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Summary – With generative AI, value shifts from writing code to structured software design, requiring developers to become software designers who model business needs, steer secure architectures, and ensure performance and compliance. These profiles lead co-design workshops to formalize user stories, define modular (microservices) architectures, govern prompts and code, and integrate AI into IDE/CI for traceability, quality (TDD/BDD, SAST/SCA/DAST), and upskilling via pairing. Structure your roles, tools, and metrics (lead time, test coverage, defect rate) to turn AI into a scalable software design lever.
The rise of generative AI has shifted value from mere code writing to the ability to structure, define, and steer software design. Where automatic function generation becomes almost instantaneous, organizations must now rely on profiles capable of turning a business need into robust architecture, specifying testable behaviors, and guaranteeing security and performance at scale.
This transformation does not aim to replace developers, but to evolve them into true software designers, orchestrating AI through prompting processes, tooling, and design reviews. In this article, discover how to rethink your roles, your tools, your engineering practices, and your metrics so that AI ceases to be a gimmick and becomes a lever for software design at scale.
Software Designer Profiles for AI
Value is now created upstream of code through needs modeling and the definition of the rules of the game. Software designers embody this responsibility, guiding AI and ensuring coherence between business requirements and technical constraints.
Deepening Requirements Analysis
Software designers devote an increasing portion of their time to business analysis, working closely with stakeholders. They translate strategic objectives into precise user stories, identifying key scenarios and acceptance criteria. This approach reduces unproductive iterations and anticipates friction points before development begins.
To succeed, it is essential to establish co-design workshops that bring together business owners, architects, and AI specialists. These sessions foster a common vocabulary and formalize information flows, dependencies, and risks. The outcome is clear specifications and greater visibility over the project scope.
In some companies, upskilling on modeling techniques (UML, Event Storming, Domain-Driven Design) accelerates this analysis phase. Teams thus gain agility and better anticipate the impact of changes while limiting technical debt generated by late adjustments.
Strengthening Intent-Driven Architecture
Software designers define software architecture based on business intentions, taking into account non-functional constraints: security, performance, operational costs. They design modular diagrams, promote microservices or autonomous domains, and ensure each component meets scalability requirements.
Example: A mid-sized financial institution tasked its teams with developing an AI-based portfolio management platform. By structuring the architecture around microservices dedicated to compliance, report generation, and risk calculation, it reduced the time needed to integrate new regulations by 40%. This example shows that an intent-driven approach secures the roadmap and facilitates regulatory adaptations.
Intent-driven architecture also relies on Decision Records (ADR) to document each critical choice. These artifacts trace trade-offs and inform newcomers, while ensuring alignment with code governance principles.
Governance and Code Quality
Beyond automatic generation, code quality remains a pillar of reliability. Software designers define style rules, test coverage thresholds, and technical debt indicators. They organize regular design reviews to validate deliverable compliance.
These reviews combine human feedback and automated analyses (linters, SCA, SAST) to quickly detect vulnerabilities and bad practices. Implementing a dependency registry and update policy ensures third-party components remain up-to-date and secure.
Finally, code governance includes a process to validate AI prompts, with traceability of requests and results. This approach preserves transparency and integrity, even when assistants generate part of the code or documentation.
Human-AI Collaboration in Development
Efficiency relies on assistants integrated into daily tools, providing contextual support while respecting internal policies. Traceability of AI interactions and rigorous access management ensure compliance and security.
AI Integration in the IDE and CI
Modern code editors offer AI-powered extensions that suggest snippets, complete tests, or generate comments. Integrated into the IDE, they boost productivity and accelerate the search for technical solutions. Implementing custom templates ensures consistency of deliverables.
On the CI side, AI-dedicated pipelines validate the coherence of suggestions before merging into the main branch. These automated steps detect deviations from best practices and security standards, preventing regressions induced by unsupervised generation.
The IDE/CI combination with AI plugins facilitates automatic API documentation, unit test writing, and deployment script generation, reducing time-to-market while maintaining a high level of reliability in the development cycle.
Traceability and Prompt Compliance
Establishing a registry of prompts and their responses is essential to audit decisions made by AI. Each request must be timestamped and associated with an author and usage context. This allows tracing the origin of a line of code or a business rule generated automatically.
Example: A public service deployed an AI assistant to draft data migration scripts. By logging each prompt and script version, the organization could demonstrate compliance with data protection requirements during a regulatory audit. This example shows how AI interaction traceability reassures authorities and secures the process.
On a daily basis, this prompt governance relies on ticketing tools or documentation management integrated into the development platform. Teams thus maintain a complete, accessible history usable for maintenance or security reviews.
Security Policies and Secret Management
Clear policies define the types of information allowed in AI interactions and require encryption of secrets. AI extensions must access keys via a secure vault, not in plaintext in configurations.
Periodic controls (SAST/DAST) verify that assistants do not generate secret leaks or expose personal data. Security teams collaborate closely with software designers to identify and block risky uses.
Finally, regular training and awareness campaigns help foster a culture where AI is seen as a powerful but guarded tool, ensuring the sustainability and trustworthiness of automatically generated systems.
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We support companies and organizations in their digital transformation
Ensuring AI Reliability Through Engineering
The robustness of AI deliverables relies on rigorous engineering: a testing culture, automated pipelines, and security controls. These foundations guarantee smooth, controlled evolution.
Test-Driven Development and BDD
TDD (Test-Driven Development) encourages writing unit tests first, then the corresponding code, promoting modular design and reliability. In an AI context, this means specifying expected behaviors before asking an assistant to generate logic.
BDD (Behavior-Driven Development) complements this by translating requirements into executable usage scenarios. Software designers define these scenarios and link them to prompts, ensuring AI produces outcomes that meet expectations.
Combining TDD and BDD helps teams limit regressions and maintain a growing test suite. Each new version of the assistant or AI model is automatically validated before deployment, reinforcing confidence in service continuity.
CI/CD Pipelines and Automated Reviews
CI/CD pipelines orchestrate static analyses, tests, and code reviews. They must include steps dedicated to evaluating AI contributions, comparing suggestions against internal standards and architectural patterns.
Automated jobs measure test coverage, cyclomatic complexity, and compliance with security standards. Generated reports feed directly into team dashboards, informing quality and performance metrics.
Seamless integration between the code review system and the CI platform triggers automatic validations as soon as a new AI snippet is submitted. This approach reduces integration delays and maintains high governance levels despite rapid generation.
Application Security: SCA, SAST, and DAST for AI
Software Composition Analysis (SCA) identifies vulnerable dependencies introduced by AI, while Static Application Security Testing (SAST) scans risk patterns in generated code. Dynamic Application Security Testing (DAST) simulates attacks to measure real-world resilience.
Example: An industrial group automated a pipeline combining SCA, SAST, and DAST on an AI-augmented application. This reduced production vulnerabilities by 60% while preserving a weekly deployment cadence. This example demonstrates the effectiveness of a comprehensive engineering foundation for securing AI.
Implementing security dashboards and proactive alerting ensures rapid response to new vulnerabilities, ensuring a defense posture adapted to the constant evolution of AI models.
Upskilling and Measuring Impact
Junior skill development relies on mentoring and katas, while key metrics guide team efficiency and quality. Continuous feedback fuels the process.
Pairing and Design-Oriented Mentoring
Systematic pairing assigns each junior to a senior to work jointly on user stories and AI prompts. This duo approach fosters knowledge transfer and architecture understanding while supervising assistant usage.
Pair sessions include real-time design reviews where the senior challenges junior choices and introduces best patterns. This practice accelerates skill growth and builds a shared software design culture.
Over time, juniors gain autonomy, learn to craft precise prompts, and interpret AI outputs, preparing the next generation and ensuring skill continuity within teams.
Refactoring Katas and Design Reviews
Refactoring katas involve exercises to restructure existing code or prompts for improved clarity and testability. These are scheduled regularly and overseen by experienced software designers.
These exercises help dissect AI patterns, understand its limitations, and identify optimization opportunities. Resulting design reviews enrich the internal pattern library and feed ADRs for future projects.
This training approach prevents treating AI as a black box and strengthens the team’s ability to diagnose and correct generation drifts before they reach production.
Key Metrics to Drive Evolution
Several metrics measure the impact of the software designer approach: lead time (from need to deployment), post-production defect rate, test coverage, and AI-related infrastructure cost. These indicators provide a quantitative view of added value.
Tracking technical debt and average prompt complexity reveals risk areas and guides action plans. Weekly reports shared with management ensure strategic alignment and visibility on achieved gains.
Combining these data points enables decision-makers to adjust resources, prioritize improvement areas, and demonstrate team performance, thus reinforcing the case for sustainable transformation.
Adopt the Software Designer Mindset to Master AI
Transforming developers into software designers is a crucial step to fully leverage generative AI. By rethinking roles, enabling human-AI collaboration, strengthening the engineering foundation, and structuring skill development, companies gain agility, security, and business alignment.
Our experts are ready to co-build this evolution with your teams and support you in implementing practices, tools, and metrics tailored to your context. Together, let’s make AI a pillar of software performance and innovation.
