Solution and Stakeholder Leadership 

• Translate business outcomes and documented requirements into AI solutions that are secure, governed, explainable, and aligned to platform best practices 

• Identify and qualify AI use cases with clients, assessing data readiness, deflection or cycle‑time potential, risk tolerance, and human‑in‑the‑loop requirements. Articulate plainly when a use case is a poor fit for AI. 

• Conduct client and internal demos of Now Assist, AI Agents, AI Control Tower and agentic workflows, clearly explaining how outputs are produced, where guardrails sit, and what the measured impact is 

• Actively participate in Agile ceremonies, flagging technical and AI‑specific risks (data quality, hallucination exposure, adoption drag, licensing consumption) during planning 

Hands‑On Development and Delivery Governance 

• Build and extend Now Assist skills, AI Agents, agentic workflows, and orchestration logic; author and tune prompts, tool definitions, and agent instructions against defined success criteria 

• Develop the supporting platform foundation: integrations, Flow Designer and Integration Hub actions, custom tools exposed to agents, Knowledge and catalog data quality, and the taxonomy that AI Search and Now Assist depend on 

• Configure and tune Predictive Intelligence models, Document and Task Intelligence, Virtual Agent and NLU/Conversational Interfaces, and AI Search relevancy 

• Extend AI beyond native capabilities via Generative AI Controller, AI Agent Fabric / MCP, and third‑party LLM or agent integrations where the use case warrants it 

• Establish evaluation discipline: baseline metrics, golden datasets, regression test suites for prompts and skills, A/B and pre/post measurement, and drift monitoring after go‑live 

• Enforce responsible‑AI guardrails — data handling and PII scoping, role‑based access to AI capabilities, audit and trace requirements, human approval gates, and configuration in AI Control Tower 

• Safeguard quality through peer reviews, automated tests, and coordinated promotions across dev, test, and prod, including cutover and rollback strategies for AI features 

• Own defect resolution during UAT and hyper‑care, including model and prompt performance issues, driving root‑cause analysis and continuous tuning 

Team Leadership and Mentoring 

• Coach junior developers on AI fundamentals, prompt and agent design patterns, and the judgment to distinguish a demo from a production‑ready solution 

• Coordinate daily development tasks, remove roadblocks, and safeguard delivery timelines 

• Facilitate training sessions and knowledge‑sharing forums that raise AI fluency across the broader delivery team 

Innovation and Cross‑Product Leadership 

• Lead AI delivery across at least one product suite beyond core platform work, understanding the process being augmented well enough to know where AI genuinely helps 

• Build reusable accelerators — skill libraries, agent patterns, evaluation harnesses, readiness assessments — and drive their adoption across engagements 

• Track each ServiceNow release for new AI capabilities, evaluate them hands‑on, and advise clients on adoption sequencing and licensing implications 

• Contribute lessons learned, benchmarks, and technical articles to internal knowledge bases and external community forums