From copilot to execution: The state of AI in wealth management

AI is moving quickly from experimentation to execution across financial services. In wealth management, that evolution is particularly significant because the business combines high-value human relationships with large volumes of client information, complex operational processes and significant amounts of unstructured content.

Advisors spend considerable time gathering, interpreting and acting on information that is distributed across documents, systems and workflows. AI has the potential to change how that work gets done, while also creating new opportunities to deliver more personalized advice and service at scale.

Advisors and client service teams are already using generative AI (gen AI) to summarize information, draft communications, retrieve institutional knowledge and surface recommendations. The next phase is more ambitious: using AI to orchestrate multistep work, improve decision-making and deliver more personalized client experiences.

For wealth management executives, this shift raises a strategic question: Where can AI create measurable business value, and what capabilities need to be in place to capture it?

However, there is an important prerequisite: AI can only be as effective as the information it can access, understand and put into context. Firms with fragmented or siloed information will find that their AI ambitions quickly encounter the same organizational barriers that have constrained their digital evolution.

Wealth managers sit on enormous volumes of client, operational and investment information. If that information can be made accessible and actionable, AI can become a valuable tool.

The first wave of gen AI in the enterprise has largely focused on productivity. Think of gen AI as a copilot that helps an employee complete a task (e.g., summarize a client file, search for information, draft an email, prepare meeting notes, etc.). Those capabilities remain valuable, and even incremental productivity improvements that can have a meaningful economic impact.

In the next stage, agentic AI offers the potential to orchestrate multiple steps in a process rather than simply generating an answer. Agents can interpret a request, retrieve information from approved sources, evaluate the available information against business rules, initiate downstream actions and escalate exceptions to a human-in-the-loop (HITL).

The economics of AI excel when the technology gets embedded in operational processes. A productivity tool may save an employee several minutes here and there, but a truly intelligent workflow can reduce handoffs, shorten cycle times, decrease rework and improve consistency across entire processes.

The objective is not to remove people from the processes. In highly regulated financial environments, human oversight remains essential. The goal is to have AI handle appropriate tasks while keeping HITL for decisions that require judgment, accountability and expertise.

Wealth management is inherently relationship-driven. Clients expect advice and communications to reflect their individual circumstances, goals, preferences and history. Advisors therefore need a broad and deep view of that relationship when preparing for a meeting or responding to a client. Relevant relationship information could include previous meeting notes, account information, portfolio data, service requests, correspondence, etc. Historically, assembling that information requires manual searching across various applications and repositories.

AI can change the economics of that preparation, bringing together relevant information from approved/footnoted sources, summarizing significant developments, identifying outstanding client needs and highlighting information that may warrant the advisor’s attention. Further, an AI system could identify significant changes in a client’s circumstances or activity and prompt an advisor to consider reaching out. It could also assemble relevant documentation or previous conversations before the interaction. The human advisor remains central.

For wealth management firms, personalization at scale could become an important differentiator. Firms have historically faced a tradeoff between highly personalized service and the operational cost required to deliver that level of service consistently. AI has the potential to reduce that cost by helping employees assemble the context required for individualized interactions.

> Learn more: The guide to reducing operational costs

There is, however, a foundational challenge behind both execution and personalization: data. Much of the information in wealth management isn’t in neatly structured databases. It may live in PDFs, forms, scanned documents, correspondence, emails, presentations, statements, handwritten information or other unstructured content.This creates a practical challenge for utilizing AI. A model may be highly capable, but if data is inaccessible, poorly structured or disconnected from the relevant business context, output quality will suffer.

For AI to make effective use of enterprise information, that information needs to be transformed into usable, contextualized data. This is where intelligent document processing (IDP) and enterprise content capabilities become increasingly important. IDP can extract and structure information from those documents, turning previously difficult-to-use content into data that can participate in digital workflows and AI applications.

Extraction, however, is only one part of the equation; context determines how information should be interpreted and used. An AI system needs to understand what information means, where it came from, how it relates to other information, whether it’s current, what business rules apply and whether it’s appropriate to use for a particular task. For example, a client’s address on a recently submitted form carries different significance from an address on an older document. A piece of client information may be appropriate for one workflow and restricted from another.

This contextual layer is essential for enterprise AI. It also becomes increasingly important as organizations introduce agentic workflows. An agent that can take action requires more than access to information. It needs defined permissions, business rules, workflow context and appropriate controls around what it can do.

This is why the foundation beneath AI deserves as much attention as the AI tools themselves. Investment in the AI foundation can create data that can be leveraged in many ways. The same contextualized information and content infrastructure can support multiple use cases rather than being tied to a single application.

Compliance and fraud

Financial institutions can also apply AI to regulatory compliance, surveillance, document review and fraud-related processes. Accuracy, explainability, auditability and human oversight matter as much as efficiency. Firms need to understand which information influenced an output, how a recommendation was generated, what action was taken and who approved the final decision.

AI will inevitably raise questions about the role of the advisor. Assuming AI reduces the time required for preparation and routine task execution, advisors may have greater capacity to serve existing clients more deeply or support a larger number of relationships.

The implications for the advisor workforce are significant. Firms will need to consider how roles, skills, training and compensation models evolve as AI becomes embedded in day-to-day workflows. Advisors will need greater fluency in working with AI, evaluating its outputs and using their judgment to override or supplement an AI-generated recommendation.

The strongest advisor model may ultimately be one in which technology handles more of the information-intensive work while the advisor concentrates on the parts of the relationship where human judgment has the greatest value.

Advisors evolve with AI that informs, initiates and orchestrates personalized actions.

• Clients: They have questions that need answered. Do I have enough to retire? What do people like me need to accumulate for retirement? What if kids move back in; how much will it cost to help support them? Will the laws around student loans change? What do you think?

• Advisors: They will blend technical and emotional skills into their emerging role to solve problems for clients. They will move away from portfolio and investment solutions that are increasingly commoditized toward engaging on a more emotional level with clients while being a financial advocate.

Fiduciary advocate and authority

Understand the business:

• Wealth management is highly regulated. Enterprises will need to implement safeguards and business rules to ensure compliance and regulatory oversight. Advisors will act as the financial fiduciary authority over their book of business.

• Advisors are expected to also be financial advocates for clients, possessing the expertise needed to guide clients.

• While advisors have a legal and ethical responsibility, we believe that this role will be partially automated to a point where advisors simply check the boxes based on AI agent responses.

Life/financial wellness coach

Understand my financial life:

• AI agents will free up advisors to spend more time understanding clients and formulating personalized solutions for them.

• Simultaneously, there is more information available to advisors that crosses business lines and is unlocked within the enterprise. Solutions are no longer restricted to the products that the advisor knows.

• AI agents can look more broadly at REITs, fixed income, alternatives, bitcoin, etc.

• Finally, with less work involved conducting administrative tasks, such as rekeying data and traversing multiple apps, advisors are available to spend more time with clients to better understand their life situations and blend them into better-fitting solutions.

Therapist

Understand me:

• Roughly 40% of the perceived value in financial relationships with advisors is emotional, according to research conducted by Vanguard.

• The human component is still critical in the relationship, and trust, compassion and empathy are not yet ascribed to AI agents (at least not today).

• As the population ages, the importance of emotion may decrease in value as younger cohorts rely more on digital relationships.

• A recent research study indicates the growing comfort levels of individuals to engage with generative AI in critical areas such as financial advice.

The enthusiasm around AI should not obscure the challenges wealth management firms face. Data quality is a huge issue. Organizations often have information distributed across legacy systems, repositories and formats. Metadata may be inconsistent and historical information may be difficult to reconcile with current records. If information is incomplete, inconsistent or difficult to access, AI applications will inherit those limitations.

Security and governance are another concern. Sensitive client information must be protected, and the regulatory obligations are significant. Organizations need clear controls around what information AI can access, how information can be used, where outputs can be shared and when human review is required.

As AI becomes more autonomous, these questions become more consequential. A system that generates a draft creates one category of risk, while a system that can initiate a workflow or modify information introduces another.

Governance needs to evolve alongside capability

Finally, there is trust. Employees and clients need confidence that AI-generated outputs are based on appropriate information and that the organization can explain how important decisions are made. Advisors also need confidence that AI won’t introduce risk. This makes governance, transparency, auditability and human oversight critical.

Organizations need to avoid treating AI as a collection of disconnected tools. The greater opportunity comes from connecting AI to an organization’s content, data and workflows so human and artificial intelligence can be leveraged successfully.

For wealth management firms, the next stage of AI adoption is likely to be less about finding the newest technology and more about constructing the right foundation underneath it. That foundation includes content management, intelligent document processing, contextualized enterprise data, workflow automation, security and governance.

Firms need to establish clear criteria for determining which AI use cases should move from experimentation into production. Starting with high-volume, information-intensive processes can provide a practical path forward. These processes often have measurable performance metrics and contain repetitive activities that are suitable for intelligent automation, while still providing clear points for human intervention.

With the right foundation in place, organizations can move beyond AI that simply answers questions or drafts content toward AI that can participate in real business processes, supporting onboarding, client service, advisor decision-making and other workflows while keeping humans appropriately involved. The most successful firms may ultimately be those that view AI as a new layer across the enterprise, connecting information, people and processes in ways that were difficult to achieve.

The strategic question for wealth management leaders is therefore becoming more specific: What can AI do when it can understands the data, puts it in context, operates within a governance framework and safely moves work forward? For wealth management, that’s where the next significant gains in productivity, personalization and client experience can begin.

About the author

Ashley Longabaugh is a wealth management and wealthtech analyst and advisor with deep expertise at the intersection of business strategy, emerging technology and commercial execution. She brings a blend of technical depth and narrative clarity to deliver impactful research and thought leadership for financial institutions and technology providers across the global wealth management ecosystem. She is an author, researcher and advisor focused on emerging technologies transforming wealth management. Her expertise includes platform modernization, AI, cloud enablement, data ecosystems, data privacy, digital wealth platforms and advisor productivity tools, alongside deep knowledge of client segmentation and the wealthtech platform market.