> A fixed-scope assessment that evaluates whether your engineering organization can safely increase AI-assisted development through governance, understanding, intent, direction, and evidence.

# AI Modernization GUIDE Assessment

Can your engineering organization safely increase the use of AI-assisted development without sacrificing understanding, governance, or delivery confidence?

## Who it's for

This assessment is designed for organizations responsible for existing applications where software failures have real business consequences.

- ✓ Teams responsible for existing applications with customers, revenue, and meaningful business dependencies.
- ✓ Organizations where software failures have real consequences, including outages, compliance exposure, security risk, data integrity issues, or reputational damage.
- ✓ Engineering and product leaders who want to leverage AI more effectively but recognize that engineering practices, not just engineering tools, determine whether AI becomes an accelerator or a liability.
- ✓ Organizations modernizing long-lived software systems that need a practical roadmap rather than a rewrite.

## Who it's not for

- ✗ Greenfield products, MVPs, or systems without meaningful production risk.
- ✗ Teams optimizing primarily for short-term delivery speed where long-term maintainability and operational risk are secondary concerns.
- ✗ Organizations looking for staff augmentation or feature delivery before understanding the health of the engineering system.
- ✗ Teams looking for AI tool training rather than engineering modernization.

## What's included

### GUIDE Assessment

We evaluate your engineering organization through the five GUIDE principles for next-generation software engineering.

#### Governance

We review the technical and organizational controls that allow engineering teams to safely increase implementation velocity.

- CI/CD guardrails
- Security practices
- Code ownership
- Deployment controls
- Operational risk management

#### Understanding

We evaluate how architectural knowledge, business rules, operational context, and engineering decisions are captured, maintained, and communicated across the organization.

We identify where critical understanding exists only as tribal knowledge and where AI-assisted development is likely to struggle because context is incomplete.

#### Intent

We assess how effectively tests, specifications, documentation, and development practices communicate engineering intent.

- Automated test coverage
- End-to-end testing
- Contracts
- Documentation quality
- Other signals that help humans and AI understand what the system is supposed to do

#### Direction

Humans set direction and own outcomes. Agents execute within those boundaries.

We review how engineers currently work with AI tools, including:

- Repository conventions
- Prompt management
- Code review practices
- Architectural decision-making
- Ownership boundaries between engineers and AI-assisted implementation

#### Evidence

We evaluate how engineering teams establish confidence in software.

- Automated testing
- Observability
- Security scanning
- Continuous verification
- Other mechanisms that provide evidence the system behaves as intended

### Modernization Roadmap

Prioritized recommendations describing:

- What to improve first
- What can safely wait
- Where modernization efforts will create the greatest leverage for both engineering teams and AI-assisted development

### Executive Summary

A clear, non-technical summary of key findings, organizational risks, and recommended next steps that engineering and executive leadership can use to prioritize investment.

## What's explicitly excluded

- Feature development or roadmap execution
- Large-scale refactors or rewrites
- Test implementation beyond small illustrative examples
- Production changes outside limited diagnostic work
- Long-term delivery or staff augmentation engagements

## Timebox

- 2-3 weeks, depending on system size and stakeholder availability
- Designed to be minimally disruptive to the existing engineering team
- Fixed start and end date

## Price Range

- Typically $20,000-$40,000, depending on system complexity and scope
- Fixed fee agreed in advance
- No open-ended billing

## Start with an AI Modernization GUIDE Assessment

Find out where AI-assisted development can safely accelerate your engineering organization, where modernization is needed first, and what evidence should guide the roadmap.

[Start with an Assessment](https://www.defmethod.com/contact)