Role-Based Programme
RB1337

AI for Operations

Smarter Processes, Productivity & Operational Decision-Making

IMAGE REQUIRED
Duration
8 Hours
Level
Basic
Delivery
Instructor-Led
Format
Workshop

Programme Objectives

  • Understand how AI can support Operations across process management, planning, productivity, reporting, quality, and continuous improvement.
  • Apply AI to analyse operational data, workflows, recurring issues, resource constraints, and performance gaps.
  • Use AI to improve SOPs, process documentation, operational communication, reports, and management summaries.
  • Develop practical skills for root-cause exploration, workflow optimisation, KPI analysis, prioritisation, and operational decision support.
  • Understand responsible AI use, confidentiality, data accuracy, safety, governance, bias, and human oversight in operations.

Tools covered

Generative AI AssistantsAI Search & ResearchProcess AnalysisOperational Data AnalysisWorkflow OptimisationRoot-Cause Analysis SupportSOP DevelopmentResource Planning SupportPerformance ReportingContinuous Improvement Planning

Who should attend

  • Operations Executives
  • Operations Managers
  • Business Operations Professionals
  • Service Operations Professionals
  • Production Operations Professionals
  • Supply Chain Operations Professionals
  • Logistics & Warehouse Professionals
  • Operations Analysts
  • Process Excellence Professionals
  • Operational Excellence Professionals
  • Workforce & Resource Planning Professionals
  • Quality & Compliance Professionals
  • MIS & Reporting Professionals
  • Operations Team Leads

Prerequisites & Participant Readiness

  • Basic understanding of business or operational processes
  • Familiarity with workflows, KPIs, reports, operational issues, or process documentation is helpful
  • Basic analytical, communication, and digital-tool skills
  • No programming or technical AI knowledge required
  • Prior AI-tool experience is helpful but not mandatory

TOC Modules

Concepts
  • Understanding Generative AI and its relevance to modern Operations
  • Exploring AI support across planning, workflows, quality, reporting, and decision support
  • Distinguishing Generative AI from ERP, workflow automation, analytics, and operational systems
  • Understanding AI limitations, hallucinations, bias, and operational risks
Practical activities
  • Identifying recurring Operations activities suitable for AI assistance
  • Comparing traditional and AI-assisted operational workflows
  • Mapping AI opportunities across the end-to-end Operations lifecycle

Scenarios

Operational Bottleneck to Process Improvement Plan

Current Workflow → Operational Data → Bottleneck Analysis → Root-Cause Hypotheses → Improvement Options → Priority Actions → Owners & Timelines → Management Review

Participants use AI to analyse a sample operational process, identify bottlenecks and performance gaps, and develop an evidence-based improvement plan with clear actions and ownership.

Operations Performance Data to Management Decision

Operational KPIs → Trend & Variance Analysis → Performance Gaps → Possible Drivers → Resource / Process Options → Priority Actions → Risk Review → Executive Summary

Participants use AI to analyse sample Operations performance data, identify significant issues and improvement opportunities, and prepare a structured decision-support summary for management review.

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