Director, AI Value Realization and Governance

Sherwin-Williams • Cleveland, OH

2h • $172,076 - $226,153 • Onsite

About The Position

The Director, AI Value Realization and Governance ensures Sherwin-Williams’ enterprise AI investments produce measurable business outcomes, sustained adoption, and changed ways of working. The role leads enterprise AI adoption strategy, measurement and value-realization frameworks, executive reporting, and change activation, while supporting the AI Center of Excellence (AI CoE) with portfolio execution, strategy analysis, and project management as needed. It partners with the AI CoE VP, the SVP of Global AI Transformation, AI Technology and Microsoft platform owners, business leaders, and enabling functions including HR, Finance, Communications, Risk, Legal, Cyber, and ETG.

The director develops practical adoption and change plans, builds AI champion networks and role-based literacy, and establishes measurement standards connecting platform telemetry and operational data to business performance. The role creates executive and Board-ready reporting, validates value and ROI with Finance and business benefits owners, and tracks initiatives from business case and pilot through production and scaled deployment. It also establishes experimentation and stage-gate practices, supports portfolio performance management, and partners across business, technology, and risk teams to promote responsible, measurable AI adoption.

The position is based at Sherwin-Williams’ Cleveland Headquarters and is not hybrid or remote. Travel is 10%. The role is not eligible for work-authorization sponsorship now or in the future. Applicants must be legally authorized to work in the United States without sponsorship.

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.
• Experience with product analytics and outcome-based KPI framework development.
• Experience designing and executing experiments, including A/B testing, randomized controlled trials, control groups, and hypothesis testing.
• Knowledge of causal inference and incremental impact measurement, including quasi-experimental methods when randomized testing is not feasible.
• Experience with statistical analysis, including statistical significance, confidence intervals, sample-size estimation, statistical power, and experiment validity.
• Experience with product performance measurement, funnel analysis, cohort analysis, segmentation, and adoption-to-value analytics.
• Experience in business case development, ROI modeling, total cost of ownership, and benefits-realization measurement.
• Ability to use data-driven decision-making and translate experiment findings into investment and scaling recommendations.
• Experience with analytics and visualization tools 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 and 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.
• Required skills include AI adoption strategy and change enablement; enterprise KPI and measurement framework development; business case, ROI, and value-realization tracking; dashboard and executive-reporting requirements definition; strong PowerPoint, Excel, analytics, and executive storytelling skills; and cross-functional facilitation and stakeholder management.
• Must be at least 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.

Nice To Haves

• Advanced degree (MBA, MS, or equivalent).
• Experience with product experimentation and analytics platforms such as Optimizely, Statsig, Amplitude, or equivalent tools.
• Experience measuring AI or GenAI performance, including quality, accuracy, latency, cost per transaction, human intervention, and business impact.
• Familiarity with 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.
• Familiarity with Microsoft Copilot, Copilot Studio, Power BI, Azure AI Foundry, or related AI platforms.
• Change-management certification or methodology experience.
• Agile, product, portfolio, or program-management experience.

Responsibilities

• Develop and lead enterprise AI adoption strategy across functions, personas, geographies, and priority workflows.
• Create and execute change enablement plans for priority AI use cases, including stakeholder mapping, communications, readiness, coaching, reinforcement, and adoption interventions.
• Build and activate networks of AI champions, business translators, and communities of practice; partner with HR, Learning, Communications, and business leaders on role-based AI literacy and workforce readiness.
• Own enterprise AI measurement frameworks spanning adoption, engagement, proficiency, workflow integration, value realization, ROI, risk, and capability maturity.
• Define recurring AI dashboards, executive scorecards, metric definitions, data sources, baselines, targets, measurement frequency, accountable owners, and validation methods.
• Prepare leadership and Board-ready reporting on adoption, actual and forecast outcomes, ROI, risks, barriers, experiment results, and recommended actions.
• Connect platform telemetry, operational data, and financial measures to business outcomes; distinguish access and usage from sustained adoption and realized value.
• Establish value-realization practices for AI initiatives, including documented business cases, hypotheses, baselines, KPI trees, benefits owners, measurement methods, and scale, pivot, or stop criteria.
• Partner with Finance and business leaders to quantify, validate, and track benefits, ROI, total cost of ownership, payback, and investment returns; prevent double counting and distinguish cost avoidance, capacity released, and cash savings.
• Track benefits from business case through pilot, production, and scaled deployment; assess forecast versus actual performance and persistence of outcomes.
• Recommend remediation, investment reallocation, scaling, or discontinuation based on demonstrated impact, economic returns, strategic relevance, and risk-adjusted value.
• Support AI CoE operating cadence, portfolio routines, prioritization, and performance management; maintain visibility into initiative status, dependencies, risks, resource needs, adoption readiness, and value realization.
• Provide project-management and analytical support for priority AI workstreams and develop reusable playbooks, templates, intake artifacts, business-case tools, and reporting standards.
• Establish an enterprise experimentation framework, including pre-implementation baselines, measurable success criteria, evaluation periods, and thresholds for success, remediation, or discontinuation.
• Partner with business, product, data science, technology, and Finance teams to design appropriate experiments, assess incremental impact, and establish standards for experiment design and interpretation.
• Define stage-gated evaluation criteria from proof of concept through scaled deployment, considering business impact, financial returns, adoption, quality, reliability, security, responsible AI, and operational readiness.
• Maintain a portfolio-wide repository of experiment designs, results, validated benefits, failure modes, and reusable measurement practices; monitor deployed outcomes for persistence or degradation.
• Connect business stakeholders with AI technology teams, Microsoft platform owners, Finance, HR, Communications, Legal, Cyber, Risk, and other enabling functions.
• Ensure adoption plans reflect business processes, employee experience, responsible AI requirements, technical feasibility, usability, telemetry, and enterprise standards.
• Develop and operationalize AI governance policies, processes, and systems for consistent assessment of risk, compliance, security, ethical use, performance, and portfolio prioritization.
• Identify and escalate cross-functional issues that impede adoption, value realization, or scaling.
• Use adoption, measurement, and value data to improve the AI CoE operating model, change approach, and scaling playbook; benchmark practices and capture lessons learned.
• Promote enterprise confidence and trust in AI through transparent communication of progress, value, risks, and lessons, and encourage safe experimentation, fast learning, measurable impact, and responsible scale.

Benefits

• The company describes a range of benefits relating to retirement, health care, well-being, and commuting; eligible offerings are available through its candidate benefits information.
• The company describes opportunities to learn, develop new skills, and grow professionally.
• The company describes an inclusive team and a commitment to its communities.

What This Job Offers

• Job Type: Full-time
• Career Level: Director