This module explores AS-IS and TO-BE process modelling as essential techniques for analysing current operations and designing future-state solutions. Learners will examine how to document existing processes, identify inefficiencies and improvement opportunities, and develop future-state models that align with organisational objectives. Through practical assignments, role plays, readings, and AI-assisted modelling exercises, participants will learn to bridge the gap between current and future processes, assess transition impacts, and design adaptable process improvements. The module also introduces emerging AI-native approaches to process modelling that support dynamic and continuously evolving business environments.

This module introduces gap analysis techniques used by business analysts to evaluate the differences between current performance and desired outcomes. Learners will explore various gap analysis frameworks, assess capability, process, and technology gaps, and develop strategies for identifying root causes and improvement opportunities. Through practical assignments, role plays, readings, and AI-assisted exercises, participants will learn how to classify, prioritise, and manage gaps effectively. The module also examines emerging AI-native approaches that enable continuous monitoring and analysis, helping organisations respond more quickly to evolving business needs and operational challenges.

This module focuses on analysing business processes to identify bottlenecks that impact efficiency, throughput, and overall performance. Learners will explore key process analysis concepts, including bottleneck heuristics, cycle time, throughput, and work-in-progress metrics, to better understand where and why delays occur. Through practical assignments, role plays, readings, and AI-assisted analysis activities, participants will develop the skills needed to identify process constraints, validate bottleneck hypotheses, and recommend targeted improvements. The module also examines AI-native monitoring approaches that provide real-time visibility into process performance and support continuous process optimization.

This module introduces benchmarking as a powerful technique for evaluating organisational performance and identifying opportunities for improvement. Learners will explore how to select reliable benchmark sources, normalise data for accurate comparisons, and interpret benchmarking results within the appropriate business context. Through practical assignments, role plays, readings, and AI-assisted activities, participants will develop the skills needed to analyse comparative performance data, validate benchmark findings, and set realistic improvement targets. The module also examines AI-enabled benchmarking approaches that support continuous performance monitoring and provide timely insights in rapidly changing business environments.

This module introduces Lean waste identification techniques used to improve process efficiency and maximise customer value. Learners will explore the eight categories of Lean waste, apply value stream mapping to visualise process flows, and identify activities that do not contribute to desired business outcomes. Through practical assignments, role plays, readings, and AI-assisted exercises, participants will develop the skills needed to analyse process inefficiencies, diagnose root causes of waste, and recommend targeted improvement actions. The module also explores emerging AI-enabled approaches to waste detection and reduction, supporting continuous process optimisation and operational excellence.

This module focuses on change impact assessment and stakeholder impact analysis as critical activities for successful business transformation. Learners will explore the dimensions of change, evaluate how proposed initiatives affect stakeholders, and develop strategies to manage adoption and minimise disruption. Through practical assignments, role plays, readings, and AI-assisted exercises, participants will learn to create impact assessments, analyse stakeholder implications, and recommend effective change rollout approaches. The module also introduces AI-enabled simulation and impact-mapping techniques that help organisations predict consequences, improve planning, and support smoother implementation of change initiatives.

This module serves as the culmination of the process analysis learning journey, allowing learners to validate their understanding of key concepts covered throughout the course. Through a knowledge check and AI-augmented process critique assessment, participants will apply process analysis techniques, evaluate business scenarios, identify improvement opportunities, and provide evidence-based recommendations. The module reinforces critical thinking, analytical decision-making, and the effective use of AI-supported tools to assess and improve business processes.