Faysal AzizAI-Assisted ERP Development and Implementation
Evaluate coding, data, documentation and support workflows
Why this course
Apply AI assistance to a prepared ERP development and implementation scenario. Generate and review code, tests, query suggestions, reports and documentation, then examine customer-feedback analysis and an incremental adoption roadmap. Activities evaluate usefulness and limitations; faster delivery or fewer errors are potential goals, not guaranteed results.
Learning outcomes
- Generate and review ERP module code and test ideas using an approved coding assistant.
- Evaluate AI suggestions for SQL queries, data migration and API integration.
- Prepare and validate sample reports and user documentation.
- Explore sentiment analysis on sample support data, recognising uncertainty.
- Create a gradual ERP AI-adoption roadmap addressing value, infrastructure, skills and compliance.
Prerequisites
Familiarity with ERP development, APIs and SQL. Access to the prepared ERP sample, approved AI tools and suitable accounts; specific features depend on the selected product and plan.
4 modules
01Day 1 — Code generation and testing3 topics
- AI in ERP development: Overview and use cases
- AI-powered code assistance (GitHub Copilot, Tabnine)
- Automated testing and debugging (Katalon, GitLab CI/CD)
Hands-on Activity:
- Use AI tools to generate ERP module code
- Implement basic AI-driven test automation
Compare coding assistants such as GitHub Copilot or Tabnine in the prepared environment. Review generated code and tests; Katalon or GitLab CI/CD executes configured automation rather than automatically making every test AI-driven.
02Day 1 — Data processing and API integration3 topics
- AI for SQL query optimization and data migration
- AI-powered API integration improvements
- AI for predictive ERP data analysis
Hands-on Activity:
- Optimize SQL queries with AI
- Set up an AI-assisted API integration
Check query semantics and measured performance before accepting optimisation suggestions. Data migration and API work use a bounded sample; predictive analysis is an introductory example.
03Day 2 — Reporting, documentation and feedback3 topics
- AI-powered ERP reports and analytics
- AI-generated documentation and user guides
- AI for customer feedback analysis (Sentiment Analysis) : Using AI to analyze customer emails, tickets, and surveys to detect frustration or urgency
Hands-on Activity:
- Generate an AI-enhanced ERP report
- Use AI to automate ERP documentation
- Perform sentiment analysis on sample customer support data
Validate reports against known sample data and documentation against the actual ERP behaviour. Use prepared non-sensitive feedback examples; sentiment does not reliably establish a customer’s intent or urgency by itself.
04Day 2 — Adoption roadmap3 topics
- How to integrate AI into ERP gradually
- Overcoming challenges: infrastructure, skills gap, compliance
- Identifying high-value AI use cases for ERP
Create an adoption roadmap for the sample ERP project, with selected high-value use cases and a gradual implementation plan.
Who this course is for
ERP developers, implementation engineers and technical leads.
A programme built around your team.
Share your training goals and requirements.