Director of AI

Open to Remote - EST/CST Required.
Dallas, Columbus, Cincinnati and Richmond

Our client is a multi-entity services business that has made significant investments in establishing an enterprise-ready artificial intelligence foundation and is now focused on scaling measurable business outcomes. Core AI-enabling technologies, including a modern data platform, enterprise productivity assistants, and leading generative AI capabilities, are in place. Executive leadership has committed to a multi-year AI roadmap and is looking for a leader who can translate investment into operational value while ensuring responsible, governed adoption.
The Director of Artificial Intelligence will be the first dedicated leader within the AI function and will own the enterprise AI program end-to-end. This includes strategy, governance, the AI Center of Excellence (CoE), adoption of AI assistants and agents, use-case delivery, and the development of a high-performing AI team.
This role provides a unique opportunity to build an enterprise AI capability from the ground up while leveraging significant foundational investments that have already been made. The Director will establish governance frameworks, evaluate and prioritize opportunities, oversee production deployments, and ensure AI initiatives deliver measurable business value.
The position partners closely with Data, Enterprise Architecture, Applications, Integration, and Cybersecurity teams to ensure AI solutions are secure, scalable, and aligned with enterprise standards.

What Is Already in Place
The organization has already established many of the foundational capabilities required to support enterprise AI initiatives, including:

• Modern enterprise data platform and analytics environment
• Enterprise productivity AI assistants and generative AI platforms
• Data governance, data loss prevention, and access control capabilities
• Cross-functional technology and security oversight processes
• Executive sponsorship and funding for AI initiatives
• Draft AI Center of Excellence charter and strategic roadmap

• Develop and operationalize an enterprise AI governance framework aligned to recognized industry standards, including NIST AI Risk Management Framework (AI RMF) principles.
• Establish governance processes covering model evaluation, performance monitoring, risk management, auditability, approval workflows, and lifecycle management.
• Maintain an enterprise inventory of AI tools, models, agents, and AI-enabled software features.
• Partner with Cybersecurity, Compliance, Legal, and Data Governance teams to ensure responsible AI practices.
• Create practical mechanisms for managing emerging and unsanctioned AI usage across the organization.
• Monitor evolving regulatory, contractual, customer, and industry requirements related to AI.

• Launch and lead the AI Center of Excellence.
• Define operating models, governance structures, funding mechanisms, and reporting practices.
• Create a continuous intake process for AI opportunities and requests.
• Establish evaluation and prioritization methods balancing business value, risk, complexity, and readiness.
• Develop AI Champions networks across business units to accelerate innovation and adoption.
• Own and maintain the enterprise AI roadmap and portfolio.

• Build repeatable evaluation processes for AI models, tools, and use cases.
• Design and maintain benchmark datasets and testing methodologies.
• Evaluate AI solutions using objective measures such as accuracy, cost, latency, risk, and operational fit.
• Establish standards for prompts, testing, deployment, monitoring, and support.
• Partner with Enterprise Architecture to define reference architectures and delivery standards.
• Manage AI licensing, consumption, and platform costs against approved budgets and investment plans.

• Lead delivery of high-value AI initiatives from ideation through production deployment.
• Prioritize use cases that improve productivity, automate workflows, and enhance decision-making.
• Define success metrics and business value targets before implementation.
• Measure realized benefits and communicate results to stakeholders.
• Ensure data quality, governance, and readiness requirements are met for all production deployments.

• Lead enterprise adoption of AI assistants, copilots, and intelligent automation solutions.
• Develop role-based enablement programs, training materials, and adoption strategies.
• Establish communities of practice, champion networks, and support mechanisms.
• Measure adoption using meaningful business and user outcomes rather than license utilization alone.
• Drive organizational understanding of AI capabilities, limitations, and responsible use.

• Build and lead a high-performing AI team aligned with business priorities and growth plans.
• Manage strategic relationships with AI technology vendors, implementation partners, and service providers.
• Own AI program budgets across software, infrastructure, services, and staffing.
• Provide strategic guidance to executive leadership regarding AI opportunities, risks, and investments.
• Serve as the organization's trusted subject matter expert for AI strategy and responsible deployment.

• Intelligent workflow automation
• AI-assisted operational decision support
• Agent-based process orchestration
• Advanced document intelligence
• Customer and employee self-service capabilities
• Enterprise-scale AI platforms and reusable AI services

• Integration standards
• Solution architecture
• Platform strategy
• Technology governance

• Data quality
• Data governance
• Data readiness assessments
• Data platform management

• Risk management
• Data protection
• Regulatory compliance
• Responsible AI controls

• Enterprise productivity AI platforms and copilots
• Generative AI services and large language models
• Cloud-based AI and analytics platforms
• Enterprise data lake, warehousing, and analytics capabilities
• Data governance and risk management tools
• Business intelligence and reporting platforms
• API and integration platforms
• ERP, CRM, operational, and field-service applications

• Bachelor's degree in Computer Science, Information Systems, Engineering, Data Science, or a related field; advanced degree preferred.
• 10+ years of progressive technology leadership experience.
• 3+ years leading AI, data, analytics, machine learning, or digital transformation initiatives.
• Direct experience designing and implementing AI governance frameworks.
• Experience evaluating, selecting, deploying, and operating AI solutions in production environments.
• Strong understanding of AI risk management, model evaluation, monitoring, and operational controls.
• Knowledge of enterprise AI technologies, including generative AI, copilots, agents, and machine learning platforms.
• Experience building business cases, managing budgets, and communicating with executive stakeholders.
• Demonstrated success building and leading technical teams.
• Strong vendor and partner management experience.

• Experience establishing or leading an AI Center of Excellence.
• Experience within construction, engineering, industrial services, manufacturing, distribution, logistics, field services, or other operationally intensive industries.
• Experience working with large-scale unstructured document processing and document intelligence solutions.
• Experience deploying agentic AI solutions and intelligent automation platforms.
• Familiarity with enterprise AI governance, monitoring, and cost-optimization practices.
• Experience operating within decentralized, multi-business-unit organizations.
• Hands-on experience with Microsoft AI technologies and modern cloud data platforms.

• Enterprise AI governance framework approved and operational.
• AI Center of Excellence launched and actively governing AI initiatives.
• Formal intake and prioritization process implemented.
• Enterprise AI inventory established and maintained.
• Repeatable AI evaluation methodology adopted.
• Multiple AI use cases delivered into production with measurable business value.
• Adoption of enterprise AI assistants significantly expanded.
• AI champion network established across business units.
• Budget and usage management processes implemented.
• Clear roadmap established for future advanced AI and automation initiatives.

• Strategic Leadership
• Executive Presence
• Change Management
• AI Governance & Risk Management
• Business Acumen
• Data-Driven Decision Making
• Program & Portfolio Management
• Technical Credibility
• Cross-Functional Collaboration
• Innovation Leadership
• Vendor Management
• Operational Excellence

Equal Opportunity Employer, including disability and protected veteran status