AI is reshaping many of the routine, time-intensive tasks across various industries – and wealth management is certainly no exception.
Yet AI tools vary widely in their capabilities and choosing the wrong type for any given task can undermine the very results firms set out to achieve. In the drive to innovate, organisations can be tempted by solutions that (over) promise efficiency but fall short on reliability – or, even more critically, introduce operational and regulatory risk.
In our recent AI in compliance webinar ‘Building on solid foundations’, Kit Ruparel, TCC and Recordsure’s Chief Technology Officer, joined Garry Evans, Chief Product and Commercial Officer, to share practical insights from the frontline of AI adoption, highlighting both the opportunities and the challenges that firms can face.
Understanding the difference
Kit emphasised a critical distinction between predictive AI and generative AI – understanding this difference is essential for wealth management firms implementing AI into their processes and procedures. Predictive AI is built on deterministic machine learning and produces the same output every time when given the same inputs. This consistency makes it ideal for tasks that demand accuracy and repeatability, such as forecasting, modelling or working with structured data. Firms can also tune confidence thresholds to understand how much they can rely on the AI’s outputs.
By contrast, generative AI is inherently probabilistic. Large language models (LLMs) were designed to be creative: writing stories, generating reports or summarising complex information. This flexibility allows them to produce varied outputs, but it also means they can give confident, human-like answers that are sometimes incorrect. As Kit noted, this “confidently wrong” phenomenon requires firms to introduce guardrails, safety filters and oversight to mitigate operational and regulatory risk.
For wealth management professionals, this distinction has practical implications. Generative AI can be a powerful tool for condensing portfolio reports, producing meeting summaries or drafting communications, which in turn reduce the time advisers spend on repetitive tasks. However, it is not a substitute for systems based on structured, reliable data where predictive AI remains the most effective solution. Combining the strengths of both approaches allows firms to enhance efficiency without compromising accuracy or compliance.
Read more on this in Kit’s Insider view: Secrets of AI agents article
Guardrails and governance
Generative AI’s unpredictability also creates risk. Conversations around sensitive topics should be carefully monitored, with guardrails and safety filters in place to prevent unsafe outputs. Organisations must implement robust governance frameworks, ensuring tools are integrated with existing enterprise ecosystems and monitored by information security teams.
That’s not to say predictive AI doesn’t require ongoing attention as well. It depends on high-quality data, subject matter expertise and careful tuning by experienced data scientists. AI is not a ‘set-and-forget’ solution. Maintaining accuracy requires continuous testing, updating and adaptation to evolving business needs, regulatory requirements and market conditions.
Choosing the right AI tool
It’s crucial to match the right AI tool to a suitable task. Predictive AI can reduce human effort dramatically, sometimes by up to 50% by automating suitability checks and validating structured data. Generative AI meanwhile, can serve as a summarisation tool by dating information into digestible formats – but it should not be relied upon to generate primary insights or make critical decisions.
Tasks such as product research, selection and the generation of suitability reports benefit from predictive AI and templated approaches, rather than generative AI. Overreliance on LLMs in these areas introduces the risk of superficial outputs, inaccuracies and the so-called “AI Verification Tax,” where humans must spend as much time checking AI outputs as they would performing the task manually in the first place.
Planning for long-term AI adoption
Adopting AI is a long-term commitment. AI models are constantly evolving: OpenAI deprecates models within a year and updates from GPT-3.5 to GPT-4 or GPT-5 require organisations to continually retest and adjust prompts and workflows. AI models also experience “drift” as business processes, customer needs and regulatory frameworks change, necessitating ongoing maintenance and investment in research and development.
AI is not the entirety of an application. Organisations spend significant effort integrating AI into workflows, designing user-friendly interfaces and testing quality to ensure outputs meet business and regulatory standards. These considerations are just as important as the AI model itself.
Using AI responsibly – regulatory and conduct considerations
For wealth managers, adopting AI goes beyond technology. It’s a question of conduct and compliance. Firms need to provide robust end-user training, implement tools that minimise potential harm and ensure transparency in how AI informs decisions. The FCA is increasingly expecting organisations to demonstrate strong governance and control frameworks, making oversight an essential part of any AI strategy.
Generative AI can be a valuable support tool but it should never operate in isolation when making critical client decisions. Relying solely on it risks introducing bias, producing unverified outputs and creating regulatory exposure. By combining generative AI with structured, predictive systems and rigorous oversight, firms can harness its benefits while maintaining reliability, compliance and trust.
The path forward for wealth management firms
The key takeaway is clear. AI can deliver tremendous efficiency and insight but only when matched carefully to the right tasks and supported by rigorous governance. Predictive AI excels in structured, repeatable and data-driven tasks, while generative AI is best deployed to summarise, consolidate and communicate insights in human-readable form.
Firms should continuously evaluate new AI tools, integrate them thoughtfully into enterprise ecosystems and maintain a long-term strategy for upkeep and compliance. As Kit concluded, the successful application of AI in financial services is as much about careful planning, oversight and understanding the tool’s purpose as it is about the technology itself.
Expert review of your AI strategy
For firms looking to validate their AI strategy or explore how to implement these best practices, we’re offering a personalised 30-minute consultation with Kit to help develop a practical, compliant and high-impact AI framework.
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