Building Scalable Operating Models for Private Markets in the Age of AI

Private Markets Growth Is Reshaping Investment Operations

The investment management industry is transforming rapidly. The rise of agentic AI, continued growth in private markets, and increasing demands on investment operations are forcing firms to rethink how data, workflows, and technology work together.  

Recent research reports from Northern Trust, Alpha FMC, and SimCorp reveals a common theme across the investment management industry: growing private markets allocations, accelerating AI adoption, and increasing investment in operational transformation. While each study looks at the industry through a different lens, they all arrive at a similar conclusion: as private markets grow and AI adoption accelerates, firms need better ways to manage data, connect workflows, and scale operations.

Northern Trust's Asset Owners in Focus 2026: Operational and Data Adaptation in the Era of Digital Disruption surveyed more than 180 asset owners across North America, Europe, Asia-Pacific, and the Middle East to examine how global asset owners are adapting to an increasingly complex investment landscape. The findings show an industry undergoing significant transformation. Northern Trust found that 94% of asset owners now invest in private markets, up from 86% in 2025, while nearly 70% identified AI adoption as a significant operational challenge. Asset owners continue to increase allocations to private markets while simultaneously investing in data, technology, and artificial intelligence to improve efficiency, strengthen operational resilience, and support increasingly sophisticated portfolios.

 AI Transformation Across Middle and Back Office Operations research report by Alpha FMC,  found that AI adoption has become common across investment management, with 91% of firms either using AI today or actively planning implementation. Yet only a small percentage have successfully scaled AI beyond pilot programs, highlighting persistent challenges around data quality, governance, workflow integration, and operational execution. 

SimCorp's 2026 Global InvestOps Report, which gathered perspectives from 200 buy-side leaders globally, found that AI adoption has accelerated significantly across the buy side, while firms increasingly cite data modernization, workflow connectivity, and operational efficiency as strategic priorities. The report highlights a growing recognition that disconnected systems and fragmented data limit the ability to scale both operations and AI initiatives.

Together, these studies reveal the focus has shifted to execution. Firms are now grappling with how to scale increasingly complex operations, operationalize AI, and create the data foundation necessary to support growth.

For firms investing in private markets, the findings reinforce a growing industry challenge. As allocations increase, so does the volume of unstructured documents, data, and workflows that operations teams must manage. 

This raises an important question: How can firms continue to scale private markets operations without increasing operational complexity at the same rate?

Key Takeaways

• Private market allocations continue to grow, increasing operational complexity.
• AI adoption is accelerating across investment management.
• Data quality and workflow fragmentation remain major barriers to scale.
• Firms need trusted, workflow-ready data to operationalize AI.
• Agentic workflows require connected systems, governance, and high-quality data.

Why Are Private Markets Creating Operational Challenges?

According to the Northern Trust's study, 94% of asset owners now invest in private markets, with allocations continuing to rise. At the same time, firms report increasing pressure around data quality, operational resilience, liquidity management, and technology adoption.

As private market portfolios grow, so does the volume of documents, data extraction, validation, reconciliation, and reporting required to support investment operations. Every document contains critical information needed for accounting, reporting, performance measurement, risk management, compliance, and investment decision-making.

Private markets generate enormous volumes of unstructured information:

• Capital account statements
• Capital notices
• Financial statements
• Schedules of investments
• Loan agent notices
• Compliance certificates
• Credit agreements
• and more

Complexity is growing faster than capacity, teams are being asked to manage growing document volumes, tighter reporting timelines, and more sophisticated investment strategies without an increase in resources.

The result is a growing need for scalable operating models that can transform unstructured information into trusted, workflow-ready data.

Why Do AI Initiatives Struggle to Scale in Investment Management?

The Northern Trust study found that asset owners are increasing investment in data, technology, and AI while continuing to face challenges around data availability, quality, and governance. While firms are investing aggressively in AI initiatives, many are discovering that technology alone cannot solve underlying data challenges.

This challenge is echoed across the industry. Alpha FMC found that firms continue to face obstacles operationalizing AI despite widespread adoption, while SimCorp's research highlights the growing need to modernize data architecture and connect workflows across the investment lifecycle. The studies reinforce a common theme that fragmented data and disconnected processes remain significant barriers to achieving operational scale.

Across the investment management industry, firms have invested heavily in dashboards, analytics platforms, and AI solutions. Yet many of these technologies continue to operate in silos. Critical information remains fragmented across documents, emails, investor portals, PDFs, and disconnected systems, preventing data from moving seamlessly across investment workflows. As a result, critical workflows often fail to communicate effectively with one another, creating inefficiencies and limiting the value organizations can derive from their technology investments.

Without trusted, structured, workflow-ready data:

• Reporting becomes slower and more resource-intensive
  
• Operational risk increases as manual processes persist
• Investment teams spend valuable time searching for and validating information
• Data quality issues create downstream reconciliation challenges
• AI initiatives struggle to move beyond experimentation

This challenge becomes even more pronounced in private markets, where critical investment data is often buried within complex, document-heavy workflows. 

The promise of AI is not simply about deploying models. It is about enabling those models to access trusted information, generate reliable outputs, and support real-world investment workflows. Before firms can fully realize the value of AI, they must first solve the data foundation problem.

What Is a Scalable Operating Model for Private Markets?

Research from Northern Trust, Alpha FMC, and SimCorp finds that Investment firms have more AI tools available than ever before, yet many continue to struggle translating AI investments into operational outcomes.

The challenge is not access to AI models. Today, firms can choose from Claude, ChatGPT, Gemini, Copilot, and numerous specialized solutions. The real challenge is operationalizing AI within investment workflows while maintaining governance, transparency, and control.

AI creates value when it can access trusted data, understand context, operate within governance frameworks, and take action across systems.

That requires:

• Structured and validated data
• Information governance
• Auditability
• Workflow integration
• Human oversight

Without these foundations, AI remains another disconnected tool.

As firms mature their AI strategies, the conversation is beginning to shift from AI-assisted workflows to agentic operations. Rather than simply generating insights, AI agents can help classify documents, validate information, route exceptions, trigger downstream processes, and support operational decision-making. However, agentic workflows require the same foundation identified across the Northern Trust, Alpha FMC, and SimCorp research: trusted data, connected systems, governance, and human oversight.

How Can Asset Owners Operationalize AI?

As private markets continue to expand, leading firms are rethinking how investment operations are structured. The future operating model is not built on larger operations teams. It is built on intelligent workflows that can automatically ingest, validate, standardize, and distribute information across the organization. 

At Alkymi, we've helped some of the world's largest asset owners, pension funds, sovereign wealth funds, private banks, and alternative investment managers transform document-heavy processes into scalable, data-driven workflows. By automating the extraction, validation, and delivery of critical investment data, our clients have been able to reduce manual effort, improve data quality, accelerate reporting, and create the trusted foundation required to support AI and future growth.  Alkymi is an AI-powered data and workflow automation platform that helps asset managers, asset owners, banks, pension funds, and alternative investment firms transform unstructured documents into trusted, workflow-ready data.

Across our client base, we are seeing a common trend: firms that invest in transforming unstructured information into trusted, workflow-ready data are better positioned to scale operations, improve transparency, and have greater value from their technology investments.

As private markets continue to grow and AI adoption accelerates, operational scale will increasingly become a competitive advantage. The firms that succeed will be those that build the data foundation capable of connecting people, processes, technology, and increasingly AI-driven workflows across the investment lifecycle.

In the next era of private markets, operational scale will not be achieved by adding more resources. It will be achieved through intelligent automation, connected workflows, and trusted, workflow-ready data.

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FAQ

What is a scalable operating model for private markets?

A scalable operating model for private markets enables firms to manage growing investment activity, document volumes, and reporting requirements without increasing operational complexity at the same rate. It combines people, processes, technology, and data to automate manual workflows, improve transparency, and deliver trusted information across the investment lifecycle. As private market allocations continue to grow, scalable operating models are becoming essential for maintaining efficiency, reducing risk, and supporting future growth.

Why is data quality important for AI in investment management?

AI is only as effective as the data it can access. In investment management, poor data quality can lead to inaccurate analysis, inconsistent reporting, operational inefficiencies, and increased risk. High-quality, validated, and workflow-ready data enables AI solutions to generate reliable insights, automate processes, and support decision-making with greater confidence. Without a strong data foundation, firms often struggle to move AI initiatives beyond pilot programs.

What are the biggest operational challenges in private markets?

Private markets generate large volumes of unstructured information, including capital calls, capital account statements, financial statements, schedules of investments, and loan agent notices. As portfolios grow, firms face increasing challenges related to data collection, validation, reconciliation, reporting, compliance, and workflow management. Many organizations also struggle with fragmented systems, disconnected workflows, and limited visibility across the investment lifecycle, making it difficult to scale operations efficiently.

How can firms operationalize AI in investment operations?

Successfully operationalizing AI requires more than deploying AI models. Firms must first establish a foundation of trusted data, connected workflows, and strong governance. This includes transforming unstructured documents into structured data, integrating information across systems, automating repetitive processes, and maintaining appropriate human oversight. Organizations that focus on data quality and workflow integration are better positioned to realize measurable value from AI initiatives.

What are agentic workflows in investment management?

Agentic workflows use AI agents to perform and coordinate operational tasks across investment processes. Unlike traditional AI tools that primarily provide insights or recommendations, agentic workflows can take action by classifying documents, extracting and validating data, routing exceptions, triggering downstream processes, generating summaries, and supporting operational decision-making. In investment management, agentic workflows have the potential to improve efficiency and scalability, but they depend on trusted data, connected systems, governance, and human oversight to operate effectively.

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