VP, Revenue Applied AI & Data

About Nintex

At Nintex, we are transforming the way people work, everywhere. As the global standard for process intelligence and automation, we're trusted by over 10,000 public and private sector organizations across 90 countries. Our customers, from industry giants like Amazon, Coca‑Cola, and Microsoft, rely on the Nintex Platform to accelerate their digital transformation journeys by managing, automating, and optimizing business processes quickly and efficiently.

We improve their lives through the technology we build. We are committed to fostering a workplace that supports amazing people in doing their very best work every day. Collaboration is constant, our workplace is fun, the environment is fast‑paced, and we value our people’s curiosity, ideas, and enthusiasm. Driven by passion and accountability, we take initiative, measure progress, and deliver results.

Our culture fosters innovation and problem‑solving, fueled by curiosity and a commitment to thinking big. Together, we move with agility, prioritize customer needs, and build unity through empathy, leaving a positive impact wherever we go.

The VP, Revenue Applied AI & Data leads the strategy, architecture, and hands‑on execution of AI‑driven solutions across the Revenue organization. This role owns the end‑to‑end applied AI function for Revenue — spanning use‑case identification, solution architecture, data foundations, model development, vendor strategy, and budget — and is accountable for delivering scalable, production‑ready AI capabilities that drive measurable business outcomes. Operating with a lean, production‑first delivery model, the VP both sets direction and personally builds, ensuring AI moves from concept to deployed, monitored, value‑generating systems embedded in everyday Revenue workflows.

• Architect, build, and deploy production‑grade AI agents and LLM‑powered solutions embedded within daily internal Revenue workflows.
• Evaluate and execute build‑vs‑buy‑vs‑platform decisions — leveraging Nintex Automation CE and K2 where appropriate, partnering with Product and Engineering on shared infrastructure, and developing custom agentic systems using Claude and other foundation models for unique internal needs.
• Own the AI function’s budget, vendor relationships, and technical roadmap for internal applications.
• Define, track, and report key performance indicators (KPIs) that link AI investments to measurable outcomes in efficiency, revenue generation, and cost optimization; present progress to leadership on a monthly basis.
• Establish clear release standards for AI initiatives, ensuring each deployment has defined outcomes, an accountable workflow owner, and production monitoring in place.
• Communicate Revenue AI strategy, investment rationale, and return on investment (ROI) to executive leadership — including the CEO, CFO, and Board — translating technical concepts into audience‑appropriate insights.
• Design and deploy AI‑driven automation across the full customer lifecycle, including pipeline qualification, proposal generation, onboarding, Q  preparation, renewal forecasting, and customer health scoring.
• Lead the automation of finance workflows, including reporting, variance analysis, contract analysis, audit preparation, and spend optimization.
• Identify and eliminate manual, high‑friction operational processes across the organization; enable scalable adoption through self‑service AI tools for business teams.
• Own and evolve the data engineering foundation, including data pipelines, warehouse architecture, data quality standards, and governed access layers.
• Develop and operationalize proprietary models trained on Nintex data (customer, product usage, and operational data) to improve retention, expansion forecasting, and business visibility.
• Establish and enforce governance frameworks for responsible, reliable, and scalable AI deployment, including model evaluation, monitoring, and data privacy and security standards.
• Operate with a lean, outcome‑focused delivery model, driving measurable impact through cross‑functional partnerships with business and technical teams.
• Define and evolve the organizational…