Reinventing Value in Wealth & Asset Management with Generative AI

The asset and wealth management industry is at a critical inflection point. Tightening margins, rising client expectations, and increasing regulatory complexity are placing unprecedented pressure on firms. The traditional playbook—built on manual processes, siloed business and technology functions, and relationship-driven models—is no longer sufficient.

For both asset and wealth managers, the challenge now goes beyond cutting costs or streamlining operations. It’s about fundamentally transforming how the business runs—from the front office to the back, from analog decision-making to intelligent, AI-enabled strategies. Clients today expect hyper-personalized service, faster response times, and proactive insights—all while firms grapple with compliance and scale.

This is where generative AI and automation are beginning to reshape the landscape. Far from just enhancing existing workflows, these technologies are enabling a smarter, more adaptive operating model—one that empowers professionals, accelerates innovation, and ultimately redefines how value is delivered.

Forward-looking firms aren’t waiting—they’re building the capabilities right now to lead in the next era of asset and wealth management.

Top Challenges Facing Wealth and Asset Managers in 2025

Asset and wealth managers are under pressure from all sides, with compounding business challenges rooted in operational and technological complexity. Today’s leaders face five critical challenges:

Fee Compression and Margin Pressure
Shrinking profitability from traditional products and platform bundling has led to tighter spreads and distribution pressures. Ever-increasing costs from legacy infrastructure add further strain, while minimal use of automation in the middle and back office and limited self-service capabilities exacerbate the issue. Many firms lack the tools needed for cost transparency and profitability analysis—a growing concern under consumer duty mandates.
Solution: Leverage workflow automation, generative AI, and shared services to drive efficiency, reduce the cost-to-serve, and enable faster time to market. Explore new products such as Privates or Multi-Strategy to create a niche category for business.
Data and Technology Integration
Firms are struggling to make timely decisions and adhere to investment guidelines due to fragmented data landscapes. The influx of market, ESG, and alternative data overwhelms legacy systems not designed for real-time or multi-source integration. Siloed data architectures across the front, middle, and back office, combined with a lack of enterprise-wide governance and inconsistent data ownership, inhibit operational agility.
Solution: Adopt modern data architecture, leverage Gen AI accelerators to enable seamless front-to-back integration, implement robust governance, and use AI/ML for intelligent enrichment and automation.
Regulatory and Compliance Pressure
Global regulatory shifts are forcing firms to transform their people, processes, and technology. Yet, regulatory data often sits in disconnected systems, making reporting complex and time-consuming. The lack of traceability, data lineage, and on-demand report generation—especially with unstructured ESG data—increases non-compliance risk and reputational exposure.
Solution: Build traceable, auditable, and real-time systems with integrated tagging and reporting capabilities, automated alerts leveraging generative AI, and compliance with horizon scanning capabilities that shorten bloated compliance assessments. Plan ahead for AI governance models.
Legacy Technology Landscape
Outdated, monolithic systems with high costs of change are slowing innovation, time to market, and increasing vendor risk. Point-to-point integrations limit scalability, and many platforms lack APIs or modular architecture needed for rapid development. Portfolio managers often lack the flexibility to adopt modern risk models, which further constrains agility.
Solution: Leverage generative AI to accelerate the pace of legacy transformation. Adopt SaaS or cloud-native platforms with modular designs and scalable ecosystems as a foundational concept.
Operating Model Inefficiencies
Manual processing, fragmented workflows, and duplicated roles continue to drive high cost-to-serve and client dissatisfaction. The lack of automation tools and limited adoption of shared services or standardized operating models limits scalability and slows time-to-market for new products. Strategic programs tend to overrun, leading to increased costs and business value erosion.
Solution: Accelerate SDLC transformation by embedding AI agents at critical points across the entire lifecycle (Analysis, Development, Testing, Deployment), straight-through processing (STP), and intelligent automation, allowing business units to collaborate more effectively. Streamline agile ways of working to deliver business value quicker.

Why Agentic AI is the Next Inflection Point in Financial Services

The convergence of these challenges is pushing executives to rethink traditional approaches. Many are embracing a new class of intelligent systems—AI agents that embed directly into operating models to drive scale, speed, and precision.

These embedded agents mark a shift from digital integration to an agentic ignition point, unlocking capabilities that were previously impossible:

From Analog to Agentic

Analog foundations: Investment strategies relied on analyst judgment, fundamental research, and manual data processing; product launches took months or years due to fragmented workflows.
Digital integration: Dashboards, data lakes, and self-service tools improved visibility and reporting but did not fundamentally change how decisions were made.
Agentic transformation: Autonomous AI agents now monitor markets, flag anomalies, simulate multi-scenario outcomes, and accelerate new strategy and product launches by 40 to 60 percent.

Tangible Business Impact

Cut down delivery time by 30 to 50 percent, significantly reducing time to market.
Enhance transparency and reduce compliance risk with automated traceability.
Reduce research and preparation time by 25 to 30 percent, improving alpha generation and client retention.

Agentic AI is not just about improving existing processes—it’s about reimagining how asset and wealth management firms think, operate, and grow.

Asset and Wealth Management Challenges as Opportunities

How Generative AI Accelerators Like Sapient Slingshot Are Driving Financial Transformation

Enter Sapient Slingshot, Publicis Sapient’s generative AI acceleration platform. Built for highly regulated industries, Slingshot helps organizations move from AI experimentation to enterprise-wide transformation—with speed, security, and control.

Unlike generic LLM pilots or chatbot demos, Slingshot is purpose-built for financial services. It combines the power of generative AI with real enterprise infrastructure to solve for:

Legacy modernization at scale
AI-powered SDLC (software development lifecycle) acceleration
Real-time decision-making through intelligent agents
Data unification across business lines
Personalized, auditable client experiences

The outcome? Reinvented value delivery across the full investment lifecycle—from onboarding and portfolio construction to compliance reporting and advisor enablement.

The business will receive value from the transformation, which includes:

Reduced tech debt and infrastructure complexity
Speeds up delivery of new features, apps, and digital services
Drives alpha generation and cost efficiencies simultaneously
Powers enterprise-level analytics
Enhances regulatory readiness

Inside the Agentic AI Blueprint: Build Smarter, Scalable Financial Operations

The P.S. Agentic AI blueprint is a modular, enterprise-ready framework that brings structure, speed, and intelligence to complex financial environments. Slingshot offers a powerful combination of tools, workflows, and foundational infrastructure that enables AI agents to drive enterprise transformation at scale.

Business Priorities: Cost efficiency | Revenue Growth | Improve quality | Reduce risk | Speed of change

Examples: Onboarding | Omnichannel Experience | Enterprise (e.g., Payments, Risk) | Operational Efficiency | Content Management

Outcomes – Delivering Customer & Colleague Journeys (Experience) by leveraging GAI

Transform & Deliver
- Proposition Agents: Impact analysis, Market Analysis, Backlog Creation, Value identification, Business case, Hypothesis generator
- Functional Agents: Spending Insights, Recommended actions agent, Advisor/Customer AI Agent
- Legacy Modernisation Agents: Code 2 Spec, Spec 2 Design, Design 2 Code, Proprietary 2 Public
- Run Agents: Payments, Risk, ESG Monitoring, Case Management
- Regulatory & Compliance Agents: Compliance Auditor, Risk Prompt library, Data models, Reusable products & accelerators

Knowledge Context Store: Domains, Onboarding, Payments, Risk
Multi Agent Foundation Framework: Agent Orchestration Service, Code Execution Service, Current & Long-term Memory
Data & Embeddings: Prompt & Embedding, Data Integration, Data Lakehouse
Security & Governance: Content Filters, Safeguard (Prompt injections & Jailbreaks), Manage Bias, Hallucination & copyright infringement
AI Services: Language, Speech, Document, Bot
Core Services: Model Garden (LLMs incl. local model), GPUs, Logging & Auditing, Deployment, Endpoints
Partner Ecosystem: Salesforce, Databricks, Adone, Murex

Key Features of the Slingshot Platform

Real-World Examples of Generative AI Success in Wealth and Asset Management

One of the world’s largest asset and wealth management firms, with over 600 billion CAD in AUM, partnered with Publicis Sapient to unlock new value through a coordinated generative AI initiative.

Using Slingshot and its agentic AI framework, the firm was able to:

Make models intelligent to assist with complex business problems, reducing the reliance on labor-intensive custom analytics reports
Unify data access across roles—portfolio managers, engineers, analysts, and executives—through consistent, clean, and governed data layers
Streamline operational processes, accelerating decision-making cycles and reducing wasted effort through process orchestration

What once took days of cross-functional coordination could now be done in minutes—while ensuring compliance and traceability throughout.