Why is the AI project cycle important?

The AI project cycle is a structural approach to building AI-based software solutions. It’s a step-by-step guide that helps to indicate the problems and challenges, collect and prepare all required data, choose the correct development models, and improve foresight, accuracy, and performance. Every stage in the AI project cycle is vital to developing a model that effectively solves business problems and tasks

What factors influence the success of AI model building?

Several factors influence the success of building an AI model. It starts with a clear business problem definition—without a specific goal, the model’s purpose becomes unclear. The quality of the data is equally important; structured, relevant, and clean datasets form the foundation for effective training. Success also depends on choosing the right methods and algorithms that match the nature of the problem and the available data. Thorough evaluation and testing help ensure the model performs reliably across different scenarios. After deployment, continuous tuning and validation are necessary to keep the model accurate and relevant. Beyond these core aspects, other important influences include the expertise of the development team, the availability of sufficient computational resources, and the speed at which the underlying data can be updated.

How is a model evaluation performed in an AI project?

The main evaluation criteria of AI models are: accuracy, precision, sensitivity, and F1 score. Also, it’s vital to check whether the model can solve predetermined business problems and challenges.

AI Project Cycle: A Comprehensive Guide to Stages and Benefits

AI is becoming an essential and indispensable part of global business. Over 92% of world enterprises will significantly increase their AI investments by 2028, including adjusting and creating AI tools for specific projects.

As a result, operational business processes undergo sustainable changes, leading to a transformation of project development procedures. Standard development processes don’t fit with artificial intelligence projects. Businesses must consider the dissimilarities and know precisely how to design and engage with AI-based software that works correctly, accurately, and without delays or unexpected expenses.

In this article, I’ll tell you how. Let’s dig in.

Exploring the AI Project Life Cycle

The AI project cycle is a framework for creating and implementing AI solutions to specific problems and tasks, and launching fast, accurate, and efficient web, mobile, and computer applications.

Each development stage is crucial because even slight mistakes and imprecision may lead to wholly inoperable and incorrect AI models. That’s why developers should use a clear, step-by-step roadmap: the AI project life cycle.

Key Stages of the AI Project Cycle

Let’s explore the key stages of the AI project cycle in detail. Developers need to consider these seven significant steps.

1. Defining the problem scope

It doesn’t matter what kind of project you’re trying to develop, or in what area. The first thing to do is to carefully identify all the problems, tasks, and challenges. Actually, it’s similar to a classical development process.

The main goal is to set specific and measurable objectives that the AI model must achieve. The most effective way to perform this is through the 4Ws problem canvas, a method of structuring and determining source data:

Who? Find the businesses or entrepreneurs affected by similar problems or struggling to solve similar tasks.
What? Perform a complex analysis of the problem. Why does it even exist? Gather as much data as possible to simplify the process of solving.
Where? Define conditions and locations where the problem arises. Is it local or global? Analyze patterns and trends that are key sources of the challenge, if any.
Why? Emphasize all benefits and advantages a company will receive when the problem is finally gone, from the average employee to the stakeholders.

By tackling these questions, problem scoping establishes a foundation that aligns the AI project with business objectives and stakeholder expectations.

2. Data сollection and acquisition

Any AI model needs a large amount of data to learn. Most importantly, the data must be clean, recent, relevant, and well-organized. Some AI models can learn by analyzing raw data, but their development is more expensive and time-consuming.

Properly acquired data is vital to AI model development. Sure, a small margin of error is acceptable even when high-quality learning datasets train a neural network. However, if the data quality is poor, the model may become completely unpredictable or, in other words, useless.

Designing an AI model requires the following steps to provide the necessary data quality:

Data collection. Every possible source of data is ok: surveys, market analytics, observational studies, manual research, APIs, CMS data, open web information, etc. The more, the better. However, narrowly specialized models can be trained with minimal chunks of data.
Data cleaning and structuring. The entire learning process depends on data quality. Raw data is valid only when training huge models working with natural languages like ChatGPT, but only clean information sets are needed for business purposes.
Feature picking means manually selecting the attributes for a standard reference dataset used for AI model initial training. It’s tedious, but getting an accurate neural network is inevitable.
Ensuring safety. Many AI models work with sensitive data, including clients’ data. So, the software must be highly secure to prevent data leaks. Businesses must also adhere to international laws and regulations like the GDPR.

3. Data exploration and preparation

Gathered data is not just numbers and letters. It’s crucial to reveal similarities, patterns, and insights hiding inside your datasets. Moreover, careful exploration may also indicate data anomalies and errors that must be cleared before this data is used for training AI models.

That’s how it’s done:

Revealing correlations. Developers may use specific software or do it manually. Well-marked data trends and patterns significantly simplify a neural network's initial training process.
Visualizing data. Infographics help highlight key insights by better displaying trends and patterns.
Solving errors and reducing anomalies. Even well-organized data has some inaccuracies. The primary goal here is to keep data integrity and deal with outliers.
Choosing the methods for further data modelling. There are dozens of different techniques used for designing and training AI models. Picking the most efficient one is vital for future neural network performance.

4. Building AI models

Here is the main stage of the AI cycle: iterative training. It’s the core of the AI project cycle.

Developers test different machine learning algorithms and datasets. To achieve high accuracy in model forecasting, they must optimize the training process as much as possible.

It means testing parameters and hyperparameters, such as training speed, packet size, number of hidden layers, activation function, etc. Different tasks may require various options to achieve the best possible results.

Sometimes, businesses design several separate AI models, where the first pre-trained AI utility is used to train the leading neural network. This approach may save time and resources, but mistakes and inaccuracies affect the final result even more than if a single AI-based model is used.

5. Model evaluation and validation

The evaluation stage is vital in the AI project cycle because it’s a way to test the performance and accuracy of a neural network. Developers use a separate dataset called testing data and measure the AI model's operability on the following criteria:

Accuracy. Developers measure the number of correct predictions and the maximum margin of error.
Precision. Calculating the proportion of false positive and false negative results is crucial because they significantly impact the AI model's overall functionality.
Sensitivity. The system's ability to detect all positive cases with no data lost. In most cases, designers measure the proportion of relevant items selected from the test dataset.
F1 score. Balancing the precision and sensitivity parameters, especially if the input data is unstructured and imbalanced.

In addition to technical specification testing, the AI model is checked to see whether it meets predetermined business goals.

If any of these testing stages show leaks in AI model performance, the model must be refined and retrained. Generally, developers use more structured and precise data for retraining, but sometimes, it means total rework, up to changing the model’s architecture or data processing methods.

6. Model deployment

Integrating the AI model into an application is a key point. That’s when a neural network is used to solve real business problems and tasks.

Developers must create an infrastructure to operate a neural network or add an AI model to an existing one. The infrastructure must be simple to use, clear, and fully functional, even for managers without any knowledge of how the AI model works.

The other key point is scalability. If needed, developers should be able to enhance the performance of a completed tool tens or even hundreds of times without major changes and additional resources.

7. Model maintenance and monitoring

In the final stage, developers must maintain and update the AI project functionality to provide accurate foresight based on new data.

There are three key points to keep a neural network functional:

Regular updates. To grant the required level of foresight accuracy, developers must refine and retrain an AI model when new data formats or specifications are used. Two primary updating methods are regular model updating with new data or reinforcement learning based on user feedback.
Checking performance. Constant monitoring of key performance indicators helps to detect functional problems in AI model operational processes. It’s crucial to notice issues that are hard to catch with the eye alone, such as gradual accuracy degradation.
Collecting feedback. Developers must be in direct contact with people who use AI projects to solve real business problems and tasks. The main goal is to make the entire software convenient to use and easy to understand, even after a brief onboarding process.

Significance of Each Stage in the AI Project Cycle

Each stage in the AI project cycle is vital for designing full-scale and efficient software that can solve business problems and tasks.

Defining problems determines a vector of research. Clear and structured datasets create the basis of the AI model. Model development allows creators to choose the best data processing methods and train a neural network to become fully functional when solving real business challenges. Its further evaluation and refinement increase foresight and accuracy and improve interaction with other tools. The deployment phase brings the AI model to its users, so it needs only occasional fine-tuning.

For example, that’s how it looks in our experience.

We helped our healthcare partner incorporate AI into an existing healthcare flow. This AI component streamlined the ECG analysis process, improving precision and patient outcomes.

The existing system had been developed over years of research and development, so the main challenge of AI model development was to perfectly fit it into the existing medical processes, protocols, and requirements.

This required a deep understanding of the existing system's architecture. Our team collaborated with medical experts to understand the complications and point out potential areas for integration.

Next, developers chose the best technical approach and integrated the designed AI model into the existing solution. At this stage, smooth integration was crucial to avoid affecting the standard medical workflow. We employed additional measures to protect sensitive patient data during the adoption and maintenance of the AI model. Developers implemented data anonymization techniques to remove personally identifiable information from the dataset used for training.

Techstack's approach to AI project cycling

After deploying the AI model into ECG analysis processes, we double-checked its capabilities to accurately detect abnormalities, classify different heart conditions, and provide timely and reliable results.

Advantages of a Well-Defined AI Project Cycle

Designing an AI model through a well-structured project life cycle has many benefits. Here are the most prominent ones:

Simpler development process. A transparent and straightforward life cycle helps to undergo all required steps to design an AI-based solution one by one, increasing the project’s prospects of success.
Risk mitigation. A well-tuned process of AI model designing helps significantly lower the risk of problems at all stages of AI-based software creation. For example, carefully cleaned and structured datasets grant a more efficient and precise training process, leading to fewer difficulties at the deployment stage.
Boosted team productivity. The AI project cycle helps set up the workflows, so every team member knows their responsibilities and roles in the development process. It helps to reduce the time required to create, tune, and deploy the solution.
High-quality AI solution. The clear project cycle greatly enhances the final performance of an AI model. As a result, the developed software solves business problems exactly as planned and requires fewer resources for maintenance and retraining.
Saved time and human effort. The AI project cycle helps to use resources wisely, so the development process becomes faster, cheaper, and less exhausting for the team.

An AI project cycle organizes the entire process of designing an AI model for a business from scratch. It is even more important because it allows developers to make the process more efficient, improve success rates, and spend fewer resources.

Common Challenges in the AI Project Cycle and Solutions

Developers often face difficulties when developing AI models for business needs. Even while using AI project cycles, it’s important to deal with them correctly, so I’ve gathered the most common of them into a table and indicated the ways to avoid their negative impact:

Key challanges and solutions in AI project cycle

Iterative Approach in AI Life Cycle

An AI project cycle is an iterative process. Designing the AI model perfectly from the first try is extremely hard. Therefore, its stages should be re-evaluated when developing complex solutions to achieve the best possible results.

For example, when creating an MVP, the AI model may be trained on a smaller dataset to save time. It helps to check the approach and ensure the computational models are correct. However, to solve real business problems and tasks, developers must retrain the model on a full-scale database.

Such iterations are essential on every development stage: when cleaning the data and preparing the databases, when training the model, when adopting it into the existing software system, or when collecting feedback from users and clients.

The AI project cycle is a transparent and straightforward plan for designing business solutions based on AI models from scratch. This systematic approach benefits both the development team and the client. As a result, the implemented AI model greatly simplifies the operational processes, saving lots of time and resources and allowing managers to achieve business goals faster and more easily.

AI Project Cycle: Stages, Benefits, and Challenges