AI Enablement Manager

BMO; Toronto; On-site; CAD 150,000 - 210,000; Full time

Job summary

MCSI in Toronto is seeking an AI Enablement Manager to lead the development and management of the AI opportunity funnel, guiding business teams to frame problems, assess value, and advance high‑impact use cases from discovery to funding. You will partner with Marketing, Governance, Risk and Capability teams to ensure readiness and business ownership throughout the lifecycle.

The role combines consulting, analysis and coordination to clarify requirements, dependencies and success measures, while

Qualifications

• Five+ years of experience in product discovery, business analysis or related field.
• Experience enabling AI capabilities and enterprise AI solutions.
• Ability to lead across cross-functional teams including business, technology, risk and governance.
• Knowledge of portfolio management, lifecycle stages and decision governance.
• Strong communication, facilitation and stakeholder management skills.

Responsibilities

• Develop and manage the AI opportunity funnel from intake to definition.
• Coordinate activities across business, technology, governance, risk and enabling partners.
• Prepare qualified opportunities for portfolio forums with clear value, requirements and dependencies.
• Create practical demonstrations and guidance to help staff apply approved AI capabilities.
• Support readiness, pilots, and adoption post-release to sustain momentum.

Skills

Product discovery

Portfolio management

Stakeholder engagement

AI concepts

Business analysis

Education

Post-secondary degree in related field

Job description

Application Deadline:

11/22/2026

Address:

33 Dundas Street West

Job Family Group:

Customer Solutions

The AI Enablement Manager owns the development and management of MCSI’s artificial intelligence opportunity funnel, helping business teams identify, assess, mature and advance high-value, business-owned AI opportunities. The role partners with teams across Marketing, Communications & Social Impact to develop opportunities into decision-ready use cases and recommends which are ready for funding and resourcing decisions.

The role provides specialized consulting, analysis, and coordination throughout the AI opportunity lifecycle. It works with business, technology, governance, risk, and other enabling partners to clarify business needs, expected value, requirements, dependencies, and readiness for progression.

The role provides continuity across the opportunity lifecycle, remaining actively involved through approval, delivery coordination, release, and early adoption. It stewards opportunity quality, readiness, requirements, dependencies, and stakeholder alignment, while technical teams retain accountability for solution design and delivery, sprint execution, and delivery-team backlogs, and business owners retain ultimate accountability for business outcome realization.

The role also supports MCSI’s AI enablement goals by developing practical demonstrations, guidance, and capability education that help employees understand and effectively apply approved AI capabilities. The role offers opportunities to deepen AI product discovery, portfolio prioritization, lifecycle stewardship, and AI product ownership capabilities through progressively complex AI opportunities and use cases.

AI Opportunity Development

• Partners with business teams to identify and frame business problems and needs that may be addressed through AI.

• Facilitates discovery discussions to clarify the business problem, target users, intended outcomes, potential value, and factors affecting opportunity readiness.

• Advises business partners on the suitability of approved enterprise capabilities, third-party capabilities, or new AI use cases for the identified need.

• Establishes and manages the MCSI AI opportunity funnel, progressing business-owned opportunities through intake, discovery, qualification, and definition.

• Partners with business teams to develop clear opportunity assessments that define the business problem, target users, expected value, requirements, dependencies, and success measures.

• Reviews supporting information to identify gaps and areas requiring further definition or analysis.

• Assesses opportunity readiness and recommends whether opportunities should advance, require further development, be redirected, or be closed.

• Maintains visibility into opportunity status, dependencies, decisions, and required actions.

• Prepares qualified opportunities for portfolio and decision forums and provides recommendations on readiness, expected value, requirements, dependencies, and next steps.

AI Lifecycle and Use Case Enablement

• Guides qualified AI opportunities through defined lifecycle stages, ensuring that business ownership, required evidence, and readiness conditions are established before formal use case approval, funding, and resourcing decisions.

• Translates qualified business needs into decision-ready opportunity statements, user needs, and capability requirements that support formal lifecycle reviews.

• Coordinates activities across business, technology, governance, risk, and other enabling partners, including required documentation, reviews, and readiness activities, to address requirements and dependencies and support opportunity progression and formal use case decisions.

• Tracks actions, milestones, dependencies, decisions, and readiness conditions for assigned opportunities and use cases.

• Identifies issues affecting opportunity progression and coordinates with stakeholders to resolve them or determine appropriate next steps.

• Supports business owners with planning and readiness for approved pilots, capability releases, and broader adoption.

• Maintains continuity across opportunity development, formal use case approval, delivery, deployment, and transition to ongoing business ownership, coordinating handoffs while remaining engaged to clarify requirements, address dependencies, and support lifecycle decisions.

• Stewards use case readiness, requirements, dependencies, and stakeholder alignment throughout the lifecycle without owning technical design, development execution, sprint management, delivery-team backlogs, or ultimate accountability for business outcome realization.

• Supports business owners following release by coordinating the gathering of feedback and monitoring of early adoption, identifying improvement opportunities, and sharing relevant findings with capability and delivery partners.

• Assesses the materiality of risks, unresolved dependencies, and decisions, and escalates matters requiring additional authority or specialized expertise.

Enterprise and Third-Party AI Capabilities

• Maintains current knowledge of approved enterprise and third-party AI capabilities relevant to MCSI business needs.

• Works with capability owners and subject-matter experts to understand available functionality, intended uses, limitations, and implementation considerations.

• Partners with business teams to identify processes and workflows that may benefit from approved AI capabilities.

• Supports the assessment and introduction of third-party AI capabilities by coordinating business needs, requirements, and input from relevant partners.

• Advises business teams on the differences, intended uses, and limitations of available capabilities to inform fit-for-purpose decisions.

• Develops practical examples and demonstrations showing how approved AI capabilities can be applied to MCSI business needs.

• Consolidates business feedback and communicates recurring needs, capability gaps, and improvement opportunities to relevant capability owners and partners.

• Monitors emerging AI capabilities and trends and assesses their potential relevance to MCSI priorities and business needs.

Enablement, Education and Adoption

• Supports the MCSI AI enablement, adoption, and literacy program through practical education, guidance, and targeted adoption activities.

• Develops and delivers practical demonstrations, capability education, and targeted learning sessions.

• Creates clear guidance, examples, and reusable materials that help employees understand and apply approved AI capabilities.

• Contributes content and expertise to AI learning activities, champion forums, communications, and other enablement channels.

• Works with business teams to identify capability knowledge gaps and appropriate enablement approaches.

• Provides practical guidance on the responsible use of approved AI capabilities.

• Gathers feedback from employees and stakeholders to inform improvements to learning materials, demonstrations, and enablement activities.

• Shares practical examples, lessons learned, and reusable approaches that can be applied across teams.

Stakeholder Engagement

• Builds effective relationships with business teams and internal and external stakeholders to surface opportunities, resolve dependencies, and support timely decisions.

• Collaborates with technology, governance, risk, change, learning, and capability partners to advance AI opportunities.

• Serves as a knowledgeable point of contact for assigned opportunities, use cases, and capabilities.

• Facilitates discussions involving different business perspectives, technical requirements, and levels of AI understanding.

• Communicates AI, technology, and process concepts in clear and practical business language.

• Provides clear, timely updates on opportunity status, readiness, dependencies, risks, decisions, and required actions.

• Connects stakeholders with the appropriate subject matter experts and decision makers when specialized input is required.

• Facilitates collaboration between business owners and enabling partners throughout the AI opportunity lifecycle.

• Maintains stakeholder alignment by confirming requirements, documenting decisions, and clarifying ownership of next steps.

Analysis and Continuous Improvement

• Analyzes information from the opportunity funnel, active use cases, and enablement activities to identify trends, barriers, and improvement opportunities.

• Defines reporting requirements and measures to monitor opportunity funnel health, progression, readiness, aging, and key dependencies.

• Recommends and supports improvements to the MCSI AI opportunity lifecycle, operating model, tools, templates, guidance, and ways of working.

• Captures and shares lessons learned from AI use cases and capability introductions to inform future opportunities and lifecycle improvements.

• Researches relevant AI developments, industry practices, and emerging capabilities to inform recommendations.

• Breaks down complex or non-routine business problems, evaluates incomplete or competing information, and recommends practical next steps in situations where established approaches may not apply.

Qualifications

• Typically, five or more years of relevant experience and a post-secondary degree in a related field of study, or an equivalent combination of education and experience.

• Experience in product discovery or business analysis, with additional experience in one or more of the following areas: innovation, strategic initiatives, capability enablement, consulting, or technology delivery.

• Experience partnering with business stakeholders to identify needs, qualify opportunities, define expected value, and develop business or capability requirements.

• Knowledge of product discovery, portfolio management, and solution delivery practices and lifecycles.

• Working knowledge of artificial intelligence concepts and enterprise AI capabilities, with the ability to assess their practical application to business needs.

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