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AI-DLC for ERP teams: what changes when AI drives the lifecycle

AWS’s AI-Driven Development Lifecycle puts AI in charge of planning and people in charge of approval. Here is what that means for SAP, Oracle, Workday and ServiceNow quality teams.

Matt Angerer
October 8, 2026 · 8 min read

Most of what has been written about AI in software testing is about tools: generate a test case, heal a broken locator, summarize a failure. Useful, but small. The more interesting question is what happens to the whole lifecycle when AI is not a helper bolted onto each step but the thing driving it. That is the question the AI-Driven Development Lifecycle (AI-DLC) tries to answer.

What AI-DLC is

AI-DLC is a methodology published by AWS in July 2025, introduced by Raja SP of AWS on the AWS DevOps Blog and described in more detail in an AWS method definition paper. AWS open-sourced a set of AI-DLC workflows in late 2025 (the awslabs/aidlc-workflows repository on GitHub), and the method was presented at re:Invent 2025.

Its central idea is to reverse the conversation. Instead of people directing AI task by task, AI proposes the plan, asks clarifying questions and implements only once a person has validated it. People stay accountable; AI does the heavy lifting.

  1. AI

    Creates a plan

  2. AI

    Asks clarifying questions

  3. Human

    Validates and corrects

  4. AI

    Implements the approved plan

Repeated for every lifecycle activity, in minutes or hours rather than weeks

The AI-DLC interaction pattern, repeated for every activity. Based on AWS’s description of the method.

It works in three phases, Inception, Construction and Operations. Work is broken from an Intent into Units and delivered in Bolts, short iterations measured in hours or days that replace sprints. There is a full walkthrough with diagrams on our AI-DLC explainer page.

Why ERP quality teams should care

Three features of AI-DLC map unusually well to how ERP programs already struggle:

  • Quality is in the room from the start.In Inception’s Mob Elaboration, the method explicitly includes QA alongside product and engineering, and its outputs include risks and measurement criteria. For ERP teams that have always been brought in at SIT, that is a big shift.
  • Brownfield is a first-class path. For existing systems, AI-DLC starts by having AI build models of the existing code and behavior for people to validate before anything changes. That is the ECC to S/4HANA problem in a sentence: understand what you have before you convert it.
  • Tests are generated, reviewed and run as part of construction. AI generates functional, security and performance tests; people review the scenarios; AI runs them, traces failures and proposes fixes for approval. Testing stops being a phase after development.

An ERP lens on AI-DLC

AWS’s material is written for building software, mostly on cloud infrastructure. ERP programs configure and extend packaged software, run on business processes that cross modules and live with vendor release cycles. Here is how I would map the quality work of an SAP program onto the AI-DLC phases. This mapping is my own, not part of AWS’s method.

Inception

  • Business process scope from the process library (O2C, P2P, R2R)
  • Simplification items and custom code impact from the Readiness Check
  • Risks mapped to processes, interfaces and roles
  • Measurement criteria written as test exit criteria

Construction

  • Configuration, extensions and custom code built as Units
  • Automated end-to-end process tests generated and reviewed per Bolt
  • Test data created or reserved by each test
  • Integration and role tests before a Unit is "done"

Operations

  • Risk-based regression on every transport and release
  • Cutover rehearsals and hypercare as operational runbooks
  • Production incidents fed back to the process library and tests

The Sign-Off Table’s adaptation for ERP programs. Not part of AWS’s published method.

The Sign-Off Table’s ERP adaptation of AI-DLC. Not part of AWS’s published method.

Where to start

  1. Get your process library in order.AI can only plan against business processes you have described. A Testing CoE’s process library is the context AI-DLC needs.
  2. Pilot on one Unit. Pick a contained change, such as a new output form or a pricing condition, and run it through Inception and Construction with AI planning and people approving.
  3. Measure flow, not effort. The promise is shorter cycles with the same or better quality. Track time from intent to tested release, and escaped defects.

Key takeaways

  • AI-DLC is an AWS methodology (2025) in which AI plans and executes and people validate at every step.
  • Three phases, Inception, Construction and Operations, with work delivered in short Bolts.
  • QA joins at Inception, and tests are generated and reviewed during construction, not after it.
  • For ERP teams, a well-maintained business process library is the foundation AI-DLC builds on.