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’s legacy
As discussed in my earlier article, The Era of Fintech, the first generation of fintech largely focused on digitising financial services and reducing friction. It transformed the customer experience by improving access and modernising delivery, but it did not fundamentally rewrite the industry’s economics. Trust, regulation, distribution and balance-sheet strength remained difficult to displace.
That first wave nevertheless laid the foundations for what comes next. Digital onboarding, APIs, cloud infrastructure and mobile-first design created the rails on which AI can now operate. The next wave may not simply digitise finance — it may make it more intelligent.
Why agentic AI matters
AI is often framed as a tool for analysis, summarising 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. It can also initiate actions across multiple systems, retain context across tasks and execute workflows rather than simply responding to prompts. That makes it especially relevant 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.
What it means for wealth management
Wealth management has always been a relationship business, but it is also a workflow business. Advisers devote significant time to research, suitability checks, reporting, compliance and administration. Advisers will increasingly work alongside AI systems that prepare meeting briefs, draft proposals, highlight risks and monitor portfolios.
Following 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 productivity and service quality while enabling firms to serve more clients without lowering standards.
This is particularly relevant following the FCA’s Advice Guidance Boundary Review, which introduced targeted support as a new regulated activity in April 2026. AI can help identify suitable client segments, generate compliant suggestions, monitor outcomes and flag when clients should move to streamlined or full regulated advice.
ESG and cleantech investing
AI also has important implications for ESG and cleantech investing. These areas rely heavily on data, classification and scenario analysis, making them natural candidates for AI-enabled tools. Improved models could help investors assess emissions exposure, supply-chain risks, transition pathways and policy sensitivity more efficiently, while also translating clients’ sustainability preferences into clearer portfolio analysis and reporting.
In cleantech, AI can support a more dynamic assessment of energy transition themes across areas such as grid optimisation, energy efficiency, battery technologies, industrial decarbonisation and climate analytics.
However, human judgement remains essential in interpreting evidence and translating it into investment decisions.
Opportunities and 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 and monitored. 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. 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.
This evolution also complements wealthtech platforms and tokenised securities, both of which point towards a financial system that is increasingly digital, modular and data-driven.
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 will 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. AI will augment rather than replace advisers, working alongside them to deliver faster, more scalable and more personalised services to 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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