In wealth management, advisers’ time is often in short supply. Client books become more complicated as they grow, and too much of the working day disappears into administration rather than the work that builds and maintains client relationships. Artificial intelligence is increasingly being used to tackle that administrative burden, from summarising meetings and preparing client communications to monitoring activity and automating proposals. This article looks at practical ways AI is changing the wealth management industry and how firms and advisers can use it responsibly.
What Is AI in Wealth Management?
AI in wealth management refers to the use of tools such as machine learning, generative AI and AI agents that can analyse financial and client data, identify patterns and take on the routine work that surrounds client service.
Rather than replacing an adviser’s judgement, these tools can prepare information, automate parts of a workflow and surface insights for a professional to review. The adviser remains responsible for interpreting the information and making decisions for the client.
Key Takeaways
- AI takes on much of the administrative load in wealth management so advisers can spend more of their time with clients.
- Interest in AI is near-universal among larger firms, though full deployment and measurable impact are still catching up.
- AI is particularly useful for note-taking, client reporting, onboarding checks and compliance monitoring.
- Clients want AI in a supporting role, not the driving seat; trust in a human adviser remains the foundation of the relationship.
- Success depends as much on clean, connected data and clear human accountability as it does on the technology itself.
AI in Wealth Management Explained
For most of its history in wealth management, AI has been used to handle relatively narrow jobs such as detecting fraud and powering early robo-advisers. Now, GenAI can handle a broader range of work. It can interpret unstructured information such as meeting transcripts and documents, summarise research, draft client communications and retrieve relevant information from a firm’s knowledge base. That means advisers can spend less time searching through documents or turning notes into follow-up actions and more time applying their expertise to the client’s situation.
More recently, AI agents have begun to appear in wealth management. Rather than completing a single task on command, an agent can carry out a multi-step job within defined permissions and controls. An agent might monitor a client account for a significant change, gather relevant account and client data or draft a follow-up action for the adviser to approve. These newer capabilities are beginning to move AI beyond individual productivity tasks and into the wider workflows that support day-to-day wealth management.
The pace of adoption reflects that shift. In an early 2025 EY-Parthenon survey of 100 wealth and asset managers, 95% said they had already scaled GenAI to more than one use case, and 78% were exploring AI agents that carry out multi-step tasks.
Why Are Wealth Management Firms Integrating AI?
The pressure to increase the capacity of the advice market is one reason firms are looking to AI. The FCA says only about 9% of consumers (roughly 4.6 million people) take financial advice, while some 7 million adults hold £10,000 or more in cash savings and could benefit from investing. The regulator describes this as an advice gap and is pursuing measures such as targeted support to help more people access financial guidance.
At the same time, wealth is moving between generations and client expectations are changing. In the US, Cerulli projects that $124 trillion will pass to heirs and charity by 2048. The UK faces a similar shift. As younger generations inherit wealth, firms face the challenge of providing a more responsive, digitally enabled service while retaining the trust and personal judgement that clients expect from a financial adviser. AI can help create that capacity by reducing the operational work surrounding advice rather than removing the adviser from the relationship.
The supply of advice is under pressure, too. The FCA’s 2025 survey found that the number of firms offering financial advice had fallen 15% between 2021 and 2025. Firms are therefore looking for ways to serve more clients without simply adding more administrative work to already stretched teams. That is where AI can help.
Finance Futures: Tomorrow’s Vision
10 Key AI Uses in Wealth Management
The following uses cover some of the areas where AI can have the greatest practical impact, from compliance and data management to adviser productivity and client service. Together, they show how firms can apply AI to both the operational work around advice and the experience clients receive.
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Regulatory Compliance
Compliance is a natural fit for AI because much of the work involves reviewing large volumes of information, checking for anomalies and documenting activity. AI can be configured to help review authorised client communications for potential issues, identify unusual patterns and help draft routine reports, while compliance staff investigate exceptions and make decisions that depend on human judgement. In the EY-Parthenon survey, compliance and risk were among the functions reporting the largest cost savings from the application of GenAI.
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Workforce Capacity
Advisers spend only about 30% of their time with clients, according to Deloitte; the rest disappears into administration. By using AI to automate wealth management tasks such as summarising meetings, retrieving information, preparing follow-ups and automating other repetitive tasks, advisers can gain back more time to build better client relationships. But the benefit is broader than simply creating more time for conversations: AI can help advisers prepare more effectively, respond faster, find relevant information and handle more of the operational work surrounding each client relationship.
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Unified Client Data Foundation
AI is only as useful as the quality and completeness of the information it can access. For a wealth manager, that means bringing together the financial, operational and client information needed to understand a relationship, including account and transaction data, client records, previous interactions, portfolio information and relevant documents. When that picture is scattered in separate systems, an AI tool might only see part of the picture, making its output less complete and harder to trust.
This is a significant challenge for wealth managers. Industry estimates suggest that only 5% of firms have achieved cross-system integration. Yet data and difficult-to-integrate technology stacks have separately been found to be a major barrier to scaling AI. A unified data foundation is therefore less an AI use case and more an enabling layer for many of the other applications in this list.
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Personalised Client Services
Personalised investment strategies are already a major focus, with 62% of wealth managers in the EY-Parthenon survey naming them as a priority. AI can support that goal by assembling relevant client information, identifying inconsistencies or missing data, modelling scenarios and preparing material for an adviser to assess when formulating a recommendation. For portfolio managers, AI can also sift through large volumes of market and portfolio data and model different allocation scenarios. The professional still determines how that analysis should inform an investment decision while AI reduces the time spent assembling and interpreting the underlying information.
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Client Acquisition
AI can make client acquisition more targeted by helping firms identify promising prospects, analyse existing client and prospect data and determine when an outreach opportunity may be relevant. For example, an AI system could identify a change in a prospect's circumstances or engagement pattern that suggests they may be ready for a conversation. GenAI can then help an adviser tailor an email or other communication before it is sent. In this way, outreach can be more relevant without turning client acquisition into a high-volume, impersonal exercise.
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Fee Compression
AI is not a fee-compression use case in the same sense as compliance monitoring or meeting summarisation. Rather, it is a response to the economics that fee pressure creates. As clients become more cost-conscious, firms need to demonstrate value while maintaining sustainable margins. AI can reduce the cost of delivering parts of the service by automating routine planning, research, reporting and administrative work. Firms can then choose how to use the resulting capacity: to serve more clients, increase the level of service for existing clients or protect margins without reducing the quality of advice.
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AI-Powered Client Reporting
Generative AI can help prepare client reports by combining structured financial data with information from client records, turning it into a draft written in clear, client-appropriate language. An adviser can then check the figures, context and wording before approving the report. Client research suggests there is a role for AI in this supporting capacity, but not as a replacement for human advice. In Unbiased’s survey of 800 UK adults seeking financial advice, 18% were open to AI producing personalised reports or summaries, while 74% preferred an adviser-led model and only 6% would choose an AI platform alone.
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Proposal Automation
Preparing a client proposal typically means digging through the client’s records, financial circumstances and previous interactions, then turning that information into a coherent recommendation and supporting document. AI can help retrieve the relevant information, organise it and produce a first draft for the adviser to review. The adviser remains responsible for checking the underlying data, assessing whether the proposal is appropriate and adding the professional judgement that cannot be delegated to a drafting tool. For firms that produce a lot of proposals, this type of AI-powered automation can save considerable amounts of time.
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Cybersecurity
For wealth managers, cybersecurity is particularly important because firms hold sensitive financial, identity and client information and process transactions on behalf of clients. AI can help monitor account activity and communications for unusual patterns and prioritise anomalies for investigation. The risk is that AI technology can also make impersonation and social-engineering attacks more convincing. Firms therefore need controls around both sides of the equation, using AI to detect suspicious activity while making sure that AI-generated communications, access requests and transactions do not bypass established verification procedures.
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AI Governance and Explainability
AI governance is about more than asking an employee to approve an AI-generated output. For a wealth management firm, it means deciding where AI can be used, what information it can access, what level of autonomy it has, how outputs are tested, who is accountable for them and how the firm monitors performance over time. Those controls become particularly important when AI is used in client-facing or regulated processes. The firm needs to be able to demonstrate that appropriate human oversight, data controls and approval processes are in place and that problems can be identified and corrected. In the EY-Parthenon survey, 77% of firms expressed concerns about data privacy, accuracy and external data use, while 86% said regulatory and compliance complexities were a major hurdle to adoption.
Benefits of AI in Wealth Management
AI can provide value in wealth management in four closely connected areas:
- Efficiency and productivity: Automating routine research, documentation, reporting and administrative work gives advisers and support teams more capacity without requiring a proportional increase in headcount.
- Client experience enhancements: Faster responses, more relevant information and more personalised communications can improve service while keeping the adviser at the centre of the relationship.
- Greater availability and scalability: AI can help firms provide rapid, high-quality support while extending their capacity to serve a growing client base. It can handle routine requests and prepare information outside normal working hours, while advisers remain responsible for higher-value decisions and client interactions.
- Risk management and compliance improvements: AI can continuously review large volumes of activity, identify potential exceptions and create supporting records, while compliance and risk professionals retain responsibility for investigation and decisions.
3 AI Considerations for Wealth Management Firms
AI can do a great deal for a wealth management firm, but three things need attending to before the technology can be relied upon: governance, data quality and compliance.
Governance
Governance establishes how AI can be used safely and who remains accountable when it is used. Firms should define which tasks AI can perform autonomously, which require human approval and what information each system is permitted to access. They also need processes for testing outputs, monitoring performance, recording decisions and responding when an AI system produces an inaccurate or inappropriate result.
For client-facing and regulated work, human oversight should remain proportionate to the risk of the task. A system that drafts meeting summaries requires a different level of governance than one that prepares materials that could influence investment recommendations. Clear ownership and documented controls allow firms to capture the productivity benefits of AI without losing accountability.
Data Quality
If client or financial data is incomplete or wrong, AI cannot reliably produce a complete picture. In wealth management, the problem often starts with fragmented systems where information is spread between CRM, portfolio, finance and other operational systems. Data may be duplicated between them, updated at different times or difficult to reconcile. An ERP can provide part of the answer by giving financial and operational data a shared foundation. This way, AI can work directly in the ERP rather than across disconnected sources.
Compliance
The FCA has said it does not intend to create a separate set of AI-specific rules, instead applying existing regulatory requirements for firms’ use of AI. For wealth managers, this means AI does not change the existing responsibilities around Consumer Duty, governance, data protection and fair outcomes. In practice, however, firms need to understand where AI is being used, what decisions or client outcomes it can influence and what controls are in place to identify errors or unfair outcomes. Using a third-party AI system does not remove the firm’s responsibility for the service it provides to clients.
Grow Your Wealth Management Firm with AI-Powered NetSuite
The obstacles to effective AI deployment share a root cause: data scattered across systems that don’t work together. NetSuite ERP Software for Financial Services brings a firm’s finance and client data into one cloud platform, providing a unified foundation for AI-powered workflows. This gives advisers a more complete view of the financial information they need to work from, while AI can take on routine work that eats into their time. For example, configured AI workflows can help flag unusual account activity for review or turn financial data into plain-language summaries.
Properly configured AI-powered workflows can operate within the permissions and controls of the platform, with audit trails providing visibility into activity. They can also support the governance and oversight firms need for regulated work. And as the firm grows, NetSuite can scale with it without requiring additional systems to be introduced or data to be rebuilt across them.
The question for most wealth management firms is no longer whether to use AI but how to rely on it responsibly. Over time, many firms will have access to similar tools. The advantage will lie in the quality of the data feeding them and the judgement of the advisers using them.
See NetSuite for Financial Services in action here (opens in a new tab).
AI in Wealth Management FAQs
How is AI used in wealth management in the UK?
In the UK at present, AI is used mainly for compliance monitoring and routine administrative tasks such as meeting notes and data extraction. Client-facing applications are growing, including personalised reporting and communications, but consumer research indicates that most people still prefer advice to remain human-led.
How can wealth management firms implement AI successfully?
The firms that succeed tend to start small, with clearly defined lower-risk use case. They establish the necessary data and governance foundations before expanding, which means deciding where AI can initially add measurable value, then verifying that underlying data is reliable, followed by the assignment of appropriate human oversight, and ultimately integrating AI into existing workflows rather than treating it as a standalone experiment.
