From Products to Solutions: Climbing the Investment Management Value Chain

For most people, driving a car is not about understanding the intricacies of internal combustion engines, the mechanics of power transmission, or the physics of tire grip on various road surfaces. Instead, the primary concern for most drivers is simply getting from point A to point B safely and comfortably. They turn the key (or press the button), step on the gas, and trust that the vehicle will perform its function without requiring deep technical knowledge on their part. Enthusiasts aside, many drivers are content with a reliable vehicle that provides a smooth ride, decent fuel efficiency, and perhaps some good tunes to make the journey more enjoyable.

Similarly, when it comes to investing, the average retail investor often approaches the financial markets with a comparable mindset. Just as most drivers aren’t automotive engineers, most investors aren’t financial experts or professional fund managers. They may not be deeply interested in the nuances of asset allocation strategies, market timing techniques, or the intricacies of derivatives trading. Instead, their focus is on the destination – the financial goals they hope to achieve through their investments.

For these investors, the primary objective is to fund important life events and milestones. This could mean saving for their children’s education, accumulating a down payment for a house, or building a nest egg for a comfortable retirement. Simply stated, they want a “vehicle” for their money that will reliably carry them towards their financial destinations, much like a dependable car carries a driver to their physical destination.

Similarly, for institutional investors, objective-driven investment management has traditionally been confined to a range of Asset-Liability Management (ALM) techniques, including Liability-Driven Investment (LDI) strategies primarily used by pension funds and insurance companies. These approaches aim to match the duration and cash flows of assets with future liabilities, reducing risk and ensuring the ability to meet future obligations. However, this narrow focus often fails to address the broader and more complex objectives of many institutional investors. 

Outsourced Chief Investment Officer (OCIO) services emerged as an attempt to provide a more comprehensive, objective-driven approach for institutions. The goal of OCIO is to offer holistic investment management services, including strategy development, manager selection, risk management, and operational support, tailored to the specific needs and objectives of each institution. 

Despite these intentions, however, OCIO has largely fallen short of its promise. Many OCIO providers have struggled to truly customize their offerings, often relying on standardized models and a limited set of strategies. They frequently lack the flexibility to fully incorporate the unique constraints, risk tolerances, and long-term objectives of individual institutions. Moreover, OCIO services have often failed to provide the level of transparency, control, and alignment of interests that institutional investors require. As a result, while OCIO has gained some traction, it has not revolutionized institutional investment management to the extent originally envisioned, leaving a real gap in the market for truly objective-driven, customized investment solutions for institutional investors.

Can AI be used to deliver more holistic, customizable, and scalable objective-driven investment solutions for both retail and institutional investors, alike?

CURRENT LANDSCAPE

First, it’s worth noting that the current landscape of the asset and wealth management industry, today, remains largely product-centric rather than solutions-centric.

This is evident in the way many financial institutions structure and market their services. Instead of starting with the investor’s life goals and working backwards to create tailored solutions, advisors often begin with a roster of investment products – mutual funds, ETFs, separately managed accounts, and various alternative investments – and attempt to fit these products into the investor’s portfolio. 

And, while ultra-high net worth (UHNW) and family office clients typically demand highly personalized services, most wealth management clients today are relegated to model portfolios often misaligned with their specific goals. For this privilege, investors pay on average 65 to 150 basis points. Any rebalancing and risk management is performed en masse for all cohorts allocated to the same model portfolio.

Truth be told, the product-first approach is deeply ingrained in the industry’s DNA, shaped by decades of focusing on relative performance against benchmarks and peer groups. Asset managers have traditionally competed by touting the merits of their individual funds or investment strategies, often emphasizing short-term performance or the pedigree of their portfolio managers. While these factors aren’t irrelevant, they often overshadow the more critical question of how well a particular investment helps an individual investor meet their personal financial objectives.

Moreover, the compensation structures within the industry may reinforce this product-centric mindset. Many financial advisors and wealth managers, after all, are incentivized to sell specific products or to maintain a certain level of assets under management (AUM), rather than being rewarded for helping clients achieve their long-term financial goals. This can lead to a misalignment of interests, where the focus is on gathering assets and generating transaction fees rather than providing holistic financial solutions.

MISALIGNMENT

The result is an industry that often speaks a different language than its clients. While investors are thinking in terms of life events, personal aspirations, and other objectives, many asset and wealth managers are still primarily discussing asset allocation, alpha generation, and risk-adjusted returns. This disconnect can leave investors feeling confused, overwhelmed, and uncertain about whether their financial strategies truly align with their objectives. Worse yet, the model often fails to deliver and may incur higher fees and potential tax inefficiencies.

The industry’s focus on investment products rather than investment solutions also tends to encourage short-term thinking. Emphasis on quarterly or annual performance metrics can distract from the long-term perspective necessary for achieving life goals. This short-termism can lead to excessive trading, higher costs, and increased anxiety for investors as they react to market fluctuations that may be irrelevant to their long-term objectives.

In essence, much of the current asset and wealth management industry operates as if selling specialty cars to people who simply need transportation. Instead of offering a reliable vehicle to get from point A to point B, they’re showcasing high-performance sports cars, rugged off-road vehicles, and luxury sedans, leaving the average investor to figure out how these options translate into achieving their personal financial goals.

This misalignment represents both a challenge and an opportunity for the industry. The shift from a product focus to a solutions focus is not just a matter of repackaging existing offerings but requires a fundamental rethinking of how financial services are developed, delivered, and measured in terms of their real-world impact on investors’ lives. It also requires a next-generation operating model that more closely integrates the client journey with portfolio management disciplines.

CLIMBING THE VALUE CHAIN

By reorienting their services around client objectives, rather than pushing products, managers have the potential to create more value for investors and build stronger, more enduring relationships. Moreover, in an age where many investment management strategies have been commoditized and firms have faced significant margin compression, an objectives-driven approach represents an opportunity for managers to climb the value chain, thereby improving profitability. 

To successfully transition to such an operating model, however, the investment process needs to be developed into a more holistic approach that actively considers and responds to client objectives, risk tolerances, and constraints in portfolio construction and management. Here, the integration of client-facing, contextual data, and portfolio management processes form a Virtuous Data Feedback Loop (Figure 1) through which both distribution and manufacturing capabilities are continuously refined.

Client engagement strategies must further be enhanced to provide more comprehensive and personalized insights through improved communication and reporting capabilities. Advanced analytics and portfolio management systems must support customized solution design. Performance metrics should evolve to focus on client outcomes and objectives rather than solely on benchmark-relative returns. 

Multi-manager platforms can also thrive in a solutions-oriented paradigm, but not if each individual investment team operates as a vertically integrated silo that lacks a holistic view of the client. In my own experience at several large multi-manager shops, such teams often lack the contractual client agreements, directives, and incentives to share client data with the top-of-house franchise. This inherently limits the ability to deliver client solutions that truly leverage the breadth of the organization’s capabilities, or to provide comprehensive risk management and a consistent client experience across all products and strategies. Indeed, this makes the franchise look more like a supermarket than a well-integrated investment platform.

Particularly important, a successful implementation of a solutions-oriented approach requires strong leadership and a supportive organizational culture. Leaders must clearly articulate the firm’s vision for such a business model and develop a comprehensive strategy for implementation. 

Effective change management is crucial to address any organizational resistance and to foster a culture of innovation and adaptability. Cultivating a deep understanding of client needs and objectives across the organization, from investment professionals to client-facing teams, is essential to developing a client-centric mindset. Understandably, aligning compensation and incentive structures with the firm’s solutions-oriented objectives and client outcomes is also necessary to drive the desired behaviors and results.

TECHNOLOGY ENABLEMENT

The integration of client-facing and external contextual data with portfolio strategies and risk management represents a significant leap forward in the ability to deliver objective-driven investment solutions at scale. By combining detailed client information – such as financial goals, risk tolerance, time horizons, and life events – with broader economic and market data, asset managers can create a more comprehensive picture of each investor’s needs and circumstances. 

This holistic view enables the construction of truly personalized portfolios that are not just collections of investment products, but cohesive strategies aligned with individual client objectives. Moreover, this integration allows for dynamic adjustments and rebalancing of investment strategies in response to changes in both personal circumstances and market conditions. For instance, a major life event like the birth of a child or an impending retirement can be automatically factored into the investment strategy, ensuring that the portfolio remains aligned with the client’s evolving financial goals.

At the core, investment managers need a modern, flexible, and scalable data architecture to pursue an objective-driven operating model. The architecture must support structured, semi-structured, and unstructured data from multiple sources, accessible via APIs. Strong security controls, data curation, and governance are table-stakes. Implementing a decentralized data architecture, such as a data mesh, can enable different domains within the organization to manage and share their data as products, improving data quality, accessibility, and governance across the firm. 

While product or strategy-specific data assets may be federated across a data mesh architecture, the platform will still benefit from shared foundational infrastructure and select data services that serve common requirements. These centralized services enable quicker onboarding of new investment teams and help to realize some economies of scale across the franchise. Data virtualization technologies can further create a logical data layer that integrates data from multiple sources without physically moving or replicating it, enhancing data agility, reducing reconciliation, and simplifying data integration efforts.

Sitting atop this data architecture, artificial intelligence (AI) and automation play critical roles in enabling the creation of a powerful, scalable feedback loop for continuous portfolio optimization. AI models can analyze vast amounts of data from diverse sources, identifying patterns and correlations that human analysts might miss. AI-powered analytics can additionally help to assess manager performance, risk profiles, and investment styles, enabling more informed manager selection decisions. These capabilities not only enhance investment selection and alpha generation, but also allow for real-time monitoring of portfolio performance in relation to client objectives, market conditions, and risk parameters. 

By aggregating and analyzing vast amounts of structured and unstructured data from multiple sources, managers can generate actionable insights to identify investment opportunities, inform investment decisions, and facilitate portfolio construction. As circumstances change – whether due to market movements, shifts in economic indicators, or updates to client information – AI systems can suggest or even automate portfolio adjustments to maintain alignment with the client’s goals. 

Sophisticated risk models and scenario analysis can also be employed to assess and manage portfolio risks across multiple dimensions. Enhanced by AI, these models can expand beyond the traditional set of financial factors to also incorporate contemporaneous contextual data, like sentiment analysis. Detailed performance attribution analysis can help understand the drivers of portfolio returns and inform investment decisions. Personalized, interactive reports can provide clients with clear insights into their portfolio performance and progress towards objectives.

Furthermore, machine learning models can continuously refine their understanding of how different investment strategies perform under various conditions, improving their ability to construct, manage, and adjust objective-driven portfolios over time. This AI-driven approach not only enhances the scalability of personalized investment solutions but also creates a more responsive and adaptive investment process that can better serve the evolving needs of clients.

By leveraging these advanced technological capabilities, asset managers can enhance their ability to deliver customized, objective-driven solutions across a diverse range of asset classes and investment strategies. Fundamentally, this approach can help firms differentiate themselves in an increasingly commoditized industry and create higher-value relationships with clients.

CONCLUSION

The investment management industry stands at a critical juncture. The traditional product-centric approach, which has long dominated the industry, is increasingly misaligned with the real needs and goals of both retail and institutional investors. As many managers confront the clear challenges associated with commoditized strategies and margin compression, the opportunity to transition from a product-centric operating model to one that is solutions-centric represents a differentiated future-state that is beneficial to managers and clients, alike.

For retail investors, this means moving beyond simple product offerings to comprehensive solutions that directly address life goals such as education funding, home ownership, and retirement planning. For institutional investors, it requires expanding beyond traditional ALM and LDI strategies to more flexible, customized approaches that fully encompass their unique objectives and constraints.

Such a transition requires a fundamental reimagining of how investment solutions are conceived, constructed, and delivered. It requires a new operating model that is structured to deliver scalable, personalized investment strategies that adapt to changing client circumstances, risk tolerances, and market conditions.

Moreover, it necessitates the application of advanced technologies, including a modern and cohesive data architecture, AI, and automation. Virtually unimaginable several years ago, and certainly difficult at that time to achieve at scale, the state of technology is quickly evolving to enable new business models that deliver unique client experiences. Successful managers will leverage such a technology platform not just to drive operational efficiencies, but as a core component of their fiduciary relationships and investment processes, enabling them to deliver truly personalized, objective-driven solutions at scale.

This evolution will not only benefit investors by providing them with more relevant and effective investment strategies, but it will also allow asset managers to differentiate themselves in an increasingly competitive market. The managers who will thrive in this landscape are those who can successfully bridge the gap between investment expertise and client objectives. This will not only add genuine value to investors’ lives, but will also secure the industry’s own relevance and profitability in an ever-evolving financial landscape.