Real work. Relevant preparation.

Named readiness programme

TCS: Manufacturing AI Practice

IT services and manufacturing

Manufacturing-focused practice connects machine learning, GenAI and agentic techniques to situations engineers need to understand. Labs provide an environment for building and testing the proposed solutions.

Responsibilities practised

• Interpret equipment and maintenance evidence
• Build and test retrieval and troubleshooting approaches
• Review outputs against the scenario and preserve human safety decisions

The preparation

Hands-on lab work connects technical foundations to manufacturing scenarios. Practice can include predictive maintenance, defect detection and questions grounded in operating procedures.

This is a preparation example. Lab participation or a completed prototype does not establish safe operation in a customer's plant or a measured improvement in maintenance performance.

Named readiness programme

Epsilon: SaaSOps Workflow Practice

Marketing technology and operations

Practice with AWS Bedrock and n8n connects ticket handling, document ingestion and retrieval to operations work. Builder and operator responsibilities need different preparation.

Responsibilities practised

• Configure and review the proposed workflow
• Check retrieved evidence and generated responses
• Route unknown cases to the responsible person

The preparation

Builders practise configuring the workflow and its connections. Operators practise using the prepared workflow, checking outputs and handling exceptions in the learning environment.

The workflows illustrate what people practise. This account does not establish production automation coverage, a rate of hallucination-free responses or realized customer time savings.

Named readiness programme

EY: Consultants Building AI Prototypes

Consulting

Practical programming and agentic AI work help consultants connect a business question to a prototype they can explain and review. The emphasis is applied capability rather than course completion alone.

Responsibilities practised

• Translate a business problem into a bounded build
• Use code, retrieval and agent techniques in practical projects
• Check outputs, explain limits and demonstrate the proposed solution

The preparation

Python foundations, API work, retrieval and agent-building exercises connect to applied projects and demonstrations. The tools and assessment criteria are selected for the programme.

This is consultant preparation, not evidence that a prototype was accepted into client production, that a client signed an agreement after a demo or that every participant demonstrated the same capability.

Read the evidence at the right level.

Programme participation, assessed human performance, agent behavior and operating impact answer different questions. Review the artifacts and criteria for the work assessed, then agree what further evidence the operating owners need.

Human capability

Skill Validation Assessments provide evidence for the responsibilities and scenarios assessed.

Agent behavior

Evaluate the selected agent against its own agreed criteria. A human practice programme does not establish that evidence.

Shared workflow

Rehearse the handoffs and exceptions, then let the organization's owners decide acceptance for actual use.