AI is reshaping wealth and asset management: what leaders need to do now
A new global study from ThoughtLab, produced with FNZ, ServiceNow, Amazon Web Services, LSEG, Publicis Sapient, and the Grant Thornton network, signals a decisive shift. Among executives surveyed, 73% say AI is critical to their firm's future, and 63% expect it to materially change how the sector works.
The research draws on inputs from 500 institutions across 16 markets. It shows AI touching every part of the value chain-from front-office personalization to middle-office risk and compliance, and back-office coding and process optimization.
Key findings at a glance
- GenAI use is set to jump from 37% today to 71% within three years; agentic AI adoption is also expected to double.
- Front office: more than half of firms use AI for customer analytics and self-service; many now craft individualized products and communications.
- Middle office: AI supports automated compliance checks and live detection of security threats and fraud.
- Back office: teams apply AI to generate code and streamline workflows.
What this means for managers
- Set the direction: Define a clear AI vision and foster a culture that rewards small experiments and fast learning.
- Fix the data and platform: Modernize your cloud stack and clean, integrate, and label data to make AI usable across teams.
- Put guardrails in place: Establish governance, risk, and compliance policies for model development, testing, deployment, and monitoring.
- Prepare people and roles: Upskill advisors, analysts, and ops with AI fluency and new workflow habits; hire where it's faster than building.
- Think agentic: Don't just automate tasks-reimagine processes so AI agents can handle work that wasn't feasible before.
Advisor productivity is the near-term win
Firms report meaningful time savings for advisors and planners. Common GenAI use cases include drafting meeting notes, summarizing dense documents, and producing personalized client messages.
Over the next three years, leaders expect AI to take on more repeatable activities-onboarding, client administration, and transaction execution-so advisors can spend more time on relationships and guidance.
"AI's evolution into an autonomous collaborator will redefine the very nature of investment management. It will act as a strategic partner driving performance, personalization, and productivity." - Lou Celi, CEO of ThoughtLab
Barriers you'll need to solve early
- Cultural resistance to change and experimentation.
- Shortage of AI skills and product-minded teams.
- Data quality and integration issues that limit model effectiveness.
- Complex operations and legacy systems that slow rollout.
- Shifting regulatory expectations that require strong controls and documentation.
How AI leaders are pulling ahead
- They translate strategy into funded, measurable AI roadmaps tied to business outcomes.
- They stand up shared data products and a cloud platform that make reuse the default.
- They run model risk management with clear roles, testing standards, and logs.
- They treat skills as a product-role-based learning paths, playbooks, and internal communities.
- They pilot agentic use cases to rework entire workflows, not just single tasks.
A practical 90-day plan
- Prioritize 3-5 use cases: One each in the front, middle, and back office; define success metrics and owners.
- Create an AI steering group: Business, data, risk, and tech leaders who meet weekly and clear blockers.
- Data first: Run a cleanup sprint for the datasets your pilots depend on; document lineage and access.
- Pilot an advisor copilot: Start with notes, summaries, and personalized outreach; capture time saved and quality scores.
- Stand up governance: Adopt a lightweight policy aligned to the NIST AI Risk Management Framework; define approval workflows and human-in-the-loop checks.
- Upskill the team: Launch role-based microlearning and office hours; assign champions per business line. If you need a structured path, see AI courses by job.
- Measure and iterate: Track advisor hours saved, onboarding cycle time, error rates, NPS, and control breaks; scale what works.
Use cases to consider now
- Front office: Next-best action for advisors, client segmentation, proposal generation, and proactive service alerts.
- Middle office: KYC/AML screening support, trade surveillance triage, and model documentation assistants.
- Back office: Code generation and testing, reconciliations, and exception handling with agentic workflows.
Why this matters
The firms outpacing peers aren't betting on a single model. They're building a repeatable system-strategy, data, governance, skills, and process redesign-then shipping small wins that compound.
If your teams need a vetted shortlist of practical tools, explore this curated list: AI tools for finance.
About the study
The study-an AI playbook for wealth and asset managers in the agentic era-analyzes responses from 500 financial institutions across 16 markets and includes insights from industry executives and AI specialists. It covers adoption patterns, operating models, risk practices, and where leaders are getting measurable results.
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