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2. Operational Redesign

1. What is this track?

Operational Redesign focuses on fundamentally improving existing processes with AI, without completely revising the business model or strategy. The core question is: "Which repetitive, time-consuming or error-prone steps in our processes can AI take over or support?"

This is the most common track and the most direct path to measurable ROI. It is suitable for organisations in the Builder profile that are ready to move from experiment to production scale.

2. Characteristics of this Track

  • Scope: Specific processes or departments.
  • Time horizon: 6–18 months per cycle.
  • Risk profile: Medium — manageable scope, controllable risks.
  • Typical driver: Capacity problems, quality complaints or explicit efficiency objectives.

3. Core Activities

Step 1 — Process Inventory

Create an overview of all relevant processes and evaluate them for AI suitability:

Criterion Low AI suitability High AI suitability
Structure Unstructured, variable Structured, repetitive
Data volume Little data Large volume with history
Decision complexity High — context and nuance Low — regular pattern
Error impact Critical, high risk Limited, recoverable

Select 2–4 processes with the highest AI suitability and highest expected savings.

Step 2 — Process Redesign (Work-First)

Adding AI to a bad process only makes it faster at being bad

Redesign the process before you implement AI. Remove unnecessary steps, clarify ownership and define the desired output exactly.

Use the following questions as a guide:

  1. Who performs this step now and how much time does it take?
  2. What decisions are made and based on what information?
  3. What errors occur most often and what is the cause?
  4. What is the desired end result in measurable terms?

Step 3 — Implementation and Measurement

  1. Build or configure the AI solution for the selected processes.
  2. Establish a baseline before go-live.
  3. Run a pilot period of 4–8 weeks with a defined user group.
  4. Measure the impact on the KPIs (time saving, error rate, quality score).
  5. Decide based on data: stop, adjust or scale.

4. Examples per Process Type

Process type Typical AI deployment Expected Collaboration Mode
Document processing Extraction, classification, summarisation Mode 3–4
Customer communication Draft responses, prioritisation Mode 2–3
Quality control Automatic inspection with exception escalation Mode 4
Reporting Automatic data collection and formatting Mode 3–4
Planning Optimisation proposals for approval Mode 2–3

5. Success Factors

  • User involvement: Involve the employees who perform the process from the beginning.
  • Small batches: Start with one process, learn from it, then expand.
  • Clear owner: Designate a process person who is responsible for the result.
  • Change management: Plan actively for resistance and communicate the 'why'.