AI won’t fix a weak back office; it will expose it.
Key takeaways
The real payoff of AI isn’t lower costs; it’s better accuracy, faster insight and lower risk.
Clear accountability and human insight are what make AI more reliable and defensible at scale.
Private equity firms are rapidly adopting artificial intelligence in fund administration, often starting with efficiency-driven use cases like automating reporting, reconciliations and/or capital activity. While these efforts can reduce manual effort and mitigate risks, they don’t always improve how the function operates.
AI does not fix weak foundations; it magnifies them. When layered onto inconsistent data, unclear accountability or outdated controls, AI can accelerate risk as quickly as output, leading to scaled inefficiency, delayed closes and pressure on limited partner trust.
AI is a force multiplier in both directions: the same capabilities that drive value can also amplify risk. As intelligence increases, so does exposure—placing governance and control at the center, not the periphery.
Firms that move beyond an efficiency-only mindset apply AI more intentionally, using it to improve accuracy, speed up decision making and strengthen controls. This shift separates experimentation from sustainable advantage in fund administration.
AI can drive high-performing fund administration, not just make it cheaper, leading to the outcomes that matter most:
Accuracy and quality
Achieve consistent, reliable reporting
Build transparency and LP confidence
Speed
Attain more responsive, scalable operations
Help teams act faster and support growth
Risk reduction
Strengthen governance and controls
Surface risks earlier
Efficiency
Liberate capacity and improve leverage
Note: the primary measure of success is not efficiency
Four building blocks of an AI-enabled fund program
Getting to these desired outcomes is a structured journey, not a one-time technology rollout. While the steps are often addressed in sequence, they evolve over time as capabilities mature.
1. Data: Get the foundation right
AI is only as effective as the data it relies on. While some view AI as a magic wand to work around data challenges, the opposite is true in practice: inconsistent, incomplete or poorly governed data leads to faster—but less reliable—outcomes. AI does not replace data discipline; it depends on it. Without a clear data foundation, firms risk automating existing issues and scaling inaccuracies rather than improving fund administration.
Avoid
- Assuming AI can compensate for poor data quality or structure
- Feeding overlapping or inconsistently defined data from multiple systems into AI
- Chasing AI quick wins without clarity on core data sources
- Lacking clear ownership for data quality and governance
Try
- Treating AI as dependent on strong data discipline
- Clearly defining data sources, definitions and ownership
- Standardizing data across accounting, reporting and compliance
- Addressing data gaps before scaling AI use cases
For example
- Standardize account mappings, valuation fields and deal identifiers across systems
- Enable targeted review instead of producing statements from inconsistent data
- Align data attributes (e.g., fair value and capital account definitions across fund accounting, investor reporting and regulatory filings) before deploying AI analytics
2. Strategy: Decide where AI should create value
Without a defined strategy, AI initiatives often default to low-effort projects that feel productive but have limited business impact. In fund administration, this typically shows up as isolated efficiency gains that optimize existing workflows without improving accuracy, insight or control. A more intentional approach anchors AI decisions to the outcomes that matter most—helping leaders prioritize where AI can meaningfully improve how the function runs, how risk is managed and how the business scales. Strategy provides the discipline to move beyond experimentation and ensure AI investments strengthen, rather than fragment, the fund administration operating model.
Avoid
- Prioritizing AI based on ease rather than enterprise value impact
- Launching disconnected pilots without a clear objective
- Treating all AI use cases as equal
- Optimizing workflows without reassessing their value
Try
- Anchoring AI initiatives to core fund administration outcomes
- Prioritizing use cases that help run, protect or grow the business
- Evaluating initiatives based on impact, feasibility and risk
- Focusing on a few high‑value priorities and then expanding over time
For example
- Prioritize AI use cases that reduce NAV close cycle time or improve accuracy of capital account balances rather than low‑impact administrative automations
- Target areas with high LP sensitivity and audit scrutiny
- Reduce post‑close adjustments and audit follow‑ups
3. Governance: Build trust before you scale
AI does not eliminate risk—it reshapes it. As AI becomes more embedded in fund administration, traditional controls and informal validation processes are no longer sufficient. AI can improve accuracy and oversight, but it can also increase the sophistication and speed of fraud, error and misuse if guardrails do not evolve alongside capability. Effective governance helps ensure AI outputs are reliable, data is protected and accountability remains clear. Without it, AI can quietly become trusted even when it is wrong—introducing risk at scale rather than reducing it. Think of it this way: AI is a force multiplier in both directions: the same capabilities that drive value can also amplify risk. As intelligence increases, so does exposure—placing governance and control at the center, not the periphery.
Avoid
- Relying on legacy or informal validation controls
- Treating AI governance as static or policy‑only
- Allowing AI to operate without clear ownership
- Reducing human review without reassessing risk
Try
- Assuming AI increases both capability and risk
- Defining where AI acts independently vs. requires judgment
- Assigning clear human-in-the-loop ownership
- Reviewing and updating controls as risks evolve
- Applying heightened scrutiny to high-risk activities
For example
- Surface exceptions early, enabling focused investigation rather than manual review
- Apply an enhanced secondary review when AI is used to flag valuation or capital activity exceptions, recognizing that automation can introduce new error or bias risks
- Allow AI to identify unusual valuation movements or capital transactions, while requiring fund accountants or valuation committees to approve any adjustments
4. Design: Turn intent into results
Even with strong data and a clear strategy, AI initiatives can fail without disciplined design. In fund administration, the risk is not a lack of ideas; it is pursuing solutions that do not reflect real workflows, control requirements or accountability expectations. Designing AI thoughtfully requires breaking work down, aligning technology with how teams operate and resisting the urge to deploy overly broad solutions too quickly. For example, consider starting with narrow agents. Well-designed AI supports the function incrementally, improves reliability and earns trust over time. Poorly designed AI introduces opacity, error and risk at scale.
Avoid
- Deploying broad, all-purpose AI to manage complex workflows
- Treating AI as a black box rather than designing for specific tasks
- Expecting expert-level performance without structure or oversight
- Designing AI without close alignment to fund operations and controls
Try
- Breaking work into discrete, task-specific AI use cases
- Designing narrow, purpose-built agents aligned to defined workflow steps
- Treating AI like a junior team member: scoped, reviewed and refined over time
- Designing jointly across fund operations, risk and technology teams
- Starting small, proving reliability, then expanding deliberately
For example
- Consider deploying narrow, purpose‑built agents for specific tasks, and then incorporate into broader workflows
- Define clear inputs, outputs and escalation rules for each agent
- Maintain explicit human review ownership for every AI‑assisted step
- Pilot AI in a single fund or asset class before expanding
Start by reframing the AI conversation
A leadership-level conversation about AI is essential before focusing on tools. Reframing the discussion helps ensure AI initiatives improve accuracy, insight and control, rather than delivering isolated efficiency gains or unintended risk. To get started, answer the following questions:
Is our back office a cost center or a competitive advantage?
- What needs to change to move toward the latter?
Are we over-indexing on cost reduction at the expense of quality and control?
- Where has AI helped us move faster?
- Has it improved reporting consistency and reliability?
- Has it strengthened or weakened LP trust?
How would our priorities change if the goal were better accuracy, faster insights or lower risk—not just efficiency?
- Which use cases would we expand, rethink or stop?
- Where could AI meaningfully improve how we serve investors?
What’s holding us back from adopting AI more holistically?
- Data maturity?
- Governance and controls?
- Team readiness and accountability?
The takeaway
AI will continue to reshape fund administration, but the speed of adoption alone will not create an advantage. The differentiator is disciplined execution. Firms that ground AI initiatives in strong data, clear strategy, effective governance and intentional design can move beyond efficiency gains to improve accuracy, insight and control. When applied thoughtfully, AI becomes a durable capability—strengthening investor trust and positioning fund administration for long-term performance.
RSM contributors
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