Finding the right automation opportunity
- Process selection criteria and value assessment.
- Task suitability: language, documents, classification, extraction.
- Cost, risk and effort estimation basics.
- When not to use AI.
AI automation & applications
Turn AI experiments into practical workflows and assistants
Design AI-assisted workflows, connect AI to business applications, and prototype knowledge assistants and document processes with proper guardrails.
Course overview
From isolated prompts to designed automation
An AI chatbot demo is easy. A reliable business workflow is harder. This program focuses on the design work between the two: selecting a suitable process, defining inputs and outputs, choosing models and tools, adding human checkpoints, and measuring whether the result is actually good.
Participants map a workflow, connect to an AI API, explore retrieval-augmented generation concepts, and build a small prototype such as a knowledge assistant or document-processing flow. Governance, cost and failure handling are covered alongside the technical build.
What makes this program different
- Starts with process selection and business value, not tool fashion.
- Includes human-in-the-loop and exception-handling design.
- Covers evaluation and monitoring, not just prototyping.
- Keeps security, privacy and cost visible throughout.
Tools disclaimer: Sessions explore AI platforms, APIs and automation tools such as OpenAI, Gemini, Microsoft Copilot and other relevant services only where available and relevant to the batch. No program promises coverage of every AI tool or technology.
Curriculum
AI automation and applications modules
Nine modules from opportunity mapping to a governed prototype.
Workflow design and human-in-the-loop
- Mapping current and proposed workflows.
- Defining inputs, outputs and decision points.
- Human review, escalation and exception handling.
- Designing for auditability and traceability.
Connecting to AI services through APIs
- Authentication, requests and responses.
- Structured outputs and validation.
- Timeouts, retries and error handling.
- Cost, rate limits and usage monitoring.
Retrieval-augmented generation concepts
- Embeddings and semantic search.
- Chunking, indexing and retrieval quality.
- Grounding answers in approved documents.
- Limitations, stale content and citation checks.
Knowledge assistants and chatbots
- Defining scope, audience and escalation paths.
- Conversation design and fallback behaviour.
- Access control and data boundaries.
- Testing with realistic and adversarial questions.
Document and data processing automation
- Extraction from invoices, forms and reports.
- Classification, routing and summarisation.
- Validation rules and confidence thresholds.
- Handling exceptions without silent failures.
Integrating with business applications
- Where AI fits alongside ERP, CRM and databases.
- Webhooks, queues and scheduled jobs.
- Idempotency and safe retries.
- Keeping systems of record authoritative.
Evaluation, monitoring and governance
- Defining quality criteria and test sets.
- Logging, feedback and drift awareness.
- Privacy, security and access reviews.
- Policies for acceptable use and approval.
Capstone: governed AI prototype
- Select a workflow and document the design.
- Build a small prototype using an approved AI service.
- Add validation, logging and a human review step.
- Present results, risks and next steps.
Learning outcomes
What you should be able to do
Assess opportunities
Decide whether a process is a sensible candidate for AI automation.
Design workflows
Map inputs, outputs, exceptions and human review points.
Integrate APIs
Call AI services with validation, retries and error handling.
Use retrieval
Explain how RAG grounds responses in approved knowledge sources.
Prototype assistants
Build a scoped chatbot or document workflow with guardrails.
Govern usage
Define quality checks, logging, access rules and review steps.
Practical work
Design, build and critique a controlled prototype
- Map a real or sample business process and mark automation candidates.
- Call an AI API and validate structured output.
- Build a small retrieval-based answer flow using sample documents.
- Add logging, error handling and a human approval step.
- Test the prototype with edge cases and document failure modes.
- Present a governance and cost summary for the prototype.
Production caveat: The capstone is a controlled prototype, not a production deployment. Production systems require additional security review, testing, monitoring, data governance and change management.
Enrolment questions
AI automation FAQ
Is this a coding-heavy course?
Yes, it is applied and technical. You should be comfortable with programming and basic APIs. The focus is on designing and integrating AI workflows, not on training machine learning models.
Will I build a production-ready chatbot?
You will build a controlled prototype. Production readiness requires additional security, evaluation, monitoring, data governance and change management work beyond the course.
Do I need my own AI API key?
Where the batch environment provides access, keys are arranged for exercises. Otherwise, you may need an approved key or use guided demonstrations. Never share or commit API keys.
Which automation or AI tools are covered?
AI platforms, APIs and automation tools such as OpenAI, Gemini, Microsoft Copilot and other relevant services are explored where available and relevant to the batch. We do not promise coverage of every tool or technology.
Can this be tailored to SAP or our internal applications?
Corporate batches can use your application context, subject to access and confidentiality. The design principles apply across ERP, CRM, databases and custom applications.
Related training
Build the surrounding skills
AI automation
Design AI workflows that can be trusted
Share your use case and technical environment. We will confirm fit and the next available batch.