Rethinking alternative investment operations

To the point

• Private markets are constrained less by growth than by structural complexity-sophisticated fund structures, fragmented data, and coordination-intensive processes.
• While GenAI enhances task-level productivity, Agentic AI tackles structural coordination by orchestrating cross-system workflows and reshaping operating models.
• Architecture is the primary constraint: scalable agents require modern core systems, robust data governance, automated workflows, and strong AI governance.
• Success depends on ruthless prioritization, clear ownership, and the disciplined use of specialists, partners, and technology.

Introduction 

Private markets are often framed as a growth story, yet beneath the expansion lies a coordination challenge. Alternatives AUM is accelerating¹, but operational pressure is driven not by scale alone, it is rooted in structural complexity. Sophisticated fund structures, fragmented data ownership, manually processed unstructured documents, and processes reliant on continuous human intervention create an operating model that struggles to scale efficiently.

The effects are tangible: capital is deployed more slowly, cost per unit of AUM rises, fund closing timelines lengthen, and operations teams absorb an unsustainable share of the burden.

Unlike traditional finance institutions, alternative investment managers are not anchored by decades of deeply embedded legacy infrastructure. This relative freedom creates a rare strategic inflection point: firms have the opportunity not merely to digitize existing workflows, but to reconsider how work should be structured in a more intelligent, adaptive operating environment.

Agentic AI makes this shift attainable. These systems pursue defined business objectives across multiple tools and datasets, coordinating tasks, handling exceptions, and escalating to humans when needed.

For COOs and CIOs, the question is therefore not simply whether to adopt the technology, but how ambitiously to apply it: to embed it within existing operating models, or to use it as a catalyst for designing fundamentally different ones.

The case for a different technology path in Alternatives

Private markets are scaling along a distinct trajectory from traditional asset classes. Global alternatives Asset under Management (AUM) have expanded steadily over the past decade and are widely expected to exceed USD 30 trillion globally by 2030². Yet the operational strain now emerging across alternative investment managers cannot be explained by growth alone.

More consequential is how that growth manifests: longer holding periods that prolong oversight requirements; increasingly bespoke fund structures that resist standardization, jurisdiction-specific regulatory overlays that compound compliance demands; and rising investor expectations for transparency, ESG reporting, and rigorous risk oversight³. Together, these forces are not simply adding scale, they are reshaping the operating burden of the industry.

Many firms have absorbed growth by expanding headcount. The result is a gradual fragmentation of information, growing reliance on individual knowledge to manage exceptions, and diminishing organizational visibility. For mid-size managers, every new fund introduces incremental operational overhead precisely when speed to market matters most. Complexity in alternatives is no longer cyclical; it has become structural.

The operational pain points are increasingly specific and measurable

• Scalability constraints driven by manual workflows and document heavy processes.
• Siloed teams and rising coordination overhead as strategies and jurisdictions expand.
• Fragmented data spanning portfolio companies, administrators, and internal systems.
• Slower speed to market as operational capacity struggles to keep pace with deal flow.
• A high exception handling burden triggered by bespoke structures and regulatory variability.
• Talent shortages in specialized roles such as risk and compliance.

Unlike traditional financial services such as banking and insurance, alternatives are not constrained by decades of tightly coupled core systems or regulatory architectures built for high frequency, standardized transactions. This relative freedom creates the conditions for a fundamentally different architectural trajectory, one in which technology reshapes how work is performed rather than merely automating fragments of legacy processes.

This is where Agentic AI enters the picture. Rather than optimizing isolated tasks, it offers a means to manage complexity at the system level. By enabling intelligent agents to operate across data sources, applications, and workflows, firms can begin to scale without a proportional increase in operational burden. The step change lies not in technological sophistication for its own sake, but in the capacity to operate differently on a scale.

From automation to agency: A structural shift in how work gets done

Most discussions of AI in asset management center on efficiency gains: faster document review, improved data extraction, and accelerated analysis. These advances matter, but they are evolutionary. Agentic AI signals a more foundational shift. Just as electricity reshaped factory design, enabling continuous motion and new production models, Agentic AI makes possible a reconfiguration of operational architecture. Factories were ultimately redesigned around continuous power rather than constrained by the limits of intermittent mechanical energy. AI now sits at a comparable inflection point. Applied narrowly, it enhances existing workflows; deployed agentically, it redefines them.

The parallel to alternatives is instructive. Early factory owners often installed electric motors exactly where steam engines had stood, capturing only marginal benefits until they reimagined the production line itself. Many firms are making a similar mistake today: placing AI into legacy workflows (document review, data extraction and analysis) rather than redesigning how work is produced end to end. The transformative question is therefore not where AI can be inserted, but what the operating model would look like if agentic intelligence were foundational from the start. Agentic systems perceive information, reason toward objectives, act across tools, and adapt based on outcomes. They pursue goals rather than execute discrete tasks. By coordinating across platforms, systems and other agents, they escalate decisions only when human judgment is truly required.

The implications for alternatives are concrete and operational. In real estate, key stakeholders, from property managers to valuers, often rely on incompatible systems. In private equity, portfolio companies maintain divergent reporting standards and accounting policies. In infrastructure, asset diversity limits standardization. In private debt, borrower data arrives in fragmented formats across syndicate partners, making covenant monitoring and credit risk assessment coordination resource intensive. Agentic systems operate across these seams, stitching fragmented processes into continuous flows.

Importantly, autonomy does not mean absence of control. Agents operate within boundaries set by humans: escalation rules, approval thresholds, audit trails. They do not replace humans, they augment professionals by absorbing complexity, allowing experts to focus on judgment and oversight.

Transforming operations: How Agentic AI addresses Alternatives' pain points

AI adoption across asset management is widespread: 95% of fund managers use generative AI somewhere in their organization⁴, with the heaviest use in operations, risk, compliance, and reporting⁵, where manual effort and reconciliation costs peak. But this widespread adoption masks a critical distinction. Generative AI is used for daily work: task-level applications augmenting individual productivity. By comparison, Agentic AI in comparison is transformational, yet deployment to transform workflows across functions remains low⁶. The gap is not technological maturity, but operating model readiness and risk management discipline.

Take investor reporting as an example. In most firms today, this process involves a fund accountant pulling data, a compliance officer reviewing regulatory requirements, an operations manager coordinating with administrators, and an investor relation professional finalizing material. The process depends on sequential handoffs, status updates via email, and exception handling through ad hoc conversations. As complexity grows, with more funds, more investors, and expanding regulatory obligations, firms typically respond by adding headcount at each stage to preserve throughput. The model scales linearly with labor, embedding operational friction rather than eliminating it.

In an Agentic operating model, an agent assumes a role rather than executing a linear function, augmenting human capability in high-volume data environments and taking on low-value, repeatable tasks. Instead of following sequential workflows, agents interact dynamically across systems and stakeholders. An agent can orchestrate data collection, apply jurisdiction-specific regulatory rules, reconcile anomalies against administrator records, and assembles draft reports in investor-specific formats.

Humans retain ownership of outcomes. They define the parameters, escalation triggers, regulatory interpretations, and tone, while focusing their attention on high-value judgement and exception review. This form of augmentation elevates professional contribution rather than displacing it. For a Head of Investor Relations, the shift is meaningful: from production coordinator to strategic advisor, and anticipating investor needs instead of chasing inputs.

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Strategy, architecture and trust: The path to scale

Agentic AI cannot compensate for weak foundations. Before autonomy becomes feasible, firms must establish an operating environment capable of supporting agent deployment. Success rests on institutional discipline and deliberately constructed layers:

• A curated data layer that exposes enterprise data and documents securely while enabling scalable access.
• API‑based system integration that surfaces functionality programmatically, removing bottlenecks created by manual entry.
• A model layer housing LLMs and prompt libraries with robust versioning, monitoring, and rollback controls.
• A tool layer that makes core applications available for agent use.
• An orchestration layer responsible for agent coordination, routing, escalation, and exception management.

Consider quarterly reporting, where data arrives from 30 portfolio companies in disparate formats. Without a coherent architecture, agents remain constrained by the very silos they are meant to overcome. With the right foundations in place, however, agents can orchestrate and execute the end-to-end flow, detecting anomalies, cross-referencing patterns, enriching data from internal systems, applying jurisdiction-specific templates, and escalating only true exceptions.

Trust is often cited as the primary barrier to AI adoption. Executives consistently point to privacy, security, and perceived loss of control as their chief concerns, reinforcing the view that AI operates as a black box beyond effective oversight. Yet these anxieties reflect unfamiliarity more than inherent limitation. With deliberate design, governance and control are entirely achievable.

A private equity firm, for example, can establish full traceability over agent-driven decisions, while an infrastructure manager can ensure transparent covenant interpretation when governance is embedded into the system architecture from the outset.

Trust in AI will not emerge overnight. Firms must build confidence deliberately and incrementally, starting with contained, lower-friction deployments and expanding autonomy only as governance frameworks mature and organizational trust strengthens through demonstrated reliability.

Deloitte's Trustworthy AI framework translates governance intent into concrete architectural requirements spanning foundational elements such as governance, controls, and regulatory compliance, as well as core dimensions including robustness, privacy, transparency, security, and accountability. These principles become operational through specific design choices:

• Explainability requires logged reasoning paths.
• Privacy demands data minimization by default.
• Robustness depends on validation checkpoints before actions propagate.

When governance is embedded architecturally rather than enforced procedurally, control becomes both stronger and scalable.

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What this means for Alternatives leaders

1. Treat this as a structured incremental journey. Success requires a clear roadmap: defining the destination, sequencing initiatives to build capability progressively, and avoiding fragmented experimentation that leads to ungovernable AI sprawl.
2. Prioritize ruthlessly and iterate continuously. Focus on areas where coordination pain is greatest and architectural dependencies remain manageable. Begin with contained, high-value domains, learn from each deployment, refine governance through feedback, and allow early insights to shape subsequent phases. Maintain a willingness to experiment, accept setbacks, and explore alternative paths as capabilities mature.
3. Secure the right capabilities. This transformation spans strategy, data architecture, governance, change management, deep industry and functional expertise. Assess whether these capabilities exist internally; where gaps remain, engage external specialists with a proven track record in comparable transformations.
4. Manage change intentionally. The scale of this shift demands a disciplined change program. Equip people early as augmentation unfolds. Roles will evolve, processes will be redesigned, and organizational readiness will influence adoption as much as technical capability.
5. Rethink rather than retrofit. Transformative value emerges when work itself is redesigned, not when AI is layered onto existing processes. Challenge assumptions about role boundaries, approval hierarchies, and process ownership. Build operating models around outcomes: humans define success and govern exceptions, while agents execute and coordinate.
6. Align AI performance with organizational structure. Outcomes are shaped by governance maturity, data discipline, and the flow of accountability across the enterprise. Without these foundations, even the most sophisticated AI will deliver only incremental gains.
7. Evaluate pragmatic options in combination. Offshoring, hybrid models, and Agentic AI each have a role to play. The question is less about choosing between them and more about orchestrating the right mix based on process complexity, talent availability, and strategic importance. Tasks that offshore effectively often automate well; the distinction lies in scalability, consistency, and long-term flexibility.
8. Adopt a modular, reusable design. Engineer for reusability from the outset by building agents as modular components. A due diligence agent, for instance, can be repurposed for covenant monitoring or regulatory tracking with minimal rework. Modularity accelerates prototyping while maximizing architectural return as components scale across functions.