TKO is investing meaningfully in AI and data initiatives across WWE, UFC, IMG, PBR, and On Location. The hardest question isn't what should we build? — it's did it work, and how do we prove it? This Manager owns the answer. You will build the measurement infrastructure, change management playbooks, and adoption tracking systems that turn deployed AI solutions into realized business value. You are the person who ensures every initiative has clear KPIs before it launches, credible adoption data once it's live, and a defensible ROI narrative for executive leadership. You will also be the COE's connective tissue to enterprise change management — partnering with Talent Culture and BU leaders to ensure that training, comms, and workflow integration are baked into every deployment, not bolted on after. This is a role for someone who is equal parts analytically rigorous and operationally creative — you can build a benefits tracking model in the morning and design an adoption campaign in the afternoon. What You'll Do 1) Value Realization — ROI Measurement Benefits Tracking (Primary) Define and own value measurement standards across the portfolio: baseline methodology, KPI design, benefits assumptions, time-savings quantification, quality uplift metrics, and adoption proxy indicators. Partner with Finance to ensure business cases are credible and auditable — defensible assumptions, consistent methodology, and clean handoffs between forecast and actuals. Build and maintain the monthly value dashboard — a single executive-ready view of realized value, adoption metrics, and portfolio ROI across all active initiatives. This is the artifact that goes to the CFO/CAO. Monitor realization vs. plan for every priority initiative: surface underperformance early with clear diagnosis and corrective options (scope reset, adoption push, additional enablement, stop/pivot). Own the benefits governance cadence monthly value check-ins with initiative owners, quarterly value readouts for executive leadership. Track adoption and usage per use case — not just is it deployed? but is it being used, by whom, and is behavior actually changing? 2) Change Management Adoption Enablement Own training, comms, and workflow integration planning for each deployed AI solution — ensuring end-users are equipped and motivated to adopt new tools and processes. Partner with Talent Culture (Jessica Rice's team) on enterprise-wide change management strategy, while owning COE-specific enablement for each use case. Design adoption playbooks tailored to each BU and use case: stakeholder mapping, comms cadence, training plan, feedback loops, and escalation paths for adoption blockers. Run post-deployment adoption sprints — targeted interventions when usage data shows an initiative isn't landing (additional training, workflow redesign, executive sponsorship activation, UX feedback to engineering). Capture and codify lessons learned from each deployment to continuously improve the COE's change management and enablement approach. 3) Decision Support Prioritization Analytics Support intake prioritization with analytical models: value scoring, feasibility assessment, risk/dependency mapping, and capacity-constrained scenario planning. Produce decision-ready analysis for the VP and DTC forums: tradeoff frameworks, sensitivity ranges, and what-if scenarios (e.g., capacity constraints, vendor spend impacts, sequencing alternatives). Build and refine the portfolio reporting system in partnership with the Portfolio Business Engagement Manager — ensuring pipeline, in-flight, and outcomes data are consistent, timely, and comparable across BUs. 4) Continuous Improvement of Measurement Adoption Ops Automate and improve reporting workflows — reduce manual data collection, improve timeliness, and increase data quality in portfolio and value tracking systems. Identify opportunities to apply AI to the COE's own measurement and adoption processes (e.g., automated usage analytics, sentiment analysis on training feedback, predictive adoption signals). Benchmark and evolve value measurement methodology as the portfolio matures — moving from early time saved proxies to more sophisticated outcome and revenue-impact metrics. What You'll Bring Required 2–6 years in management consulting (with strong analytical / business case work), corporate strategy ops, finance partnering, portfolio analytics, or a transformation office role with explicit value realization accountability. Strong analytical ability KPI design, benefits modeling, adoption measurement, and executive dashboarding. You're comfortable building a model from scratch and defending its assumptions to a CFO. Proven ability to translate data into narrative — you don't just produce charts, you produce recommendations with clear so what and now what. Experience designing or executing change management and adoption programs for technology or process transformations — training plans, comms strategies, stakeholder engagement. Exceptional written communication executive-ready decks, value narratives, and decision memos that are crisp, honest, and actionable. Comfort operating in matrixed, cross-functional environments — you can partner effectively with Finance, Data, Technology, HR, and BU leaders without owning any of them. Preferred Experience measuring AI, automation, or productivity initiatives — time-savings, quality uplift, automation rate, cycle-time reduction, NPS/satisfaction impact. Familiarity with BI tools and data infrastructure (Tableau, Power BI, Snowflake, or equivalent) — enough to partner effectively with analytics and data engineering teams. Exposure to enterprise change management frameworks (Prosci, ADKAR, or equivalent) — formal certification not required, but structured thinking about adoption is. Experience with enterprise AI platforms (e.g., Sana, Glean, Microsoft Copilot) and how adoption is measured in practice.
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