Morgan Stanley and Oliver Wyman project tokenized assets to hit $2.3 trillion by 2030, driven by collateral mobility and AI-driven operating model shifts.

The global wealth and asset management landscape is entering a period of deceptive stability. On the surface, the numbers are staggering: global managed assets stood at $159 trillion in 2026, with household financial wealth reaching $341 trillion. Projections suggest these will climb to $247 trillion in assets under management (AUM) and $469 trillion in household wealth by 2030. However, a deeper dive into the data reveals a precarious reality. Much of the recent growth has been a byproduct of market performance and currency effects rather than the acquisition of new capital.

According to a joint report by Oliver Wyman and Morgan Stanley, titled Breaking from the Pack: The Race for Above-Market Growth, over 80% of AUM growth in 2025 was driven by market swings. This environment allows firms to appear healthy while they fail to win actual market share or convert growth into sustainable profit. To break away from this pack, the industry is pivoting toward two structural pillars: the tokenization of real-world assets and the enterprise-wide scaling of artificial intelligence.

The $2.3 Trillion Tokenization Forecast

Tokenization—the process of recording asset ownership, such as bonds or fund shares, as digital tokens on a blockchain—is moving from theoretical experimentation to institutional utility. The base-case projection puts the tokenized real-world assets market at approximately $2.3 trillion by 2030, a massive leap from the current $40 billion valuation. While some analysts expected a broad migration of all investment products to the blockchain, the actual growth engine is far more specific.

The primary driver is not the democratization of investment, but the optimization of the plumbing behind the scenes. The report identifies collateral mobility and asset utility as the dominant forces, accounting for $1.7 trillion of the projected total. In the current system, cash, U.S. Treasuries, and money-market holdings are fragmented across various custodians and settlement systems. Moving or substituting collateral to secure a trade can take hours or even days.

By utilizing tokenized assets, financial institutions can transfer and pledge collateral across different venues in near real-time. This efficiency reduces the necessity for firms to pre-fund transactions or maintain oversized liquidity buffers, creating a leaner, more responsive capital structure. Other contributors to the $2.3 trillion figure include reserve and treasury management ($400 billion), global securities distribution ($200 billion), and operational efficiencies ($80 billion), while private market transferability adds a modest $10 billion.

Why speed beats cost in the blockchain race

A common misconception regarding blockchain adoption is that lower fees will be the primary catalyst. However, the data suggests otherwise. When comparing tokenized funds to traditional financial products, a slight fee premium often exists for the tokenized versions, likely due to the high costs of building and managing the necessary blockchain infrastructure. Consequently, cost savings are not the short-term driver.

The real value proposition is velocity. The ability to move assets instantly is far more valuable to a global institution than a marginal reduction in transaction fees. We are already seeing early evidence of this in the market; for instance, the HINC token, a tokenized credit fund from Securitize, is being used as collateral on Loopscale, allowing eligible investors to borrow the USDG stablecoin without needing to redeem their original stake.

Scaling AI beyond the pilot phase

Parallel to the rise of tokenization is the evolution of Generative AI. For several years, the industry treated AI as a collection of isolated pilots or point solutions—small-scale experiments that improved a single task but didn't change the business. The industry has now reached a tipping point where AI must be scaled across the entire enterprise.

This shift is forcing firms to rethink their operating models entirely. The goal is no longer just productivity, but the integration of AI to drive better investment decisions and more effective client engagement. As detailed in previous strategic analyses, Gen AI is a critical component of a broader strategy to build resiliency and capture growth in a market where revenue and profit are increasingly decoupling.

The human crisis in wealth management

While the technology evolves, the human element of wealth management is facing a systemic crisis. A significant wealth transfer is underway in the US, with Cerulli Associates projecting $124 trillion will change hands over the next two decades. A disproportionate amount of this—roughly $40 trillion—will pass to widows, as women typically outlive their spouses.

The current operating model is ill-equipped for this transition. Historically, the industry was built around a male-centric client model, often resulting in transactional and impersonal interactions with surviving spouses. This is compounded by a shrinking pool of advisers and a crushing administrative burden. Research from Fidelity in 2025 indicates that 59% of an adviser's time is consumed by administration and compliance, leaving only 27% for high-value client work.

The financial system is running on an operating model built for a completely different era and now caught in the crosshairs of four converging headwinds.

This gap creates a desperate need for the AI scaling mentioned previously. Without automating the administrative sludge, advisers cannot possibly manage the emotional and complex needs of the next generation of wealth holders, particularly during the grueling process of retitling accounts and navigating tax rules after a loss.

Breaking the growth plateau

To achieve above-market growth, firms must address the fact that AUM growth is currently a tide that lifts all boats, regardless of merit. The firms that will succeed by 2030 are those that can successfully integrate the following components:

• Collateral Optimization: Transitioning to tokenized Treasuries and money-market funds to eliminate liquidity buffers.
• Operational Overhaul: Using AI to shift the adviser's role from administrator to high-value relationship manager.
• Targeted Product Development: Focusing on the narrower set of products and client segments where economics are actually concentrating.

The transition to a $2.3 trillion tokenized market is not a guarantee but a base-case scenario. Higher adoption could push this figure to $5.5 trillion, while a slower rollout might see it stall at $1.1 trillion. Regardless of the final number, the direction is clear: the future of asset management is digital, instantaneous, and AI-augmented.

Global Implications for US and UK Enterprises

For entrepreneurs and firms in the USA and UK, these trends signal a shift in how capital is managed and deployed. In the US, the focus on tokenized U.S. Treasuries as collateral aligns with the deep liquidity of the American bond market, providing a massive opportunity for fintechs to build the middleware that connects legacy custodians with blockchain ledgers. The US regulatory environment remains a focal point, as the clarity around the classification of tokenized assets will determine the speed of the $1.7 trillion collateral mobility shift.

In the UK, the push toward digital assets aligns with the City of London's ambition to remain a global fintech hub. UK firms can leverage the shift toward tokenization to improve the efficiency of private markets, which currently suffer from high barriers to entry and low liquidity. For both markets, the AI imperative is urgent. The disparity between the volume of wealth being transferred and the capacity of human advisers to manage it creates a massive market gap for AI-driven wealth-tech solutions that can automate compliance and administration, allowing human experts to focus on the complex tax and emotional needs of the new wealth holders.

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Sources: Oliverwyman (2), Newisty ·