AI-First Delivery Operations for an AI/ML Organization

Overview

Working alongside GlobalLogic, Miros supported the AI/ML organization within ZEISS in improving delivery operations and establishing a more structured AI-first execution model.

As AI initiatives expanded, the organization needed clearer governance, faster execution processes, stronger operational cadence and better internal knowledge organization to support scalable delivery.

The Core Problem

The AI/ML organization was operating in a fast-moving environment where delivery practices and operational structures had not yet fully matured around AI-native workflows.

Key challenges included:

• Lack of a standardized AI-first SDLC

• Inconsistent delivery cadence across initiatives

• Limited governance structure for AI/ML delivery

• Operational inefficiencies slowing execution

• Fragmented internal knowledge and documentation

The organization needed a more structured operating model capable of supporting scalable AI product delivery.

Strategic Approach

The engagement focused on improving execution structure, operational clarity and delivery consistency across the AI/ML organization.

Key initiatives included:

• Designing an AI-first software delivery lifecycle (SDLC)

• Establishing delivery governance structures

• Introducing clearer operational cadence and execution rhythms

• Improving delivery flow and coordination speed

• Building an internal product structure wiki and centralized knowledge base

• Supporting standardization of operational processes across teams

The objective was to create a more scalable and predictable AI delivery environment.

Quantitative Results

Most importantly AI-first SDLC established for delivery operations

• Delivery coordination and execution speed improved

• Governance structure introduced across AI/ML workflows

• Internal documentation and operational visibility significantly improved

• Teams gained a more predictable and scalable delivery framework

Qualitative Impact

Delivery operations became more structured and organized

• Teams gained clearer execution alignment and cadence

• Internal knowledge became easier to access and maintain

• Leadership gained stronger visibility into delivery workflows

• The organization established a stronger operational foundation for scaling AI initiatives