Director, AI Adoption and Value Realization
The Director, AI Adoption and Value Realization is responsible for ensuring Sherwin-Williams’ enterprise AI investments translate into measurable business outcomes, sustained adoption, and changed ways of working across the organization. This leader owns the adoption strategy, measurement framework, value realization discipline, executive reporting, and change activation model for AI initiatives, while supporting the AI CoE with portfolio execution, strategy analysis, and project management discipline as needed. This role partners closely with:
The SVP, Global AI Transformation to support executive reporting, Board-ready progress updates, and enterprise transformation priorities
The AI Center of Excellence, VP to operationalize adoption, measurement, and value realization priorities across the enterprise
The Senior Director, AI Technology and Microsoft platform owners to connect platform telemetry, usage data, and technical delivery progress to business adoption and value outcomes
Business leaders, HR, Finance, Communications, Risk, Legal, Cyber, and ETG to drive role-based adoption, AI literacy, responsible use, and measurable business impact
Job duties include contact with other employees and access confidential and proprietary information and/or other items of value, and such access may be supervised or unsupervised. The Company therefore has determined that a review of criminal history is necessary to protect the business and its operations and reputation and is necessary to protect the safety of the Company’s staff, employees, and business relationships.
Position is not remote/hybrid employees will report into our Cleveland, Ohio Global HQ.
AI Adoption Strategy & Change Activation
• Develop and lead the enterprise AI adoption strategy across functions, personas, geographies, and priority workflows.
• Translate AI strategy into practical adoption plans that help employees embed AI into day-to-day work, not simply access tools or complete training.
• Design and execute change enablement plans for priority AI use cases, including stakeholder mapping, communications, readiness activities, coaching, reinforcement, and adoption interventions.
• Build and activate a network of AI champions, business translators, and communities of practice across functions to scale adoption locally.
• Partner with HR/Learning, Communications, and business leaders to drive role-based AI literacy, executive enablement, and workforce readiness.
AI Measurement Framework & Executive Reporting
• Own the enterprise AI measurement framework across adoption, engagement, proficiency, workflow integration, value realization, ROI, risk, and capability maturity.
• Partner with Microsoft platform owners, ETG, Finance, HR, and business teams to define recurring AI dashboards and executive scorecards.
• Establish standard reporting routines and metrics definitions for Microsoft Copilot, Co-Work, Copilot Studio, agent activity, token consumption, and other enterprise AI platforms as appropriate.
• Prepare recurring leadership updates that clearly communicate adoption progress, business outcomes, risks, barriers, and recommended actions.
• Move measurement beyond activity metrics by incorporating behavior change, workflow adoption, proficiency, satisfaction, and business impact indicators.
Value Realization & ROI Discipline
• Establish consistent value realization practices for AI initiatives, including value hypotheses, business baselines, KPI trees, benefits owners, measurement methods, and scale/fail criteria.
• Partner with Finance and business leaders to quantify expected and realized value from AI use cases, including revenue growth, productivity, cost reduction, cycle time, quality, customer experience, and risk reduction where applicable.
• Track performance from pilot through production to ensure AI solutions deliver measurable and sustained impact.
• Identify adoption and value barriers early and recommend corrective actions to the AI CoE, business owners, and governance teams.
• Support Board and executive reporting on AI ROI, adoption, maturity, and value realization progress.
AI Portfolio Execution & Performance Management
• Support the AI CoE operating cadence, portfolio routines, prioritization meetings, and performance management processes.
• Partner with the AI CoE VP to maintain visibility into AI initiative status, dependencies, risks, resource needs, adoption readiness, and value realization progress.
• Assist with project management and analytical support for priority AI workstreams, while keeping adoption and measurement as the primary focus of the role.
• Help establish reusable playbooks, templates, intake artifacts, business case tools, and reporting standards for AI initiatives.
• Drive transparency and accountability across AI initiatives by ensuring owners, milestones, measures, decisions, and next steps are clear.
Business–Technology–Risk Partnership
• Serve as a connector between business stakeholders, AI technology teams, Microsoft platform owners, Finance, HR, Communications, Legal, Cyber, Risk, and other enabling functions.
• Ensure AI adoption plans reflect business process realities, employee experience needs, responsible AI requirements, technical feasibility, and enterprise standards.
• Partner with AI Technology teams to ensure solutions are designed for usability, adoption, measurement, telemetry, and sustained impact—not just technical delivery.
• Partner with Responsible AI and governance teams to reinforce appropriate use, trust, compliance, and risk-aware adoption.
• Identify and escalate cross-functional issues that slow adoption, value realization, or scaling.
Continuous Improvement & AI Capability Building
• Use adoption, measurement, and value data to continuously improve the AI CoE operating model, change approach, and scaling playbook.
• Benchmark internal adoption patterns and external leading practices to identify opportunities to improve AI maturity and workforce readiness.
• Capture lessons learned from pilots, scaled implementations, and business feedback to improve future AI initiatives.
• Help build enterprise confidence and trust in AI by making progress, value, risks, and learnings visible to leaders and employees.
• Promote a practical, outcomes-focused AI culture that emphasizes safe experimentation, fast learning, measurable impact, and responsible scale.
Required:
- Bachelor’s degree required
- 8–12+ years of experience in enterprise transformation, change management, adoption, analytics, business strategy, technology-enabled transformation, consulting, or related fields.
- Proven experience leading adoption, change management, measurement, or value realization for large-scale enterprise initiatives.
- Strong understanding of AI, analytics, automation, and/or digital platforms, with ability to translate technology capabilities into business adoption and measurable outcomes.
- Experience developing KPI frameworks, dashboards, executive reporting, business cases, and value realization models.
- Demonstrated ability to partner across business, technology, Finance, HR, Communications, Legal, Cyber, Risk, and external partners.
- Experience influencing senior leaders and driving alignment across complex, matrixed organizations.
- Strong analytical, communication, storytelling, facilitation, and executive presentation skills.
- AI adoption strategy and change enablement
- Enterprise KPI / measurement framework development
- Business case, ROI, and value realization tracking
- Dashboard and executive reporting requirements definition
- Strong PowerPoint / Excel / analytics and executive storytelling skills
- Cross-functional facilitation and stakeholder management
Preferred:
- Advanced degree (MBA, MS, or equivalent) preferred
- Experience supporting AI, generative AI, Microsoft Copilot, Copilot Studio, automation, data/analytics, or digital transformation programs.
- Experience building champion networks, communities of practice, role-based training, or enterprise capability-building programs.
- Experience working with centers of excellence, transformation offices, PMOs, or shared services models.
- Familiarity with responsible AI, privacy, security, governance, and risk considerations related to AI adoption.
- Microsoft Copilot, Copilot Studio, Power BI, Azure AI Foundry, or related AI platform familiarity
- Change management certification or methodology experience
- Agile, product, portfolio, or program management experience