Will AI in wealth management follow the pager or the mobile phone?

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If you were a medical professional in the 1980s, a pager was probably part of your daily life. A one-way communication device that told you someone was looking for you but gave you no way to respond. The pager remained on the market for years, but it never made a real breakthrough beyond emergency services and specific business functions. The NHS was still running 130,000 of them in 2019, representing roughly 10% of the total left in use globally. The Health Secretary called them obsolete and urged their replacement.

The mobile phone took the same basic need, the ability to communicate remotely, and solved it so comprehensively that by 2025 there are a projected 7.49 billion mobile users worldwide. The difference between these two technologies is not just one of timing or marketing. It is a difference of kind. The mobile phone succeeded because it met the three criteria of a General-Purpose Technology: pervasiveness across sectors, continuous improvement in functionality, and the ability to generate an ecosystem of innovation around it. The pager failed on all three.

That framework is directly relevant to AI in wealth management. The question is not whether AI is impressive. It clearly is. The question is whether it meets the General-Purpose Technology test in a way that pagers did not.

Pervasiveness: AI is already everywhere in wealth management

AI adoption across financial services increased by 37% between 2020 and 2021, according to KPMG's research across seven industry sectors. Within wealth management specifically, AI is now embedded in investment decision-making, client service, risk assessment, compliance monitoring, and portfolio construction. Real-time machine learning applied to datasets spanning finance, supply chains, agriculture, healthcare, and retail gives wealth managers systematic insights that were simply not available a decade ago. The breadth of application is the first marker of a general-purpose technology, and AI passes this test convincingly.

Improvement: the functionality keeps expanding

AI-powered chatbots, robo-advisors, and virtual assistants have moved from novelty to operational infrastructure. Revenue generated by robo-advisors multiplied 15 times between 2017 and 2023. These tools now handle routine client inquiries, provide account information, manage basic investment decisions, and deliver financial education, freeing human advisors to focus on complex situations where judgement and relationship matter most. Studies in adjacent sectors have demonstrated the pattern: equipping medical professionals with digital assistants instead of pagers resulted in faster response times and reduced failure rates. The wealth management sector is seeing the same dynamic. The tool improves, so the human's time is redirected to higher-value work.

The surrounding ecosystem has evolved in parallel. Data lakes, cloud analytics, and on-demand processing power have removed the infrastructure constraints that previously limited AI deployment. Microsoft committed EUR3.2 billion to AI and cloud infrastructure in Germany alone by 2025. The computational resources that AI requires are no longer a bottleneck for firms willing to invest. The technology that interprets images, processes natural language, and handles IoT data flows has matured alongside the core AI models. This is an ecosystem, not a single tool, and ecosystems are far harder for competitors to replicate or for markets to ignore.

Innovation: tokenisation accelerates the trajectory

In the medium term, the convergence of AI and blockchain-based tokenisation of investment funds will accelerate AI's trajectory in wealth management. AI identifies patterns and generates insights. Blockchain provides the immutable infrastructure to store and verify those insights across multiple parties. Tokenisation opens markets to investors previously excluded by cost or complexity. Bain estimated in 2023 that tokenisation could unlock a $400 billion market in alternative investments alone, creating entirely new asset classes that are accessible, liquid, and transparent in ways the current infrastructure does not support.

Each short-term AI capability, from portfolio optimisation to client engagement to risk analytics, becomes more powerful when deployed on tokenised, blockchain-verified infrastructure. The combination is not additive. It is multiplicative. And it is the kind of ecosystem-level innovation that separates a general-purpose technology from a clever tool.

The trust question: 2008 casts a long shadow

Every technology that depends on data and algorithms raises legitimate concerns about privacy, security, bias, and the boundaries of human oversight. AI in wealth management is no exception. But the most important obstacle is not technical. It is trust.

The 2008 financial crisis was caused by products so complex that very few people understood them. The subprime market collapsed not because the mathematics was wrong but because the people making decisions, buying, selling, rating, and regulating, did not understand what they were holding. Could AI in wealth management create the same dynamic? Could advancement in AI produce investment products and strategies that are beyond human comprehension, leading to a concentration of risk that nobody recognises until it is too late?

The parallel is not alarmist. It is structural. If the models that drive investment decisions are opaque to the advisors using them, to the clients whose money is at stake, and to the regulators responsible for market stability, then the trust deficit that undermined the financial system in 2008 could emerge again in a different form. The vetting of data sources, the explainability of AI-generated recommendations, and the regulatory frameworks covering privacy, compliance, and ethics are not optional additions to an AI strategy. They are the conditions under which AI earns its place in wealth management at all.

Future AI models in this sector must be interpretable and explainable to both clients and regulatory authorities. The firms that build this into their architecture from the start will have a structural advantage over those that treat it as a compliance afterthought. The pager died because it solved too narrow a problem. AI in wealth management has the pervasiveness, the improvement trajectory, and the ecosystem innovation to follow the mobile phone's path. Whether it does depends on whether the industry solves the trust problem before the trust problem solves it for them.