Director, AI and Data Enablement

Job Responsibilities

The Director, AI and Data Enablement will build and scale enterprise artificial intelligence (“AI”), digital capabilities, and a data operating and governance model that enables measurable business impact across Koppers.

Enterprise AI and Data Enablement Strategy

• Build the enterprise AI and data enablement roadmap, governance structure, and operating model, with a focus on business value and platform-based capabilities.
• Identify, prioritize, and evaluate AI and analytics use cases across manufacturing, commercial, supply chain, finance, safety, legal, and corporate functions through a clear intake, governance, and value-assessment process.
• Partner with senior leaders to shape the enterprise AI agenda, prioritize the highest-value opportunities, and align investments with strategic business outcomes.

AI Enablement and Adoption

• Drive adoption of AI capabilities embedded in existing enterprise platforms, including Microsoft, ERP, EHS, CRM, supply chain, analytics, and related systems.
• Identify opportunities to leverage existing enterprise technology capabilities and vendor innovations before pursuing custom AI development.
• Establish and lead an AI Center of Enablement that supports business users with education, consultation, governance, and use-case prioritization.

Business Partnership and Value Realization

• Partner with business and functional leaders to identify practical AI opportunities to improve productivity, safety, quality, cost, customer experience, and operational performance.
• Apply product management principles to develop reusable data products, AI-enabled workflows, decision-support tools and scalable capabilities to solve business problems.
• Develop communication, training, and change management strategies that drive successful adoption of AI technologies and data-driven decision making.
• Measure and communicate outcomes including productivity improvements, cost savings, revenue opportunities, risk reduction, quality improvements, and operational efficiencies.

Data Governance and Enterprise Data Enablement

• Partner with business and technology leaders to strengthen enterprise data governance, data quality, master data management (MDM), and data stewardship practices.

• Establish standards for data ownership, quality, metadata management, lifecycle management, and governance processes.
• Support development of reusable data assets and enterprise data capabilities that improve scalability of AI and analytics initiatives.
• Collaborate with enterprise architecture, data platforms, and application teams to ensure data platforms support enterprise AI and analytics objectives.
• Support the development and execution of enterprise data strategies that improve accessibility, quality, consistency, and business value.

AI Governance, Risk, and Responsible AI

• Establish responsible AI standards, controls, and governance in partnership with Legal, Cybersecurity, Compliance, HR, Internal Audit, and business leadership.
• Maintain governance and approval processes for AI use cases, third-party AI solutions, and emerging technologies.
• Monitor evolving AI regulations, industry trends, and emerging risks and incorporate them into enterprise governance practices.

Qualifications

• Bachelor’s degree in information systems, Computer Science, Data Science, Engineering, Business Analytics, Mathematics, Statistics, or a related field; Master’s degree preferred.
• Experience in manufacturing, chemicals, or other asset-intensive industries preferred.
• Proficiency in technology, data, analytics, digital transformation, AI, enterprise applications, or related disciplines.
• Experience driving adoption of AI capabilities across Microsoft, ERP, CRM, EHS, supply chain, analytics, collaboration, and productivity solutions preferred.
• Expertise in enterprise AI adoption, data enablement, analytics, governance, digital transformation, or technology initiatives that delivered measurable business outcomes.
• Familiarity with Microsoft Azure AI, Microsoft Copilot, Microsoft Fabric, Power BI, Databricks, Snowflake, Oracle, Salesforce, SQL, Python, or similar technologies preferred.
• Strong program management, stakeholder management, and change management skills.
• Familiarity with enterprise data governance, master data management (MDM), and data quality programs preferred.
• Strong understanding of enterprise data management, data governance, AI governance, enterprise applications, security, privacy, and enterprise technology architecture.
• Strong knowledge of generative AI, agentic AI, predictive analytics, machine learning concepts, data visualization, and enterprise AI pla