Is Your Data Ready for Artificial Intelligence?

In a previous post, Catherine Fankhauser, Partner and Practice Leader of Family Office Advisory Services at Ernst & Young (EY), joined our CEO, Anthony Abenante, to outline five steps Family Offices should take when starting an AI journey. "Having good data" topped the list. In this post, I want to go deeper on what that means in practice.

Artificial intelligence represents more than an incremental technology upgrade, it offers a fundamental transformation in how family offices can operate. Advanced AI systems promise capabilities that were once the stuff of science fiction: real-time portfolio optimization across all asset classes, predictive analytics for market movements and liquidity needs, automated compliance monitoring, intelligent tax optimization, and natural language interfaces for complex financial queries, analysis, and reporting.

These capabilities, however, are built on a foundation of quality data. Unlike traditional software that can function on partial or inconsistent inputs, AI systems depend fundamentally on comprehensive, well-structured, and properly labeled datasets. The AI readiness gap facing family offices is therefore not primarily about AI technology itself -- it is about the underlying data infrastructure required to make AI effective.

Family offices differ from other financial organizations in four critical ways, and understanding these differences is essential to addressing their data challenges:

• Diverse asset portfolios. Family offices manage holdings that extend well beyond public securities to include private equity, real estate, hedge funds, collectibles, family businesses, and other alternative investments. Each asset class generates different types of data with varying levels of reporting frequency, standardization, and transparency.
• Multiple external service providers. Family offices typically work with custodians, prime brokers, fund administrators, tax advisors, and legal counsel, each maintaining separate data systems with different formats and update frequencies. This creates a naturally fragmented data environment where consolidation is a persistent challenge.
• Sensitive family information. Beyond financial assets, family offices manage estate plans, philanthropic activities, and personal data that require strict confidentiality. These privacy requirements can conflict with the data aggregation and sharing mechanisms that AI systems typically rely on.
• Wide variation in size and sophistication. Many single-family offices operate with lean teams and limited technology resources, making large-scale data initiatives especially challenging.

The most pervasive data challenge facing family offices is fragmentation. Investment holdings data are frequently dispersed across an array of disconnected systems, creating several problems for AI implementation. Machine learning algorithms require integrated datasets to identify patterns and generate insights and when data resides in isolated silos, AI systems lose the holistic view essential for meaningful analysis.

Data silos also increase the risk of inconsistency and duplication. The same investment may be recorded differently across systems, which confuses AI models and can lead to erroneous conclusions.

The Multi-Custodian Problem 

Many family offices maintain relationships with multiple custodians to access specialized services, manage counterparty risk, or accommodate the preferences of individual family members. While this diversification offers operational benefits, it significantly complicates data management.

Historical Data Gaps 

AI and machine learning models typically require substantial historical data to train effectively and identify meaningful patterns. Family offices may have years of investment history, but accessing that data in structured, usable formats is often difficult. Legacy systems may have been replaced, historical records may exist only on paper or in PDFs, and data standards may have shifted over time.

Even when historical data exists electronically, it may rely on outdated categorizations or lack key fields needed for modern analysis. Without adequate historical data, AI models cannot perform back-testing or learn from past market cycles.

Privacy Requirements 

Family offices manage highly sensitive information that extends well beyond financial data to include personal family matters, health information, estate plans, philanthropic intentions, and business strategies. Protecting this information can limit or complicate access to certain datasets needed for AI implementation.

Family Offices Must Evolve 

Many family offices rely on legacy technology systems that were never designed with AI integration in mind. These systems may lack modern APIs (Application Programming Interfaces) or data export capabilities that AI tools require.

The rapid evolution of AI also means that a leadership team's expertise can quickly become outdated. Continuous learning is essential. Family office staff need opportunities to develop new skills, experiment with emerging technologies, and stay current with industry developments, which requires investment in training, professional development, conference attendance, and collaboration with academic institutions or industry groups. Organizations that cultivate a learning culture -- one where experimentation is encouraged and failure is tolerated -- are better positioned to adapt to technological change.

Finally, family offices are not static entities, and data infrastructure must accommodate growth without requiring constant re-architecture. An AI platform that performs well with $500 million in assets across 50 positions may struggle when a portfolio grows to $2 billion across hundreds or thousands of positions spanning multiple asset classes and jurisdictions. Scalability must be a design priority from the outset.

A Strategic Framework for Addressing Data Challenges

Phase 1: Data Governance and Stewardship

Effective data management requires clear governance structures that define roles, responsibilities, and decision-making authority. Who owns the data? Who is responsible for data quality and integrity? Who approves sensitive data access requests? Who sets data retention policies? Clear answers to these questions are essential.

In family offices, governance is further complicated by the involvement of multiple stakeholders: family members, investment staff, external advisors, and service providers, each of whom may have different views on data priorities, privacy requirements, and acceptable uses. Establishing and maintaining consensus requires active governance mechanisms.

For AI specifically, governance policies must address additional questions: What data can be used for model training? How should models be validated before deployment? What level of human oversight is required for AI-generated recommendations? How are model decisions documented and explained? Who can deploy new AI capabilities?

Governance must also extend to third-party service providers. How are they using your data? What safeguards are they maintaining, particularly for the most sensitive information?

Phase 2: Foundation Building

Before deploying sophisticated AI capabilities, family offices must establish solid data foundations. This phase focuses on four priorities:

1. Comprehensive data inventory. Catalog all data sources, document their contents and update frequencies, identify gaps and quality issues, and map data flows between systems. This baseline understanding clarifies what data resources exist and where improvement is needed.
2. Data consolidation. Deploy a data lake or similar solution to create a unified view of family office assets and eliminate siloed information.
3. Data quality processes. Implement validation rules, exception reporting, and correction workflows. Define data ownership and accountability. Establish metrics for measuring quality and track improvements over time.
4. Data catalogs. As data ecosystems grow in complexity, the ability to find and understand available data becomes critical. Data catalogs inventory available datasets, document their contents and lineage, and facilitate discovery. For AI implementations, they help data scientists identify relevant datasets, support model debugging, and facilitate impact analysis when data sources change. Maintaining comprehensive data catalogs, however, requires dedicated effort and appropriate tooling.

Phase 3: Targeted AI Pilots

With foundational data infrastructure in place, family offices can begin experimenting with AI through targeted pilot projects. Pilots serve multiple purposes: demonstrating value, building internal expertise, uncovering unforeseen challenges, and refining implementation approaches.

Successful pilots share common characteristics. They address well-defined problems with measurable outcomes, leverage data that is already relatively clean and accessible, have executive sponsorship and appropriate resources, and include mechanisms for capturing lessons learned.

Example pilot projects might include:

• Portfolio rebalancing optimization using machine learning to minimize tax impact
• Document processing automation for extracting data from fund statements or K-1 tax forms
• Anomaly detection for identifying unusual transactions or market movements
• Natural language processing for analyzing investment research reports or earnings call transcripts

Phase 4: Scaling and Integration

Successful pilots provide the foundation for broader AI adoption. The scaling phase focuses on expanding AI capabilities across additional use cases, integrating AI insights into decision-making workflows, and building organizational muscle memory for maintaining and improving AI systems.

This phase requires balancing expansion with sustainability. Adding new AI capabilities without corresponding investments in data infrastructure, governance, and talent can produce poor results. Successful scaling requires disciplined program management, continued focus on data quality, and ongoing capability development.

Best Practices and Recommendations

Start with Data, Not Algorithms

The allure of sophisticated AI models can tempt organizations to prematurely focus on algorithm selection and model development. Resist this temptation. No algorithm, however advanced, can compensate for poor-quality or inaccessible data. Invest first in data infrastructure, quality, and governance, including the unglamorous but essential work of data cleaning, standardization, and consolidation.

Embrace Incremental Progress

Transformative AI capabilities are built incrementally, not through sweeping all-at-once implementations. Start with focused projects that deliver tangible value quickly, use early wins to build momentum and secure resources, and iterate based on experience. This approach reduces risk, facilitates learning, and maintains stakeholder engagement.

Build Privacy and Security In

Security and privacy cannot be afterthoughts. Design data architectures with privacy preservation from the outset. Implement encryption, access controls, and audit logging while consistently evaluating outside vendors rigorously on their security practices.

Develop Governance and Internal Capabilities

Establish a clear governance structure and processes early. Invest in internal capabilities through hiring, training, and hands-on experience. Even small family offices can cultivate basic data literacy and AI fluency among existing staff; internal expertise enables more effective vendor management and supports long-term sustainability.

Measure and Monitor

Establish metrics for evaluating AI initiatives. Track data quality indicators, model performance, user adoption rates, and business impact. Regular monitoring surfaces issues early, supports continuous improvement, and demonstrates value to stakeholders. What gets measured gets managed and that principle extends to third-party providers as well.

Build vs. Buy

Organizations must determine the right approach to their data management challenges. Building internal capabilities offers control and customization but requires significant investment. Purchasing packaged solutions from third-party providers can enable faster, more cost-effective deployment but may sacrifice flexibility. A hybrid approach, combining in-house capabilities with external service providers -- potentially guided by consultants or advisors -- is another viable path. The right choice depends on organizational size, available resources, and technology sophistication.

How Archway Can Help

The data challenges described in this post are precisely the problems the Archway Platform was designed to solve. For nearly 25 years, Archway has helped family offices and financial institutions aggregate, consolidate, standardize, and manage their key financial data and documents.

The Archway Platform addresses core data readiness challenges in several ways:

Consolidated data in a single environment. The platform brings together accounting and investment data across custodians, asset classes, and currencies, eliminating the fragmented, multi-silo environment that makes AI implementation so difficult. With a single, reconciled source of financial truth, AI tools have the clean, comprehensive dataset they need to function effectively.

Structured, auditable financial data. Archway's foundational general ledger automatically books journal entries as transactions are processed, producing well-labeled, consistently formatted data as a natural byproduct of normal operations, exactly what AI systems require.

Alternative investment data ingestion. Through our alternative investment ingestion tools and partnerships with companies like Canoe Intelligence and Arch, the Archway Platform streamlines the extraction and processing of alternative investment data. Capital calls, distribution notices, K-1s, and fund statements represent some of the most persistent data gaps for family offices seeking AI-ready data.

Built for complexity and scale. The Archway Platform is designed for the unique demands of UHNW family offices: multi-entity structures, multi-generational ownership, and multi-asset portfolios. As your data needs grow, the platform grows with them.

For family offices looking to build the data foundation that makes AI possible, partnering with Archway provides both the technology and the outsourced services to get there. [Request a demo →]

Conclusion

Artificial intelligence holds enormous promise for family offices. Realizing that promise, however, requires addressing data challenges that are both technical and organizational in nature.

These challenges, while substantial, are not insurmountable. With the right strategy, resources, and commitment, family offices of all sizes can build the data capabilities necessary to thrive in an AI-enabled future. The question is not whether AI will transform wealth management, it will. The more salient question is which family offices will be prepared to harness its potential.

Ethan Wishnick, Chief Operating Officer, Archway