• Lead, coach, and develop a team of Field Enablement & Adoption leaders, setting high standards for technical fluency, field credibility, and execution discipline. • Build deep capability across the team in workflow‑first enablement, AI‑assisted experiences, role‑based learning, and reinforcement strategies. • Own the design and execution of field readiness and adoption strategies for new tools, workflows, and AI‑enabled capabilities. • Deliver structured, scalable enablement programs (e.g., learning series, role‑based training, office hours, sandbox environments). • Ensure field teams can practice new workflows and tools in realistic environments before they are expected to use them live. • Ensure AI‑enabled workflows (e.g., copilots, automation, guided decision support) are introduced with clear intent and guardrails and trust and usability considerations. • Translate emerging digital and AI capabilities into practical, confidence‑building field experiences. • Define and track adoption and readiness signals (usage patterns, confidence indicators, friction points). • Establish feedback loops with the field to identify where enablement, workflow fit, or reinforcement needs adjustment. • Partner with cross functional teams including Field Intake and Business Requirements, Experimentation and Pilots, and Enterprise Delivery.

• Lead, coach, and develop a team of intake and requirements professionals, building strong capability in requirements engineering, stakeholder discovery, and business-to-technical translation • Establish clear quality standards (templates, definition-of-ready, acceptance criteria, traceability) so requirements are consistently buildable and testable • Own the single CE-facing intake for workflow and digital requests, ensuring requests enter through one structured path and are shaped once, correctly • Implement transparent triage and prioritization so demand is assessed consistently and sequenced using explicit criteria (value, readiness, capacity, dependencies) • Convert field needs into technical-ready requirements packages which include: problem statement and user/workflow context; scope boundaries and constraints; success criteria and acceptance tests; key dependencies and risk/edge cases; rollout/readiness considerations • Ensure intake and requirements explicitly cover AI/workflow use cases (e.g., copilots, guided workflows, automation concepts) in a structured way—defining intent, guardrails, and measurable value • Partner with experimentation/pilot teams so early AI/workflow concepts are validated, refined, and converted into scale-ready requirements before enterprise build • Establish feedback loops so learnings from pilots/adoption are captured and reflected in improved requirement patterns, templates, and intake criteria—strengthening the system over time • Ensure handoffs include the right evidence and readiness signals so solutions can transition cleanly from 'validated' to 'enterprise scale' • Use the end-to-end lifecycle framing (define → design → build/enable → pilot → scale → measure/improve) to ensure requirements anticipate what must be built and how success will be evaluated—without taking ownership of enterprise analytics or platforms

• Orchestrate alignment and own integrated execution across the full lifecycle: intake shaping → pilot validation → adoption readiness → enterprise implementation handoff • Define and enforce readiness standards that determine whether a solution is truly scale-ready • Maintain disciplined defect and risk management to prevent post-launch instability and recurring “fix-forward” cycles • Convert validated solutions into complete, implementation-ready handoff packages • Act as the technical arbiter when scope, feasibility, sequencing, or quality trade-offs must be resolved • Partner with engineering and enterprise delivery teams to align on backlog readiness, technical reviews, environments, UAT planning, release sequencing, and cutover readiness • Establish closed-loop feedback from pilots, adoption signals, and scaled outcomes to continuously improve execution standards and playbooks • Strengthen execution maturity over time by institutionalizing repeatable, disciplined pilot-to-scale practices across the organization.

• The Practice Director will be responsible for setting and maintaining the strategy and goals of the practice by creating, owning, and executing on initiatives that drive their practice forward. • Manage the Practice Managers that oversee project managers, consultants, solution architects, developers, and team leads. • Provide direction for practice methodologies, serve as an escalation point for issues, recognize the need for increased skillsets within the team, review Statements of Work (SOW) and negotiate credits, ensure that their practice meets our utilization targets, and maintain and grow financial goals. • Additionally, assist in decision-making regarding the use of applications to optimize performance and efficiency. • Develop and communicate the strategic direction and goals of the practice in alignment with organizational objectives. • Manage Practice Managers, providing guidance, support, and mentorship to ensure optimal performance and development. • Provide direction and leadership in the adoption and implementation of best practices and methodologies within the practice. • Serve as an escalation point for complex issues or challenges, providing timely resolution and guidance to the team. • Identify gaps in skills within the team and develop strategies for upskilling and training to meet evolving business needs. • Develop, monitor, and manage the financial goals of the practice, including revenue targets, profitability, and cost management. • Assist in decision-making regarding the selection and use of applications and tools to support practice operations, ensuring efficiency and effectiveness.