Associate Director, Corporate Strategy- Enterprise AI Transformation

The AI TO is responsible for accelerating and scaling responsible AI adoption across Wolters Kluwer by helping functions identify high-value opportunities, reinvent workflows, coordinate enabling resources, govern risk, and deliver measurable business impact. Functions and business owners remain accountable for execution, adoption, and outcomes; the AI TO provides the rigor, expertise, visibility, and support required to accelerate progress. The Associate Director role will work closely with functional leaders, business owners, Finance, Data, Technology, HR, and other stakeholders to determine where AI can materially improve business performance and to build the fact base required to make investment and scaling decisions. The successful candidate will translate ambiguous questions such as "Could AI fundamentally improve this workflow?" into rigorous, evidence-based answers. This will require understanding how work is performed today, identifying the operational and financial drivers of performance, establishing credible baselines, defining the right KPIs, quantifying value at stake, pressure-testing assumptions, and measuring whether expected value is ultimately realized. This is a hands-on strategy and analytics role. The ideal candidate combines the structured problem solving and business judgment of a strategy consultant with a strong quantitative orientation and a willingness to dig deeply into data, processes, assumptions, and economics. Primary Accountabilities Identify and diagnose high-value opportunities Partner with functional leaders, process owners, and frontline subject-matter experts to understand how work is performed today and where AI-enabled workflow redesign could materially improve business outcomes. Conduct business and process diagnostics to identify bottlenecks, sources of cost, delays, capacity constraints, quality issues, risk, or lost revenue. Help distinguish incremental productivity opportunities from more transformational opportunities to redesign end-to-end workflows around human judgment, AI agents, data, and automation. Assess the scale and materiality of opportunities and identify the key value drivers that determine whether an initiative merits investment. Define KPIs and establish credible baselines Translate broad transformation ambitions into a small number of meaningful business and operational KPIs. Determine how relevant measures are calculated today, where the underlying data resides, who owns it, and what constitutes a credible baseline. Gather, reconcile, and analyze information across multiple sources to establish current performance, including volumes, cycle times, throughput, productivity, quality, conversion, capacity, costs, customer outcomes, and other relevant measures. Identify data gaps, limitations, and assumptions and develop pragmatic approaches for measuring performance where perfect data is not available. Ensure initiatives have measurable success criteria before investment and implementation decisions are made. Quantify value at stake Build transparent, driver-based models that translate changes in operational performance into financial and strategic outcomes. Quantify potential value from revenue growth, productivity, capacity creation, cost reduction, quality improvement, risk reduction, customer impact, or employee experience as appropriate. Develop Year 1 and longer-term value estimates, expected operating costs, required investment, and net business impact. Clearly distinguish between cash savings, capacity released, cost avoidance, revenue improvement, and other forms of value. Document the critical assumptions behind each value case and identify the sensitivities that have the greatest effect on expected outcomes. Develop and challenge business cases Develop rigorous, directional business cases for priority AI opportunities, considering value, feasibility, investment, risk, readiness, adoption, and implementation complexity. Pressure-test assumptions and challenge sponsors and business owners constructively where supporting evidence is weak. Identify the critical conditions that must be true for an initiative to deliver its expected value. Compare opportunities consistently to help leadership prioritize limited investment and execution capacity. Support build / buy / partner analysis where relevant, incorporating expected economics, differentiation, operating costs, and dependencies. Design value measurement and evaluate results Define measurement approaches for pilots and scaled deployments, including baseline, target, leading indicators, operational KPIs, business outcomes, and measurement cadence. Establish the analytical bridge between AI adoption, workflow change, operational performance, and financial impact. Compare realized results with expected performance and diagnose the causes of variance. Determine whether evidence supports scaling, modifying, pausing, or stopping an initiative. Partner with Finance and business owners to ensure realized value is credible, traceable, and understood consistently. Build the enterprise AI value view Aggregate opportunity-level analyses into a transparent enterprise view of expected and realized AI value. Identify the largest emerging value pools, material assumptions, risks, dependencies, and gaps across the AI portfolio. Provide leadership with fact-based perspectives on where the enterprise is creating value, where performance is falling short, and where additional intervention or investment is required. Help maintain clear accountability for business outcomes and KPI movement across priority initiatives. Generate executive insight and recommendations Lead analyses across multiple sources of quantitative and qualitative information; identify meaningful patterns, test hypotheses, surface limitations, and translate findings into practical recommendations. Develop concise, executive-ready decision materials for the AI TO, functional leadership, Executive Leadership Team, and other senior stakeholders. Communicate complex analyses simply, clearly articulating the answer, supporting evidence, implications, and recommended actions. Independently lead analytical workstreams from problem definition through recommendation. Build repeatable approaches and institutional capability Develop practical frameworks, templates, benchmarks, and analytical tools that allow AI opportunities to be evaluated consistently across functions. Capture learnings from initiatives and continuously improve the AI TO's value-realization methodology and operating routines. Monitor emerging approaches to AI economics, measurement, workflow transformation, and value realization and selectively incorporate relevant practices into the AI TO playbook. Skills and Competencies Exceptional structured problem-solving skills and ability to turn ambiguous business questions into clear hypotheses, analyses, and recommendations. Strong quantitative and analytical orientation, including demonstrated ability to build driver-based business and financial models from imperfect or incomplete information. Strong business judgment and ability to identify the few metrics and value drivers that matter most. Ability to quickly understand unfamiliar business processes, operating models, and economics. Intellectual curiosity and willingness to dig deeply into source data, process details, assumptions, and calculations. Strong understanding of the relationship between operational performance and financial outcomes. Ability to synthesize quantitative and qualitative information into clear implications and recommendations. Excellent written and verbal communication skills, including the ability to create concise, executive-ready materials. Strong interpersonal skills and ability to build credibility with senior leaders, functional stakeholders, Finance, technical teams, and frontline subject-matter experts. Confidence constructively challenging assumptions while maintaining productive stakeholder relationships. Strong ownership mindset and ability to independently lead complex analytical workstreams. Experience using generative AI tools to accelerate research, analysis, synthesis, and problem solving preferred. Familiarity with BI tools, SQL, or other analytical tools is helpful but not required.