New report: AI adoption framework for software engineering

Adopt the agentic software development lifecycle

Five levels for building software with AI, responsibly and at scale.

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Trusted by architects and engineering leaders worldwide

  • Philips
  • ING
  • Siemens
  • NXP
  • KLM
  • Konecranes
600B+
lines of code benchmarked
40,000+
systems analyzed
ISO/IEC 17025
accredited software quality lab
The five adoption levels

Five levels of AI adoption. Where are you?

Each level changes who is responsible for quality, which risks matter, and what governance you need. The tag under each level shows where the market is today.

  1. Level 1

    AI chat

    Generates ideas, explanations, and code snippets.

    Happening at a large scale
  2. Level 2

    AI-assisted

    Centrally provisioned coding assistants help write and improve software.

    What the market is shifting away from
  3. Level 3

    Agentic

    Agents plan, execute, and iterate.

    What the market is adopting now
  4. Level 4

    AI-native

    AI across the full SDLC.

    Where the market is heading
  5. Level 5

    Self-improving and self-healing

    AI learns from its own behavior and fixes issues it observes at runtime.

    Few organizations are here yet
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Reaching enterprise AI maturity cannot be achieved through a single pilot project, tool, or purchase. Use this framework to assess your current position and collaboratively decide with your team on the next steps.

Jasper Geurts, CTO, Software Improvement Group
Jasper Geurts CTO, Software Improvement Group
The four principles behind the framework

How to move fast without losing control.

Four principles shape how organizations should approach AI in software development, and why governance can't be an afterthought.

Principle 01

AI amplifies what's already there

Good foundations go faster with AI. Poor ones accumulate more problems, more quickly.

Principle 02

The harness matters more than the model

Models change. The harness around them (guides, context, sensors, monitoring) is what stays, compounds, and is yours. It is also what shapes the quality of what they produce

Principle 03

The agentic SDLC requires different ways of working

Unlike basic AI-assisted coding, agentic workflows operate autonomously across the entire pipeline. That demands a different way of working.

Principle 04

Governance must scale with adoption

More AI requires proportionally stronger controls across six dimensions: cost, productivity, security, open source, architecture, and tech debt.

Who it's for

Built for the leaders governing software in the AI era.

Each chapter speaks to a different seat at the table. Start with yours, then read the rest to understand what your peers are working against.

CTO / CIO

You're weighing the investment case for wider AI rollout.

63% of organizations report no enterprise-level EBIT impact from AI. (McKinsey, The state of AI in 2026)

CISO

You're watching for data exposure and IP risk before it becomes an incident.

AI-generated code carries roughly double the security risk of human-written code. (SIG, State of Software 2026)

CFO / VP Engineering

You need to justify AI spend against outcomes, not activity.

Agentic coding tasks can consume up to 1,000x more tokens than a standard code chat. (arXiv:2604.22750)

Architects & engineering teams

You're building the harness and holding the line as agents scale.

AI productivity gains of roughly 60% at 10,000 lines of code collapse by the time a system reaches 100,000 lines. (Stanford/Denisov-Blanch)

Two products, one backbone: Sigrid® Axis and Sigrid Core.

See how they fit together
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Get the AI Adoption Framework.

  • Identify where you are in your AI adoption journey
  • Understand how to move forward responsibly
  • Know which governance structures you need
  • Get ready for today and start preparing for what's possible tomorrow

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Read our methodology

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Watch Core in action

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