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April 04, 2023

AI Development Process: What Should It Look Like?


Edwin Lisowski

CSO & Co-Founder

Reading time:

9 minutes

The impact of Artificial Intelligence (AI) is already evident in our daily lives. [1] [The technology has been integrated with a variety of products and services in an attempt to help us accomplish more in less time. A recent study shows that labor productivity in developed countries can increase by 40% in the next 15 years due to AI’s influence [2]. Therefore, it comes as no surprise that AI and Machine Learning (ML) technologies are also revolutionizing the software development process.

With the help of AI, developers can configure high-performance, safe, and reliable software programs faster and at a fraction of the cost.

Here is everything you need to know about how AI enhances the software development process.

Elements of Successful AI Development Process

With all the hype surrounding AI, it’s easy to jump into AI solutions without understanding what it takes to create a successful AI development project. Here are the three major parts involved in an AI development process:

Big data

Before embarking on an AI journey, it’s important to understand the importance of data management in an AI development process. To be clear, big data encompasses the essence of this process.

Therefore, businesses must be ready to work with data scientists, engineers, and other specialists to gather, clean, and analyze the available data. This way, the data is organized, stored, and used accordingly.

Notably, sorting and organizing your business data goes a long way in preventing issues like duplicates, null values, and conflicting sources.

Machine learning (ML)

This is a subset of AI that helps computer systems understand and improve further from past experiences without being programmed to do so. To learn, these computer systems are usually fed huge volumes of data which they use to identify various trends, make forecasts, and carry out various tasks.

It’s important to note that the quality and size of the dataset are vital for creating computer systems that carry out relevant tasks accurately and effectively.

For example, if you plan to build a machine learning system that can forecast home prices in a particular city, the training data should include a lot more than the size of the home. It should also include other primary factors such as the number of bedrooms, location, number of bathrooms, the kitchen layout, size of the garage, and many others.

Deep learning (DL)

Machine Learning (ML) usually relies on the available datasets to make decisions and carry out various tasks. Consequently, human actions are needed in circumstances when there is inadequate data or when an ML model is likely to make an error. This is where deep learning comes in.

Deep learning uses artificial neural systems to recognize, understand and predict vital human actions. Unlike machine learning, deep learning is capable of reflecting on past experiences and making better decisions. It can easily carry out tasks that come naturally to humans, like speech, pattern, and image recognition.

This makes deep learning best suited for applications like health care, autonomous driving, military operations, security, virtual assistants, fraud detection, and advertisement. [3]

Read more about Benefits of AI in Software Development and Product Building

AI Development Process: steps to follow

It’s evident that artificial intelligence has had a great impact on software development. AI solutions in speech recognition, fraud prevention, client support, computer vision, and big data have accelerated productivity in the software development space and enhanced customer experience. This makes AI software development processes more crucial for the creation of more efficient and secure software applications.

Here are the steps to follow for a successful AI software development process:

Successful AI software development process - graph

Define a use case

The most important aspect of any AI software development process is to solve a given problem. However, to do this, you ought to define the problem first. You can do this by using the customer’s perspective and pain point.

This way, you can tell what they’re looking for in a software application and come up with better ways to generate value for them. This will help you come up with a great product that resonates with your customers and drives up your company’s revenue.

Confirm data availability

After defining the problem, you intend to solve, the next step is to ensure that you have the necessary resources and processes to gather the data needed to conduct the required analysis.

Software development requires a significant amount of time to debate the available data. For this reason, ensure you use the most accurate dataset in the right amount. This enables the entire AI software development process to move smoothly.

Perform data exploration

Before you proceed to the programming part of your AI software development project, it’s important that you perform data exploration. As one of the first steps of data preparation, data exploration involves surveying and investigating available data before working with it.

Doing this helps validate and verify AI data assumptions, making it easier for you to determine if the available data accurately depict the events.

Outline a methodology for building and validation AI/ML models

For developers to approve their hypothesis and improve execution, they must run several tests to uncover errors and fix them in good time. Thus, the success of an AI or ML software development model requires extensive feature engineering. [4]

So, your AI software development project will benefit more from enlisting a subject matter expert than using an algorithm to interpret a methodology for building and validating an AI or ML model.

Pick between data-driven and model-driven AI

Data-driven AI aims to identify correct answers based on the quality of data it has seen and the training it has received. On the other hand, model-driven AI aims to improve its decision-making by creating new models and algorithmic manipulations.

Model-driven AI is better suited for software development because it encourages collaboration, enhances productivity, and delivers better applications.

Follow the key steps of AI software development

There are three steps you need to follow in the implementation of AI software development. They include:

Key steps of AI software development - infograph

  • Develop an AI strategy: It helps you focus on the core objectives you intend to achieve with AI.
  • Assess your AI readiness: Understanding and ensuring your AI readiness will help you decide the best way to implement AI, pick the best team for the job and deliver a profitable project.
  • Prepare a proof-of-concept: This is required to test whether the AI software development process is feasible and likely to be successful.


It might be interesting for you: Software Designing and AI – How To Make The Most Of It?

Ensure implementation of AI software development on the cloud

The time is gone when companies interested in AI/ML systems had to spend a lot of money on high-end servers to support the technology. With the help of cloud technology, people can satisfy their AI/ML computing needs without breaking the bank.

AI software development on the cloud

Test the AI software

Artificial intelligence has also changed how software development companies test their programs. Today developers can test their software products using AI-based technologies. This type of testing is usually smarter and more effective. With the help of AI-based testing, developers can test many software products simultaneously and market them faster.

Automate and unveil the AI software

Once the development team has built and tested the AI/ML-based software model, it can be unveiled for production. A great idea would be to start small and then increase the rollout of the model after receiving positive feedback from users.

Regularly retrain the AI software

After building AI-based software, the work doesn’t stop there. Unlike people, AI models are not capable of learning different things simultaneously. If an AI/ML model is trained to do one particular job, it will do that job only.

Therefore, if you want your AI software to perform other tasks, you must be ready to train it for those particular tasks. On that note, regularly retaining AI software is a crucial part of an AI software development process.

How AI improves software development

The following are the various ways in which AI technology simplifies software development and deployment process:

Produces precise estimates

As development teams continue to work on more software development projects together, they can easily predict a project’s budget and timeline. However, sometimes these forecasts are not accurate due to unforeseen external factors.

On the other hand, an AI model has the ability to learn from the data fed to it. Therefore, it can accurately estimate the budget and timeline for a software development project.

Strategic decision-making

Using the data fed to them, AI technologies can learn from the mistakes of failed software development projects and help with assessing the performance of current ones. This helps businesses to come up with better strategies to minimize the risks and make success an integral part of the project.

Increase speed and scale of software development

Most of the processes involved in software development, such as coding, testing, and bug fixing, are time-based. When done manually, these tasks can take days or even months to complete.

Fortunately, machine and deep learning technologies can help accelerate some of these processes, especially the testing of software products. Instead of quality assurance analysts running these tests manually, AI technologies can do it automatically and save time.

Better error management

When you feed data to an AI model, it can identify data errors and disparities. If these errors are fixed in good time, it means that the team doesn’t have to abandon the project midway to roll back. This helps save time and resources in the long run.

Final thoughts

AI is rapidly becoming an invaluable tool for many modern software companies. Developers can now incorporate AI solutions in many sections of their software development processes to save time and cut down operational costs. In the near future, AI will become a must-have for every software company that wants to compete on the global stage. Check out our AI consulting services to discover more.


[1] Wen AI Becomes Part of Our Daily Lives. URL:  Accessed March 26, 2023
[2] AI Productivity. URL:  Accessed March 26, 2023
[3] Application of Deep Learning. URL: Accessed March 26, 2023
[4] What is Feature Engineering. URL: March 26, 2023


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