Senior Director, AI Enablement & Platforms

The Work

The Senior Director, AI Enablement & Platforms is a senior enterprise leader within the Enterprise AI team, accountable for making approved AI capabilities accessible, governed, reliable, supportable and adoptable at scale across EQB. The role leads enterprise AI enablement and adoption, and provides stewardship of the platforms and operating services that underpin a trusted employee and developer experience.

The role sets the strategy and operating model for AI platform onboarding, access, service management, support, reliability, observability, lifecycle management, vendor coordination and cost transparency. It partners with Data & AI Governance, AI Engineering, AI Architecture, Cybersecurity, Risk, Privacy, Legal, Infrastructure and business teams to scale AI responsibly and securely without duplicating their accountabilities.

The role leads the Manager, AI Platform Operations and Manager, AI Enablement, combining operational excellence with workforce readiness, learning pathways, reusable enablement patterns, communities of practice, communications and stakeholder engagement. Success is measured through responsible adoption, service health, user experience, control effectiveness, productivity and demonstrable business value-not through hands-on solution engineering or innovation-lab delivery.

Enterprise AI Enablement and Adoption

• Lead the enterprise AI enablement strategy and roadmap, translating EQB priorities into practical adoption, readiness and change plans for employees, developers and leaders.
• Establish scalable learning pathways, role-based guidance, reusable enablement patterns, communities of practice, champion networks, communications and engagement mechanisms that build AI fluency and responsible use.
• Partner with business and Technology leaders to identify adoption barriers, prepare teams for new AI-enabled ways of working and embed approved capabilities into day-to-day workflows.
• Define the end-to-end enablement experience - from discovery and intake through onboarding, learning, support, adoption and continuous improvement - using user feedback and service data to improve outcomes.
• Measure adoption quality, user experience, productivity and value realization; distinguish meaningful, sustained use from access or activity alone and provide transparent reporting to senior stakeholders.

AI Platform Strategy, Stewardship and Operations

• Own enterprise stewardship and the service operating model for EQB’s evolving portfolio of approved AI platforms and tools, including productivity copilots, developer copilots, cloud AI services, agentic AI frameworks and approved commercial solutions.
• Set platform roadmaps and service expectations in partnership with AI Architecture and AI Engineering, balancing enterprise reuse, user needs, resilience, security, control requirements, vendor direction and total cost of ownership.
• Oversee platform onboarding, environment readiness, identity and access processes, service catalogue entries, support pathways, incident and problem management, change and release coordination, knowledge management, capacity and continuity planning.
• Establish operational telemetry, observability, service-health reporting and escalation practices covering availability, performance, usage, cost, reliability and - in partnership with accountable teams - quality and safety signals for production AI services.
• Lead platform and tool lifecycle management, including intake, fit-for-purpose assessment, onboarding readiness, controlled change, rationalization, renewal coordination and retirement, while maintaining clear ownership and supportability.
• Coordinate AI-focused vendor relationships, service reviews, licensing and consumption management, and platform FinOps. Partner with Procurement, Finance and third-party risk functions where their formal accountabilities apply.
• Maintain sufficient breadth across platforms such as Microsoft 365 Copilot, Microsoft Foundry, GitHub Copilot, Google Cloud AI services, approved third-party tools and agentic frameworks to lead specialists and keep the portfolio current without creating a fixed product inventory.

Responsible and Secure Scaling

• Embed governance, security, privacy, legal, risk and architecture requirements into platform onboarding, access, service operations and enablement journeys in partnership with the accountable control and assurance functions.
• Operationalize approved responsible AI requirements and platform controls; maintain evidence, service documentation, runbooks, inventories and issue-management practices needed for effective oversight and auditability.
• Ensure new or materially changed platforms and tools follow applicable review, approval and lifecycle processes before enterprise use; escalate unresolved risk, service or ownership issues through the appropriate forums.
• Coordinate production-readiness expectations for AI services - including monitoring, support, rollback, continuity, cost visibility and human-oversight needs - without assuming ownership for solution engineering, architecture approval or governance policy.

Enterprise AI Operating Model and Partnerships

• Lead an integrated operating rhythm across AI Enablement & Platforms, AI Engineering, AI Architecture and Data & AI Governance, with clear decision rights, hand-offs, service expectations, prioritization and escalation paths.
• AI Enablement & Platforms owns platform stewardship, operations, access, adoption, readiness, support and the enablement experience.
• AI Engineering owns the engineering and delivery of reusable AI capabilities, solutions, agents and implementation patterns.
• AI Architecture owns target-state architecture, standards, technology direction and architectural assurance.
• Data & AI Governance owns governance frameworks, policies, controls, oversight and responsible AI requirements.
• Partner with business and Technology leads across EQB’s lines of business and corporate functions so domain priorities, adoption needs and value outcomes inform enterprise platform and enablement roadmaps.
• Influence enterprise priorities and investment choices through evidence on demand, operational health, adoption, risk, cost, duplication and realized value.

Let's Talk About You!

• Relevant post-secondary education in technology, business, engineering, data/AI or a related field.
• 10-15 years’ experience with significant progressive leadership experience in enterprise technology, AI, digital platforms, service management, enablement or transformation, including experience leading leaders and influencing senior executives in a complex matrix organization.
• Demonstrated experience establishing or scaling enterprise platforms and services, with practical knowledge of onboarding, access, support models, reliability, observability, incident/problem management, lifecycle management, vendor coordination and cost management.
• Strong understanding of generative and agentic AI, enterprise copilots, cloud AI platforms, AI service management, model and tool lifecycle considerations, and the operational implications of moving AI capabilities into sustained use.
• Experience driving enterprise adoption through change management, communications, learning pathways, communities of practice, stakeholder engagement and measurable user outcomes.
• Working knowledge of responsible AI, cybersecurity, privacy, third-party risk, technology risk, regulatory expectations and control environments relevant to a Canadian financial institution.
• Proven ability to define operating models, clarify decision rights, build cross-functional partnerships and manage outcomes across architecture, engineering, governance, infrastructure, risk and business teams.
• Commercial and financial acumen to guide vendor relationships, licensing, consumption, platform FinOps, prioritization and value-realization decisions.
• Strong executive communication, judgment and analytical skills, with the ability to translate complex platform, risk and adoption issues into clear choices and actions.
• Sufficient technical fluency to set direction, challenge recommendations and lead specialists; hands-on software or model engineering expertise is not required.
• Core Competencies
• Enterprise leadership and influence - aligns diverse stakeholders around clear outcomes, decisions and accountabilities.
• Enablement mindset - removes friction and equips people to use approved AI capabilities confidently, safely and effectively.
• Platform stewardship and operational excellence - treats AI platforms as enduring enterprise services, with disciplined reliability, supportability and lifecycle management.
• Responsible judgment - balances speed, value, customer and employee experience, security, privacy, risk and regulatory obligations.
• Strategic and systems thinking - connects platform, operating-model, talent, governance and adoption choices across the enterprise.
• Value orientation - uses evidence to prioritize, rationalize investment and demonstrate productivity, operational and business outcomes.
• Clear communication and stakeholder empathy - makes complex issues understandable, listens actively and builds trust across technical and non-technical audiences.
• Learning agility and humility - remains current as AI markets and tools evolve, invites challenge and adjusts direction when evidence changes.
• Accountability and urgency - acts decisively, escalates transparently and follows through on commitments without compromising controls.