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Automation, RPA & Process Intelligence — Designing Intelligent and Automated Operations

Automation, RPA & Process Intelligence develops a practical approach to discovering, redesigning, and automating business processes using process mining, task analysis, robotic process automation, AI, and broader intelligent-automation methods. Participants learn to identify automation opportunities, analyze process variation and bottlenecks, select the right automation approach, and build credible business cases. The program connects process intelligence with implementation governance, human-in-the-loop design, performance measurement, and continuous improvement so automation delivers sustainable operational value rather than isolated task-level efficiencies.

Prague

5 days

November 2, 2026 – November 6, 2026

Price

EUR 5,500

Initial Enrollment Request

Learning Objectives

Map and analyze business processes to identify bottlenecks, variation, rework, and automation potential.
Distinguish process mining, task mining, RPA, workflow automation, AI, and hyperautomation use cases.
Evaluate automation candidates using value, feasibility, complexity, control, and exception criteria.
Build process and automation designs that incorporate human judgment, controls, and exception handling.
Develop business cases and performance measures for intelligent-automation initiatives.
Plan governance, implementation, monitoring, and continuous improvement for an automation portfolio.

Detailed Daily Schedule

Day 1 — Process Intelligence and Automation Opportunity

Establish the relationship between process management, automation, and operational performance.
Map end-to-end processes, handoffs, decisions, exceptions, and pain points.
Identify automation candidates without automating inefficient processes blindly.
Compare RPA, workflow automation, AI, and intelligent-automation approaches.
Create an initial automation opportunity inventory.

Day 2 — Process Mining and Evidence-Based Discovery

Understand event logs, process variants, conformance, and process-mining fundamentals.
Use process data to reveal bottlenecks, loops, delays, and hidden variation.
Combine process mining with task-level observation and stakeholder knowledge.
Translate process evidence into redesign and automation hypotheses.
Build a fact-based process improvement and automation case.

Day 3 — RPA and Intelligent Workflow Design

Identify rule-based tasks suited to robotic process automation.
Define triggers, business rules, data inputs, outputs, and exception paths.
Design human-in-the-loop controls for judgment-intensive activities.
Examine document processing, AI-assisted decisions, and workflow orchestration.
Produce a future-state process and automation design.

Day 4 — Business Case, Delivery and Governance

Estimate automation benefits across cost, capacity, quality, speed, and compliance.
Assess implementation complexity, dependencies, risks, and change impact.
Define automation development, testing, deployment, and hypercare stages.
Establish ownership, controls, security, and change-management responsibilities.
Prioritize initiatives into a balanced automation portfolio.

Day 5 — Hyperautomation and Continuous Improvement

Explore how process mining, RPA, AI, analytics, and orchestration work together.
Define performance measures for automated and hybrid processes.
Monitor exceptions, process drift, bot performance, and realized benefits.
Establish a continuous-improvement loop using process intelligence.
Develop an intelligent-automation roadmap for a selected operational area.