AI Implementation Lead

Fairview Health Services•Minneapolis, MN

About The Position

The AI Implementation Lead is responsible for translating enterprise AI strategy into practical, scalable, and safe implementation across clinical, operational, and administrative environments. This role leads the end-to-end execution of AI and machine learning initiatives, from opportunity identification and workflow design through validation, deployment, adoption, monitoring, and continuous optimization. The ideal candidate brings a strong blend of project leadership, healthcare delivery knowledge, and applied understanding of AI/ML concepts, with the ability to bridge technical teams and frontline stakeholders to ensure solutions are not only technically sound, but also operationally effective, trusted by end users, and aligned to organizational priorities. This position works in close partnership with data science, engineering, clinical informatics, operations, compliance, legal, privacy, security, and IT teams to implement AI solutions that are explainable, compliant, integrated into workflows, and measurable in impact. The role requires experience managing ambiguity, coordinating cross-functional delivery, and operationalizing governance requirements related to model performance, bias, safety, and regulatory expectations. The AI Implementation Lead must be able to guide pilots and production deployments with a strong focus on change management, user adoption, workflow fit, and ongoing monitoring to support sustainable value realization and responsible use of AI.

Requirements

B.S./B.A. in computer science, engineering, information systems, health informatics, business, or a related field; or equivalent experience.
5 years of project management experience delivering complex technology, analytics, or digital initiatives.
2 years working directly with data, analytics, AI, or machine learning teams.
Experience managing projects with ambiguity, iterative experimentation, pilots, or agile delivery models.
Working knowledge of AI and machine learning concepts, including model development, validation, performance metrics, bias, drift, and explainability.
Experience collaborating with healthcare, clinical, operational, IT, or compliance stakeholders and working with sensitive data.
Strong project planning, risk management, communication, and cross-functional facilitation skills.

Nice To Haves

Experience in a healthcare delivery environment such as a health system, hospital, or ambulatory network.
Experience leading AI or machine learning projects, such as clinical decision support, risk stratification, forecasting, or natural language processing.
Familiarity with EHR or EMR platforms and healthcare data standards such as HL7, FHIR, ICD, CPT, LOINC, or SNOMED.
Knowledge of AI governance, model risk management, and applicable healthcare regulatory considerations.
Experience with agile tools and methods such as Scrum, Kanban, Jira, or Azure DevOps.
Relevant certifications such as PMP, PMI-ACP, CSM, or healthcare/AI-related credentials.

Responsibilities

Partner with clinical, operational, technical, and business stakeholders to define AI use cases, success metrics, and delivery plans.
Lead AI initiatives through data discovery, experimentation, validation, deployment, and post-launch monitoring.
Coordinate data access, governance, privacy, and compliance activities, including handling sensitive health information.
Manage project risks related to model performance, bias, explainability, safety, and regulatory requirements.
Work with clinicians and operations leaders to embed AI solutions into workflows, EHRs, and operational systems.
Oversee pilots, phased rollouts, user feedback, and adoption efforts to improve usability and trust.
Track key performance indicators such as model performance, drift, adoption, workflow impact, and outcome measures.
Partner with data science and ML Ops teams on monitoring, retraining, and change control processes.
Communicate status, risks, trade-offs, and results to technical teams, executive leaders, and governance groups.
Support change management, user training, and enterprise best practices for AI documentation, intake, and prioritization.