A global AI race defined by speed
AI and digital finance are moving from experimentation to system-level redesign. Lloyds Banking Group’s push to place customer deposits on blockchain rails and pair them with AI-driven processes illustrates this shift. The bank expects tokenised deposits and AI to reshape how Britons buy homes, compress execution times, and remove intermediaries. Its chief executive compares the potential impact to the arrival of the smartphone, predicting a five-year window where financial services become more personalised and intuitive.
These developments show how integrated technologies—tokenisation, blockchain, smart contracts, and AI—are converging into a new infrastructure layer. It is no longer a question of digitalising old processes; it is the creation of new ones.
But while the technology frontier is moving rapidly, nations are not moving at the same pace.
Context — The divergence between regulatory ambition and technological acceleration
The US and China are already operating at full throttle. They are scaling large language models, deploying autonomous agents into financial workflows, and enabling private-sector experimentation at speed. China has spent a decade building nationwide digital-payments infrastructure. The US has now accelerated stablecoin regulation through the Genius Act, signalling that the state intends to enable, not constrain, digital-asset innovation.
The UK is trying to keep pace. Lloyds’ nationwide pilot of tokenised deposits demonstrates that the country can run system-wide experiments. Its ambition is clear: redesign mortgages through smart contracts; streamline value exchange; embed AI guidance for customers. It is a genuine attempt to modernise financial rails.
Europe, by contrast, is still fastening the seatbelt. While the US and China are already racing, and the UK is accelerating, the EU is putting on the gloves, adjusting the helmet, and checking compliance on the starting grid. Regulatory structures—MiCA, AI Act, cross-border harmonisation—are designed for safety, but at the cost of inertia.
There is a real possibility that by the time Europe is technically ready to run, the race will be largely decided.
Our view — The adoption bottleneck is not technology; it is regulatory drag
Our experience deploying AI in asset managers across the UK, Luxembourg, and Switzerland shows that Europe’s gap is not capability. European firms have top-tier quant teams, deep compliance expertise, and strong digital-transformation budgets. The bottleneck is structural.
European regulation demands that firms solve governance before deployment. The US and China solve governance during deployment. That difference changes velocity.
Even when firms understand the value of AI agents—automated research, automated compliance checks, autonomous workflow routing—the timeline to production stretches because of overlapping supervisory expectations: explainability, audit, human-in-the-loop requirements, and cross-border data constraints.
But what is often missed is that delay carries a compounding cost. AI adoption benefits from cumulative acceleration: when one workflow speeds up, every interconnected workflow speeds up. The US and China are already stacking these compound gains. If Europe delays long enough, the productivity delta will not be linear; it will be exponential.
Implications — What Europe’s asset managers must do now
Build AI governance and infrastructure early
Waiting for regulatory clarity is the slowest strategy. Firms should pre-build governance frameworks, model validation layers, and auditability systems. This ensures that once approval arrives, deployment is immediate.
Prioritise interconnected automation
Adopt agentic AI not as isolated tools but as linked systems—research, risk, compliance, reporting. Europe must unlock the compounding effect, not isolated productivity pockets.
Engage regulators actively
Regulation is moving, but slowly. Firms that shape the conversation with supervisors will gain operational clarity faster than those that wait passively.
Prepare for tokenisation + AI convergence
The Lloyds example is a preview. Tokenised deposits, tokenised funds, AI-driven smart-contract workflows, and machine-driven settlement will define the next infrastructure layer. Asset managers in Europe need to build literacy now, not after MiCA extensions arrive.
Conclusion — Europe must stop preparing and start running
The future of AI in asset management will not be won by the best models but by the fastest adoption cycles. Today, the US and China are already at full speed. The UK is accelerating with system-wide pilots. Europe is still adjusting equipment on the starting line.
If Europe does not shift from regulation-first to deployment-with-governance, it risks entering the race only after the podium is full. The next five years will determine whether European financial institutions remain competitive or become slow-moving observers in a global AI-driven financial system.
The window to accelerate is open, but it will not stay open for long.
