Director AI Value Realization and Governance

The Director, AI Value Realization and Governance 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 AI Center of Excellence, VP to operationalize adoption, measurement, and value realization priorities across the enterprise 
• The SVP, Global AI Transformation to support executive reporting, Board-ready progress updates, and enterprise transformation priorities 
• 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 

Minimum Requirements:

• Bachelor’s degree
• 8–12+ years of experience in enterprise transformation, change management, adoption, analytics, business strategy, technology-enabled transformation, causal experimentation setup, consulting, or related fields.
• Product analytics and outcome-based KPI framework development.
• Experimentation design and execution, including A/B testing, randomized controlled trials, control groups, and hypothesis testing.
• Causal inference and incremental impact measurement, including quasi-experimental methods where randomized testing is not feasible.
• Statistical analysis, including statistical significance, confidence intervals, sample size estimation, statistical power, and experiment validity.
• Product performance measurement, funnel analysis, cohort analysis, segmentation, and adoption-to-value analytics.
• Business case development, ROI modeling, total cost of ownership, and benefits realization measurement.
• Data-driven decision-making, experiment interpretation, and translating analytical findings into investment and scaling recommendations.
• Analytics tools and visualization platforms, such as SQL, Python, R, Tableau, or Power BI.
• 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.
• Must be at least eighteen (18) years of age
• Must be legally authorized to work in the United States without sponsorship now or in the future for employment visa status

Preferred Qualifications:

• Advanced degree (MBA, MS, or equivalent)
• Experience with product experimentation and analytics platforms, such as Optimizely, Statsig, Amplitude, or equivalent tools.
• Experience measuring AI/GenAI performance, including quality, accuracy, latency, cost per transaction, human intervention, and business impact.
• Familiarity with AI platforms such as Microsoft Copilot, Copilot Studio, or Azure AI Foundry.
• Experience establishing enterprise experimentation frameworks, measurement standards, and evidence-based product or portfolio investment practices.
• 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.

Technical/Skill Requirements: 

Required

• 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

• 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

Travel Requirements

• 10%

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.
• Establish and own an enterprise AI measurement framework connecting AI investments to strategic business outcomes, operational KPIs, workforce effectiveness, customer experience, and financial performance.
• Develop standardized KPI trees linking AI adoption and workflow integration to measurable operational improvements and business outcomes, with clearly defined leading and lagging indicators.
• Establish metric definitions, data sources, baselines, targets, measurement frequency, accountable owners, and validation methods to ensure consistency and comparability across AI initiatives.
• Distinguish tool access, usage, engagement, and training completion from sustained workflow adoption, proficiency, productivity improvement, quality enhancement, and realized business value.
• Partner with AI Technology, platform owners, business teams, and Finance to integrate platform telemetry, operational data, and financial measures into a unified AI performance dashboard.
• Develop executive and Board-ready scorecards that report actual versus target outcomes, realized and forecast value, ROI, experiment results, confidence in reported benefits, risks, and corrective actions.
• Establish a recurring performance review cadence to identify underperforming initiatives, diagnose adoption and value gaps, and recommend corrective actions, additional experimentation, investment changes, or discontinuation.

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.
• Establish an enterprise AI value realization framework requiring each material AI initiative to have a documented business case, value hypothesis, baseline, target outcomes, measurement methodology, accountable business benefits owner, and defined scale, pivot, or stop criteria.
• Partner with Finance and business leaders to quantify, validate, and track expected and realized benefits, including incremental revenue, margin contribution, cost reduction, productivity, capacity released, cycle-time reduction, quality improvement, customer experience, and risk reduction, as applicable.
• Establish consistent ROI and total cost of ownership methodologies that account for implementation, licensing, infrastructure, model consumption, integration, human review, training, change management, ongoing operations, and risk mitigation costs.
• Distinguish gross opportunity, modeled benefits, pilot-validated benefits, realized financial benefits, and independently validated incremental value. Prevent double counting across initiatives and distinguish cost avoidance, capacity released, and actual cash savings.
• Partner with Finance to validate financial assumptions, attribution methods, benefit realization, payback periods, and investment returns using agreed measurement standards.
• Track benefits from business case through pilot, production, and scaled deployment, including forecast versus actual performance, benefit realization timelines, and persistence of outcomes after implementation.
• Establish accountability with business benefits owners for converting AI-enabled productivity and operational improvements into tangible business value, such as cost reduction, additional throughput, improved service, or incremental growth.
• Recommend investment reallocation, remediation, scaling, or discontinuation based on demonstrated business impact, economic returns, strategic relevance, and risk-adjusted value.

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.
• Establish an enterprise experimentation framework for AI initiatives that defines how business hypotheses are formulated, tested, validated, and translated into investment and scaling decisions.
• Require material AI pilots and deployments to establish pre-implementation baselines, measurable success criteria, target outcomes, evaluation periods, and predefined thresholds for success, remediation, or discontinuation.
• Partner with business, product, data science, technology, and Finance teams to design rigorous experiments using randomized controlled trials, A/B testing, holdout groups, matched comparison groups, phased rollouts, or other appropriate quasi-experimental methods.
• Measure the incremental impact attributable to AI by distinguishing AI-driven improvements from changes in demand, seasonality, workforce composition, process redesign, other technology investments, and broader business conditions.
• Establish standards for experiment design, sample size, statistical power, measurement windows, data quality, evaluation methodology, and interpretation of results, appropriate to the use case and decision risk.
• Evaluate AI solutions against relevant alternatives, including existing workflows, non-AI automation, alternative models, and the cost of taking no action, where appropriate.
• Define stage-gated evaluation criteria for progression from proof of concept to pilot, production, and scaled deployment, incorporating demonstrated business impact, financial returns, user adoption, solution quality, reliability, security, responsible AI, and operational readiness.
• Establish a portfolio-wide repository of experiment designs, results, validated benefits, failure modes, and reusable measurement practices to accelerate learning and reduce duplicated investment.
• Monitor outcomes after deployment to validate benefit persistence, detect performance degradation, and identify when changes to workflows, models, or adoption interventions are needed.

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.
• Develop and operationalize enterprise AI governance policies, processes, and systems to ensure AI initiatives are consistently assessed for risk, compliance, security, ethical use, performance, and portfolio prioritization before investment, deployment, and scaling decisions
• 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. 

This position is not hybrid/remote and will be located at our Cleveland Headquarters office.

This position is not eligible for sponsorship for work authorization now or in the future, including conversion to H1-B visa.

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. 

Salary Frequency: Annually

Minimum Salary: 172,076.06

Maximum Salary: 226,152.93