Franklin Templeton Moves Agentic AI From Pilot to Platform With Wand AI
Franklin Templeton has entered a multi-year partnership with Wand AI to deploy agentic AI across its global asset management platform. What began as pilots has progressed to full-scale enterprise use, spanning research, operations, and digital initiatives with strong governance in place.
"Our partnership with Wand AI brings agentic AI out of the lab and into live production - embedded across research, operations, and transformative initiatives. With strong governance in place, we are demonstrating that AI can deliver secure, scalable, and measurable value," said Vasundhara Chetluru, Head of AI Platform at Franklin Templeton.
Why this matters to management
This is a signal that agent-based AI is moving beyond proofs of concept in financial services. The goal isn't novelty; it's dependable outcomes at scale with the right controls.
By 2026, Franklin Templeton expects Wand-powered agents to support core workflows in investment research, operational efficiency, and enterprise transformation - all under rigorous governance and control frameworks.
What "agentic AI" looks like in practice
Wand AI provides an operating system for an autonomous workforce: agents can be created, orchestrated, and governed centrally, then embedded into live processes. Think of it as AI labor that collaborates with human teams, follows policy, and improves through feedback loops.
Rotem Alaluf, CEO of Wand AI, framed the mission as turning AI from experiments into an integrated workforce that operates at scale in regulated environments - coordinated, auditable, and aligned with business goals.
Where value shows up first
- Investment research: Faster synthesis of filings, calls, and alternative data; analyst-ready briefs; continuous monitoring with alerts.
- Operations: Workflow automation across repetitive, rules-heavy tasks; lower cycle times; fewer manual handoffs.
- Risk and compliance: Policy-aware agents, audit trails, access controls, and human approval points baked into workflows.
- Digital transformation: A scalable way to identify, test, and evolve AI-driven processes without rebuilding systems from scratch.
Execution playbook for leaders
- Start with governed use cases: Pick high-signal workflows with clear policies (e.g., research brief generation, client reporting QA, reconciliation checks).
- Stand up an AI control plane: Centralize agent orchestration, identity and access management, data permissions, and logging.
- Design human-in-the-loop checkpoints: Define approval gates for material decisions and disclosures; make overrides easy to apply and audit.
- Instrument the work: Track cycle time, accuracy, exception rates, coverage, and cost-per-task from day one.
- Integrate with systems of record: Connect to research platforms, data lakes, OMS/EMS, CRM, and document management via APIs.
- Codify policies: Convert compliance, data residency, and model risk standards into enforceable rules agents must follow.
- Upskill teams: Train managers and practitioners on agent prompting, exception handling, and process redesign.
Governance and risk controls that stick
- Model risk: Establish model inventory, validation routines, and scenario testing for agent behaviors.
- Data security: Enforce least-privilege access, PII protection, and environment isolation; log every action.
- Compliance: Pre-approved templates, disclosures, and retention rules embedded in agent workflows.
- Operational resilience: Clear rollback plans, failover modes, and performance SLAs for critical processes.
- Vendor strategy: Avoid lock-in with modular architecture and clear exit paths; measure cost-to-value by use case.
How to measure ROI
- Productivity: Time saved per task, coverage ratio (sources monitored per analyst), and throughput per FTE.
- Quality: Error rates, rework, compliance exceptions, and audit findings.
- Speed: Lead time from request to insight, cycle time for reporting or reconciliations.
- Cost: Cost per processed document/ticket, avoided external spend, infrastructure efficiency.
- Adoption: % of workflows with agents in production, satisfaction scores from end users, override rates.
What to watch next
Expect deeper integration with research tooling and data catalogs, broader operational coverage, and tighter policy automation. The most important signal: consistent, auditable results that hold up under regulatory scrutiny while freeing teams to focus on higher-value work.
About the companies
Franklin Templeton serves clients in 150+ countries with more than 1,500 investment professionals and $1.66 trillion in AUM as of September 30, 2025. Learn more at franklintempleton.com.
Wand AI provides an operating system for the agentic workforce with centralized control, transparency, and live deployments across regulated sectors. Explore their platform at wand.ai.
For managers building an AI roadmap
If you're standing up similar capabilities, a structured learning path can speed up adoption and reduce rework. Browse finance-focused AI tooling and training here: AI tools for finance.
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