AI Across the SDLC

We use AI directly inside the SDLC from requirements and architecture to development, QA, DevOps, and delivery management. Structured AI-assisted workflows, curated project context, human approval gates, and measurable quality controls accelerate delivery, reduce manual effort, detect risks earlier, and improve visibility.

What Andersen’s proven AI-delivery framework offers

Andersen combines domain expertise, AI-enabled workflows, and human-in-the-loop governance to accelerate delivery, improve visibility, and control SDLC risks without compromising quality, security, or control.

AI across the entire SDLC

Andersen applies AI to requirements, architecture, development, QA, DevOps, and delivery management workflows, reducing manual effort, speeding up handoffs, and improving SDLC visibility.

Delivery visibility and risk detection

We use AI-enabled delivery monitoring to identify bottlenecks, delays, delivery risks, and operational issues earlier across projects, making it possible to react faster and keep execution under control.

AI-assisted DevOps and operations

Our team integrates AI into troubleshooting, CI/CD pipelines, infrastructure workflows, and engineering operations to support faster issue resolution, stronger consistency, and more stable delivery environments.

Enterprise-ready delivery model

Andersen’s skilled technology experts deliver AI-enabled workflows with built-in security, compliance, and operational controls for large-scale and regulated complex environments.

Human-controlled quality and governance

We embed traceability-focused procedures, approval gates, and measurable quality controls into AI-enabled delivery processes to keep outputs reviewable, accountable, and aligned with project requirements.

AI-assisted delivery workflows

Andersen’s AI-native engineering practice offers structured delivery workflows with shared project context, automation support, and human review gates to keep execution coordinated and controlled.

AI tools embedded into our SDLC

Requirements workflows

AI agents process documentation, stakeholder inputs, and business context to reduce discovery delays, structure requirements, assess feasibility, estimate effort, and prepare artifacts faster.

Architecture and design

AI transforms requirements into architecture documentation, C4 diagrams, and reusable solution building blocks, helping teams reduce architecture ambiguity and keep decisions aligned.

Software development

Intelligent coding assistants support software developers with scaffolding, boilerplate generation, unit test drafting, code review, and quality checks within Andersen’s own AI-native SDLC.

How we apply AI across the SDLC

15–25% earlier risk detection

AI-assisted requirements analysis and human review workflows identify delivery risks, missing logic, unclear acceptance criteria, and requirement gaps earlier in the SDLC. This gives you a stronger foundation before development starts, reduces ambiguity in user stories, and keeps business, product, and engineering expectations aligned from the beginning.

20–30% less routine work

Modern AI-driven workflows automate repetitive and time-consuming documentation, reporting, coordination, and status-tracking tasks across the entire SDLC. This allows Andersen’s project teams to spend less time on routine operational work and focus more on strategic decisions, delivery quality, and measurable engineering progress toward client project goals.

Customers we have worked with

How AI supports product delivery

At Andersen, AI is reliably integrated across requirements, architecture, development, testing, release management, and delivery operations to improve visibility, reduce repetitive work, and keep delivery governance stronger and more transparent.

Stage

Requirements

Architecture

Development

Testing

Release

Management

Without AI

3–4 weeks per epic

2–3 weeks (ADRs + diagrams)

100 hours per feature (baseline)

2 weeks per release

MTTR 4–6 hours per incident

3–4 hours/week on status reports per PM

With AI

1.5–2 weeks per epic

1–1.5 weeks

50–65 hours per feature

1–1.2 weeks per release

MTTR 2–3 hours

15–30 min/week

AI workflows

Requirement drafting, user story generation, gap detection

ADR generation, C4 diagram creation from code

Code generation, AI review, auto-docs

Test generation, regression scoring, self-healing

Risk scoring, pipeline automation, self-healing

Portfolio summaries, delay predictions, auto-status

Deliverables

User stories, ACs

ADRs, C4 diagrams

Code, unit tests, API updates

Test cases, scripts, reports

Safe deploy configs

Dashboards, briefings

* These figures and metrics represent market averages and may vary depending on the specific project or request.

Meet our expert

Marcin Wawryszczuk
Head of AI Department

years of experience

100+

AI projects in his portfolio

10+

research papers

Marcin is an experienced AI architect with global leadership experience, holding both MBA and PhD degrees.

Specializes in GenAI/ML Architecture;
Expert in Agentic AI and RAG ecosystems building;
Active Assistant Research Professor.

What our experts say about AI

Explore insights from our experts on applying AI across software delivery, from discovery and development to testing, DevOps, and project management, with a focus on visibility, risks, speed, and human control.