• Represent the trustworthiness and confidence of serious expertise in practical AI applications in customer conversations (especially as an escalation point above an SE SME) • Spearhead the early-stage implementation and adoption of LD4AI at scale, working closely with existing customers to ensure activation and churn prevention (alongside feedback) • Develop and document Solutions Engineering playbooks and best practices that scale beyond our first customers • Function as a tremendous partner to the LD4AI Chief of Staff, surfacing revenue-related insights with respect to the maturation of the product accordingly • Support LD4AI POVs ran by on-the-ground SEs • Collaborate extensively with product and engineering teams to ensure product concepts are technically feasible and align with LaunchDarkly's strategic goals • Drive continuous improvement by monitoring product performance, user experience, and market response, iterating based on actionable data and insights • Confirm that LD4AI’s roadmap adheres to market demand and deliver exceptional user experiences, especially for the AI developer persona – vocalizing objections with data • Deliver integrations against common, quantified customer requests in the form of code contributions, architecture diagrams, and whitepapers • Function as a key technical asset in (with the Head of AI and the partnership team) technical partnerships with advantageous potential partners (like Anthropic, DataBricks, etc…) • Publicly evangelize LD4AI at mainstream industry conferences, webinars, partner engagements, and strategic meetings • Function as the primary owner of the disbursement of enablement material to the SE team, particularly to the designated by team/region SE LD4AI SMEs (for which you have partial, dotted-line accountability) • Co-own Revenue Enablement of LD4AI along with the Head of AI and the LD4AI tetrad • Work with the AI Researcher, the PMM team, and the Head of AI to build and maintain competitor playbooks

• Partner with executives, functional leaders, and teams to identify high-value automation and workflow optimization opportunities. • Map, design, and prototype AI-driven workflows using tools like n8n, Gumloop, Zapier, or similar platforms. • Build and deploy automation pilots that streamline internal processes, reduce costs, or improve employee experience. • Teach teams how to build and maintain their own automations — driving sustainable AI fluency across Invoca. • Stay current with the fast-moving AI tools market, including LLMs (ChatGPT, Claude, Gemini), task-oriented agents, and automation technologies. • Collaborate with IT, Data, and Operations to ensure automations are secure, scalable, and integrated with Invoca systems. • Develop and maintain documentation, playbooks, and experiment frameworks for automation best practices. • Measure and communicate impact, including time savings, adoption, and operational efficiency improvements.

• Own customer success from post-sales handoff through post-go-live • Define and align success metrics, ROI targets, and usage KPIs • Track efficiency gains, accuracy improvements, cost savings, and experience impact • Communicate outcomes through QBRs, exec readouts, and customer newsletters • Own regular customer readouts of AI usage patterns, adoption trends, and workflow performance • Analyze false positives, false negatives, failures, and negative feedback across agent behavior, integrations, and UX • Separate system gaps vs. process, training, or expectation issues • Partner with Value Engineering and AI Implementation teams to drive prioritized improvements across agents, orchestration, prompts, UX, and integrations • Design and execute change-management and rollout plans with customer leadership • Drive adoption across teams, roles, and geographies • Serve as the first escalation point during implementation, go-live, and hypercare • Manage communication across business, IT, security, and executive stakeholders • Identify opportunities for additional SOWs and new use cases • Consultatively sell outcomes using Challenger-style methodologies • Act as the voice of the customer to Product and Engineering • Translate VOC, usage data, and failure patterns into actionable insights

• Supports the design, testing, deployment, and continuous improvement of AI‑enabled solutions that enhance patient recruitment, clinical operations, quality, and business workflows across a multi‑site clinical research network. • Focuses on applied AI, especially LLM‑enabled workflows, automation, and decision support—ensuring solutions are reliable, explainable, compliant, and usable by support and site-based teams. • Partners with operations, quality/QA, recruitment, finance, and IT to identify AI opportunities, translate them into testable solutions, evaluate performance, operationalize them safely in a regulated environment (HIPAA/GCP/21 CFR expectations).