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Artificial Intelligence Whitepaper
  • August 10, 2026
  • 12 min read

Pathway to AI-native engineering: From traditional SDLC to Coherent's Continuous Delivery Loop

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Contributing Experts

Maria Reyes VP Product, AKTIV

Sam Osei Product Strategist

Tara Patel Data Lead, Fitness Company

James Kim Principal Engineer

Retention isn't a marketing problem. It's a product architecture problem.
Retention isn't a marketing problem. It's a product architecture problem.
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See how Coherent's Continuous Delivery Loop (CDL) can turn scattered AI adoption across a traditional SDLC into a well-managed, high-performing AI-native engineering system.

AI helps engineers work faster, but this speed hasn’t led to quicker delivery, better quality, or more reliable releases. In many organizations, the delivery system hasn’t kept pace with AI, and the gap is widening. 

This whitepaper introduces the Continuous Delivery Loop (CDL), Coherent Solutions’ framework for moving from traditional SDLC to AI-native software engineering. 

The CDL was developed and tested within Coherent’s own delivery system. It connects AI-powered workflows into a coordinated system that helps close the gap between speed and real performance.

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What you’ll read about

The Digital Value Gap
Learn why AI helps individuals work faster but can cause bottlenecks across the whole SDLC, and why adding more tools isn’t the answer.

The CDL operating framework
Find out how CDL’s four activity centers (Problem Identification, Validation & Exploration, Design & Engineering, and Observation & Scale) organize AI-native engineering into a managed system.

AI maturity and measurement
Learn how to see where your organization fits in the CDL Maturity Model, set a clear delivery baseline, and track real performance improvements, not just activity.

Governance built into delivery
See why having governance built into every step of the delivery loop, like security checks, license rules, and audit logging for each AI action, leads to better results than reviewing only at the end. CDL helps make accountability and speed work together.

What the data shows

If companies do not adapt their software delivery system to support AI’s speed, that speed can quickly become a liability, impeding quality and progress. The CDL operating framework can help companies bridge the gap between speed and system design, without losing the discipline of the classic SDLC.

10%
ceiling on productivity gains from AI tools alone
20%
additive productivity gain with an AI-native operating model
30%
avg delivery improvement using CDL
Max Belov

Human strategy, oversight, and decision-making are more important than ever to ensure the efficiency of AI-enabled delivery systems.

Max Belov, Chief Technology Officer, Coherent Solutions

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If your organization is producing more with AI but not necessarily delivering better, this whitepaper will set you on the path to AI-native engineering.