AI for family offices: Strategy & governance

Why AI governance matters now

AI has moved from hype to infrastructure. For family offices, the question is no longer whether to adopt AI, but how to do so responsibly. In wealth management, 91% of asset managers now use or plan to use AI, up from just 55% in 2023. This rapid acceleration reflects finance’s appetite for efficiency and insight, but also reveals a governance blind spot.

Family offices are not immune. Many already fund AI ventures in longevity, climate, and biotech. Yet when it comes to internal operations, adoption is uneven. Some offices are piloting new tools, others remain hesitant, citing privacy, regulatory uncertainty, and lack of in-house expertise. This creates a paradox: families driving the AI revolution externally while lagging internally. Governance, not technology, is the bridge.

A Simple poll from this year found that one-third of family offices were exploring AI but hadn’t committed, while just over a quarter were actively using tools. Another 21% described themselves as interested but cautious. This fragmented adoption confirms that while institutional finance treats AI as infrastructure, family offices are still experimenting, often without governance frameworks in place.

The governance challenge: adoption outpacing oversight

Adoption is not the same as successful integration. A Deloitte study shows that only 38% of AI projects in finance deliver expected ROI, with more than 60% delayed by shortages in talent. For family offices, where resources are leaner, the risks of wasted time, reputational fallout, or even regulatory exposure are higher.

A Simple poll at the backend of 2024 underscores this point: 41% of family offices cited security and privacy as the main barrier to AI adoption. Another 19% pointed to the lack of tailored solutions for family office needs, while 19% admitted a lack of in-house expertise. These are not just technical obstacles; they reveal gaps in governance, operational readiness, and culture.

What often gets overlooked is the role of data strategy. AI systems are only as effective as the data they are built on: messy, siloed, or incomplete data will undermine results. For family offices, where information spans portfolios, trusts, and personal records, refining data pipelines is as important as choosing the right tool. Without that foundation, pilots stall, ROI is missed, and risks multiply.

A practical first step is to run a lightweight data audit before any AI pilot, mapping where data lives, who owns it, and whether it’s fit for machine learning. This turns governance from theory into tangible preparation.

The lesson: Without structured oversight, adoption risks creating exposure rather than efficiency.

Build vs Buy vs Partner

One of the most strategic choices family offices face is whether to build proprietary AI tools, buy off-the-shelf solutions, or partner with vendors.

• Build: Maximum control over data and functionality but high cost ($100K–$500K+), long timelines (6–18 months), and ongoing maintenance demands.

• Buy: Quicker deployment (3–9 months), lower upfront costs, but raises questions about vendor resilience, integration, and data privacy.

• Partner: A hybrid, increasingly popular model where sensitive functions are developed internally while standard capabilities are outsourced.

Globally, 94% of financial services firms expect third-party AI use to increase. Family offices must apply disciplined evaluation frameworks, not convenience, when making these decisions.

For example, some family offices have already experimented with building custom reporting dashboards to protect sensitive data, while others lean on custodians’ embedded AI for faster implementation. Each route carries risks: building strains lean teams, buying risks lock-in, and partnerships demand careful vetting of vendor resilience. For most family offices, the pragmatic path will be hybrid – retaining control of core functions while outsourcing commodity features.

Data governance & ownership

Data is both the fuel and the fault line of AI. The IBM Cost of a Data Breach Report (2025) found that 97% of organisations reporting AI-related breaches lacked proper access controls. GDPR fines reached €1.2 billion in 2024, increasingly targeting financial institutions with weak safeguards.

Family offices, given their exposure to deeply personal and financial data, are especially vulnerable. Breaches can damage both reputation and continuity.

A Simple poll (Nov 2024) found that 47% of family offices were interested in AI but had not yet implemented it, while only 8% had reached core integration. This early-stage reality makes embedding governance now essential. Without it, experimentation risks becoming “shadow AI,” where staff use public tools informally, exposing sensitive family information.

One real-world parallel comes from financial services: several firms have already had to ban employees from pasting client data into public AI tools after discovering sensitive documents circulating outside controlled systems. For family offices, a single lapse like this could expose succession plans or personal financial data, damage that is almost impossible to contain once leaked.

Ethical guardrails

AI governance is not just about compliance. It also involves ethical guardrails: ensuring transparency, explainability, and fairness.

For family offices, the reputational consequences of biased outputs or opaque decision-making are amplified. Unlike large banks, which may absorb reputational shocks, family offices operate in high-trust ecosystems where perception directly affects influence and partnerships.

Ethical frameworks, lightweight but explicit, can reduce risks. These might include rules on human-in-the-loop oversight, requirements for explainability in investment decision support tools, and codes of conduct for AI-assisted communications.

One risk example: an AI-driven investment tool that inadvertently down-weights female or minority-led businesses due to biased training data. Even if unintended, such an outcome could undermine both returns and reputation. For family offices, embedding principles of explainability and fairness is a way to align technology adoption with family values. A simple first step is to adopt an internal “AI code of conduct” — a lightweight policy that commits to transparency, requires human review of critical decisions, and bans the use of sensitive family data in public models.

Regulation & compliance

The regulatory landscape is moving quickly, and in different directions across jurisdictions.

• EU: The AI Act (2024) introduced a tiered approach, with high-risk systems facing obligations around explainability, oversight, and human control. Compliance deadlines run up to 36 months.

• US: Federal deregulation dominates, but 38 states introduced AI-related laws in 2025, creating patchwork compliance.

• UK: The FCA has taken a “sandbox” approach, embedding oversight in existing financial regulations.

For cross-border families, this “compliance complexity” means systems must either be designed to the strictest jurisdiction or adapted region by region.

In a Simple poll, 29% of family offices said their biggest continuity risk was a lack of shared long-term vision. Another 15% cited outdated systems and weak governance. This suggests that external regulations are only one part of the challenge: internal governance weaknesses can undermine continuity just as much.

Many family offices already face complexity in tax, reporting, and succession planning that spans multiple jurisdictions. AI regulation adds another layer: a portfolio reporting tool compliant with US standards might breach EU transparency rules if used in Europe. For globally dispersed families, this makes “compliance by design” essential — building or selecting tools that can meet the toughest standards from the outset rather than patching compliance later.

The takeaway for family offices is simple: treat compliance as a front-end design choice, not a back-end repair. The cost of over-engineering governance is far lower than the cost of retrofitting it under regulatory pressure.

Culture & change management

AI adoption is as much cultural as technical. Globally, 41% of employees expect AI to improve their jobs, while 20% expect negative effects. Yet 75% of CFOs still cite security and privacy as their top AI concern, highlighting a trust gap between leadership and staff.

For family offices, culture is just as critical. In the same Simple poll on adoption barriers, nearly 20% admitted that lack of expertise was holding them back. This shows that leadership concerns and workforce readiness are intertwined.

Left unaddressed, this gap leads to “shadow AI,” where staff use public models without approval. The solution is transparency from leadership, role-specific training, and framing AI as augmentation, not replacement.

In practice, generational dynamics often shape adoption. Next-gen family members and younger staff may push for experimentation, while older leaders hesitate. This can create fragmentation unless pilots are structured to include all stakeholders. For example, training workshops combined with a 90-day AI pilot can demonstrate benefits while addressing security fears. At the same time, leaders should openly explain the risks of “shadow AI” — such as uploading sensitive trust documents into public chatbots — to make clear why governance matters.

Ultimately, cultural readiness is what makes governance frameworks stick. Without trust and shared understanding, even the most sophisticated policies risk becoming box-ticking exercises rather than living safeguards.