Vice President, AI & Automation

Location: Must be based in Texas (DFW, Austin or Houston)

Élan Partners is seeking a Vice President, AI & Automation to lead the enterprise AI and intelligent automation strategy for a growing, technology-enabled organization. This highly visible leadership role advances an established AI Center of Excellence and turns business priorities into scalable, production-ready AI solutions with measurable results. The ideal candidate is a technically fluent AI leader who has personally delivered intelligent automation and applied AI in production, with recent hands-on experience integrating modern LLMs and agentic frameworks into enterprise business workflows. This person will combine strategy, technical depth, team leadership, and business acumen to scale AI capabilities across the organization.

Required Experience

• 10+ years of progressive technology, transformation, or consulting leadership experience.
• Demonstrated success leading AI, automation, or digital transformation initiatives from strategy through production implementation.
• Recent hands-on experience delivering LLM and agentic AI solutions in production business environments.
• Strong working knowledge of enterprise AI platforms, LLM ecosystems, agentic AI patterns, MCP, orchestration, API integration, and human-in-the-loop controls.
• Experience integrating AI with enterprise applications, workflows, RPA/automation, document processing, knowledge platforms, and systems of record.
• Strong understanding of cloud architecture, preferably Azure and/or AWS, along with data, identity, security, deployment, monitoring, and integration patterns.
• Working knowledge of LLMOps and model evaluation, including model selection, prompt/output evaluation, monitoring, and guardrails.
• Strong understanding of AI governance, privacy, access controls, vendor risk, auditability, and responsible AI.
• Proven experience building, scaling, and leading technical teams.
• Demonstrated ability to deliver measurable business results from AI and automation investments.

Preferred Qualifications

• Bachelor's degree strongly preferred; MBA or advanced technical degree a plus.
• Management or technology consulting background.
• Experience in a PE-backed or sponsor-owned organization.
• Experience in legal services, claims, healthcare, insurance, financial services, or another data-sensitive environment.
• Experience deploying secure agentic AI solutions at production scale.
• Experience scaling an existing enterprise AI/automation capability rather than solely building one from the ground up.
• Experience operating in a fast-paced, mid-market organization.

Key Responsibilities

• Own and execute the enterprise AI and intelligent automation strategy and roadmap, prioritizing initiatives based on business value, feasibility, risk, and organizational needs.
• Lead and scale the AI Center of Excellence, including AI product ownership and business analysis functions, while partnering with matrixed development resources.
• Partner with executive and functional leaders to identify, scope, and deliver high-value AI initiatives with measurable improvements in cost, capacity, cycle time, quality, revenue, and operational efficiency.
• Expand and mature existing AI infrastructure, including agentic AI frameworks, Model Context Protocol (MCP) servers, internal knowledge libraries, model selection, evaluation/testing, and platform resilience.
• Establish standards for agent requirements and context, including structured specifications, tool/MCP definitions, prompts, and instruction libraries.
• Integrate AI into enterprise workflows, systems of record, intelligent document processing, knowledge sources, and automation platforms.
• Establish and maintain enterprise AI governance covering sensitive data, human oversight, agent actions, identity and authorization, auditability, model risk, and responsible AI use.
• Partner with Finance and business leaders to define and validate ROI and measurable business benefits before scaling AI investments.
• Evaluate AI platforms, models, vendors, and partners and make informed build-versus-buy decisions.
• Build AI capabilities throughout the organization through shared tools, reusable patterns, training, and adoption.
• Lead AI initiatives across separate QA, UAT, staging, and production environments with disciplined deployment and release practices.