Lead day-to-day work using AI as a core operational layer — not as an add-on tool Apply AI to structure thinking, accelerate analysis, and improve decision quality Identify where AI augments human judgment vs. introduces risk Set standards for responsible AI use: transparency, validation, and override Continuously translate emerging AI capabilities into practical workflow improvements ________________________________________ 1. Work Design Lead the organization through transformational work design — redefining how work should operate, not documenting how it currently works Challenge legacy workflows, assumptions, and handoffs that no longer serve speed, quality, or human contribution Partner with business leaders to identify structural constraints in current operating models and redesign them at the root Define future-state workflows that intentionally rebalance automation and human judgment Establish clear principles for what should be automated, augmented, or protected as expertise Drive alignment on redesigned workflows before any build begins — ensuring the team is not scaling broken processes Ensure all designs reflect the core principle: maximizing human contribution alongside automation 2. Automation Development & Integration Build and configure automation workflows using platforms such as UiPath, Power Automate, Make, or similar Integrate workflows with enterprise systems (CRM, ERP, HRIS, data platforms) via APIs and webhooks Implement human-in-the-loop checkpoints, override mechanisms, and escalation paths Monitor and maintain automation reliability, uptime, and performance ________________________________________ 3. AI / ML Enablement Evaluate where AI/ML improves automation quality or coverage Integrate intelligent components (LLMs, NLP, document processing, classification models) Define confidence thresholds and fallback logic for safe human intervention Monitor model performance, drift, and reliability in production ________________________________________ 4. Measurement & Insights Design baseline and pilot measurement frameworks Track metrics: processing time, error rates, throughput, override frequency Analyze human contribution metrics (freed capacity, role elevation) Build dashboards to inform scaling decisions ________________________________________ 5. Change Management & Enablement o Process analysis o Automation engineering o Data analysis / BI o Applied AI/ML o Change management Strong ability to structure ambiguous problems Familiarity with AI/ML concepts and applied use cases Proficiency in data tools (Excel required; SQL/BI preferred) Basic scripting required (Python strongly preferred; Vertex AI, APIs, or similar platforms) Strong communication skills across technical and non-technical audiences Enterprise systems (Salesforce, SAP, Workday, ServiceNow) Process mapping or process mining tools Python or JavaScript scripting ________________________________________ Translate business requirements into workflows and implementation plans Collaborate across roles from discovery through pilot and scale Support documentation, testing, and operational readiness Contribute reusable patterns and best practices ________________________________________
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