Operational Alpha Can Provide a Crucial ‘Competitive Advantage’

Inefficient financial operations are costing businesses an estimated $100 million per year, according to a survey of more than 1,000 senior executives conducted by Oxford Economics and Fidelity National Information Services Inc.

High costs are driving a growing emphasis on “operational alpha,” the value that investors generate by building streamlined systems and processes to effectively reduce costs and improve decisionmaking in the face of potential risk. In today’s increasingly complex and volatile market landscape, leading firms are leveraging technology to design more efficient operations. Much more than a mere competitive edge, the move toward tech-enhanced operational efficiency is fast becoming understood as a core investment strategy benchmark.

“When institutional investors evaluate managers, they increasingly look for those who invest in digital infrastructure,” says Matt Stauffer, senior vice president and head of back office solutions for FIS Capital Markets. “AI and automation can now extract side-letter terms, streamline approval flows and surface insights from unstructured deal data. Managers who digitize and standardize these workflows are better positioned to handle growth and deliver consistent performance.”

Defining, Measuring Operational Alpha

On its face, operational alpha encompasses the reduction of costs, risk mitigation and enhanced decisionmaking that investors achieve through streamlined systems and processes. In practice, it boils down to an organization’s ability to withstand volatility.

“It’s not really about reducing risk, but being able to make the right decisions around that risk according to the circumstances of the market at any given time,” says Bill Mann, chief investment strategist at financial advice company The Motley Fool.

As Mann sees it, operational alpha effectively keeps investors on the right path—especially during downturns—while also creating products with lower volatility to match investor tolerance. Finally, it addresses the instability of investor preferences, helping people understand their own behavior and make more rational choices.

Stauffer agrees that investors and firms are beginning to understand how to make smarter investment management choices as a core outcome of improved operations.

“When it comes to decisionmaking, the value of operations is now being recognized in ways it never was before,” he says. “Previously, operations focused just on cost and risk management, but now firms are using operational data to better inform investment decisionmaking and portfolio management.”

The potential results are both significant and measurable. Stauffer cites proprietary research from FIS and The ValueExchange showing that buy-side firms are targeting $1.6 million in annual operational savings, “demonstrating the real financial impact of getting this right.”

Data Management Bottlenecks

Shriram Bhashyam, the chief operating officer of Sydecar, a special purpose vehicle and fund administration platform for venture capital, names data governance and data access as two major drivers of operational inefficiency. Bhashyam explains that as private markets have grown in volume, with a greater variety of investor types and increased complexity in investment structures, these data management challenges have become more prominent.

“Critical data [are] spread across disparate sources,” Bhashyam says. “This fragmentation leads to slow turnaround times and increased reconciliation issues.”

In addition, many organizations still rely on manual processes for data entry and retrieval. A 2024 survey by Prague-based intelligent document processing firm Rossum found that 58% of global finance leaders were still turning to spreadsheets as their primary automation tool, while 26% of finance departments reported using no automation tools at all.

For investors, the continued over-reliance on manual documentation is a stubborn source of operational friction.

“Each side letter carries unique terms and obligations, but most managers still track them in spreadsheets or static documents, making it nearly impossible to enforce consistently and creating real risk of missed commitments,” Stauffer says. “Co-investments present another major friction point, because they move quickly with customized terms, yet many managers lack integrated systems linking legal, finance and reporting functions.”

The scattershot nature of these documentation and retrieval systems can make a lasting impact: Stauffer points out that many firms continue to store deal memos as unstructured files, without the ability to tag, search or analyze them for later reference. As a result, organizations squander the opportunity to draw from their own critical decisionmaking intelligence, losing out on valuable institutional knowledge that could improve future investment decisions.

Technological Transformation

As machine learning technology continues to advance, investors are turning to artificial intelligence tools to help alleviate operational pain points. In a recent Deloitte survey of C-suite executives, a majority of investment managers reported that they expect AI to strengthen their operation’s critical thinking and problem-solving (64%), creativity (55%), teamwork (55%) and adaptability (55%). The same survey found that 57% of investors anticipate efficiency and productivity gains from adopting AI, while 38% foresee improvements to products and services, and 38% see potential for enhanced fraud detection and risk management.

“Trainable AI can be used to identify data anomalies across silos, match trades and speed up reconciliations,” Bhashyam says. “Additionally, many rote and repetitive tasks can be readily handled by technology, including capital calls, distribution notices and many ‘happy path’ [know your customer] and [anti-money-laundering] checks.”

Early adopters such as The Motley Fool provide a case in point for investors’ potential gains from adopting tech-enhanced systems. The firm has developed its own AI tooling to improve speed and efficiency in administrative and operational workflows such as client onboarding, account transfers and documentation, as well as to generate proprietary indexes for its passively managed funds and to analyze investor behavior.

“AI has helped a lot in terms of us going behind our investors and figuring out what people are good at and where signal is coming from,” says Mann.

The firm has also leveraged AI to make its funds more tax-efficient.

“One of the magical problems that you can have from holding a company for a long time is that it appreciates a lot,” Mann says. “So one of the areas where AI workflows have translated directly to the bottom line for our investors is tax-loss harvesting and tax-advantaged trading.” While firms tend to focus disproportionately on expense ratios, Mann points out that tax implications are often overlooked: “But taxes are ultimately much more expensive to investors than most expense ratios could ever be—especially if you do them wrong.”

Though AI offers a potential vehicle to improve operational efficiency, it is not a stand-alone silver bullet. Firms looking to fix disjointed data management systems will need to invest in integrated architecture that unifies data storage and management with business processes, says Stauffer. Organizations will also need to resolve fundamental gaps in data quality, completeness and consistency to maximize the value of exception management and reconciliation tools.

Those that put in the legwork and the systems are poised to yield results, according to Stauffer: “The firms that use their operational infrastructure as a competitive advantage, rather than just a cost center, are the ones creating sustainable operational alpha that translates directly to better investor outcomes.”

Tags: Artificial Intelligence, biotechnology, investment operations