- Family offices are rapidly embedding AI into everyday workflows for investment diligence, reporting, and operations, often via tools already inside productivity and research platforms.
- Highest-value use cases include deal sourcing and screening, legal document summarization, research synthesis, and drafting investment committee materials.
- Adoption is tempered by the need for strong governance and data readiness, with privacy, compliance, auditability, and human-in-the-loop review as core requirements.
- AI is both an investment theme and an internal productivity lever as offices de-risk, raise liquidity, and reset return expectations.
Read More
The primary source “AI In The Family Office: Lessons For Asset, Wealth Managers” from the 11th Family Wealth Report Family Office Investment Summit 2025 offers a detailed look at how family offices are integrating AI-driven tools. Experts from PwC point out that institutional use cases must often be adapted—or “right-sized”—to fit smaller, more agile family offices. Usefulness is highest in simplifying deal workflows, accelerating due diligence, legal agreement review (trust, operating, partnership agreements), and drafting of investment committee memos. Early AI applications are less about model-driven prediction and more about automating and synthesizing content.
These observations align with broader survey data. Notably, the 2025 RBC & Campden Wealth Report shows that North American family offices have tripled use of AI in operations compared to 2024, and expect average returns of ~5% in 2025, reflecting a more cautious investment posture. The Goldman Sachs Family Office Insights Report reports that 86% of surveyed family offices have exposure to AI as an investment theme, and 51% use AI in their own investment processes. These data points underscore that AI’s role is dual: both as an investment area (theme of allocation) and as a functional tool in operations. It also highlights a gap between interest and actual internal use.
On the strategic front, several levers appear decisive. Governance emerges as essential: centralized oversight (often cross-functional), human review (“human-in-the-loop”), access controls, and auditability are repeatedly cited. Data readiness is foundational: clean data, versioning, masking, consistent metadata, and secure storage. Without this, AI outputs risk being inaccurate, untrustworthy, or even dangerous in terms of compliance.
Risk factors include over-reliance on AI outputs without sufficient human judgement; lack of transparency of models (“black box” issue); privacy and security of sensitive information; potential regulatory exposure around fiduciary duties and data laws; and the risk that investment expectations are mismatched with AI’s realisable benefits. Another open question is cost: developing custom tools vs using embedded tools in platforms, balancing short-term savings versus long-term investments.
For asset and wealth managers (including investment banking partners), this evolution has material implications. First, they must offer AI-enabled due diligence, data aggregation, and reporting tools; second, fee models will need to account for contributions of AI, transparency, and risk control; third, there is growing demand for vendor partners who can deliver explainably, securely, and auditable AI. Finally, client segmentation will matter: larger family offices with more complex structures will push for bespoke AI workflows; smaller ones will lean on low-risk, simple tools embedded in platforms they already use.
Open questions include: how to benchmark AI-augmented performance versus traditional practices; what constitutes acceptable levels of AI governance and liability; how to manage model risk and drift over time; and how generational transitions within families (younger heirs) will shift both risk appetite and expectations of tech-enabled wealth management.
Supporting Notes
- Speakers from PwC outlined that in private markets, AI is already being used for sourcing, screening, due diligence, IC memos, investor communications, and summarizing legal agreements.
- Many family offices are starting with “citizen-led” adoption—using tools embedded in Microsoft 365, Google Workspace, CRMs, and research platforms—to achieve safe, auditable flows and productivity gains.
- Data readiness: clean taxonomies, versioning, consistent naming, and defining who has access are prerequisites for scaling AI workflows.
- In the RBC/Campden report, family offices expect about 5% returns in 2025, down from 11% in 2024. Improving liquidity (48%) and de-risking portfolios (33%) were key objectives.
- Goldman Sachs survey: 86% of family offices have exposure to AI as an investment theme; 51% use AI in their investment processes.
- Internal usage still limited in many reports: in BlackRock’s Global Family Office Report, only ~1/3 of family offices use AI internally, though many more are investing in its infrastructure.
- Strategic governance practices include defining roles, prioritizing use cases by impact and risk, centralized oversight, tracking KPIs (cycle times, error rates, adoption) and maintaining human reviewer control.
due to foot-noting style, the numbers align with sources: 1 = family wealth report; 2 = RBC & Campden; 3 = Goldman Sachs; 4 = BlackRock; 5 = CompleteAITraining summary.