Manager - AI Strategy & Solutions Delivery

Manager - AI Strategy & Solutions Delivery

Reporting to VP, AI Center of Excellence, this role drives enterprise-scale AI and automation strategy and execution.

Define roadmap, architect solutions, and lead cross‑functional delivery to unlock measurable efficiency and innovation. Partner with data scientists, ML and platform engineers, and senior business leaders to deliver high impact use cases, and lead data science teams in model development, evaluation frameworks and production pathways to value.

• Develop AI‑driven strategies to support business value.
• Identify, qualify, and prioritize high‑impact AI, analytics, and agentic automation opportunities across business domains.
• Quantify full value at stake and value levers (automation, augmentation, revenue lift, risk mitigation), with clear dependencies and path to realization.
• Bring together AI, data engineering, solution architecture and relevant business domain expertise to design fit‑for‑purpose solutions leveraging LLMs, RAG, and agentic automation.
• Translate ambitious north‑star visions into executable plans, building alignment among relevant executives and business stakeholders on timing and delivery.
• Achieve maximum scale from AI solutions by proactively identifying and addressing barriers to scale and generalizing solutions to multiple applications.
• Partner with vendors and internal platform teams on best practices on platform governance to ensure enterprise‑grade scalability, security, and compliance.
• Serve as solutions architect and product owner for critical initiatives—defining scope, non‑functional requirements, SLAs/SLOs, and acceptance criteria.
• Partner with AI and ML engineering leaders to manage versioning, iteration planning, and solution delivery, creating transparency for business stakeholders and proactively mitigating delivery issues.
• Build productive relationships with key business stakeholders to help them co‑own the AI solutions, enabling the business to provide feedback and guidance at all stages of development.
• Bring business expertise and focus on value capture to AI problem‑solving during development, helping the team maximize impact and minimize unnecessary complexity.
• Augment our business stakeholders’ teams with AI and change management expertise, helping them realize maximum value from AI solutions.
• Lead interactions with AI and ML engineering teams to develop and codify more effective ways for AI and strategy teams to collaborate.
• Monitor industry trends and emerging technologies to ensure the organization remains at the forefront of AI and automation advancements.
• Oversee the needs for documenting knowledge, creating templates, and authoring playbooks to help the team scale.
• Develop and maintain three synchronized roadmaps: stakeholder engagement & adoption, use case portfolio, and technical capabilities/platform evolution.
• Conduct peer training and authoring thought leadership along with other members of the team to augment the full team’s skills.
• Develop new delivery frameworks and help the team build new service offerings.

• Years using data and AI to solve complex business problems and drive measurable impact in large, regulated environments.
• Proven track record influencing and aligning executives and cross‑functional teams to deliver outcomes at portfolio scale.
• Hands‑on leadership of technology strategies in AI/ML, analytics, BI, and automation—spanning both low code (e.g., Copilot Studio/Power Platform) and pro code stacks.
• Strong communicator—executive presence, crisp storytelling, and the ability to translate complex technical concepts into actionable business strategy.
• Familiarity with large datasets, ML modeling, and modern AI patterns (LLMs, RAG, vector databases, embeddings, prompt/response optimization, agents & tools).
• Practical understanding of evaluation and safety for LLM/ML solutions (offline/online testing, adversarial testing, guardrails, telemetry, policy enforcement).
• Comfortable with analytics scripting (Python/R preferred) and data/ML platforms (e.g., Azure, AWS, Databricks/Snowflake, MLflow, feature stores, CI/CD).
• Deep experience with Agile at scale and TBM/portfolio…