AI Business Enablement Lead

Raymond James•Vancouver, BC

1d•CA$125,000 - CA$140,000•Hybrid

About The Position

The AI Business Enablement Lead is responsible for accelerating practical, responsible AI adoption across Raymond James Ltd., with a focus on advisor productivity, operational efficiency, service quality, and employee effectiveness. Reporting to the SVP, Head IT Delivery Office / CIO, the role owns the RJL AI enablement roadmap and works closely with business leaders, advisor teams, operations, technology, product management compliance, risk, privacy, and Raymond James Financial (RJF) AI teams. The role focuses on three priorities: increasing adoption of approved productivity tools, identifying and implementing practical AI use cases in business workflows, and preparing RJL to take advantage of AI capabilities embedded in core platforms. Success in this role will be measured by adoption, business value, risk-managed implementation, and the firm’s ability to use AI confidently in a regulated wealth management environment.

Requirements

Bachelor’s degree in Data Science, Computer Science, Engineering, Business Administration, or related field — or equivalent experience.
8+ years of experience in technology enablement, digital transformation, business analysis, product management, consulting, operations transformation, or related roles.
3+ years of experience working with AI, automation, data, analytics, or emerging technology use cases.
Financial services or wealth management experience strongly preferred.
Experience translating business problems into practical technology-enabled solutions.
Experience evaluating, prototyping, or implementing LLM-enabled tools, workflow automation, or productivity solutions.
Experience facilitating workshops, working sessions, pilots, or adoption programs with cross-functional stakeholders.
Wealth management business processes, advisor workflows, branch operations, client lifecycle, and service model.
Canadian financial services regulatory environment relevant to AI adoption, including CIRO, OSFI expectations where applicable, privacy, records, supervision, and third-party risk.
AI productivity tools and enterprise platforms, including Microsoft Copilot, ChatGPT/OpenAI-enabled solutions, meeting assistants, knowledge search, workflow automation, and emerging AI capabilities.
Responsible AI practices, data handling, privacy, security, model risk, vendor risk, and human oversight in regulated environments.
Change management, adoption measurement, training design, and business-value tracking.
Analysis: Uses data, adoption metrics, and business insight to identify high-impact AI opportunities, understand adoption trends, and drive prioritization decisions.
Communication: Conveys complex AI concepts clearly to technical and non-technical audiences — from advisor teams to executive leadership; strong executive presence.
Client Focus:
Exercising Judgment & Decision Making: Makes timely, risk-informed decisions that balance innovation speed with governance, compliance, and business impact.
Technical & Professional Knowledge: Demonstrates expertise in AI platforms, prompt engineering, automation, and emerging technology — with the ability to personally build working solutions.
Building Effective Relationships: Builds strong partnerships across IT, business units, advisor teams, compliance, and vendor teams to achieve shared goals.
Influence Without Authority: Inspires adoption and drives alignment across complex, cross-functional environments — including executives, architects, advisors, and business leaders — without relying on direct reporting authority.

Nice To Haves

Master's degree or MBA preferred.

Responsibilities

Participate in the development of the RJL AI strategy, applying functional expertise to test the viability of the strategy and contributing creative ideas and insights to support the strategy formation process.
Maintain and execute the RJL AI strategy and phased roadmap.
Communicate the RJL AI strategy, along with broad actions needed to implement it; inspire a diverse workforce to commit to these actions to achieve business goals.
Partner with the RJF AI teams to assess, adopt, and adapt enterprise AI capabilities for the Canadian business.
Advise senior leaders on AI capabilities, mega-trends, risks, vendor developments, and practical workflow opportunities.
Partner with product management to add AI that is imbedded in 3rd party enterprise tools to the RJL roadmap.
Provide regular reporting on adoption, blockers, risks, and emerging opportunities.
Lead AI adoption and literacy programs for advisors, branch teams, operations, supervision, compliance, technology, and corporate functions.
Develop role-based guidance, training, prompt libraries, playbooks, and practical examples for approved AI tools.
Work with RJF AI teams to leverage existing resources, standards, training, and lessons learned.
Establish and lead the RJL AI Enablement Group and AI Community of Practice to share use cases, adoption patterns, lessons learned, and reusable materials.
Identify and support AI advocates across business units to increase safe and consistent adoption.
Partner with business leaders, advisor teams, product management, operations, supervision, compliance, risk, and technology to identify AI use cases tied to measurable business outcomes.
Maintain a prioritized AI opportunity backlog, including business owner, expected value, feasibility, data requirements, risk considerations, dependencies, and implementation path.
Facilitate use-case intake, discovery, prioritization, pilot planning, and post-implementation measurement.
Support pilots and controlled rollouts of approved AI capabilities, ensuring adoption plans, training, communications, controls, and success measures are in place.
Coordinate with compliance, risk, privacy, legal, technology, and RJF AI teams to ensure AI use cases follow applicable policies, controls, and Canadian regulatory expectations.
Operate a structured intake and assessment process for AI tools and use cases, including business value, data usage, privacy, supervision, records, model risk, vendor risk, and operational risk considerations.
Maintain clear guidance on approved tools, permitted uses, restricted uses, and escalation paths. Reduce shadow AI risk by providing approved alternatives, practical training, and transparent communication.