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Vice President, Artificial Intelligence & Data

Patrick Industries, a publicly traded company headquartered in Elkhart, Indiana, invites you to join a team of dedicated Team Members who are passionate about delivering high-quality products and exceptional customer service. As a leading solutions provider serving a diverse range of markets across the United States, our commitment to innovation, quality, and sustainability has positioned us as a high growth, diversified and empowered Team of more than 10,000! Your adventure awaits!

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: 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

Reporting to the Chief Information Officer, the Vice President of AI & Data 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 — and 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 – Scale AI-, data-, and automation-driven capabilities that unlock new business value.
• Value Optimization – Ensure measurable ROI, efficiency gains, and capital discipline.
• Agility & Efficiency – Enable rapid, iterative delivery through modern DevOps practices.
• Resilient Operations – Keep AI and data solutions secure, stable, and well-governed.

Areas of Responsibility

The mandate spans the operating capabilities the VP will stand up to govern, deliver, and sustain AI and data at enterprise scale.

Govern & Direct — set the agenda, control the rules, steer the portfolio

• AI & Data Strategy & Investment — 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.
• Data Governance, Policy, Standards & Risk — 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.
• Portfolio & Program Management — Prioritize, sequence, and stage-gate the portfolio; control scope, budget, and resources; manage cadence, milestones, and dependencies; and track value realization and benefits.
• Training, Change & Adoption — 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.

Deliver & Run — build, run, and sustain the capabilities that produce value

• Enterprise Data Platform & Architecture — 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.
• Product Ownership: LLM Platform & Utilities — Own the roadmap for shared LLMs, agents, APIs, and utilities, with monitoring, observability, evaluation, and quality controls, plus utilization analytics, financials, and vendor management.
• Product Ownership: AI Solutions — 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.
• Technical Ownership — 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.
• Knowledge & Content Management — Own curated knowledge bases and sources of truth, content lifecycle and access controls, retrieval infrastructure, and data-quality stewardship with ongoing SME-driven curation.

Building the Team & Delivery Engine

A central part of the mandate is to build the people and platform that make delivery repeatable. The VP will 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. The team stands up the reusable data platform, pipelines, and engineering playbooks that bend the cost curve so each successive use case is faster and cheaper than the last, while Patrick retains the architecture, intellectual property, and institutional knowledge.

Staying at the frontier of AI and data

• 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.

Traceability to the IT Strategy

Every responsibility traces to Patrick’s IT Strategic Pillars and the enterprise Digital Backbone (Architecture | Data Governance | Talent) across the Stabilize → Accelerate → Differentiate journey — and, through them, to profitable growth, operational discipline, capital stewardship, and teams built for today and tomorrow.

Strategic Pillar

How this role advances it

Innovative Advantage

Scales AI, data, and automation that expand margin, insight, and competitive differentiation, unlocking new growth across customer, aftermarket, and operations.

Value Optimization

Formulates and governs the AI and data investment for measurable ROI; enforces portfolio discipline, benefits tracking, and total-cost-of-ownership control.

Agility & Efficiency

Operates a product-centric, DevOps-enabled delivery model with a predictable cadence and rapid time-to-value.

Resilient Operations

Keeps AI and data solutions secure, reliable, and well-governed through standards, controls, SRE, and incident response.

Candidate Profile

• 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.

Executing for Results

• 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.

Leadership

• 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.

Relationships & Influence

• 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.