Are you ready to use artificial intelligence (AI) to transform your business? Do you want to use AI for improved decision making? Or do you want to build an AI-powered product?
Definitely, you want to build an AI solution. As you see today, the global market of AI software spending is expected to hit $297.9 billion by 2027, as per Gartner’s report.
However, developing AI software goes through diverse complexities in the entire development roadmap. Whether you want to automate tasks, improve user experiences, or make data-driven decisions, developing AI software for any of these goals isn’t an easy process.
The entire AI software development process includes multiple stages, from setting clear objectives and preparing data to selecting algorithms and deploying models. And, each of these stages has different hurdles to take care of.
But don’t worry. We have got your back. This is why we have written this blog to help you know the complete AI development process.
As AI continues to reshape industries, understanding the key development steps will help you resolve the complexities while developing robust AI solutions.
At Rushkar Technology, we believe to move with the pace of the latest technologies. As a reliable AI development company, we have experience in building AI software solutions customized to meet your business needs.
Let’s go ahead.
Why is Investing in Artificial Technology AI Important?
Today, artificial intelligence is a transformative technology that helps businesses to leverage AI as much as possible.
Like leading organizations, Netflix, Meta, Google, Appel, Nvidia, and Amazon are making heavy investments in AI technology. Developing powerful AI solutions helps these organizations improve product innovation, productivity, and overall business operations. Why?
The simple answer is AI allows businesses to automate routine tasks, analyze large datasets, and make more informed decisions. Let’s understand this point with an example.
For example, AI technology can improve customer support via chatbots, personalize marketing campaigns, and predict market trends.
According to a report by Gartner, global AI software spending is projected to grow to $297.9 billion by 2027. And, over the next five years, market growth will grow from 17.8% to 20.4% in 2027, along with a 19.1% CAGR.
This level of growth is due to the adoption of AI in multiple sections, including healthcare, finance, retail, logistics, and more. As a founder, you would have noted that the potential return on investment in AI software is substantial.
Organizations that integrate AI into their operations can achieve cost savings, improve customer satisfaction, and gain a competitive edge. Nonetheless, the journey to building AI software requires careful planning and execution.
Now, you might wonder about the process of developing AI software solutions for your business or as a product to sell in the market, let’s check the following process.
How to Build AI Software: 7 Steps Process
Developing AI software is a complex process that includes multiple steps, from planning to executing and deployment. Below, let’s learn about the key stages that you need to follow to create an AI software solution.
1. Define Your Objective to Use AI
The first step in building AI software is to clearly define your objective. You can ask yourself these basic questions to define your objective.
- What problem are you trying to solve with AI?
- Are you looking to automate a specific process, enhance customer interactions, or improve decision-making?
Your objective will guide the entire development process, from selecting the right AI technologies to designing the software architecture. Understand this point with an example.
For instance, if your goal is to develop an AI-powered recommendation system for an e-commerce platform, your focus should be on algorithms that can analyze user behavior and suggest products accordingly.
As a result, you can use the power of data of your customers and provide suggestions or personalized offers within the eCommerce platform.
Why is it important to have a clear objective before proceeding to the development process?
Defining a clear objective also helps in setting expectations. This helps to measure the success of your AI software. The next step would be to collect and prepare data for its ultimate benefit.
2. Gather and Prepare Data
Data collection and preparation is an important step as AI systems rely on data. The quality of your AI software is connected to the quality of data you feed into the algorithms.
Identify the sources of data that are relevant to your objective. This could include customer data, transaction records, social media interactions, or sensor data, depending on your use case.
Once you have your data, the next step is to clean and preprocess it. The process of cleaning and preprocessing data includes:
- Removing duplicates
- Handling missing values
- Normalizing the data to ensure consistency
That’s why data preparation is often the most time-consuming part of AI development. However, it’s equally important for developing a reliable AI model.
Your next step after preprocessing data is selecting the proper AI algorithms.
3. Choose the Right AI Algorithms
Choosing AI algorithms is an important stage that defines the success of your software solution. There are different AI algorithms suited to different types of problems.
For instance, if you want to develop a predictive model, you might use machine learning algorithms like decision trees, support vector machines, or neural networks. If your objective is to understand natural language, natural language processing (NLP) techniques would be more appropriate.
Depending on your objective and the problem you want to solve, it’s important to choose the AI algorithm accordingly. This is where you need to experiment with multiple algorithms to see which one performs best on your data. The entire process of choosing AI algorithms is known as model selection.
Technically, at this stage, it involves training various models on your data, identifying the performance, and selecting the right AI model that delivers the best results.
4. Develop and Train the Model
Once you are done with selecting the appropriate algorithms, next develop and train your AI model. This step includes feeding your cleaned and prepared data, which we did in the last step, into the algorithm and allowing it to learn patterns and relationships.
During the training process, the model will adjust its parameters to minimize errors and improve accuracy. Training an AI model can be resource-intensive which requires heavy computational power and time.
That’s why it’s important to monitor the training process and make adjustments as required to avoid overfitting the models. Monitoring the training process helps you to know where the model performs on training data, and whether it provides results on unseen data or not.
5. Test and Validate the Model
After training, the next step is to test and validate your AI model. Testing involves running the model on a separate set of data that it hasn’t seen before to evaluate its performance. The goal is to ensure that the model generalizes well and produces accurate results in real-world scenarios.
Validation is an ongoing process that involves fine-tuning the model to improve its performance. The validation process includes:
- Adjusting the algorithm’s parameters
- Adding more data or refining the data preprocessing steps
It’s important to continue testing and validating the model until it meets your performance criteria.
6. Deploy the AI Software
Once your model has been trained, tested, and validated, it’s time to deploy the AI software. Deployment involves integrating the AI model into your existing systems or building a new application around it.
This stage requires close collaboration between your development team and IT department to ensure smooth integration and scalability.
It’s important to monitor the AI software after deployment to ensure it continues to perform well. Regular updates and maintenance may be required to address any issues that arise and to adapt the software to changing business needs.
7. Monitor and Optimize
Building AI software is not a one-time effort. After deployment, it’s crucial to continuously monitor the software’s performance and optimize it as needed. This might involve retraining the model with new data, adjusting algorithms, or updating the software to incorporate new features.
Monitoring also involves tracking key performance indicators (KPIs) to measure the software’s impact on your business. For example, if your AI software is designed to improve customer engagement, you might track metrics like customer satisfaction scores, conversion rates, or retention rates.
How Much Does It Cost to Develop AI Software?
The cost of AI software development varies from $5000 for simple AI chatbots to $500,000 for advanced applications. This cost depends on multiple factors such as the complexity of the AI model, the quality of data required, and the computational resources used. These elements significantly impact the overall budget.
