Senior Analyst, AI Business & Transformation

Lifepoint Health•Brentwood, TN

1d•Hybrid

About The Position

The AI Business Analyst is the strategic bridge between business stakeholders and the AI Transformation engineering team. This role identifies high-value automation opportunities across the organization, translates business challenges into actionable engineering requirements, and produces executive-ready communications that demonstrate the team’s impact and ROI. Leveraging AI tools natively in their own daily workflow, this analyst operates as both a domain expert and an AI power user — helping the organization understand what is possible while ensuring the engineering team builds what actually matters. Candidates who use AI tools daily and demonstrate clear enthusiasm for enterprise AI transformation are strongly preferred over those with a traditional BA background alone.

Requirements

Bachelor’s degree in Business Administration, Information Systems, Healthcare Administration, Operations, or related field.
3+ years in a business analyst, management consultant, operations analyst, project manager, or similar role with significant cross-functional stakeholder engagement.
Experience working alongside IT or technology teams is strongly preferred.
Demonstrated, hands-on proficiency using AI tools (Claude, ChatGPT, Microsoft Copilot, or similar) to produce professional-quality documents, analyses, executive presentations, and communications.
Exceptional written communication skills — ability to craft compelling, concise executive narratives from complex technical and operational concepts.
Process analysis and workflow documentation skills: ability to map current-state business processes, identify friction points and waste, and articulate clear future-state requirements.
Familiarity with enterprise platforms such as ServiceNow, Microsoft 365, or ERP systems at a functional user level — sufficient to understand integration potential and data flows.
Working understanding of AI/ML concepts including large language models, autonomous agents, automation, and data pipelines — sufficient to hold credible conversations with both engineers and executive leaders.
Understanding of AI security and responsible use considerations: data privacy, access control, appropriate use policies, and the importance of organizational AI governance guardrails.
Excellent stakeholder management, facilitation, and meeting leadership skills — comfortable leading discovery workshops with both technical and non-technical audiences.
Highly organized with the ability to manage a portfolio of parallel initiatives across different business units and stages of maturity.
Self-starter mentality — comfortable in a fast-moving, emerging-practice environment where the playbook is being written in real time.
Genuine intellectual curiosity about AI and automation, evidenced by personal experimentation, self-directed learning, or a clear track record of bringing AI tools into previous work.

Nice To Haves

Advanced degree a plus but not required.
Demonstrated, hands-on daily use of AI tools (Claude, ChatGPT, Copilot, or equivalent) may offset traditional experience requirements for exceptional candidates.
PMP, CBAP, or Lean Six Sigma Green/Black Belt is a plus.
Microsoft AI Fundamentals (AI-900) or similar AI literacy certification is desirable and supported.
An appetite for continuous learning in AI is essential.

Responsibilities

Conduct structured discovery sessions with business unit leaders and operational teams to identify manual processes, bottlenecks, and automation opportunities with measurable business impact.
Translate business requirements into clear, actionable engineering specifications the AI engineering team can build against — including agent scope, data inputs and outputs, success criteria, edge cases, and security considerations.
Use AI tools natively (including Anthropic Claude, Microsoft Copilot, and other LLMs) to draft executive summaries, business cases, use case briefs, ROI analyses, and presentation materials for senior leadership.
Maintain and manage the team’s use case pipeline: prioritizing opportunities by business impact and technical feasibility, tracking progress from discovery through deployment, and communicating status to stakeholders.
Serve as the primary point of contact for business units seeking AI transformation support — qualifying inbound requests, setting appropriate expectations, and managing the engagement model with external teams.
Develop and maintain standard templates for use case intake, requirements documentation, agent specification, and executive reporting to accelerate the team’s ability to move from idea to build.
Collaborate with the AI governance team to ensure all proposed use cases align with organizational AI policies, data handling guidelines, responsible AI principles, and compliance requirements.
Build and maintain stakeholder relationships across business units, IT, and senior leadership — facilitating alignment and removing communication-driven blockers that slow the team.
Track business outcomes delivered by deployed AI agents and automation — working alongside the team’s analytical tools to validate that implemented solutions achieve stated ROI.
Stay current with enterprise AI capabilities, industry use cases, and emerging best practices to proactively surface transformation opportunities to the team and leadership.