AI Workflow Automation Lead

Xcel Energy•Denver, CO

2d•Onsite

About The Position

The Enterprise Risk AI Workflow Automation Lead is a senior individual contributor responsible for turning a substantial share of Enterprise Risk work into scalable AI-enabled operating models. The role translates strategic intent, objective-state designs, and early proof-of-concept work into governed solutions that improve cycle time, consistency, traceability, and organizational reach. This role sits at the intersection of risk operations, enterprise systems, and applied AI execution. It designs agentic workflows that retrieve information, reason over structured and unstructured data, interact with internal platforms, approved plugins and connectors, and use LLM APIs and selected system actions while preserving human review, escalation, and control requirements. The role also evaluates when plugin-based integrations are appropriate, defines usage boundaries, and ensures those integrations remain reliable, governed, and auditable. The role also tracks current AI capability limits, forecasts where those limits may move over the next three to six months, and sequences implementation accordingly.

Requirements

Bachelor’s degree in business, analytics, information systems, computer science, operations, engineering, economics, or a related discipline; Or Seven years of an equivalent combination of education and relevant experience.
Relevant experience implementing AI-enabled workflows, agentic systems, automation solutions, enterprise knowledge workflows, or adjacent process-transformation capabilities in a business environment.
Working knowledge of LLM model APIs, approved plugin and connector integration patterns, authentication and permissions concepts, structured and unstructured data exchange, and safe execution controls for agentic workflows.
Demonstrated ability to establish human and AI operating boundaries, create protocols and expectations for handoffs and review, and update those protocols as capabilities evolve.
Ability to assess current AI capability boundaries, recognize material failure modes, and translate a three-to-six-month capability outlook into a staged implementation roadmap.

Nice To Haves

Experience designing or operating multi-step agentic workflows that retrieve information, reason over data, call APIs or tools, and generate auditable outputs across enterprise environments.
Experience with AI evaluation, testing harnesses, prompt and version management, failure-mode documentation, model or agent monitoring, and human-in-the-loop quality assurance.
Experience interfacing AI workflows with internal systems such as document management platforms, workflow or ticketing tools, enterprise knowledge retrieval solutions, reporting environments, structured data sources, and approved plugins or connectors that support enterprise workflow execution.
Experience writing Python automation scripts or moderate-complexity internal code to support AI workflow orchestration, tool calling, or prototype-to-production implementation.
Familiarity with Microsoft enterprise tooling such as Power Platform, Power Automate, Power BI, Copilot, related plugin or connector ecosystems, or comparable enterprise AI and automation ecosystems.
Experience creating operating protocols, review expectations, or implementation standards for human and AI collaboration in controlled environments.
Experience within Enterprise Risk Management, Compliance, Internal Audit, Controls, Governance, utilities, energy, or another highly regulated environment.
Understanding of AI governance and risk management frameworks.

Responsibilities

Convert current-state Enterprise Risk processes, target-state operating concepts, and early prototypes into backend AI workflows that reduce manual effort and increase departmental throughput.
Define and implement interactions between agentic workflows and internal systems, including document repositories, workflow and ticketing tools, reporting solutions, structured data sources, knowledge repositories, and approved plugins or connectors that extend AI workflows into enterprise tools and content environments.
Translate business workflow needs into implementation requirements involving LLM APIs, approved plugins and connectors, selected internal and external APIs, structured data exchange, tool-calling patterns, permissions, logging, and safe execution boundaries. Partner with technical teams when deeper engineering support is needed.
Establish the design for human and AI interaction, create protocols for review points, approval gates, exceptions, escalation, and accountability, define expectations for human oversight, and periodically update those patterns as technology and tools evolve.
Continuously evaluate what current AI tools can reliably automate, document failure patterns, and forecast where practical capability boundaries may move over the next three to six months, so implementation plans stay aligned with the next wave of usable functionality.