AI Program Manager - Dallas, TX

Conifer Health Solutions•Dallas, TX

About The Position

We are seeking a strategic and operationally focused AI Program Manager to lead cross-functional programs from vision to impact across a healthcare enterprise, partnering with the AI Product Owner to lead the end-to-end execution of AI initiatives, ensuring projects align with business goals. The ideal candidate is a strong leader and critical thinker who excels at driving clarity, mitigating risk, and enabling teams to deliver business outcomes. This role will bridge the gap between business stakeholders and technical teams, serving as the coordinator for AI solution delivery; partnering closely with clinical and operational stakeholders, finance, and procurement; and working with external vendors to pilot, scale, and monitor solutions with measurable return on investment. The individual must ensure potential AI solutions are compliant with regulations and Tenet policies. The AI Program Manager will not define the enterprise AI strategy or product roadmap; instead, this role is responsible for translating strategic direction into repeatable execution frameworks, governance processes, operational standards, and measurable outcomes. The ideal candidate combines healthcare operational knowledge, strong program management discipline, vendor management experience, and the ability to navigate cross-functional enterprise environments.

Requirements

Bachelor’s degree in Computer Science, Engineering, Mathematics, Information Systems or appropriately relevant field
5+ years of program or project management experience managing technical programs in healthcare, specifically with data science, AI/ML environments, AI-enabled initiatives, or digital transformation
Experience in program management methodologies (Waterfall, Agile, hybrid)
Experience leading cross-functional enterprise programs involving clinical, operational, and technology stakeholders
Experience managing pilots, proof-of-concepts, innovation initiatives, or emerging technology programs
Strong understanding of healthcare operations, governance structures, and stakeholder management
Demonstrated ability to establish operational processes, frameworks, and standardized methodologies
Strong analytical, organizational, communication, and executive presentation skills

Nice To Haves

PMP, Lean, Agile, or similar program management certifications preferred
Experience supporting AI, automation, machine learning, generative AI, or advanced analytics initiatives
Familiarity with clinical workflows, healthcare operations, and clinical informatics
Experience working with enterprise portfolio management practices
Experience in large, matrixed healthcare organizations
Versed with AI advancements in generative AI, machine learning, etc
Experienced working with healthcare data and familiar with healthcare regulations
Adept at critical thinking; demonstrated ability to analyze information, question assumptions, and evaluate arguments to form well-reasoned judgments and solve problems effectively without relying on status-quo or others' direction
Operationally disciplined and process-oriented; skilled at coordinating complex cross-functional initiatives
Comfortable navigating ambiguity in emerging technology environments
Able to balance innovation with governance and operational rigor
Focused on measurable outcomes, adoption, and scalability
Strong executive communication skills and C-Level interaction

Responsibilities

Partner closely with the AI Product Owner (who maintains responsibility for business case development, ROI modeling, roadmap planning, and governance direction) from ideation to implementation
Align pilot execution activities with enterprise portfolio priorities and operational objectives
Serve as a liaison between business operators, clinical informaticists, technical teams, vendors, and leadership stakeholders
Facilitate executive and operational communications regarding pilot status, risks, outcomes, and adoption readiness
Drive stakeholder engagement to ensure operational alignment, user readiness, and successful adoption
Works collaboratively with business and technical stakeholders, regardless of reporting structure
Lead end-to-end coordination and execution of AI pilots and solutions across clinical, operational, and corporate functions
Establish and maintain a standardized AI framework, including intake, evaluation, execution, measurement, adoption, and transition processes
Develop repeatable methodologies, templates, and governance checkpoints for AI delivery
Ensure solutions are delivered consistently with defined objectives, timelines, success criteria, and stakeholder accountability
Bridges strategy and execution across initiatives to maximize efficiency and chance of achieving business goals
Coordinate dependencies across portfolio management, operational teams, IT, security, legal, procurement, and finance
Manage pilot lifecycle activities from initiation through operational handoff or closure
Pro-actively develop and drive program delivery improvement
Oversee third-party AI vendor engagements, pilot coordination activities, and implementation timelines
Monitor vendor deliverables, pilot performance, and adherence to enterprise standards and expectations
Manage external partner relationships to ensure accountability and alignment with organizational objectives
Track pilot performance metrics, KPIs, adoption indicators, operational outcomes, and value realization measures
Partner with operators and clinical informaticists to evaluate workflow impact, user adoption, and measurable business outcomes
Develop dashboards and executive reporting mechanisms to communicate pilot progress, risks, outcomes, and scalability recommendations
Ensure pilots have clearly defined success criteria, measurable outcomes, and post-pilot evaluation processes
Support organizational readiness and adoption planning for AI-enabled workflows and solutions
Identify operational barriers, change impacts, and stakeholder concerns related to AI implementations
Promote standardized processes and enterprise consistency in how AI pilots are introduced, managed, and evaluated
Help establish sustainable operational models for successful pilots transitioning into broader deployment