Who joins
Business sponsors, process owners, frontline users, data and technology teams, security and risk leads.
02 / PROCESS TRANSFORMATIONINDIA + ASEAN
Transform enterprise processes with Agentic AI.
Redesign how work moves across people, data and systems. Start with an Agentic AI Process Transformation workshop, then build and operate workflows with clear human accountability.
Plan a process transformation workshopFROM INFORMATION TO ACTION
THE STARTING POINT
Set the stage with an Agentic AI Process Transformation workshop.
Bring process owners, frontline users, technology and security teams together to examine one business workflow. Map the current work, question unnecessary steps, identify where AI can help, and define where people must stay in control.
Turn the workshop into a prioritised use case, a risk assessment and a scoped pilot. Connect approved data and systems, evaluate agent behaviour and validate the outcome with users before extending the workflow or its permissions.
START WITH A WORKSHOP
Agentic AI Process Transformation workshop
What we prepare
Choose one process and bring representative, approved examples. Identify system owners, sensitive data, decisions and actions that require human approval.
What you leave with
A current and proposed process map, a prioritised use case, data and integration requirements, safety boundaries and a pilot plan. Agree owners, baseline measures, testable acceptance criteria and how outputs and actions will be verified.
What comes next
Validate one workflow with users, evaluate failure and misuse scenarios, then agree a controlled rollout and ongoing operations. Funding and production approval are separate decisions.
WHAT WE DELIVER
A clear scope.
Something you can use.
A workshop and process blueprint
Map the current and proposed workflow, accountable owners, success measures and risks. Agree what to simplify, automate or keep with people.
One scoped Agentic AI pilot
Connect the approved data and systems for a document, knowledge or operational workflow. Validate it with the people doing the work.
Security and safety controls
Limit data and tool access, evaluate unreliable outputs and misuse, and require human approval for consequential actions.
Controlled rollout and operations
Agree release gates, monitoring, audit records, exception handling and a way to pause or recover the workflow.
WHERE IT APPLIES
Document operations
Extract and validate invoices or procurement documents; route low-confidence cases to a reviewer.
Procurement workflows
Bring together demand, purchasing and supplier information so teams can investigate exceptions and make better decisions.
Internal knowledge
Find answers across approved policies and procedures, with source references and permissions respected.
SECURITY AND SAFETY
Security by design.
Safety throughout.
Protect systems and data. Evaluate how AI behaves and the consequences of its actions. Agree controls and evidence for the risks in each engagement.
Bound agent actions
Agree data access, allowed tools, spending limits and actions requiring human approval. Start with the minimum permissions needed.
Test failure and misuse
Evaluate grounding, unreliable outputs, prompt injection and unsafe tool use against the workflow. Include fairness and harmful-output checks where relevant to the decisions involved.
Keep people in control
Record important decisions, route exceptions to an owner, monitor behaviour and provide a way to pause actions and recover when something goes wrong.
DEFINE SUCCESS FIRST
Measure the work.
Then the difference.
We agree a baseline, target, measurement method and accountable reviewer, alongside security and safety acceptance criteria. These are measures to consider, not promised results.
Verifiability in practice
Link factual AI outputs to approved source records where available, and flag missing or uncertain evidence for human review. Record tool actions, approvals and evaluation results; check cited sources because a citation alone does not prove correctness.
- Time per completed workflow
- Manual touches and exception rate
- Accuracy on an agreed evaluation set
- Adoption by the people doing the work
GOOD QUESTIONS
Before we begin.
Do we need to know which AI model to use?
No. We start with the business task, data, risk and cost constraints. We compare suitable proprietary and open-weight models, including language quality and where they can run. Model changes must pass the workflow’s evaluation and human approval checks.
Can this connect to our existing systems?
We assess the available APIs, permissions and integration constraints during discovery. The first pilot focuses on the systems needed for one useful workflow.
Is AWS funding guaranteed?
No. Where relevant, we can work with your AWS account team to explore suitable programs. Eligibility, scope and approval must be confirmed before any funding is assumed.
YOUR NEXT STEP