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 both 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 a few of the ways businesses can adapt 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?
Before we jump ahead, let's start with the basics. 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.
Natural Language Processing (NLP) is booming in its development. By manipulating data, NLP helps translate human language into information that computers can read and interpret.
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 the application of AI systems in business.
Artificial Intelligence for Business
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. To begin with, let's look at the benefits that an enterprise can get from implementing predictive modeling, data mining, neural networks, visual recognition, or any other AI-based technology.
Artificial Intelligence is widely used in businesses. Most often, companies use it to personalize and improve customer service, automate processes, increase productivity, and analyze data. And what AI undoubtedly provides to such companies is a competitive edge.
It is believed that the potential of AI software solutions for business is enormous. There is no doubt about it, as the development of AI technologies is rapidly gaining momentum. But what figures can we rely on to come to such conclusions?
Some figures are worth paying attention to:
- 91.5% of market leaders invest in AI on an ongoing basis.
- 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.
In addition, the AI market is on the rise, as evidenced by the following statistics:
- The global market size in 2022 was $119.78 billion;
- The market is expected to reach $1,597.1 billion by 2030;
- U.S. AI market share to be $190.61 billion by 2025.
Artificial Intelligence, Machine Learning, and Big Data software development are top priorities for many organizations. There are three main reasons for adopting AI technologies: changes in business strategy, modernization related to cloud technologies and data migration, and inflation and cost pressures.
What are the business benefits of adopting AI technology?
Are you considering 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
- Smaller 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.
Artificial Intelligence Software Development in 2023
AI software in healthcare
AI-based software solutions - applications, platforms, or integrated systems - are essential for managing finances and people, as well as managing large amounts of data. In turn, the healthcare sector is one of the most promising industries for the implementation of AI technologies due to the complexity and sensitivity of data and the need to process it in an orderly manner. Prosthetics, implantation, and robotics are just some of the expected applications of AI in healthcare.
Cognitive computing in manufacturing
AI-based software solutions in 2023 and beyond may be a way to solve the following problems:
- Shortage of skilled workers and the need to replace them;
- Automation of routine tasks so that skilled workers can handle high-level tasks.
Deep learning and neural networks as abstract matters of artificial intelligence application development can be applied as follows:
- Data collection
- Data loading
- Sparse data processing and analysis
Looking ahead, AI software solutions may replace workers in production, but this is unlikely in the case of highly skilled workers.
Predictive analytics challenges in business
The use of artificial intelligence in predictive analytics is likely to continue in 2023. Businesses need accurate AI-based analytics solutions to
- Achieve sustainability and accurate forecasting;
- Ensure real-time business monitoring;
- Manage risks and avoid crises.
The data analytics capabilities of AI-based software make it sought after in all industries, but especially in manufacturing.
Cognitive computing and computer vision are two areas of AI application in the year to come. Machine vision is already being implemented in solutions such as medical applications for the visually impaired and the development of self-driving cars. Artificial Intelligence models are expected to gradually become more complex and voluminous so that they can address even more sophisticated computer vision challenges.
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. In fact, 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.
Things to know before creating an AI system for your business
Data is the 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.
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.