Is AI-DLC Right for Us?

This self-assessment helps you evaluate whether AI-DLC is a good fit for your team and organization. Answer honestly for accurate guidance.

How to Use This Assessment

Answer each question in all three sections
Tally your scores
Read the recommendation for your score range
Consider the nuances in the detailed breakdown

Score each question from 0-2:

0 = Not at all / Rarely
1 = Somewhat / Sometimes
2 = Definitely / Frequently

Section 1: Team Readiness

How prepared is your team to adopt AI-DLC?

Question 1: Claude Code Usage

How much is your team currently using Claude Code or similar AI coding assistants?

Score	Answer
0	Not at all - we don't use AI assistants
1	Some experimentation - a few people try it occasionally
2	Regular use - most team members use AI assistants regularly

Your score: ___

Question 2: Process Openness

How open is your team to trying new development methodologies?

Score	Answer
0	Resistant - prefer to stick with current practices
1	Cautious - willing to try if benefits are clear
2	Eager - actively looking for process improvements

Your score: ___

Question 3: Documentation Culture

How well does your team document decisions and work?

Score	Answer
0	Minimal - documentation is rare and inconsistent
1	Adequate - important things get documented
2	Strong - thorough documentation is standard practice

Your score: ___

Question 4: Quality Focus

How much does your team prioritize code quality over speed?

Score	Answer
0	Speed first - we ship fast and fix later
1	Balanced - we try to maintain quality while moving quickly
2	Quality first - we invest in doing things right

Your score: ___

Section 1 Total: ___ / 8

Section 2: Work Suitability

How well does your work fit AI-DLC's approach?

Question 5: Requirements Clarity

How clear are your requirements before development begins?

Score	Answer
0	Vague - requirements emerge during development
1	Partial - some clarity, but much is discovered later
2	Clear - well-defined requirements before starting

Your score: ___

Question 6: Task Decomposition

How well can your work be broken into discrete, independent units?

Score	Answer
0	Difficult - our work is highly interconnected
1	Moderate - some work can be decomposed, some can't
2	Easy - most work naturally breaks into units

Your score: ___

Question 7: Verifiability

Can you define programmatically verifiable success criteria for most work?

Score	Answer
0	Rarely - most success is subjectively judged
1	Sometimes - some work has clear criteria, some doesn't
2	Usually - most work can have automated verification

Your score: ___

Question 8: Iteration Tolerance

Does your workflow allow for iterative refinement?

Score	Answer
0	No - work must be right first time
1	Limited - some iteration is acceptable
2	Yes - iteration is expected and built into process

Your score: ___

Section 2 Total: ___ / 8

Section 3: Organizational Support

Does your organization support AI-DLC adoption?

Question 9: Management Buy-In

Does management support investing in process improvements?

Score	Answer
0	No - focus is purely on output
1	Somewhat - open to improvements that don't slow delivery
2	Yes - actively encourages process improvement

Your score: ___

Question 10: Learning Time

Can your team dedicate time to learning new methodologies?

Score	Answer
0	No - we're at full capacity
1	Limited - some slack for learning
2	Yes - learning time is built in

Your score: ___

Question 11: Pilot Opportunity

Can you identify a low-risk project to pilot AI-DLC?

Score	Answer
0	No - all work is high-stakes
1	Maybe - could find something with effort
2	Yes - obvious candidates exist

Your score: ___

Question 12: Champion Availability

Is there someone who could champion AI-DLC adoption?

Score	Answer
0	No - no one has bandwidth or interest
1	Maybe - someone could potentially take this on
2	Yes - clear candidate exists

Your score: ___

Section 3 Total: ___ / 8

Scoring

Calculate Your Total

Section	Score
Section 1: Team Readiness	___ / 8
Section 2: Work Suitability	___ / 8
Section 3: Organizational Support	___ / 8
Total	___ / 24

Results

Score: 20-24 - Strong Fit

AI-DLC is likely a great fit for your team.

Your team is well-positioned to adopt AI-DLC. You have:

Existing AI tool usage providing foundation
Team openness to new methodologies
Work that suits structured approaches
Organizational support for adoption

Recommended approach:

Start immediately with Phase 1 (Individual Developer)
Plan for full team adoption
Expect quick wins and smooth adoption

Score: 14-19 - Good Fit with Preparation

AI-DLC could work well with some preparation.

You have good foundations but some gaps to address. Look at which sections scored lowest:

If Team Readiness is low:

Start with education about AI-DLC benefits
Address concerns before formal adoption
Consider starting with most receptive team members

If Work Suitability is low:

Focus on work types that fit best initially
Develop skills in writing verifiable criteria
Build decomposition habits gradually

If Organizational Support is low:

Build a business case with expected benefits
Start small to demonstrate value
Seek management alignment before team rollout

Recommended approach:

Address lowest-scoring area first
Start with a limited pilot
Demonstrate value before expanding

Score: 8-13 - Conditional Fit

AI-DLC may help, but significant preparation needed.

There are notable gaps that need addressing. Consider:

Prerequisites before adoption:

Build AI coding assistant usage habits
Develop documentation practices
Create space for learning and experimentation
Secure management support

Alternative considerations:

Lighter-weight AI practices might be better starting point
Focus on team fundamentals first
Revisit AI-DLC in 6-12 months

If you proceed anyway:

Start with one enthusiastic individual
Keep scope very limited
Be prepared for slower adoption
Don't mandate team-wide adoption yet

Score: 0-7 - Not Yet Ready

AI-DLC is likely not the right approach right now.

Your situation has too many obstacles for AI-DLC to succeed. This isn't a judgment - it's about fit.

Focus instead on:

Building basic AI assistant familiarity
Developing quality-focused practices
Creating organizational space for process improvement
Addressing time pressure issues

Revisit when:

Team has AI coding assistant experience
There's management support for process investment
Work pressure allows for methodology learning

Detailed Breakdown

Low Team Readiness (Section 1 < 4)

Symptoms:

Team doesn't use AI assistants
Resistance to process changes
Poor documentation practices
Speed prioritized over quality

Before AI-DLC:

Introduce AI assistants gradually
Build documentation habits
Demonstrate quality investment value
Address change resistance

Low Work Suitability (Section 2 < 4)

Symptoms:

Requirements are always unclear
Work is highly interconnected
Success is subjective
No room for iteration

Consider:

AI-DLC may not fit your work type
Start with specific work that fits better
Adapt the methodology to your context
Alternative methodologies may be better

Low Organizational Support (Section 3 < 4)

Symptoms:

Management focuses only on output
No time for learning
All projects are high-stakes
No potential champion

Before AI-DLC:

Build business case
Identify pilot opportunities
Find or develop a champion
Create learning time

Special Considerations

For Startups

Score < 14? That's okay for early-stage startups. Focus on:

Building product-market fit first
Using AI assistants informally
Establishing basic quality practices
Adopting AI-DLC when you have product direction

For Enterprise

Score > 18? Consider broader implications:

Governance requirements
Multiple team coordination
Training at scale
Tooling standardization

For Regulated Industries

Add these questions:

Can AI-DLC artifacts support compliance? (Probably yes)
Do you need special workflows for regulated work? (Consider adversarial)
How do you audit AI-assisted development? (AI-DLC helps with this)

Next Steps by Score

Score Range	Next Step
20-24	Adoption Roadmap
14-19	First Intent Checklist (start small)
8-13	Developer Guide (individual only)
0-7	Revisit prerequisites first

Reassessment

Plan to reassess in:

3 months if you scored < 14
6 months if significant organizational changes occur
After major process changes

Keep this assessment and compare with future results to track progress.