disclosures are a regulatory expectation, given that transparency is among the core UK regulatory principles. Ultimately, advisers need to be able to demonstrate that that AI enhances rather than replaces professional advice.
Related to AI compliance requirements, financial advisers and senior management must understand how the AI works: no one is expected to become an expert in writing AI code overnight but understanding its limitations and governance is vital. AI training is important so that humans can challenge, override, and monitor AI-generated outputs with confidence.
Preparing for the next regulatory phase
While the current UK framework is principle-based, there are clear signs that legislation and more prescriptive regulation will follow, particularly for powerful, general-purpose AI and the realm if IP after the recent High Court decision in Getty Images v Stability AI.
Therefore, firms should build risk-governance functions today, before legislation mandates them. That means establishing AI leadership by senior management, establishing clear policies and defining escalation processes for model failures or unexpected outputs. It also means putting in place the contractual protections mentioned earlier for AI vendors and clients.
Additionally, AI literacy must be treated as a management priority. Management and front-line advisers should be trained in model risk, explainability, bias mitigation, and the ethical use of automation
Firms should also monitor international regulatory trends. While the UK has opted for a flexible, principle-based regime, other jurisdictions (notably the EU) have introduced AI laws. Advisory firms with cross-border operations need to plan for regulatory divergence too, in definitions, documentation, compliance burdens, and liability exposure. It remains to be seen whether the EU AI Act will in practice become a de facto requirement for UK advisers with EU clients.
The road ahead – AI action plan
Adopting AI without solid legal foundations will be costly – legally, commercially and reputationally. These foundations depend on organisations:
- establishing strong governance frameworks with clear oversight and accountability.
- conducting thorough risk assessments for all AI systems and AI vendors, covering bias, security, and operational resilience.
- ensuring rigorous testing and continuous monitoring of AI models to maintain compliance.
- developing clear documentation and audit trails for AI-driven decisions.
- training senior management and staff to understand AI technology and its associated risks.
Ultimately, those firms that safely use AI to supplement professional advice rather than replace will lead the road ahead.
First published – FT Advisor – 18 Dec 2025 – The AI shift financial advisers cannot ignore
