For small and mid-sized asset managers, modernizing data infrastructure is no longer simply about keeping pace with the market, it is a matter of margin preservation. As fee compression, product complexity, regulatory expectations, and investor reporting demands intensify, the operating model behind the investment engine is under greater scrutiny. Firms that continue to rely on manual processing, spreadsheet-based controls, and fragmented data workflows are carrying a cost structure that becomes harder to defend with every new client, strategy, and reporting obligation. In fact, PwC’s 2025 Asset & Wealth Management Revolution research reveals that profit as a share of AUM has fallen roughly 19% since 2018, even as global AUM keeps climbing.1
The issue is not only that manual processes are inefficient. It is that they create a compounding drag on operating leverage. In a manual data management paradigm, growth itself can become margin dilutive. Expanding into new asset classes, onboarding clients, and responding to more RFPs increase the volume, variety, and urgency of data work, placing unsustainable pressure on data teams that lack automation or a unified data layer to manage integrations across systems.
The operating model was not built for today’s data burden
Many asset management operating models were built around human intervention as the connective tissue between systems. Portfolio accounting platforms, order management systems, custodians, administrators, market data providers, risk tools, client reporting applications, and spreadsheets became part of a patchwork architecture. Over time, operations teams learned to make that architecture work through extraction, transformation, comparison, and correction.
That approach may have been manageable when product lines were simpler and reporting windows were longer. But asset managers now handle more asset classes, external data sources, customized mandates, and frequent reporting requests. Faster settlement cycles, tighter risk oversight, and rising transparency expectations leave less room for delayed or inconsistent data.
Manual workflows do not scale gracefully in today’s environment. As data volumes rise, teams add more checks, spreadsheets, handoffs, and review cycles. The result is an operating model that becomes more expensive precisely when the business needs more leverage.
Manual reconciliation is not a back-office inconvenience, it is a margin issue
Reconciliation is one of the clearest examples of how manual processing turns into margin leakage. On paper, reconciliation is a control function: ensuring that positions, cash balances, transactions, fees, and reference data align across systems and external parties. In practice, when reconciliation is manual, it becomes a recurring tax on the business.
Teams spend hours gathering data from different sources, normalizing formats, matching records, identifying breaks, researching discrepancies, and documenting resolutions. Each exception may require outreach to custodians, administrators, counterparties, internal teams, or clients. In many organizations, these processes remain dependent on spreadsheets, email chains, institutional memory, and manual signoffs.
The direct labor cost is significant, but the opportunity cost is often greater. Skilled operations team members become consumed by repetitive tasks. Reporting cycles slow down. Errors become more likely. Audit and compliance reviews become more burdensome because evidence is scattered across systems and spreadsheets.
For a firm already facing pressure from declining fees, rising personnel costs, and technology investment needs, these inefficiencies flow directly into operating margin. They may not appear as a line item labeled “manual processing,” but they show up in headcount growth, delayed closes, duplicated work, error remediation, higher oversight costs, and the inability to scale without adding people.
Scale is widening the competitive gap
Large asset managers are not immune to operational complexity, but they can spread technology investments across larger asset bases and dedicate larger teams to automation, governance, and platform consolidation. Many are moving from isolated automation projects toward enterprise-wide unified data foundations that support AI-enabled workflows, embedded and automated controls, and more scalable client coverage.
That matters because the competitive gap is increasingly based on operating leverage. Firms that can onboard clients, support new products, process larger data volumes, and deliver timely reporting without proportional cost increases will be better positioned to defend margins and invest in growth. Firms that cannot will face a difficult tradeoff: absorb higher costs, accept slower growth, or reduce service levels.
Data automation changes the economics of growth
Automation changes the economics of growth by reducing manual effort at key points in the data lifecycle. Reconciliation is a natural starting point because it touches so many downstream processes: NAV oversight, performance reporting, risk analytics, fee calculations, regulatory reporting, client statements, and management dashboards.
Modern reconciliation workflows can ingest data from multiple sources, standardize it, apply matching logic, detect anomalies, prioritize exceptions, and maintain an audit trail. Instead of reviewing every record manually, operations teams focus on the exceptions that matter most. Lineage, approvals, and resolution history are preserved as part of the process.
The benefits compound when automation is connected to a unified data foundation. Rather than adding point solutions to individual processes, firms can bring ingestion, orchestration, persistence, semantic modeling, metadata management, governance, lineage, and API access into one environment. Core data processes can be automated once and reused across reporting, analytics, operations, distribution, and client service. The unified data layer connects and governs data from the fund accounting, order management, custodian, administrator, risk, and reporting systems already in place, rather than replacing them.
For small and mid-sized managers, the objective should not be to replicate the technology budgets of the largest players. It should be to create a reusable operating foundation: the ability to support more assets, clients, strategies, and data without a corresponding increase in manual work or expense.
The risk dimension: errors, delays, and reputational exposure
The cost of manual processing is not limited to efficiency. It also introduces risk. In asset management, data quality issues can have consequences well beyond the operations team. A pricing discrepancy, stale reference data, incorrect fee calculation, or delayed reconciliation can affect portfolio reporting, client communications, regulatory filings, and internal decision-making.
As regulatory expectations rise, that fragility becomes harder to justify. Auditors, boards, and clients increasingly expect evidence of consistent controls, clear data lineage, timely exception management, and repeatable processes. A manual environment may still produce accurate results, but proving that accuracy can require significant effort.
A unified data foundation helps make controls part of the workflow itself. Matching rules, exception thresholds, approvals, timestamps, lineage, and audit logs become embedded in the process. Human judgment remains essential, but it can be applied where it adds value, such as in resolving genuine exceptions, interpreting complex cases, and improving the process over time.
AI raises the stakes for data modernization
Artificial intelligence is adding urgency to the modernization agenda. Many asset managers are exploring AI for research, client engagement, compliance monitoring, portfolio analytics, and operational support. But it is challenging to deliver meaningful business value with AI if the underlying data environment is fragmented, inconsistent, and dependent on manual intervention.
There is a practical limit to what firms can achieve by layering new tools on top of old infrastructure. If data is trapped in disconnected systems, if reconciliation occurs after the fact, or if teams cannot trust core datasets, AI use cases will remain constrained. Pilots may be possible, but production deployment will be difficult.
This is where the cost of inaction becomes strategic. Firms with unified, governed, current data are better positioned to deploy AI into operational workflows, automate routine analysis, and identify risks earlier. Firms that delay modernization may not only carry higher operating costs; they may also find themselves unable to adopt the tools that could help close the scale gap.
A practical modernization path for smaller managers
Modernization does not need to begin with a sweeping transformation program. For many small and mid-sized asset managers, the most effective approach is to start with high-friction, high-volume workflows where the margin impact is visible: reconciliation, fee processing, financial reporting, client reporting, assets and flows reporting, and RFP/RFI response support.
Firms should begin by quantifying the true cost of manual work. How many hours are spent gathering and validating data? How many exceptions recur each cycle? How often do teams rely on spreadsheets outside controlled systems? How long does it take to produce trusted reporting? How much growth could the current operating model support without additional headcount?
From there, firms can prioritize workflows that create the greatest operating leverage and connect them through a unified data foundation. The practical path is not to replace the systems asset managers already rely on, but to create a governed data and control layer that connects them, harmonizes data, embeds controls, and turns fragmented workflows into reusable services. The goal is to reduce manual touchpoints, improve data consistency, preserve auditability, and support multiple functions. Each additional use case should make the foundation more valuable, not add another integration burden.
For small and mid-sized asset managers, the margin question is increasingly unavoidable. Manual processing may feel familiar and inexpensive in the short term, but over time it becomes a hidden tax on growth. A unified data foundation offers a more durable path: automate core data processes once, reuse them across the business, and scale with greater confidence as complexity increases. Learn how InterSystems can help your firm create a unified data foundation that supports your firm’s growth.
1 PWC The Profitability Paradox: Competing for Relevance and Returns
