AI Development Life Cycle: Stages, Process, and Best Practices

Do you know? Each $1 you invest in AI development brings back an average $3.7. That's why the number of businesses that want to adopt AI solutions is increasing. But the majority of them are not aware of where to start and how things will go on. Just like any other software development, there is an AI development life cycle- the route map for your AI development project.

You may ask why it is necessary to know details about the AI development life cycle. Just imagine the difficulties if you are travelling to a new place and you do not have a route map. Similarly, unless a business is familiar with the AI development life cycle, its AI development journey will be like a ship wandering in the sea without direction. Since planning for deployment and future upgrades, the knowledge of the AI development life cycle makes each action easier.

Here, we will see the AI model development life cycle, its stages, best practices, and how to set up your AI in the software development life cycle for the best results. We will also discuss how mapping AI in the software development life cycle and generative AI in the software development life cycle help businesses. So here, you will get complete guidance for your AI development project.

What Is the AI Development Life Cycle?

The AI development life cycle is all about a well-defined process of developing any AI system. For example, even if you are constructing a building, there is a step-by-step process. What if you miss site planning and get into foundation work? Similarly, there are some structured step-by-step processes for building an AI solution.

The AI development life cycle is beyond the common step-by-step processes of planning, design, development, training, testing, deployment, and maintenance. Different from standard software development, AI systems require some additional processes like continuous learning, data engineering, model evaluation, and performance monitoring. The AI development life cycle is the best way to ensure that every AI model is:

• Accurate
• Data-driven
• Ethical
• Unbiased
• Efficient
• Scalable
• Monitored continuously
• Improved over time

So, businesses adopting AI systems get maximum benefit when they understand and follow the AI software development life cycle. Whether you are planning to build an AI chatbot, AI agent, generative AI solution, AI model, or an AI tool, this development life cycle will be beneficial in multiple ways.

Why the AI Software Development Life Cycle Matters

Just look around today, all the businesses want to build AI systems for their specific purpose. But the real statistics say the success rate of AI development projects is very low, and just in the case of generative AI development projects, 70-95% projects fail. The major reason for this failure is jumping into the depths of AI development without knowing the AI development life cycle.

Many businesses think that the AI development journey is just to hand over the whole project to an AI development company. It will be a foolish decision if a business thinks that we have an AI requirement; we will hand over the project to a trusted AI development company, they know how to do it, and there is no need to know more about the AI development life cycle. How can you evaluate your AI development partner companies on the right track?

The majority of AI projects fail when businesses skip this proven structure. Do you know? What happens when a business misses this AI development life cycle? Some of them are:

• Business rivals may launch similar AI products faster.
• The AI development cost may go beyond the initial budget.
• Late adoption becomes expensive.
• The possibilities of repetitive tasks, rework, and extra work are high.
• May lead to integration issues.
• The quality of outcomes may be lower.
• The challenges in resource management will be high.

What if a business invests thousands of dollars in AI development with a great expectation and ends up with these challenges? A strong AI software development life cycle is the only way to deal with all these challenges and make your project 100% successful.

Benefits of the AI development life cycle

The AI software development life cycle knowledge and its implementation help businesses to:

• Reduce project risk
• Stick to the budget
• Improve model accuracy
• Enhance automation
• Speed up development
• Ensure continuous model updates
• Eliminate rework

So if you want to see your AI development project successful, mapping AI in the software development life cycle and following the AI development cycle are unskippable.

AI Development Life Cycle vs Traditional SDLC

The majority of businesses think that the AI development process is almost the same as normal software development. First of all, you know the nature of AI solutions is more diverse than that of common software. Also, there are many factors that make the AI life cycle far different than a classical SDLC.

Especially when we compare the data dependency of each development process, we can notice the difference. See this table to understand the difference between a development cycle and SDLC.

This is why businesses must focus on mapping AI in the software development life cycle. So, if you are getting into developing AI solutions for your business, always get guidance from a leading AI development company with a deep understanding of classic SDLC, AI development cycle, mapping AI in the software development life cycle, and generative AI in the software development life cycle.

Stages of the AI Development Life Cycle

Below are the essential stages of the AI development life cycle, followed by best practices and process details. Each stage plays a vital role in ensuring that the AI model development life cycle remains effective.

Where to Begin AI Development

Both businesses and entrepreneurs have ideas to develop their AI systems. But most of them are not aware of where to start and who to share with. The most important step of the AI model development cycle is to choose a reliable AI development life cycle partner, share your raw AI development idea, and confirm the validity of the idea in real life.

Whatever may be your AI development idea( AI chatbot, AI model, AI agent, or custom AI solution), share it with a top AI development company that can validate the idea, conceive your idea, and deliver the best output. Once you share the requirements and ideas for your AI systems, the steps of the AI development life cycle start.

1. Set a Route Map and Goal

The first step of the AI software development life cycle is to set up a route map for the AI development project, fix milestones, and finalize the ultimate goal. All these begin by understanding the idea the business brings. It can be a solution for a problem they are facing, a new AI digital product idea, or a solution to expand their business.

So, the AI development partner will study the requirements in this stage of the AI development life cycle. That's why you need an AI development partner with top AI development consultancy services. Key major activities of this stage are:

• Define use cases
• Determine KPIs
• Assess feasibility
• Identify ROI

2. Data Collection & cleaning

Once you know what the expected outcomes are, it's time to set the foundation of your AI development. Data is the foundation of the AI software development life cycle, and you need to arrange it properly. The quality and management of data are important in deciding the success of your AI development life cycle.

Especially, Generative AI models require huge datasets, and it is important for generative AI in the software development life cycle. Even the most advanced AI models fail if the data management fails. The AI development services provider you hire must have data management and cleaning efficiency.

The data collection, cleaning, and management tasks include:

• Allocating all sources
• Collect raw data from all sources
• Explore data patterns
• Identify missing data
• Fix inconsistent values
• Understand the factors that decide outputs
• Arrange shattered data
• Data cleaning
• Data labeling

This stage of the AI model development life cycle prepares the whole dataset for AI development and model training. Since models learn directly from these data, you need to consider this stage of the AI development life cycle as crucial.

3. Model Selection & Design

Now comes the selection of the model and design of your AI system. The right AI architecture and design can determine the product's success. If you want to make this stage of the AI software development life cycle easy, you need to partner with an AI development company with multiple industry project experiences. So they can help you in:

• Selection of a model
• Deciding the design
• Selection of ML/DL algorithms
• Choosing LLMs or generative AI frameworks
• Designing neural network layers
• Picking performance metrics

For example, if a business wants to develop generative AI models, they need to collaborate with a generative AI development agency that follows generative AI in software development life cycle and choose transformer architectures, LLMs, or diffusion models.

4. Core Development

Now we enter the core development stage of the AI software development life cycle. Here, experienced AI developers design and develop the AI system based on the choices you made in the last step. Sticking to all the factors you have decided on is the key to success at this stage. The major activities of the core AI development stage are:

• Code writing
• UI building
• API development
• DB integration
• Bug fixing
• Feature integration
• Performance tuning

In core development, as a business with a future vision, you need to make sure that the design and architecture are scalable. This AI development life cycle stage will be smooth if you hire AI developers with proficiency in all AI models.

5. Model Training & Testing

Model training and model testing are the major pillars of the whole AI software development life cycle. Here, the data you have collected and cleaned will be fed to the AI model you have developed. By this, you can train the model so that it brings the expected outcomes.

In this AI model training stage, the performance of the AI model will be evaluated and optimized by running it in all possible real-life situations. The model training includes:

• Parameter tuning
• Hyperparameter optimization
• GPU/TPU-powered training
• Avoiding overfitting & underfitting

Before deployment, the model must be tested to keep it 100% perfect. This testing stage eliminates all the possibilities of rework after launch and makes the AI software development life cycle smooth. The AI model testing includes:

• Accuracy checking
• Bias & fairness testing
• Stress testing
• Real-world simulation

No matter how much time you spend in this training and testing stage of the software development life cycle. But you need to conduct this stage more carefully and prepare the AI model for a perfect launch.

6. Deployment & Integration

Once you have validated your AI model in all the possible real-life scenarios and you feel satisfied with its performance, you can deploy the AI model to your real business systems. If you hire dedicated AI integration services, they will analyse the integration and deployment possibilities in the initial stage itself.

If they feel your legacy systems are outdated and not capable of smooth integration, they may suggest system upgradation. So it is always a wise idea to choose a complete software development and modernization company with AI development expertise. For example, if you want to develop an AI chatbot, the AI development team will analyse your systems and see the integration choices. This integration or deployment can be:

• On-premises
• Cloud-based
• Edge-based

Integration and deployment have to be done with 100% accuracy. Especially in the case of generative AI apps, such as chatbots or content automation AI systems, strategic deployment is part of generative AI in software development life cycle.

7. Monitoring, Maintenance & Improvement

As the name suggests, AI development is a cyclic process, where you need to work on your AI model even after the launch. You know the trends in AI solutions are changing day by day, and all businesses are in a race to adopt it first. So the AI models you develop may need changes over time.

That's why proper monitoring and performance optimization are important. This stage in the AI model development life cycle is a long-term process, including:

• Tracking accuracy
• Detecting data drift
• Retraining models
• Updating infrastructure

In the long run, you can't skip this process. If you want to make this stage of the AI development life cycle easier and cost-effective, always choose a partner who delivers AI maintenance and support services.

Best Practices for the AI Development Life Cycle

Now you know the steps of the AI development life cycle. But this is not enough to succeed in the AI development journey. There are some proven best practices to make the AI development life cycle smooth and bring maximum outcomes. When you follow the best practices in each step of the AI development life cycle, the risk will be minimal, the cost will be minimal, and the results will be maximum.

Only experienced AI development agencies know these best practices. Because it's beyond some written theories and comes out of practical experience in developing AI solutions for business in various industries. As a leading AI development agency, Fulminous Software is revealing the best practices for the AI development life cycle:

• Always move with the guidance and assistance of a leading AI development company.
• You need to choose high-quality and unbiased datasets, which will bring you accurate and fair outcomes.
• If you set up a proper MLOps pipeline, it automates deployment and model updates.
• Always prioritize transparency and ethical AI, which is essential to building trust by following responsible practices.
• Don't miss continuous retraining and optimization of the AI model; this keeps models always effective.
• Analyse the adaptability of your SDLC; if it is outdated, make it capable of proper AI integration.
• Make sure that your AI model architecture and design are scalable architectures that support growth and eliminate the need for rework.

Best Practices for the AI Development Life Cycle and Cost Reduction

When you follow best practices across the AI development life cycle will save a great amount of money by avoiding misaligned expectations and useless experimentation. By creating clear goals, KPIs, and data needs before development begins, teams save money spent on development time wasted due to unclear project direction. Effective data management and MLOps best practices also reduce costs by automating burdensome tasks, like data cleaning, model retraining, and performance monitoring.

Automation eliminates manual work, which leads to fewer FTE’s and saves in terms of costly performance degradation resulting from not maintaining or updating models. If you add AI early into system design and adopt disciplined development processes, these challenges of integration, rework/common cause correction cycle time, and operational failures can be minimized. This avoids expensive delays in deployment and ensures that the AI system will scale without massive hardware changes in the future.

If you follow these practices, you can have an effective AI software development life cycle, and the AI systems will be the best. You can get the services of top-notch AI development companies like Fulminous Software to follow these best practices and get advanced AI solutions.

Mapping AI in the Software Development Life Cycle

Mapping AI in the software development life cycle, making sure that the AI components fulfill business goals, technical requirements, and system architecture. It enables teams to see where AI provides value, how it fits within existing workflows, and what is necessary for effective solution delivery.

By laying out AI in the SDLC, it’s possible for companies to specify at what exact points AI-based automation, predictions, or intelligent features would function. This disciplined method will help in:

• Cost reduction
• Time saving
• Avoid misunderstandings
• Minimize rework
• Easy shifting from established development practices

Mapping AI in the SDLC also helps to plan data pipelines, model training and testing, deployment, and monitoring realistically. And it makes sure that AI models aren't treated in isolation, but as integrated components that operate in coordination with APIs, backend systems, and end-user interfaces. By correctly positioning AI on the software development life cycle (SDLC) map, organizations benefit from better quality control, more scalable solutions, and long-term model performance.

How Generative AI Fits Into the Software Development Life Cycle

The rise of generative AI in software development life cycle is helping industries in building effective software. Generative AI in software development lifecycle cuts down the development time and costs by automating several tasks. Generative AI improves SDLC by:

• speeding up code generation
• automating test case creation
• generating synthetic data
• enabling intelligent chat-driven development
• simplifying documentation

So if you are looking to develop software for your business, generative AI in software development life cycle can bring plenty of advantages. Fulminous Software is a top generative AI development company, and we know how to implement generative AI in software development life cycle.

Fulminous Software: The Best AI Development Life Cycle Partner

Another thing that decides the AI development project's success is who is accompanying you in the AI development life cycle. Only a leading AI development company with a complete AI solution can assist you as a perfect AI development life cycle partner. Do you know why businesses choose Fulminous Software as their AI development life cycle partner?

• Knowledge: Fulminous Software is well aware of the AI development life cycle, and we strictly follow it.
• Experience: Fulminous Software is a trusted AI development company with experience in assisting more than 90+ businesses in their AI software development life cycle and delivering the best outcomes.
• Complete solutions: Our AI development team will stay with you throughout the AI model development life cycle, like a perfect partner from ideation to long-term support.
• Diverse Services: Fulminous Software is proficient in the following specific development life cycles of AI chatbots, AI agents, AI models, AI apps, and AI software.
• Problem-solving: Our AI development team knows all the challenges in the AI development life cycle and is skilled to overcome all of them.
• Affordability: Fulminous Software always offers affordable prices by strictly following this AI software development life cycle and stage-wise budgeting.
• Transparency: In all stages of the AI development cycle, the service, pricing, and communication of Fulminous Software will be transparent.
• Customization: Fulminous Software is capable of customizing ai systems for industry-specific results.

Fulminous Software is all set to serve you throughout the AI development life cycle with 100% dedication. Explore more about our AI development services now!

Conclusion

In short, the AI development life cycle is far different from a normal software development project, and following the AI development life cycle is the only way to the success of an AI development project. Knowing and following the AI development life cycle is all about moving your AI development project with a clear vision, milestones, and route maps.

So no more AI project failures for those who stick to this proper AI development process. If you follow this proven AI software development life cycle, the risk will be minimal, and the result will be maximum. But you need an experienced guide to accompany you in this whole AI software development life cycle.

Fulminous Software is a leading AI development company that follows the AI software development life cycle and delivers the best AI solutions. Our AI development team is well aware of the AI software development life cycle, mapping AI in the software development life cycle, and generative AI in software development life cycle. No matter whether you want to develop an AI chatbot, an AI agent, a custom AI model, or an AI-powered software, Fulinous software is here to help you. Contact us now and get the best AI systems.

FAQs

Q1. What is the AI development life cycle?

The AI development life cycle is the process used to build AI solutions. When businesses follow the AI development life cycle correctly, they get the best AI systems.

Q2. How is the AI software development life cycle different from traditional SDLC?

The AI software development life cycle differs from traditional SDLC because it focuses heavily on data quality, iterative training, and continuous monitoring.

Q3. What does the AI model development life cycle include?

The AI model development life cycle includes data collection, feature engineering, model selection, training, evaluation, and retraining.

Q4. How does the AI product development life cycle help businesses?

The AI product development life cycle helps businesses transform ideas into fully functional AI-driven products. By following the AI product development life cycle, companies can make better AI products than their rivals.

Q5. How does mapping AI in the software development life cycle improve project success?

Mapping AI in the software development life cycle ensures AI components fit current architectures and business workflows. When businesses focus on mapping AI in the software development life cycle, they eliminate project risk.

Q6. What role does generative AI play in the software development life cycle?

Generative AI in software development life cycle accelerates software development by automating various stages, and companies are using generative AI in software development life cycle to build software faster.