How to Build AI Software: A Step-by-Step Guide [Updated 2024]

Wondering how to build AI software? You are one step away from leaving your competitors behind. Creating a machine capable of generating human-like behavior and speech opens up huge prospects for large and small businesses across all industries - from manufacturing and e-commerce to education, healthcare, and the arts. Robots, chatbots, and virtual assistants are just some of the ways companies are embracing artificial intelligence.

In this article, we'll cover how to create AI software solutions tailored to your business needs, and thus get the most out of natural language processing, neural networks, computer vision, and many other AI-related technologies.

It's time to invest in artificial intelligence. If you've been looking for a sign, this is it. Stay tuned.

What is Artificial Intelligence?

Artificial Intelligence (AI) is a branch of computer science that deals with cognitive tasks such as pattern recognition, learning, and generating human-like responses.

AI encompasses many disciplines, including data analysis, computer science, statistics, software engineering, neuroscience, psychology, and - perhaps not obviously - philosophy.

Artificial intelligence (AI) can perform tasks normally requiring human intelligence. These include speech recognition, visual perception, text and speech translation, and decision-making. One of the great things about AI is that it produces more with less.

Artificial Intelligence is, first and foremost, a variety of algorithms:

• Machine Learning
• Deep Learning
• Natural Language Processing
• Neural Networks
• Cognitive Computing
• Data Science
• Big Data
• Computer Vision

Natural Language Processing (NLP) is undergoing rapid development. By manipulating data, NLP helps transform human language into information computers can read and interpret. To do this, NLP mixes computational linguistics, machine learning, and deep learning models. The NLP market is projected to grow approximately 14 times between 2017 and 2025.

One of the most prominent examples of AI applications is GPTs. GPTs stands for Generative Pre-trained Transformers. These are machine learning algorithms that react to input data and transform it into human-like text. The most well-known GPTs today are GPT-3 and GPT-4. GPT-3 is a large language model processing text, while GPT-4 is a large multimodal model processing not only text data but also images.

As a huge artificial intelligence model, GPT-4 is already widely used in business, as it processes 8 times more words than its predecessors and is so good at understanding text and images that it can produce websites based on images alone.

This is what AI-powered natural language processing tools look like in a nutshell. Now let's move on to an overview of the AI ​​software development landscape.

AI Software Development in 2024 [Market Overview]

Let's skip the long-winded sections like "Can I create my own AI?" or "How can I create my own AI products?" Instead, let's get straight to the point.

The global AI market is growing and is expected to reach $826 billion by 2030:

Other statistics confirm that AI continues to be widely used in various industries, and this trend will continue in the coming years. From 2023 to 2030, we can observe an annual market growth of about 37%.

One more piece of data is worth noting. As stated, the AI market size is poised to grow to $1,591.03 in 2030.

It seems that we have a huge potential for AI software solutions. So, it's time to take a look at how AI is being applied.

AI for business needs

Companies are increasingly confident that AI has the potential to transform business operations. In particular, 64% of companies expect AI to help increase their overall output. At the same time, 25% of companies are turning to AI product development due to a lack of human resources.

Let's take a look at what else is driving companies to use or develop AI software:

• AI tools are becoming more accessible to the average user, easy to use, yet effective;
• AI is now a standard feature of modern business systems;
• The need to reduce operational costs and speed up workflows.

At the same time, the development and implementation of AI go hand in hand with the retraining of employees within the company. This is the case for nearly 40% of the business representatives surveyed.

Some numbers to consider as well:

• 91.5% of market leaders are continuously investing in AI.
• 35% of companies are implementing AI, and 42% of respondents are considering implementing AI in the future.
• 72% of executives believe Artificial Intelligence will be the most significant business advantage in the future.

Now let's look at how businesses use AI in general:

Undoubtedly, this list is growing almost daily given the pace of AI adoption.

But what about how developers themselves view AI and how this technology is expected to be applied in this context?

AI software development trends, facts and figures

When it comes to trends in the AI software development market, engineers cite GenAI (40%) and AI assistants for software development (38%) as important industry trends.

Interestingly, developers are overwhelmingly confident (65%) that AI is having a positive impact on their work:

• 61% say AI makes their jobs easier;
• 55% say AI allows them to spend time on more important work.

However, 23% see AI as a threat to their jobs.

Below are some of the types of tasks where developers are using AI:

• Coding (33%);
• Writing documentation (29%);
• Research (28%);
• Creating tests (23%);
• Troubleshooting/debugging (21%).

As for the technologies used to build AI software, these include ChatGPT, GitHub Copilot, Bard, and many others.

How to create Artificial Intelligence Software: 6 key steps

The process of creating AI software is somewhat different from standard software development. To the standard stages of research, design, idea testing, development, support, and other steps, one more should be added - the AI creation stage.

In general terms, this path can be outlined by the following tasks: defining the problem, choosing a software development company, selecting a technology stack, building an AI algorithm, creating an AI mobile application or web-based system, implementing the product, and maintaining it.

However, let's go through this process step by step.

Step 1. Discovery stage

This stage cannot be avoided, regardless of whether you decide to create AI software or any other software. The main idea is to align the client's business goals with the intentions of the AI program being created.

At this stage, you need to

• Define the problem
• Select a software development company
• Agree on business goals
• Describe the upcoming MVP
• Set up a project roadmap
• Choose a model of cooperation with an IT outsourcing company (the Staff Augmentation model has proven to be a good one)
• Decide on the technology stack for web or mobile development that you may need
• At least roughly estimate software development time.

Before we dive into building your own AI, we'd like to point out that it's perfectly fine to set overly broad goals when planning AI software development. It's up to your partner, i.e. the offshore team you choose to create an AI software solution, to do their best to make these goals clear, tangible, and achievable.

Step 2. Proof of Concept (PoC) stage

This stage is an additional one if you are going to create traction-promising AI software. It is the backbone of machine learning platform development. At this stage, you need to build an AI algorithm and test the selected AI models. How to do it? Train your own AI by providing it with relevant data and then watch the results.

Given that AI is a set of smart algorithms, we can expect such expert systems to self-select the most appropriate way to process tasks, switching from a less suitable to a more suitable option.

If it turns out that the AI program being created fails to work properly, give up on its further development. Creating software from scratch associated with technologies such as deep learning, natural language processing, neural networks, robotics, etc. is too expensive to tolerate a mistake at the very beginning of the software development process.

Step 3. AI software prototype development stage

What makes an AI software solution great is its design. In the case of an AI system, not the entire design is created at once, but only its key screens and features.

This is done so that designers and engineers can test the design idea step by step, interact with users, and then make the necessary adjustments to the interface to ensure the system is as user-friendly as possible. This is a perfect match for creating an employee management system, be it ATS, ERP, or even CRM.

By the way, the incremental design creation for AI applications can save the customer's budget. So, when you start thinking about how to create AI software at affordable prices, think about what we have just discussed.

Step 4: Artificial Intelligence platform coding stage

When the AI algorithms and design are in place, the only thing left is the development phase. So please fasten your seat belts. It may seem that way. However, take your time.

The coding process will go like clockwork if you have chosen the right technology stack, hired the proper engineers, trained the AI algorithms, and selected all the necessary components to equip your soon-to-be artificial intelligence system. Good job! Still, consider some of the following points.

Project management approach

Let it be an agile project management method, as it provides transparency and flexibility in the AI development process. With an agile approach, your software development team with AI expertise can easily make changes to the product at any time as needed.

An artificial intelligence system from scratch

If you are looking to get a fine-tuned AI platform developed from scratch, you need to keep in mind that engineers may need to create neural networks or train AI models properly. This may take additional time, so it is worth clarifying this point in advance to avoid embarrassing surprises.

Ready-made customized components

As discussed earlier, the MVP of an upcoming AI system is initially developed with only key features. Non-key features may be covered by off-the-shelf options. This will save time and budget. Instead, data mining experts can focus their efforts on training AI models - the core of your software solution.

Step 5. Deployment and testing stage

This is the pre-final stage, where the last errors are caught and the ability of the AI system to withstand the flow of users is checked. For this purpose, both manual and automated testing is carried out in a real environment.

Step 6: Get the Artificial Intelligence program up and running

To get your own artificial intelligence system up and running, it is best to hire industry-specific software developers. They know how to get your app on Google Play and the App Store, if necessary, and make any AI, machine learning, or computer vision-related platform available to users on the go.

Once an AI app or AI-powered employee management system has been released, it's worth continuing to update it, monitor its effectiveness, and respond to any unexpected user behavior patterns.

An experienced offshore (IT outsourcing) team of software developers will ensure a continuous process of AI product maintenance. The technologies they deal with must include deep learning, natural language processing, neural networks, automation, cognitive computing, data science, robotics, and other specialized technologies. With such expertise, training or building an AI model will not be a challenge for them, but rather a matter of daily routine.

So, if you're on your way to recruitment software development or thinking about how to build a SaaS system based on AI solutions, hire developers with proven AI expertise.

Use Cases of AI Software Development

It's not enough to just talk about why we should be building AI software in 2024, because we need to look at specific examples of AI software for specific industries. So let's do just that.

AI in Recruiting

Artificial intelligence in recruiting usually refers to the application of AI technologies (most often machine learning and natural language processing) in the form of a comprehensive and multi-functional AI-based recruiting platform. There are very specific reasons for this.

First of all, it makes sense to create AI recruiting software if your team is faced with the need to sort through a bunch of applications every day.

Manual sorting is time-consuming but even worse, fatigue leads to inattention or missing out on truly valuable candidates. Artificial intelligence automates the selection process and quickly finds qualified candidates.

In addition, human bias in hiring decisions is still a sore point. Unlike humans, AI applies objective criteria to evaluate candidates, promoting fairness and increasing diversity in organizations.

AI ensures an exact match between job requirements and the experience of a potential specialist. This allows recruiters to hire people with the right skills with less effort.

And of course, let's not forget that the most common application of AI in recruiting is the use of chatbots. They respond quickly to candidate inquiries and maintain a dialogue that lets candidates know they are seen and valued. This communication can take place around the clock, helping to increase candidate satisfaction and reinforce a positive employer brand.

The company as a whole will benefit from this innovation because:

• The team is replenished with excellent specialists (considering how difficult it is to find qualified personnel in the market, this is a convincing argument, isn't it?)
• AI reduces job posting-to-hiring time, which means urgent work issues are resolved faster and client projects are implemented as expected.
• The work environment becomes more inclusive and friendly, which meets the current needs of society.
• You will save money.

It seems that AI in recruiting is already irreplaceable.

AI in Elearning

How is AI being used in e-learning? Here are a few ways AI is improving the quality of online education:

• AI can be used to create interactive language tutors that provide feedback on pronunciation and grammar.
• Some virtual tutors provide guidance and answer questions, thus offering students support.
• Synthetic speech technology can be used to convert text into natural speech, which is useful for audio lessons.
• Simulators and interactive materials are also essential because they help students practice skills in realistic scenarios.
• When programming or mastering specific skills, AI recommends exercises based on the learner's characteristics, providing personalized learning.

AI can also adapt learning content based on student performance, helping to improve learning outcomes and automate grading, saving time.

The use of AI in e-learning makes education more effective by meeting the learning needs of both students and instructors.

AI in Fintech

Let's find out how artificial intelligence technology is being applied in the fintech industry, and thereby answer the question of why you should create your own AI software.

Data analytics

Leverage big data analytics to gain deeper insights into customer behavior, market trends, and risks. Machine learning algorithms help. They sift through massive data sets to identify patterns, predict outcomes, and facilitate data-driven decision-making.

Digital banking

With AI capabilities, a traditional physical bank is no longer an absolute necessity - it can be accessed from a smartphone. Users can manage their accounts, resolve tax issues, and get support from banking experts from the comfort of their own homes. All of this not only increases customer engagement and satisfaction but also reduces operational and maintenance costs for the financial institution itself.

Robotic consultants

Artificial intelligence-based robotic consultants are employed to automate and simplify routine office tasks while human consultants handle more important matters. Robotic consultants can also be helpful for more specialized tasks, such as predicting financial risk or investment profitability.

Identifying and preventing fraud

Artificial Intelligence can detect fraud instantly because it is designed to recognize patterns and respond to unexpected user behavior. For the banking industry, this AI capability is particularly useful because digital transactions occur almost every second, making the system vulnerable to unauthorized actions and fraud. Therefore, creating AI programs for the financial industry makes sense, at least from the perspective of improving cybersecurity. Interestingly, AI algorithms can work around the clock, monitoring the slightest suspicious activity, while bank specialists can deal with higher-order tasks.

Regulatory Technology (RegTech)

Regulatory compliance and the preparation of documents for inspections are often time-consuming tasks for financial institutions. AI software automates these tasks and reduces the risk of human error.

Automated regulatory compliance software can now be considered a must-have in the fintech arena. RegTech includes everything from regulatory advice provided by chatbots to automated compliance data management and optimized regulatory data processing.

Credit scoring and risk assessment

One of the reasons financial organizations may want to create artificial intelligence software is to cover credit scoring issues for customers. Artificial intelligence models assess a particular customer's financial capabilities, credit history, and risk factors fairly accurately, allowing a financial organization to decide whether or not to grant or deny credit to customers.

AI in Healthcare

Now, let's explore five powerful examples of how artificial intelligence is being used in healthcare, where it's proving particularly useful.

Medical image analysis

An important part of improving patient outcomes is analyzing medical images, such as MRIs or X-rays. By using AI to analyze these images, physicians can more quickly and accurately identify diseases when their signs are seen in the images.

Personalized treatment plans

The beauty of using AI in healthcare is that the technology relies on the patient's data and creates very detailed treatment plans based on it. AI can also predict the body's response to drugs or treatments and optimize patient care accordingly.

Remote Patient Monitoring and Telemedicine

In addition to personalized treatment plans, AI enables remote monitoring of patients without the need for hospitalization. With AI-powered wearable devices worn by patients, healthcare providers can monitor their health in real-time and provide immediate access to medical care when needed.

Drug discovery

AI can be leveraged to accelerate the discovery or creation of new, more effective drugs by easily analyzing large amounts of biological and chemical data. This helps to discover potential drug candidates, predict their efficacy, and accelerate drug development.

Predictive analytics to improve healthcare quality

No more guesswork when it comes to predicting disease and treatment outcomes. AI makes highly accurate predictions by analyzing all available patient data. With this feature of AI technology, healthcare providers and entire facilities can optimize treatments and surgeries and improve patient care.

Challenges in AI Software Development

When it comes to building artificial intelligence software, it's not just the disruptive benefits of this technology that come to the fore. There are also very specific challenges that those who choose to build AI software must face. Below, we discuss the key challenges and ways to overcome them.

Ethical issues

Can you create your own artificial intelligence product? Of course, you can. AI integration is no longer just a hot topic, it is a necessity. However, you need to be careful about how artificial intelligence will handle the personal data of your users (your customers or patients).

This is where you need solid legal support and awareness, as you are dealing with sensitive issues such as privacy, transparency, possible further use of the data, and the unintended consequences this may lead to, etc.

High resource requirements for training AI models

In AI training, large amounts of data are first integrated into the algorithm, and then the parameters are iteratively adjusted to improve the performance of the AI model. This is called tuning.

This process of building and training an AI model has both advantages and disadvantages. On the one hand, it enriches AI models with valuable data. On the other hand, this learning process can be too costly for organizations because it requires valuable expertise and time.

AI's limited understanding of code it generates

We should not treat artificial intelligence as a human being. It is a technology that learns from human experience and knowledge, but it cannot guarantee that the code it writes will be of high quality and that AI products will be free of security issues. AI is also incapable of thinking ahead and scaling, so if you decide to develop AI software, you can't do it without human intervention.

It's up to the experts to check code quality and ensure data security to ensure that the data needed to build an AI system is secure, of high quality, and generally compliant with industry standards.

Potential for errors and damage

There can be a misconception in society that artificial intelligence is smarter than humans, doesn't get tired, and doesn't make mistakes because it's based on automating processes, and automation seems to eliminate human error. Yes, that makes sense. But technology also makes mistakes, such as providing inaccurate or incomplete information, making biased judgments, and acting on human cues that may be wrong. How do we solve this problem? Humans should monitor what AI generates and verify the information it receives for accuracy and reliability because the cost of error can be too high.

Integration with existing systems

Another challenge comes to the fore: integrating AI software with other systems that make up the business ecosystem and ensure the smooth operation of your organization. As it turns out, it's not enough to develop an AI application or web solution, you also need to integrate that product into the existing system.

This is a challenge because you need to resolve compatibility issues, ensure uninterrupted communication between AI and other system components, solve problems with different data formats, and so on.

The existing architecture can undergo significant changes. However, if the integration is handled by experienced specialists, the process will be much less complicated.

Shortage of AI software development expertise

If you are interested in building your own AI software, you should first look for experienced AI developers who know how to train AI properly. According to the statistics above, there is currently a shortage of such specialists, as companies are in high demand for such developers. So, get ready to face the challenging path of finding and hiring AI developers.

However, perhaps our tips on how to create your own AI software with the expertise of skilled AI developers described below will help you overcome this challenge somehow.

What are the Business Benefits of adopting AI Technology?

Are you thinking about building AI software? Or maybe you're wondering how to create a SaaS system enriched with AI models. If so, you should definitely take a look at the key business benefits you can get from implementing AI-powered cognitive computing systems.

So, building AI software or using data science in areas where you think it is warranted means gaining the following business benefits:

• New sales channels and business opportunities
• Better decision-making process
• Increased customer loyalty
• Increased productivity and efficiency due to reduced workload
• Manual work automation and reduced risk of human error
• Simplified and faster processing of big data
• Decision-making based on ML and AI, i.e. more accurate and faster data
• Highly accurate predictions of potential risks and benefits at your fingertips
• Small budgets.

Remember that the benefits you can expect depend solely on the goals you pursue by implementing AI, neural networks, machine learning, etc. in your business processes.

Now let's move on to creating Artificial Intelligence software.

Things to Know before Creating an AI System for your Business

Data is key

Artificial Intelligence is data-driven. This is a multi-level structure of algorithms aimed at making decisions based on available data and without human intervention. Planning is therefore crucial.

You have to decide:

• What data you will use
• Where to get this data from
• How to clean the data
• How to categorize it, etc.

Data collection and processing is the cornerstone of the project team's work. Professionals involved in the creation of artificial intelligence programs must be proficient in AI methods, ensuring workflow consistency and expected outcomes.

AI model should be trained on customer data

Developers contributing to your AI system can achieve great results by training the AI model on their own data. However, the product is aimed at achieving the client's goals, and therefore the AI model should be retrained on your data. This leads to the results you need as a client.

AI model should be processed separately

Developing an AI model separately from the main product has its advantages:

• Each part of the system develops at its own pace.
• Changes to the AI model are made apart from the rest of the product.
• Bad code or changes in the main product code do not affect AI.
• The main product and the AI model can be launched at different times. This is expected to speed up business results, as users will get the product earlier with AI model updates being released gradually.

The team should be well-versed in coding, testing, and AI

In addition to splitting the AI model from the core product, it's also worth knowing how well they function together. Therefore, the AI software development project team should include engineers, data scientists, data analysts, testers, and architects. And, of course, the project manager must also be equipped with all this knowledge.

AI system development takes more time

To train a neural network, you need to collect data, clean it, and label it. It can take up to several days. And this is only the preparation for training the AI model. Training a neural network can also take several days or weeks for each training cycle. This is the nature of creating AI-based software.

And last but not least.

Whereas a traditional software system can be easily customized and debugged as needed, deep learning remains something of a black box, as it sometimes delivers unexpected results. This is because the AI system functions differently. In this case, it is hardly possible to determine which sequences and combinations of data led to the result.

The remedy is to collaborate with experts who know how to create artificial intelligence systems, natural language processing programs, and deep learning systems, and then initiate controlled attempts to influence the artificial intelligence model.

Best practices for AI development

Although we've already covered what you need to know before building AI software for your business, there are a few other key points worth emphasizing. It won't be out of place.

• You should be clear about the problems you want AI to solve. At the same time, you should set specific and measurable goals to ensure that those problems are actually solved. All of this will keep your project aligned with business needs and its creation consistent and justified every step of the way.
• Make sure your data is high quality, clean, and relevant. If you do not have the expertise to do this yourself, hire specialists. Why pay so much attention to data? Because data, when it is of high quality, leads to accurate, secure, and reliable results.
• Choose suitable algorithms that meet your needs first. As a result, the AI in your software will be more productive and beneficial to your business, and you'll have fewer technical issues to deal with.
• To avoid legal issues and maintain user trust, consider ethics. AI is just a set of algorithms that must be trained and controlled.
• Speaking of AI training, it should be regularly updated with fresh and relevant data, as this will allow your AI product to be accurate and relevant, which will definitely have a good impact on the business.
• Start with an MVP to see what your AI product can do. Collecting and analyzing user feedback, testing the product, and running it through complex operations will help you mitigate potential risks and ensure system uptime. In addition, this consistency allows you to increase the capacity of the system as your business grows and scales.
• Expand as needed.

Sounds reasonable, doesn't it?

Hire AI Developers to create AI Software for your Business

Creating artificial intelligence software requires experience and industry knowledge. The DDI Development team has both. We will provide you with a team of experienced AI developers who will analyze your business niche, determine the most appropriate AI platform structure, estimate the time to develop an AI solution and work exactly as planned. At the same time, keep in mind that we are talking about experts:

• With a good command of English;
• Working according to the Agile project management methodology;
• Supporting clients throughout the entire AI development process.

Even if we are in different time zones, we will find a way to stay in touch, meet your needs, and keep you informed.

Bottom line

As artificial intelligence is significantly reshaping the business world, there are more and more reasons to build your own AI software. Are you wondering if you can build your own AI software? It is possible, but there are also obstacles you may face when trying to embrace this brand-new technology.

A tried and true option is to outsource the creation of new AI software to a software development team. Please note that an AI system requires the creation and training of an AI model, not just the development of a mobile app or web platform. Leave it to the experts.

Do you need a consultation? Contact us to learn all the ins and outs of AI software development.

FAQ

How can I make AI software?

To build AI software, you need to go through a certain process. First, there's the discovery phase, where you define the problem that the software you're building needs to solve and look for a software development team to bring the system idea to life. Next is the Proof of Concept (PoC) phase, where you (the experts you hire) train your AI. Then you move on to the prototype AI software development phase, the coding, deployment, and testing phase, after which you will have your AI software up and running. Here, in a nutshell, is the path you need to follow if you want to build AI software.

What are the different types of AI software?

AI software can be categorized in different ways, such as by function and industry. Here are a few types of software:

• Machine learning platforms (TensorFlow, PyTorch, etc.) used for predictive analytics in various industries;
• Natural language processing tools (e.g., Grammarly) to improve writing and communication;
• AI software for diagnostic analytics used in healthcare, etc.

What tools are commonly used for AI development?

Tools commonly used in AI development include Python, TensorFlow, PyTorch, NLTK, spaCy, OpenCV, Jupyter Notebooks, scikit-learn for machine learning, Keras for neural networks, Docker for containerization, and many others.

Will AI replace software engineers?

For now, this scenario is unlikely because AI still requires human oversight, control, and checking for errors, inaccuracies, or biases.

What is the future of AI software development?

The future of AI-enabled software development looks very promising, as evidenced by the statistics cited in the text. It seems that the entire software development industry will soon revolve around AI technologies, given their current high demand in business and society.