Course Outline
Introduction
-
Defining business process automation using ChatGPT
-
Components of AI systems: Models, agents, tools, and workflow structures
-
Distinguishing between fixed procedural workflows and autonomous agentic processes
-
Strategy for determining appropriate levels of automation autonomy
-
The essential role of human oversight and decision-making in AI workflows
-
Extending capabilities beyond single interactions: Implementing reusable workflows and recurring execution
Managing Context and Memory
-
Differentiating between ephemeral chat context and persistent memory storage
-
Maintaining explicit process state records
-
Ensuring the retention of critical decisions and data across multiple execution cycles
-
Addressing context window limitations and the risk of information loss
-
Facilitating continuity of work across separate conversation sessions
-
Clear distinction between permanent operational rules and transient process status
Structuring Work within ChatGPT
-
Mitigating risks associated with combining excessive tasks and shifting requirements in a single thread
-
Detecting missing decision points and inconsistencies in output generation
-
Decomposing complex operations into smaller, clearly defined tasks
-
Effective transfer of goals, source materials, decisions, and results between tasks
-
Determining when to utilize separate conversations or distinct workflows
Utilizing Plugins, Projects, and Spaces
-
Functional overview of plugins, projects, and Spaces
-
Organization of reusable instructions and tool configurations
-
Integrating workflows with authoritative business information sources
-
Managing related conversations and shared collaborative materials
-
Efficient sharing of documentation and deliverables
-
Configuring the operational environment for specific business processes
Developing Workflows with Persistent Memory
-
Establishing clear workflow objectives
-
Mapping inputs, procedural steps, expected outputs, and acceptance criteria
-
Recording process state to ensure continuity between executions
-
Applying historical decisions to subsequent operational runs
-
Protocols for handling errors and incomplete executions
-
Resuming workflow operations after interruptions
-
Constructing a prototype workflow for a specific business function
Scheduling and Recurring Execution
-
Comparing manual initiation versus automated recurring execution
-
Setting execution frequency and time zone parameters
-
Defining notification protocols and termination rules
-
Managing scenarios where new data is unavailable
-
Re-executing workflows utilizing saved state data
-
Analyzing and comparing outcomes across repeated executions
Assessing Workflow Quality and Consistency
-
Establishing rigorous quality standards
-
Validating required fields, numerical accuracy, data sources, and output formatting
-
Identifying duplicate or contradictory results
-
Distinguishing between acceptable linguistic variation and procedural errors
-
Analyzing root causes of variability in AI-generated outputs
-
Testing workflows against representative business scenarios
Practical Application Workshop
-
Selecting a recurring business task for automation
-
Designing the automated workflow structure
-
Defining process memory and state retention mechanisms
-
Executing and validating the workflow
-
Comparing results from multiple execution cycles
-
Identifying errors and areas for optimization
-
Finalizing the automation for operational deployment
Problem Resolution and Troubleshooting
-
Addressing missing or incomplete contextual information
-
Correcting inaccurate or outdated process state records
-
Resolving inconsistencies in results across executions
-
Managing missing data or inaccessible information sources
-
Preventing duplicate processing of data
-
Recovering from failed or partially completed workflow runs
-
Navigating tool constraints and account feature limitations
Conclusions and Future Actions
-
Reviewing the completed workflow prototype
-
Identifying additional tasks suitable for automation
-
Refining quality and acceptance criteria
-
Planning integration into daily operational procedures
-
Exploring advanced enhancements for a second phase, including code-supported processing and agent development
Requirements
-
Expected Participant Knowledge Prior to Training
- Fundamental familiarity with ChatGPT
- Programming knowledge is not required as a prerequisite.
- Participants must provide a computer, internet connection, and a ChatGPT account with access to the features utilized during the workshops.
- Verification of plugin, Spaces, and scheduling/automation feature access is recommended before training, as availability may depend on account and organizational settings for government.
Intended Audience
The training is suitable for:
- Specialists and managers integrating ChatGPT into daily operations
- Process owners and business analysts responsible for optimizing team workflows
- Professionals preparing reports, summaries, documentation, data compilations, and recurring business updates
- Team leaders implementing AI and managing work with shared information sources
A concise summary for catalog purposes:
Prerequisites: Basic knowledge of ChatGPT. No programming experience required.
Audience: Managers, specialists, process owners, business analysts, reporting/documentation professionals, and AI implementation leaders.
Testimonials (1)
Able to pivot upon audience suggestions - ie able to create a real AI agent scenario on the spot.
