A Practical Guide to AI-Enabling the Software Development Lifecycle
How AI is changing the way software is created
Artificial intelligence is impacting every phase of the software development lifecycle—from discovery and solution design to execution and adoption. This guide explores practical ways businesses can leverage AI tools to accelerate innovation, improve efficiency, and deliver exceptional products.
The Phases of Software Development Lifecycle
Software development lifecycle is typically broken into four distinct phases:
Discovery: Phase for understanding the problem space by gathering user needs, business requirements, and market insights through research and stakeholder engagement.
Solution Design: Phase for exploring solutions, creating prototypes, defining how the solution will work, including architecture, specifications, and user experience design.
Execution: Implementation of the designed solution through development, testing, and deployment activities to create the working product.
Adoption: Transition of the solution to users through training, support, and change management while measuring success and gathering feedback.
This diagram highlights the four key phases of traditional software development. In the following sections, we will explore how AI enhances each phase.
Let's start with the discovery phase focused on identifying insights and customer needs to address.
Discovery: Identifying Insights and Needs
AI-powered tools are accelerating customer centric product definition from collecting and evaluating customer feedback to prioritizing and justifying investments.
Translating Customer Feedback into High Impact Features
Tools like ProductBoard consolidate customer input from interview notes, sales calls, product review boards, social media, customer success interactions, support issues, and ad-hoc discussions via email or messaging. They structure the information from these various sources into a unified feedback repository to analyze, get insight on, and surface the right opportunities. Leveraging these AI tools, product managers can analyze, synthesize and translate customer and stakeholder feedback into a compelling articulation of customer needs, and opportunities.
Further automation can auto-link customer feedback to feature candidates, and accelerate the writing of problems summary, pain points, solution themes and desired outcomes.
Other tools like Ignition and Enterpret address similar needs.
Accelerating Investment / Product Briefs
To justify new investments, product managers need to assemble the business case behind their proposed investment. This business justification usually takes the form of an investment or product brief that includes the following:
Clear statement of the opportunity and customer needs
Target user and buyer personas
Competitive positioning and differentiation
Key requirements and capabilities
Pricing and packaging impact
Revenue / cost impact
Expected outcomes and key assumptions made
Launch and change management plans
The process of assembling this information is iterative and time consuming. Generative AI simplifies creating investment briefs by automating research and documentation tasks.
At a minimum, product managers should build templates with their preferred generative AI tool using custom GTPs, Perplexity Spaces or Claude Projects. Alternatively, a specialized tool like ChatPRD come with pre-built templates tailored for product management workflows.
Bash AI is another specialized tool that addresses similar needs.
With a solid understanding of customer needs established in the discovery phase, teams can now focus on translating these insights into actionable designs during the solution design phase.
Solution Design: Evaluating Solutions
Generative AI is transforming the ability to generate prototypes and evaluate solutions rapidly. They fundamentally change how product teams can flesh out solutions to customer needs, buildout functional requirements, and build and test working prototypes. For product managers who see their jobs as just building requirements (which is wrong!), these tools "skip the PM part!" and help maximize the impact of AI on prototype development.
Rapid Application Prototyping
The rapid prototyping space is evolving incredibly rapidly with both incumbents and new vendors fighting to stay relevant or disrupt the space. Amjad Masad, CEO of Replit, recently observed that every 6 months, AI based coding tools are taking a significant step forward. And new entrants continue to enter an increasingly crowded space. Onlook is a good example. They just joined YCombinator Winter 2025 cohort to build the Cursor for designers.
Traditional design tools like Figma are being challenged by prototyping tools like Replit. Reflecting this trend, Paul Graham posted on X, in a post that went viral, that a CEO of a moderately big tech company had decided to replace Figma with Replit.
The lines are increasingly blurring between design tools and rapid application prototyping tools with innovation happening on all fronts. Coming from a design first approach, Figma AI latest AI design generation and prototyping capabilities are quite compelling as illustrated below.
Many rapid prototyping tools like v0 also support the ability to import Figma designs offering best of both worlds options.
With a validated solution in hand, the product team will move from the solution design phase to the execution phase, implementing and hardening the solution. AI accelerates execution by transforming how development, testing, and deployment are handled.
Execution: Implementing and Hardening Solutions
Taking a prototype used for validation and testing to a hardened solution requires investments to fine tune the experience, integrate with production tool chains like CI/CD, provide effective observability and monitoring, ensure security, compliance, scalability and reliability. The impact of AI on the development of production ready services continue to increase and transform how software is developed and hardened.
Development
Matt Madrigal, Chief Technology Officer at Pinterest, recently reported in The Information that over 20% of the code submitted by Pinterest’s 1,800 engineers in Q4 2024 was written by GitHub Copilot, Microsoft’s AI coding assistant. This is consistent with Google CEO reporting that over 25% of new Google code is generated by AI. This shift allows engineers to focus on higher-value tasks while reducing time spent on repetitive coding.
However, more profound changes are likely to happen with the rise of autonomous AI agents like Devin or upcoming innovation like Github Copilot Padawan. With Software Engineering agents becoming reality, we should expect a continuing increase in AI contribution to development, and for that change to radically impact the makeup of software development teams.
Code Security
There is abundance of research on the security risks of AI generated code. This makes Static and Dynamic Application Security Testing (SAST / DAST) more relevant than ever. These tools provide security trained AI models to detect and address security vulnerabilities in any code, generated or not. An example of such tool is Snyk DeepCode AI.
Quality automation
In the State of DevOps in 2024, DORA identified a significant decrease in delivery stability (minus 7.2%). It attributes this decrease to an increase in size of change batches, more prone to creating instability. AI is a significant contributor to these larger change lists. Large change lists increase the importance of quality automation tools that can improve test automation, rapidly create tests from requirements, self-heal broken tests, and minimize test brittleness.
Rainforest QA is a good example of an AI native tool that is redefining software quality assurance, making it faster, more accurate, and cost-effective.
Once a solution is deployed to customers, it is paramount to ensure a reliable experience and to promote effective utilization of the solution to achieve business goals. This is the focus of the adoption phase.
Adoption: Reliability and Effective Utilization
AI can significantly impact post deployment and launch activities and enable more proactive approaches to system reliability and effective utilization.
Incident Response
Nothing is worse than a production incident in the middle of the night requiring multiple team members on an incident response call. AI agents are offerings great promises to automate troubleshooting and remediation tasks, analyze data, identify root causes and enable rapid resolution of production issues. Resolve.ai's AI Production Engineer is a good example of such agent.
Solution Adoption
This is a topic for a future article and an area where AI can have far reaching impact on improving adoption and business outcomes. Opportunities include:
Improved personalized user experiences: leveraging AI algorithms to analyze user behavior and preferences to deliver personalized content and interfaces, improving user satisfaction and retention.
Improved onboarding: using AI-powered chatbots and virtual assistants to guide users through product features, provide contextual help, and recommend underutilized functionalities.
Proactive engagement and support: leveraging historical data to predict user needs, enabling proactive support and engagement to minimize churn and downgrades or increase upsell / cross-sell.
Feature adoption: analyzing user interactions to identify underutilized features and recommending them effectively.
Conclusion
In conclusion, AI is not just a tool but a transformative force reshaping the software lifecycle. Businesses adopting these tools are gaining a decisive edge in delivering robust, market-ready products at unprecedented speeds. By embracing AI across the discovery, solution design, execution, and adoption phases, businesses can deliver better innovative products faster and cheaper.
To stay competitive, start by integrating AI tools into one phase of your software development lifecycle—such as discovery or execution—and scale adoption as you see measurable results.