About the Role
We are looking for a Director of Enterprise AI Adoption to lead the integration of artificial intelligence across our business operations. This is a pivotal leadership role at the intersection of technology, change management, and business strategy. This individual will be responsible for defining our enterprise AI roadmap, evangelizing adoption across departments, and ensuring AI-powered tools deliver measurable productivity and business value at scale. This opportunity is ideal for someone who combines a deep understanding of AI/ML capabilities with strong stakeholder management skills and a track record of driving technology adoption in complex, matrixed organizations.
This remote position is open to individuals who live in, or are open to relocating to, the following states: Arizona, Delaware, Florida, Georgia, Kentucky, Nevada, New Jersey, New York, North Carolina, Ohio, Rhode Island, South Carolina, Tennessee, Texas, Virginia, and Washington.
This position requires travel to our headquarters in Melville, NY 6 times per year for internal meetings and team building activities. We reimburse reasonable and necessary travel expenses, and you’re also welcome to work on-site anytime beyond these visits – our doors are always open.
Responsibilities
AI Strategy and Roadmap
- Develop and own the enterprise AI adoption strategy aligned with SupplyHouse's business objectives and long-term growth plans.
- Identify high-impact AI use cases across procurement, operations, sales, customer service, and finance, and prioritize based on ROI and feasibility.
- Serve as the primary internal authority on enterprise AI tools, platforms, and best practices including large language models, agentic workflows, and AI-powered analytics.
- Monitor the AI technology landscape and advise executive leadership on emerging opportunities and risks.
People, Change Management, and Culture
- Design and execute enterprise-wide change management programs grounded in proven methodologies to drive sustained, high-quality AI adoption across all levels and functions.
- Act as a culture carrier for AI transformation championing a mindset of curiosity, continuous learning, and responsible experimentation throughout the organization.
- Build internal champions, AI ambassadors, and center-of-excellence structures to scale AI literacy and usage across every business unit.
- Develop training curriculum, playbooks, and role-based enablement programs to upskill employees on AI tools and workflows at every level, from frontline staff to senior leadership.
- Create psychological safety around AI adoption by proactively addressing employee concerns about job displacement, skill gaps, and the pace of change positioning AI as a tool that augments human capability rather than replaces it.
- Partner closely with the Human Resources team to embed AI competency into hiring profiles, onboarding programs, performance frameworks, and career development pathways.
- Lead executive communications, all-hands presentations, and town halls related to AI strategy, adoption milestones, and organizational impact.
- Identify and mitigate human-side risks of AI adoption including change fatigue, resistance patterns, and inequitable access to enablement resources across teams.
Cross-Functional Leadership
- Partner with department heads across IT, Operations, Human Resources, Legal, and Finance to embed AI into core business processes.
- Collaborate with the IT and Data Engineering teams to ensure secure, compliant, and scalable AI infrastructure.
- Manage relationships with external AI vendors, consultants, and technology partners.
- Coordinate with Legal and Compliance teams to establish responsible AI governance policies, data privacy standards, and risk frameworks.
Measurement and Optimization
- Define KPIs and success metrics for AI adoption initiatives and report progress to the C-suite and Board as needed.
- Establish feedback loops and continuous improvement processes to optimize AI tool performance and user experience.
- Build business cases and ROI models to justify AI investments and scale successful pilots enterprise-wide.
Team Leadership
- Build, lead, and mentor a high-performing team of AI program managers, analysts, and change agents.
- Foster a culture of experimentation, curiosity, and responsible innovation within the team and across the organization.
Requirements
- Bachelor's degree in a related field.
- 10+ years of progressive experience in technology strategy, digital transformation, or enterprise software adoption.
- 5+ years in a leadership or director-level role with demonstrated experience managing cross-functional programs.
- Hands-on experience with enterprise AI/ML tools.
- Proven track record of designing and executing large-scale change management programs in complex organizations.
- Exceptional executive communication and presentation skills; ability to translate technical concepts for non-technical stakeholders.
- Strong analytical mindset with experience building business cases and measuring technology ROI.
Who thrives in this role
SupplyHouse is a people-first company with deep roots in the trades. We move fast, but we bring our teams along with us. The ideal candidate for this role is not just a technologist – they are a trusted colleague, a patient teacher, and a relentless advocate for making AI work for everyone, not just the tech-savvy individuals. You will succeed here if you:
- Lead with empathy and communicate change in ways that resonate with frontline workers and executives alike.
- Are comfortable with ambiguity and energized by building something new in an organization that is still defining what AI means for its people.
- Believe that culture and adoption are the hardest, and most important, parts of any technology transformation.
- Listen before you prescribe, and take the time to understand the unique needs, fears, and motivations of each team you partner with.
- Bring optimism and patience to resistance, treating skepticism as useful signal rather than an obstacle.
- Hold yourself and your team accountable to outcomes, not just outputs (e.g.: adoption rates, workflow efficiency, and employee confidence matter more than tools deployed).