Process Intelligence And AI Enablement Specialist

Job Description

The Process Intelligence & AI Enablement Specialist is responsible for transforming operational processes, policies, and Standard Operating Procedures (SOPs) into structured, scalable, and AI-ready workflows. This role combines process excellence, low-code/no-code solution design, and AI enablement to ensure business processes can be effectively executed by both human teams and AI-powered systems.

The specialist will analyze complex operational workflows, create structured process maps and decision trees, and design knowledge frameworks that support automation, AI assistants, intelligent workflows, and future LLM-based solutions. The role also serves as a key contributor in validating AI outputs, auditing process accuracy, and maintaining governance standards to ensure reliable and compliant AI-assisted operations.

The ideal candidate possesses strong process mapping expertise, systems thinking, analytical skills, and a solid understanding of AI technologies, prompt behavior, workflow automation, and human-in-the-loop quality management.

• Process Mapping & Knowledge Design
• Analyze, document, and optimize operational processes, SOPs, policies, and business rules.
• Convert complex procedures into structured process maps, decision trees, workflow diagrams, and knowledge frameworks.
• Design AI-ready process documentation that can be utilized by automation platforms, AI assistants, and Large Language Models (LLMs).
• Create clear process logic, exception handling paths, and escalation frameworks to improve operational consistency.
• Identify process gaps, ambiguities, risks, and inefficiencies, and recommend improvements.
• Low-Code / No-Code Workflow Development
• Design and maintain workflows using low-code and no-code platforms.
• Build process guidance tools, workflow applications, decision-support systems, and operational knowledge repositories.
• Ensure workflows remain scalable, user-friendly, and aligned with evolving business requirements.
• Collaborate with stakeholders to translate operational requirements into structured digital workflows.
• AI Enablement & LLM Readiness
• Structure operational knowledge and decision logic to support AI-assisted and LLM-driven workflows.
• Develop frameworks that improve AI understanding of business processes, policies, and operational rules.
• Support prompt design, workflow orchestration, and AI knowledge architecture initiatives.
• Partner with business, operations, and technical teams to identify opportunities for AI adoption and intelligent automation.
• AI Quality Assurance & Audit
• Conduct audits and evaluations of AI-generated outputs, recommendations, and workflow decisions.
• Validate AI performance against established SOPs, policies, and business requirements.
• Develop testing methodologies, evaluation criteria, and quality standards for AI-assisted processes.
• Monitor AI accuracy, consistency, hallucination risks, and adherence to operational guidelines.
• Support human-in-the-loop review processes to continuously improve AI performance and reliability.
• Governance, Monitoring & Continuous Improvement
• Establish governance standards for process documentation, workflow design, and AI-enabled solutions.
• Maintain version control and change management processes for SOPs, workflows, and AI knowledge assets.
• Track operational and AI performance metrics, including accuracy, compliance, cycle time, adoption, and quality outcomes.
• Produce reports, dashboards, and insights that communicate process effectiveness and AI performance to leadership.
• Drive continuous improvement initiatives through data analysis, stakeholder feedback, and operational audits.
• Stakeholder Collaboration
• Partner with Operations, QA, Training, Product, Policy, and Technology teams to ensure process alignment.
• Facilitate process discovery workshops and requirement-gathering sessions.
• Support onboarding, training, and adoption of workflow tools and AI-enabled solutions.
• Act as a subject matter expert on process intelligence, workflow optimization, and AI-enabled operations.
  
  

• Process Intelligence - Ability to analyze, structure, and optimize complex operational workflows into scalable business processes.
• AI & LLM Readiness - Understanding of how AI systems consume, interpret, and execute structured knowledge and decision logic.
• Workflow & Systems Thinking - Ability to design interconnected workflows, decision frameworks, and operational ecosystems.
• Quality & Governance - Strong focus on accuracy, compliance, auditability, and continuous improvement.
• Data-Driven Decision Making - Ability to leverage operational metrics, audits, and insights to improve processes and AI performance.
• Cross-Functional Collaboration - Strong stakeholder management skills with the ability to influence across operations, business, and technology teams.
• Communication & Documentation - Ability to communicate complex concepts clearly through process maps, documentation, presentations, and visual frameworks.
  
  

• Bachelor's Degree in Computer Science, Information Systems, Engineering, Data Science, Business Analytics, Industrial Engineering, or a related field, or equivalent practical experience.
• Minimum of 2–4 years of experience in Process Excellence, Business Process Management, Operations Excellence, Automation, Knowledge Management, or related disciplines.
• Strong experience creating process maps, workflow diagrams, decision trees, and operational documentation.
• Hands-on experience with low-code/no-code platforms, workflow tools, or business process management solutions.
• Familiarity with AI technologies, LLMs, prompt engineering concepts, or AI-assisted workflows.
• Strong analytical, problem-solving, and systems-thinking skills.
• Excellent written, visual, and verbal communication skills.
• Ability to simplify complex operational processes while maintaining accuracy and compliance.
• Experience in e-commerce, logistics, customer support, returns and refunds, trust and safety, or operational excellence environments.
• Experience working with AI-enabled products, conversational AI, knowledge systems, or intelligent automation solutions.
• Familiarity with prompt engineering, AI evaluation frameworks, model testing, annotation workflows, or AI quality assurance methodologies.
• Experience building dashboards, reports, and executive presentations.
• Exposure to AI governance, model monitoring, or responsible AI practices.