The End of Agile? Waterfall Rehabilitated by AI

The Paradox That Could Revolutionize Our Methods

For decades, we buried waterfall. Too rigid, too slow, unsuited to modern complexity. Agile emerged as the only way: short iterations, minimal specifications, continuous adaptation.

But what if agile was just a workaround?

A pragmatic response to a simple constraint: the difficulty of quickly producing complete, quality specifications.

AI could change this equation. By potentially accelerating the analysis and specification phase, it opens interesting new methodological possibilities to explore.

Why Did Agile Really Emerge?

An interesting perspective: agile was partly born from our collective difficulty in specifying quickly and correctly.

The Challenges of Traditional Specifications

In the classic waterfall approach:

3-6 months to produce a complete requirements document
Dozens of meetings with sometimes disconnected stakeholders
Voluminous documents difficult to maintain and validate
Specifications sometimes obsolete before development even begins
High cost of these analysis phases

Result: Projects that started after 6 months of analysis, only to discover that a significant portion of requirements needed adjustments.

The Agile Solution: A Different Approach

Agile proposed a pragmatic answer: specify as you go.

Minimal user stories replacing detailed specifications
"Just enough documentation" to stay flexible
Short cycles enabling continuous adaptation
Rapid client feedback on concrete increments

The undeniable advantage: The client sees something quickly. Even if imperfect, even if incomplete, they can touch, test, and give feedback on something concrete rather than on documents.

The trade-offs of this approach:

Multiplication of development-feedback cycles
Frequent refactoring related to evolving understanding
Technical debt to manage
Some produced code requires adjustments
Need for stakeholder adaptation to this iterative approach

AI: An Accelerator That Opens New Possibilities

Artificial intelligence could transform waterfall's historical bottleneck: the specification phase.

What Took Months Could Take Days

AI-assisted functional analysis:

Use case generation: potentially hours instead of weeks
Assisted identification of edge cases and error scenarios
Production of detailed specifications: possibly 2-3 days instead of 2-3 months
Accelerated cross-validation and contradiction detection

Accelerated technical specifications:

System architecture: assisted generation in 1-2 days
UML diagrams, ERDs, and data flows: assisted production
Technical documentation: significant reduction in manual effort
Data modeling: assistance for coherence

Stakeholder validation:

Interactive mockups generated from specifications
Functional prototypes in hours
Complex scenario simulations
Quick confirmation before any development

The Equation That Could Change

Before AI (observed):

Complete analysis: 3-6 months
Agile development: 6-18 months by iterations
Total: 9-24 months minimum

With AI (projection):

AI-assisted complete analysis: 1-2 weeks
AI-assisted development on complete specifications: 4-8 weeks
Potential total: 6-10 weeks

The opportunity: An AI-assisted waterfall cycle could have the duration of an agile sprint, but with the scope of a complete project.

A Possible Hybrid Approach: Agile in Speed, Waterfall in Method

This transformation could enable an interesting hybrid approach:

Phase 1: Complete Specification (1-2 weeks)

AI-assisted business analysis:

Detailed business objectives and ROI
Current and target business process modeling
Stakeholder identification and their needs
Requirements traceability matrix

Exhaustive functional analysis:

Detailed functional specifications for each component
Main and alternative use cases
Complete and validated business rules
Precise and measurable acceptance criteria

Complete technical architecture:

Documented and justified technology choices
System, application, and data architecture
Testing, deployment, and monitoring strategy
Security and compliance plan

Phase 2: User Validation (3-5 days)

Generated functional prototype:

Complete but unconnected user interface
Complete navigation and flows
Realistic data simulations
Structured validation sessions

The best of both worlds:

This is where AI could reproduce agile's key advantage, but amplified:

The client sees something concrete in days (like in agile)
But that "something" represents the complete vision of the software as an interactive mockup
They can navigate, test flows, validate the complete user experience
Their feedback covers the entire solution, not fragments
Validation possible before a single line of code is written

Adjustments and refinements:

Rapid AI-assisted modifications
New generation of updated specifications
Final confirmation before development

Phase 3: Assisted Development (4-6 weeks)

Code generation on validated specifications:

Frontend code: assisted generation from validated mockups
Backend services: assisted generation from architecture
Automated tests: assisted generation from specifications
Potential significant reduction in development time

Fewer feedback cycles, potentially more quality:

Better code alignment with specifications from first cycle
Less major architectural refactoring needed
Less code thrown away or rewritten
Validation tests rather than discovery tests

Potential Benefits of AI-Assisted Waterfall

Possible Time Savings

Comparison for a typical project (average enterprise application):

| Methodology | Analysis | Development | Total | Refactoring | |-------------|----------|-------------|-------|-------------| | Traditional waterfall | 6 months | 12 months | 18 months | 20% | | Pure agile | 1 month | 15 months | 16 months | 40% | | AI-assisted waterfall | 2 weeks | 2 months | 2.5 months | 10% |

Note: These figures are projections based on observed AI capabilities and remain to be validated in different contexts.

Potential Reduction in Technical Debt

The AI-assisted waterfall approach could generate less technical debt than agile:

Architecture defined from the start: potentially less structural refactoring
Complete specifications: less code to rewrite following discoveries
Documentation: assisted generation and maintenance
Tests: integrated design from the beginning

Superior ROI for Stakeholders

Stakeholders get:

Complete visibility from project start
Validation before development investment
Interactive prototype in days - they see the entire software quickly
Feedback on the whole - no need to wait multiple sprints to see the global vision
Faster delivery of complete solution
Fewer surprises and in-flight changes
Predictable budget with fewer overruns

Conditions for Success

This transformation isn't automatic. It requires:

1. Mastery of AI Tools for Analysis

Ability to guide AI in specification production
Rigorous human validation of generated outputs
Domain expertise to detect hallucinations
Knowledge of patterns and anti-patterns

2. Process Rigor

Don't give in to the temptation to "start coding"
Invest necessary time in complete specification
Structured validation with stakeholders
Strict governance of post-validation changes

3. Team with Right Skills

Analysts capable of orchestrating AI
Architects validating technical choices
Developers mastering AI-assisted development
Product owners understanding specification value

4. Acceptance of New Paradigm

Challenge the dogma "agile is always the answer"
Recognize that complete specifications are no longer prohibitive
Accept that some projects benefit from waterfall approach
Choose methodology based on context, not ideology

When to Use AI-Assisted Waterfall?

This approach is particularly suited for:

✅ Well-Defined Projects

Stable and well-understood business domain
Strict regulatory or contractual requirements
Solution replacing well-documented existing system

✅ Projects with High Technical Complexity

Multiple integrations with existing systems
Critical performance or security constraints
Complex distributed architecture

✅ Projects Where Change Is Costly

Mobile development with long approval processes
Embedded systems or firmware
Applications requiring certifications

✅ Projects with Multiple Stakeholders

Need for alignment between several departments
Large budgets requiring multiple approvals
High reputational or financial risks

Agile Retains Its Place

Important: This waterfall rehabilitation doesn't signal agile's death.

Agile remains relevant for:

Exploration of uncertain opportunities where the problem isn't yet well defined
Radical innovation in emerging domains
Product experimentation where learning is the main objective
Startups in discovery phase seeking product-market fit

The Intelligent Hybrid Approach

The real opportunity is methodological flexibility:

AI-assisted waterfall for well-defined and stable parts
Agile for uncertain and exploratory parts
Choice based on context and project constraints

The Future of Software Development

AI doesn't replace methodologies, it could alleviate some of their historical constraints.

Waterfall was never bad in itself. It was simply too slow and rigid for certain contexts of modern software development. Agile was never perfect. It represents an excellent compromise given the difficulties of rapid specification.

AI could enrich our methodological options.

A Possible New Reality

Complete specifications in days rather than months
User validation possible before development
Potentially more predictable development cycles
Less waste, more delivered value

Tomorrow's Developer

Modern developers could benefit from:

Mastering AI orchestration for analysis
Understanding when to specify and when to iterate
Rigorously validating generated specifications
Choosing methodology based on project context

Conclusion: Exploring New Possibilities with Pragmatism

Agile was our pragmatic response to a technical constraint: the difficulty of specifying quickly. AI could alleviate this constraint.

AI-assisted waterfall isn't a step backward, but a new opportunity to explore.

An opportunity that would allow us to choose our methods based on context and real constraints. An opportunity that could allow us to deliver faster. An opportunity that could combine certain advantages of waterfall and agile.

The question becomes: "how to use AI to enrich our methodological toolbox and choose the best approach for each situation?"

This vision of course remains to be validated in real conditions across many projects. But early feedback is encouraging and deserves exploring these new possibilities with curiosity and rigor.

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