AI Project Failure Rates Hit 85%: A Practical Guide to AI SDLC for Executives

AI in SDLC: Transforming Software Development

Today, you can consider it generally accepted to structure the process of designing, developing, testing, and deploying high-quality software using the SDLC. As a rule, it consists of the following key stages: planning, requirements gathering, design, development, testing, deployment, and maintenance. Each stage ensures a clear roadmap, reducing errors and improving project outcomes.

AI changes the SDLC in two essential ways:

1. Creating AI-powered software products, including:

• Intelligent applications that learn and adapt
• Solutions that process and analyze large amounts of data
• Systems that automate complex decision-making processes

2. Accelerating software development with AI tools, including:

• Automated code generation and optimization
• Improved testing and debugging capabilities
• Efficient documentation processes

Below, you’ll discover key features of the AI SDLC from these two perspectives, exploring how AI powers state-of-the-art software and how AI solutions streamline tasks in the software development process.

AI Software Development Life Cycle: A Detailed Walkthrough

Managing the AI software development lifecycle requires a structured approach that aligns with your business objectives. At the same time, the focus on AI models and data that underpin AI solutions significantly differentiates the AI SDLC from the non-AI software build cycle.

First and foremost, it is necessary to ensure compliance with the technical requirements of the AI SDLC, which ensure seamless development and deployment of AI solutions and encompass:

• Model training infrastructure focuses on high-performance computing resources, including GPUs and cloud environments, to handle large-scale training tasks efficiently.
• Data pipeline specifications involve designing robust systems for data collection, preprocessing, and storage to ensure continuous, reliable data flow.
• Integration points outline how the AI solution will interact with existing systems, ensuring compatibility and smooth deployment.
• Performance benchmarks set clear metrics for accuracy, speed, and scalability, helping teams measure and optimize AI performance against business goals.

Check out the essentials at each stage of the AI SDLC.

Stage 1. Discovery

Initially, your journey begins with defining the problem scope and business requirements. This vital phase helps you identify specific challenges and set measurable objectives. The discovery process in AI-powered software projects encompasses, in particular, assessing data availability, exploring AI models’ feasibility, identifying the associated risks, and more.

Stage 2. Data gathering

The next step is collecting relevant data to train and validate AI models from various sources. This stage forms the foundation of your AI solutions because data quality decisively influences the AI project outcomes, including the performance, accuracy, and reliability of AI models.

Stage 3. Data preparation

At this stage, your AI engineers transform the raw information into formats suitable for training AI models. Data preparation covers:

• Cleaning and removing inconsistencies
• Organizing data structures
• Labeling data points for training AI models, etc.

Stage 4. Model development

During this phase, your AI software development project team selects appropriate AI algorithms and designs the AI model architecture. Essential considerations include:

• Model complexity requirements
• Performance expectations
• Resource availability
• Scalability needs, and more.

Stage 5. Model training

Your AI models get exposed to prepared datasets during the training phase. This iterative process needs continuous refinement, which allows for well-trained AI models with high accuracy.

Stage 6. Model evaluation & refinement

During this stage, AI experts use fresh test datasets to assess AI models` accuracy, performance, and reliability. Such an evaluation identifies possible inconsistencies and spots areas for improvement. This iterative process, which may include necessary adjustments or retraining with additional data, ensures the AI system meets quality benchmarks and can operate robustly in real-life cases.

Stage 7. Model deployment

The deployment aims to integrate trained AI models into a live production environment. It involves configuring the AI models for real-world data inputs, ensuring system compatibility, and monitoring performance. This stage turns the AI solutions into functional products, ready for end-user interaction and ongoing optimization in a production environment.

Stage 8. Post-implementation maintenance

Post-implementation maintenance becomes a continuous process of keeping the AI models accurate, secure, and efficient after deployment. AI software engineers deal with monitoring the performance of AI models, addressing data issues, and retraining the systems as needed. They update datasets, fine-tune AI models, and adapt AI solutions to evolving business needs, ensuring expected performance, reliability, and accuracy.

Generative AI SDLC: Key Features and Challenges

As businesses across industries increasingly invest in Generative AI (Gen AI), this technology quickly becomes a pivotal part of operations. McKinsey reports that 65% of companies are integrating Gen AI.

For building advanced AI solutions with Gen AI models, it’s essential to pay attention to the following features and challenges of the Gen AI SDLC.

Data diversity and volume requirements

Data quality is paramount when implementing generative AI. Obtaining data from diverse sources ensures more accurate outputs. Your training data needs to be comprehensive enough to maintain the model’s consistent performance and accuracy.

Focus on creativity and output quality

One significant concern with generative AI is the risk of homogenization in creative outputs. Due to the influence of existing data patterns, AI models may produce similar content across different users. To combat this, developers must focus on diversifying training datasets and selecting settings to drive unique outputs.

Handling bias in generative AI models

Bias remains a vital issue in generative AI development. There is always a risk that AI solutions can perpetuate existing societal biases, notably in gender and racial representation. To succeed, your generative AI development projects need robust bias detection mechanisms and regular audits.

Ensuring ethical use and compliance

Your ethical considerations should extend beyond simple basic compliance. Be prepared to address challenges such as protecting sensitive data privacy, preventing harmful content generation, transparency in decision-making processes, adherence to norms, and responsible standards.

Importance of iterative testing and feedback loops

The iterative approach allows the continuous enhancement of AI solutions based on user inputs and performance metrics. Regularly assessing the generated content against quality benchmarks, using end-user feedback to refine AI algorithms, adversarial testing, and detailed performance monitoring – here are the components that will strengthen your Gen AI product improvement strategy.

Note that generative AI models need continuous monitoring and refinement. Organizations implementing proper safeguards and iterative improvements get the most out of Gen AI capabilities.

Generative AI in SDLC: Facilitating and Accelerating Software Development

Solutions for speeding up the software development process are always of great interest. How can it be otherwise if over 30% of tech projects run late or exceed budget? Against this backdrop, studies that reveal that developers who use Copilot write code up to 55% faster than those who don’t are encouraging a rethink of AI SDLC, expanding the scope of AI tools.

Carrying out the software development process according to the AI SDLC, you can make sure that Generative AI proves valuable in several directions:

1. Code Generation and Optimization

• Automated creation of boilerplate code
• Smart code suggestions based on context
• Performance optimization recommendations

2. Testing and Quality Assurance

• Creation of complete test scenarios
• Generation of synthetic test data that keeps statistical properties
• Automatic detection of security risks

3. Documentation and Maintenance

• Automatic generation of technical documentation
• Code explanation and annotation
• Maintenance suggestion automation

Incorporating Generative AI in SDLC allows teams to deliver software development projects faster. Research shows that teams using generative AI tools complete 126% more projects weekly.

However, the benefits of AI SDLC management go beyond just getting more work done. Since automated code generation cuts the time spent on repetitive tasks, your software engineers can focus on strategic objectives and breakthrough ideas.

Gen AI SDLC: Building Software with Cutting-Edge Tools

AI-based toolkit for software development projects promptly expands. Below, you can view what types of AI-powered tools you can rely on when integrating AI in SDLC.

Tools for AI-assisted code generation

AI solutions like GitHub Copilot and OpenAI Codex stand at the forefront of AI-assisted development. These tools enable your software engineers to:

• Reduce coding time
• Generate complex code snippets automatically
• Receive intelligent code suggestions in real-time

High-performing foundation models (FMs)

LLAMA, GPT, and Amazon BedRock are reshaping software development practices. These models offer unprecedented capabilities in:

• Natural language processing
• Code understanding and generation
• Problem-solving assistance
• Software documentation creation

Gen AI tools for prototyping and UX design

AI capabilities in modern design tools have transformed the prototyping process. Your team can create stunning designs faster with Figma plugins and DALL-E integration. AI-assisted design tools cut prototype creation time impressively.

AI solutions for project management and collaboration

AI-powered tools facilitate project management and team coordination. Widely used software products in this field, such as Asana and Microsoft Project, utilize AI capabilities for planning,  tracking, and resource allocation across projects.

Platforms for automated testing and quality assurance

Software quality assurance receives a substantial boost with AI-powered testing platforms (e.g., Testsigma, Parasoft, etc.). Such AI tools excel at creating complete test scenarios, spotting potential bugs early, automating regression testing, and more.

The right mix of AI tools can improve the software development process, particularly software engineers’ productivity, while maintaining code quality and project timelines. At the same time, the latest powerful tools work best when experienced developers guide them. It is advisable to consider AI-powered tools as productivity boosters rather than replacements for human expertise. Balancing automation with the human touch remains a good tip for organizing the software development process in 2025.

SDLC AI: Why Choose Riseapps for Your AI Projects

Riseapps stands out as a reliable partner for AI development, backed by extensive expertise and a solid success record. Whether your business requires a plug-and-play AI system for rapid deployment or a fully customized solution tailored to unique operational needs, Riseapps has the proficiency to deliver. Prioritizing flexibility, scalability, and strict regulatory compliance, Riseapps empowers organizations to reduce costs, boost ROI, streamline processes, and enhance operations with cutting-edge AI technology.

1. Expertise in AI software development

Riseapps has a proven track record of delivering advanced AI solutions across various industries. Their team specializes in building intelligent systems that drive efficiency, automation, and innovation. Riseapps offers end-to-end services, covering every stage of the AI software development life cycle (AI SDLC).

2. Riseapps’ AI toolkit

Riseapps stays at the forefront of AI technology, leveraging the most advanced tools for software development, including:

• GPT – For natural language processing and content generation.
• Stable Diffusion & Midjourney – For AI-powered image generation.
• DALL·E – For creative AI design and media assets.

3. Client-centric approach

Riseapps prioritizes client success by delivering tailored AI systems that meet specific business objectives. Their flexible methodologies ensure a personalized development process with measurable results.

4. Flexible forms for starting AI implementation

Riseapps offers multiple ways to begin your AI journey based on your current needs and goals:

• AI Consulting Session with CTO – Gain expert insights into AI strategies
• AI Product Workshop – Identify opportunities for AI-driven transformation
• AI R&D Prototyping – Develop early-stage AI models
• Proof-of-Concept Development – Validate AI ideas with functional prototypes
• MVP Development – Build a minimum viable product to test in real market conditions

Partnering with Riseapps means working with an innovative, experienced, and client-focused team dedicated to turning AI concepts into reality.

You can see an example of the results achieved through the AI software development cycle by looking at the AI healthcare product built by Riseapps below.

AI SDLC: Success Stories and Lessons Learned

Actual results speak louder than promises and theoretical reasoning. Explore how leaders across industries achieved remarkable outcomes through Riseapps’ AI software development expertise.

PareIT AI medical summarization software

PareIT is an AI-driven medical record analysis platform designed for personal injury attorneys in the U.S. It uses machine learning models trained on over a million medicolegal data points to automate case file analysis, reducing the need for manual reviews. PareIT can process thousands of pages in under two hours, offering cost-effective and accurate medical summaries tailored for personal injury, malpractice, criminal, and worker’s compensation cases.

Implemented Solutions

1. AI-Powered Automation:

• Integrated machine learning algorithms for faster and more accurate data extraction from medical records
• Implemented AI models to identify key legal information such as diagnoses, treatments, and handwritten notes

2. Advanced Search Capabilities:

• Multi-component search for legal cases, medical data, doctors, and diagnoses
• AI-enhanced context search for deeper data insights

3. Real-Time Support with AI Bots:

• Implemented an AI conversational bot trained in legal and medical scenarios
• Automated handling of 95% of customer support queries

Project Results

• 40x Faster medical summarization compared to manual methods
• 3x Cheaper than legal professionals and clinicians for data analysis
• 85% AI-powered data accuracy achieved

Riseapps successfully partnered with PareIT to implement AI capabilities to enhance automated medical summarization and data extraction. The collaboration resulted in a faster, more efficient, and cost-effective solution, empowering legal professionals with accurate data analysis while ensuring regulatory compliance.

Element AI operating system for risk management

BlackSwan Technologies, a leading AI company, developed the ELEMENT™ platform, the world’s first AI operating system for enterprise risk management. It integrates multiple AI technologies like machine learning, natural language processing, and knowledge graphs to enhance fraud detection and compliance efficiency.

Implemented Solutions

1. Transaction Monitoring Module:

• Developed a robust system with four components: event management, backtracking, error handling, and error management
• Enabled clients to process millions of transactions per second while detecting and preventing money laundering activities

2. High-Performance Optimization:

• Implemented AI optimization algorithms for faster transaction analysis
• Increased processing capacity to 6 million transactions per hour from several days

3. Abstract Architecture Implementation:

• Improved development speed and maintenance efficiency
• Ensured data integration across multiple sources and faster data-loading processes

Project Results

• 6 Million transactions per hour: Achieved through AI-powered optimization
• 50-Fold reduction in deployment time: Simplified processes for faster customer onboarding
• 55% Decrease in alert costs: Reduced false positives with enhanced compliance algorithms

Riseapps empowered BlackSwan with cutting-edge AI tools, transforming ELEMENT™ into a powerful, secure, and scalable risk management solution.

Whitelabel AI Solutions for Impressive AI SDLC Acceleration

Business owners and executives can expedite AI software acquisition and deployment by leveraging whitelabel solutions. These pre-built, customizable products allow rapid integration without the need for extensive development.

Advantages of whitelabel AI solutions:

• Speedy implementation: Deploy AI capabilities swiftly, reducing time-to-market
• Cost efficiency: Avoid the expenses associated with developing AI tools from scratch
• Customization: Tailor the solution to align with your brand identity and specific business needs
• Reliability: Utilize proven technologies with established performance records

You can learn how AI whitelabel solutions work with a real-world example from Raisapps. Voice.AI is a whitelabel AI voice agent developed by Riseapps. This commercial-off-the-shelf (COTS) product exemplifies how businesses can enhance customer interaction through advanced voice technology.

Key features of Voice.AI:

• Natural Language Processing: Engages customers in human-like conversations, improving user experience
• 24/7 Availability: Provides round-the-clock assistance, enhancing customer satisfaction
• Easy Integration: Seamlessly incorporates into existing systems, ensuring a smooth transition
• Scalability: Adapts to growing business requirements without losing performance