How AI could build on fintech’s digital rails to reshape advice, investing and adviser productivity
Fintech promised to transform financial services. In many ways it did — but the change was more infrastructural than revolutionary. Digital onboarding, APIs, platform-based investing and mobile-first design modernised the industry without fundamentally rewriting its economics. Now AI is emerging, and its greatest potential may lie not in replacing what fintech built, but in running on top of it. Fintech laid the rails; AI can now make them intelligent.
Fintech’s legacy
Fintech changed the customer experience, but it did not fully rewrite the industry. It reduced friction, improved access and helped push financial services towards a more digital model. Yet many of the core economics of the sector remained intact because trust, regulation, distribution and balance-sheet strength were still difficult to displace.
That said, fintech laid essential foundations. Digital onboarding, APIs, cloud infrastructure and mobile-first design modernised the industry. Those digital rails matter now because AI can build on top of them. The next wave may not simply digitise finance — it may make it more intelligent.
Why agentic AI matters
AI is often discussed as a tool for analysis, summarisation or automation. Agentic AI goes further. It can pursue a goal across multiple steps, decide what action to take next and adapt to changing inputs with limited human prompting. Unlike conventional chatbots, agentic AI can initiate actions across multiple systems, retain context across tasks and execute complete workflows rather than simply responding to individual prompts. That makes it especially relevant to financial services, where workflows are complex and highly data-driven.
In wealth management, that could mean an AI system preparing meeting briefs by retrieving CRM records, summarising market developments, identifying portfolio drift, highlighting potential suitability issues and producing a first draft of client communications before an adviser reviews them. This is not simply about making tasks faster. It is about changing how work is organised, how advice is delivered and how clients experience the service.
What it means for clients
For clients, AI may first appear as better access to information and guidance. Tools can already summarise markets, compare products, explain risks and help people think through portfolio decisions in plain language. That could be especially valuable for investors who do not currently have access to traditional advice.
Agentic AI could make that experience more proactive. Instead of waiting for a quarterly or annual review, a client may receive a prompt when portfolio drift, market movements or a significant life event suggests a reassessment is needed. Advice becomes increasingly continuous and event-driven rather than being tied to a calendar.
There is also a market-structure angle. Firms with strong data, clear governance and scalable systems are likely to benefit most. Those with fragmented legacy systems may struggle if AI raises expectations around responsiveness and personalisation across the industry.
What it means for wealth management
Wealth management has always been a relationship business, but it is also a workflow business. Advisers spend a great deal of time on meetings, research, suitability checks, reporting, compliance, administration and follow-up. AI can remove friction from many of those tasks, allowing advisers to focus more on judgement, planning and client relationships.
That matters because good advice is not simply about producing a suitable investment recommendation. It is about understanding a client’s objectives, tax position, family circumstances, risk tolerance and emotional responses to uncertainty. Agentic AI can support that process, but it cannot replace the human qualities that make advice credible and effective.
The most likely outcome is augmentation rather than replacement. Advisers will increasingly work alongside AI systems that prepare meeting briefs, draft proposals, highlight risks and monitor portfolios in the background. Following periods of market volatility, for example, AI could identify clients whose portfolios have drifted outside agreed tolerances, prepare tailored communications and schedule adviser reviews automatically. This could improve both productivity and service quality, especially for firms seeking to serve more clients without lowering standards.
This is also relevant in the context of recent regulatory change. The FCA’s Advice Guidance Boundary Review, which came into force in April 2026, introduced targeted support as a new regulated activity, allowing firms to offer suggestions based on the common characteristics of defined consumer segments without the full burden of personalised advice. Designing and scaling targeted support journeys is precisely the kind of complex, data-driven workflow where AI can help: segmenting clients, generating compliant suggestions, monitoring outcomes and flagging when a client may need to move to streamlined or full regulated advice.
The FCA has noted that simplified advice can complement targeted support, and AI may be the tool that makes that transition seamless in practice.
ESG and cleantech investing
AI also has important implications for ESG and cleantech investing. These areas depend heavily on data, classification and scenario analysis, making them natural candidates for AI-enabled tools. Better models could help investors assess emissions exposure, supply-chain risks, transition pathways and policy sensitivity more efficiently.
AI can also help wealth managers translate clients’ sustainability preferences into more transparent portfolio analysis and reporting. As ESG data continues to expand in both volume and complexity, intelligent tools may become increasingly valuable in helping advisers interpret information rather than simply collect it.
Cleantech is another area where AI may matter. From grid optimisation and energy efficiency to battery technologies, industrial decarbonisation and climate analytics, AI can help identify opportunities and monitor execution. For investors, that may create a more dynamic way to evaluate companies and long-term themes linked to the energy transition.
Better analytics, however, do not automatically produce better investment decisions. AI can identify relevant signals, but it cannot resolve the underlying debates around sustainability, impact or trade-offs. Human judgement remains essential when interpreting evidence and translating it into investment decisions.
Opportunities and the risks
The opportunities are clear: lower costs, better personalisation, faster service and broader access to advice. AI could help wealth managers serve mass-affluent clients more profitably while supporting more inclusive models that sit between full regulated advice and pure execution-only investing.
The risks are equally important. Poorly governed AI can create unsuitable recommendations, compliance failures, privacy concerns and overreliance on opaque models. In financial advice, trust is fragile, and firms will need to ensure that efficiency never comes at the expense of accountability.
That means AI must be tested, monitored and explainable. Data quality, model governance and auditability should be treated as core capabilities rather than afterthoughts. Human oversight remains essential for key decisions, exceptions and anything affecting client suitability or regulatory responsibility.
The next wealth stack
The future wealth stack may be built from fintech infrastructure and AI working together. Fintech gave the industry digital onboarding, APIs, open data and more efficient back-office systems. AI can now orchestrate those capabilities to make financial services more adaptive, more personalised and ultimately more useful.
This also connects to the broader evolution of ownership, access and portfolio construction. The financial system is becoming more modular, more data-driven and more responsive to client needs. That is likely to reshape not only advice but also product design, distribution and ongoing client engagement.
The biggest winners are unlikely to be the firms that automate most aggressively. They are more likely to be the firms that combine technology with trust, governance and human judgement.
Final thoughts
Fintech digitised financial services. Agentic AI may make them adaptive. Firms that invested in digital infrastructure over the past decade now have an opportunity to transform it into an intelligent advice platform.
The future of advice is unlikely to be AI versus humans. It is more likely to be AI working alongside humans: faster, more scalable, more personalised and better able to serve a broader range of clients. The question is no longer whether AI will become part of wealth management, but how effectively firms combine it with expertise, governance and trust.
DISCLAIMER: This article is for informational purposes only and constitutes financial guidance, not regulated financial advice. P27 is not FCA-authorised, and Mauro Tortone is not a financial adviser. This does not constitute a personal recommendation to invest. Tokenised securities are regulated instruments, and all investments carry risk. Before investing, consult a financial adviser registered on the FCA Directory if you are based in the UK.
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