Vice President, Artificial Intelligence & Data

Patrick Industries•Elkhart, IN

6d•Hybrid

About The Position

Patrick Industries is building its enterprise AI and data capability from the ground up and is searching for the executive to lead it. This is a rare “zero-to-one” mandate inside a profitable, acquisitive company with 65+ years of entrepreneurial execution and 85+ operating brands. It involves a staged, multi-year investment behind a use-case portfolio carrying more than $150M of identified value across 70+ initiatives, spanning customer-centric operations, aftermarket commerce, and back-office automation. The Vice President of AI & Data will set the operating model, formulate the AI and data investment strategy, build and scale the delivery team, own the data foundation on which it all depends, and run the engine that turns strategy into production-grade and measurable value. The role reports to the Chief Information Officer and governs, prioritizes, and delivers the enterprise AI, data, and automation initiatives that drive measurable business value across Patrick Industries. The role is the execution engine behind the enterprise AI strategy and the steward of the data foundation beneath it, translating prioritized use cases into scalable, production-grade solutions through a DevOps-enabled, agile delivery model, and ensuring a disciplined delivery capability that is fast without being fragile. Operating at the intersection of business and technology, the VP carries full lifecycle accountability from intake and prioritization through build, deployment, and scaled adoption. The leader is expected to stay at the leading edge of a fast-moving field, continuously evaluating new models, agentic frameworks, and tools and translating them into pragmatic, well-governed advantage. The leader drives clear traceability from each use case to defined KPIs and business outcomes, strengthens the data-governance leg of the enterprise Digital Backbone, and aligns delivery to Patrick’s IT Strategic Pillars: Innovative Advantage, Value Optimization, Agility & Efficiency, and Resilient Operations.

Requirements

Proven executive leadership in AI, data, automation, advanced analytics, or digital product delivery, with a track record of taking solutions from pilot to enterprise scale.
Strategic command of AI and data investment — able to shape a multi-year roadmap and budget, prioritize for ROI, and make disciplined build / buy / partner decisions.
Deep experience with modern data platforms and governance (lakehouse/fabric, MDM, cataloging, data quality and lineage) and the modern AI stack (LLMs and agentic systems, RAG, MLOps/LLMOps, cloud) — with the habit of staying at the frontier.
Strong experience operating DevOps and agile delivery at enterprise scale, with a disciplined, metrics-driven delivery capability.
Experience leading within federated or decentralized business environments and influencing senior business stakeholders.
Deep understanding of enterprise governance disciplines — security, data, architecture, and compliance — and executive communication skills suited to C-suite and Board engagement.
A builder who thrives in a relatively undefined, zero-to-one environment and is energized by standing up a team, a platform, and an operating model.
Sets clear and challenging goals while committing the organization to improved performance; tenacious and accountable in driving results.
Comfortable with ambiguity; adapts nimbly and leads others through complex situations, taking smart, well-considered risks.
Viewed as having high integrity and forethought; acts transparently and consistently, always considering what is best for the organization.
Leads by example, demonstrating Patrick’s principles of effective leadership: Leading for Positive Influence and culture, Leading with Humility, Embracing Responsibility, Communicating with Excellence, Leading with Accurate and Social Awareness, Building Healthy Accountability, and Servant Leadership.
A diplomat who promotes healthy debate toward “win-win” outcomes and inspires teams with an approachable style.
Thrives in a relatively undefined environment, unafraid to “roll up sleeves” across a wide range of topics, projects, and deliverables.
Self-reflective and open to feedback; empowers individuals and teams and drives continuous improvement.
Builds strong relationships with stakeholders through emotional intelligence and clear, persuasive communication; inspires trust and followership.
Brings notable business understanding and developed relationships across industries and technologies.

Responsibilities

Own the enterprise AI and data strategy and roadmap, the multi-year investment plan and budget allocation, the operating model and decision rights, and an outcome thesis tied to defined value levers.
Own data governance — ownership and stewardship, quality, master data management, access, and lineage — alongside acceptable-use policy, an approved-tool catalog with exception workflow, security/model/vendor risk, and a controls library and risk register.
Prioritize, sequence, and stage-gate the portfolio; control scope, budget, and resources; manage cadence, milestones, and dependencies; and track value realization and benefits.
Build AI and data literacy from the executive team to the frontline, role-based training paths, change and communications plans, and a champion network that drives durable adoption.
Own the data foundation AI depends on — the lakehouse/fabric bridging 40+ ERPs, the semantic layer, master data management and entity matching, cataloging, and observability — and sequence AI delivery behind data readiness.
Own the roadmap for shared LLMs, agents, APIs, and utilities, with monitoring, observability, evaluation, and quality controls, plus utilization analytics, financials, and vendor management.
Ensure every production solution has a named owner, a managed backlog and release plan, KPI ownership and user-feedback loops, and disciplined reuse, consolidation, and sunset decisions.
Set reference architecture, integration patterns, and standards; run SDLC, DevOps, and CI/CD for AI workloads; manage environments, infrastructure-as-code, and reliability (SRE); and own production support and incident response.
Own curated knowledge bases and sources of truth, content lifecycle and access controls, retrieval infrastructure, and data-quality stewardship with ongoing SME-driven curation.
Recruit and scale a dedicated team from a small founding core to roughly twenty professionals over three years — solution architecture, AI/ML and software engineering, data engineering and architecture, DevOps/MLOps, product management, and data and solution governance — operating a lean internal model that orchestrates strategic delivery partners and brand adoption rather than depending on them.
Maintain an active scan of frontier models, agentic frameworks, and tooling with a disciplined evaluation pipeline that separates durable capability from hype, keeping the approved-tool catalog and reference patterns current without compromising security or governance.
Translate emerging capability into pragmatic roadmap and investment decisions, and continuously upskill the team so Patrick’s practice compounds rather than ages.