How to build an app with AI: step-by-step guide 2024

Introduction

According to Forbes, AI is expected to contribute $15.7 trillion to the global economy by 2030. The greatest economic gains from AI will be in China which will receive a boost of 26% rise to GDP in 2030, followed by North America (14.5% boost), totaling $10.7 trillion and accounting for almost 70% of the global economic impact. Let’s have a look at the interactive map below:

Furthermore, the increasing demand for generative AI products has the potential to inject approximately $280 billion in new software revenue. This surge is fueled by specialized assistants, novel infrastructure products, and coding accelerators known as copilots. Major beneficiaries of this trend are anticipated to be companies such as Amazon Web Services, Microsoft, Google, and Nvidia, especially as enterprises continue to migrate more workloads to the public cloud. 

The surge in demand for AI/ML development in the whole world mirrors the fast digitization sweeping through key sectors like banking, finance, healthcare, automotive, and telecommunications.

Artificial Intelligence remains at the forefront of both business and daily life worldwide, witnessing a surge in popularity alongside the emergence of new trends like Conversational AI and Generative AI. In this article, we will try to show you how AI can be implemented in practice, how to create an app with AI, and how to build AI applications. Let’s start our journey!

What is Artificial Intelligence: components and structure

Artificial intelligence or AI, refers to the simulation of human intelligence in machines, enabling them to perform tasks that typically require human intelligence. These tasks include learning, reasoning, problem-solving, perception, and natural language processing (NLP).

AI uses other techniques such as machine learning, computer vision, natural language understanding, expert systems, and robotics, each with its own principles and methodologies for simulating human-like intelligence in machines. These techniques are often called the key elements of artificial intelligence. Let’s dive into them deeper.

Machine Learning

Machine Learning or ML, is one of the most common AI approaches. ML can be called an approach of AI that focuses on enabling machines to learn from data without being explicitly programmed. It can also be used while working with complex LLMs and LVMs. There are different types of machine learning algorithms, including supervised learning, unsupervised learning, and reinforcement learning, each suited to different types of tasks and data.

• Supervised learning involves training AI with labeled datasets to predict outputs. 
• Unsupervised learning uses unlabeled datasets, requiring AI to detect patterns and learn outputs from scratch. 
• Reinforcement learning operates by providing positive or negative rewards based on AI performance. This approach enables AI tools to learn which actions yield positive rewards and encourages them to perform specific actions.

As we may see from the AWS ML lifecycle architecture diagram, the model development phase includes training, tuning, and evaluation. The model deployment phase includes the staging environment for model validation for security and robustness. Monitoring is key in the timely detection and mitigation of drifts. Feedback loops across the ML lifecycle phases are key enablers for monitoring. 

Feature stores provide consistent and reusable features across ML development and deployment phases. The model registry enables version control and lineage tracking for model and data components.

Deep Learning

Deep learning is a part of machine learning that revolves around neural networks with many layers (hence "deep"), allowing them to learn representations of data at multiple levels of abstraction. These neural networks are inspired by the structure and function of the human brain.

Deep learning involves training neural networks with many layers to learn representations of data at various levels of abstraction. The process includes the following steps:

1. Input data is fed into the network.
2. Through forward propagation, the data passes through hidden layers, where computations and activations occur.
3. The output layer produces predictions or classifications.
4. A loss function measures the discrepancy between predicted and actual outputs.
5. Backpropagation adjusts network parameters based on the loss, enabling learning.
6. Iterative training minimizes loss and optimizes parameters.
7. Validation and testing assess model performance and generalization.

Let’s have a look at comparison schema to see the difference between ML and Deep Learning to understand its specifics better:

So, as far as we may see, a key difference between deep learning and traditional machine learning lies in their approach to data representation and feature extraction. Traditional machine learning algorithms typically require manual feature engineering, where relevant features must be identified and extracted from the data beforehand. In contrast, deep learning algorithms automatically learn representations of data directly from raw input, eliminating the need for manual feature engineering. Today, we may see more and more growing ML startups that use deep learning algorithms.

NLP

NLP or Natural Language Processing - is a huge branch of artificial intelligence that focuses on enabling computers to understand, interpret, and generate human language in a way that is both meaningful and useful. We have already examined it in our previous articles, so feel free to jump there if you would like to see the comprehensive guide on NLP, NLU, and NLG.

NLP plays a fundamental role in various AI applications, including AI chatbots, AI assistants, prompt engineering, sentiment analysis, machine translation, and text summarization. By using technologies from machine learning and deep learning, NLP enables AI systems to process and analyze vast amounts of textual data, enabling them to communicate with humans more effectively and perform tasks on how to make an AI app. 

Therefore, NLP stands as an integral component of AI, facilitating human-computer interaction and enabling AI applications to comprehend and generate language in a manner that approximates human-level understanding. 

Robotics

Robotics is another big AI approach and involves the design, construction, operation, and use of robots to perform tasks in the physical world and business automation processes. AI plays a crucial role in robotics by enabling robots to exhibit intelligent behaviors, make decisions, and adapt to their environment. 

The core AI technologies such as machine learning, computer vision, path planning, and reinforcement learning are commonly used in robotics to enable robots to perceive their surroundings, navigate autonomously, manipulate objects, and interact with humans in a more intelligent and natural manner. 

Therefore, robotics is considered a subfield of AI, as it involves the integration of AI technologies to create intelligent machines capable of performing tasks autonomously or semi-autonomously. 

Expert Systems

Expert systems are a type of AI technology that emulates the decision-making ability of a human expert in a specific domain or field, like eCommerce, Banking, Automotive, or Education. These systems are designed to solve complex problems by reasoning through knowledge and rules derived from experts in the domain. 

Expert systems typically consist of a knowledge base, that stores facts and rules about the domain, and an inference engine, that uses this knowledge to make decisions or provide recommendations.

The knowledge base of an expert system contains structured information about the domain, including facts about the problem space, rules that define relationships and constraints, and heuristics that guide the reasoning process. The inference engine processes input data and applies logical reasoning to draw conclusions or make decisions based on the knowledge stored in the knowledge base.

Expert systems are commonly used in different fields such as fashion, finance, engineering, and troubleshooting, where they assist human experts in decision-making, problem-solving, diagnosis, planning, and interpretation tasks. By capturing the expertise of human experts into a computer system, expert systems can provide valuable insights, recommendations, and solutions to complex problems, thereby augmenting human expertise and improving decision-making processes.

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Why do we need AI in application development: tips and benefits

According to our recent AI statistics, AI technology will eliminate 85 million jobs while making 97 million new ones by 2025. Moreover, AI-related progress can affect almost 15% of the global workforce by 2030. These numbers help us understand that AI integration into any business becomes a necessary thing and we all need to understand how to build an app with AI today. 

Artificial Intelligence has revolutionized the functionality of computer systems, providing human life with seamless business automation and task performance akin to human capabilities. Acting as a monumental structure of supercomputers, AI streamlines operations, minimizing errors, and fostering automation across various complex tasks, thus epitomizing next-generation progressions.

The main benefits of AI are evident in numerous industries, primarily through its ability to:

1. Reduce Human Error. AI-driven systems exhibit near-zero errors when programmed accurately. Providing predictive analysis, AI models preempt errors, ensuring accuracy and efficiency. Time and resources are conserved, yielding precise results.

2. Automate Repetitive Tasks and Processes. AI facilitates the automation of regular tasks such as data collection, customer service, and software testing.  Feel free to learn more about how AI can be used in software testing and its benefits. AI automation frees up human resources for tasks requiring cognitive abilities, increasing productivity and ROIs.

3. Handle Big Data Smoothly. AI possesses the capability to process and analyze large volumes of data swiftly. It efficiently extracts relevant information for analysis and further processing, facilitating informed decision-making.

4. Enable Quick Decision-Making. AI accelerates the gathering of insights, providing consolidated data and predictions promptly. Availability round-the-clock ensures swift processes by using special AI decision-making applications.

5. Use AI Assistants. AI-based chatbots streamline customer service by addressing routine queries promptly. Voice assistants like Siri and Alexa offer personalized assistance, responding to voice commands efficiently.

6. Improve Processes and Workflows. AI improves operational efficiency, productivity, and revenue generation for businesses. Numerous processes are streamlined, leading to error-free workflows.

There are many ways AI applications can be used in your business. Let’s have a look at the schema below with the main categories:

Overall, companies and enterprises can reap a multitude of advantages. Here are the main tips for using AI in app development for them:

• Improved user experience achieved through deep personalization of content, advertisements, and recommendations.
• Cost reduction and error mitigation through the automation of business processes.
• Increased efficiency across operations, data handling, and customer support processes.
• Discovered new revenue streams by using improved recommendations and increasing consumer satisfaction.
• More protected security measures for sensitive data, contributing to overall security enhancements.

How to build an AI application: step-by-step guide

Now, it is time to move to the most interesting part of our article - how to build an app with AI. Below, we will try to show you the most popular steps that are used in AI development, and in this way, it should be easier to understand how to build AI applications in 2024.

The core components of AI app development are the following:

• Discovery Phase
• AI Model Design
• Iteration Development
• Model Integration
• QA Testing and Support

Let’s dive into each step deeply.

Discovery Phase

Before proceeding to the AI app development steps, any project should be started with technical discovery and investigation. This is an important element in our product creation approach that we recommend to follow. It will reduce the speed of AI development and avoid the gap between expectations and reality.

The Discovery Phase involves several crucial steps:

1. Problem Definition. The first thing we need to do is clearly define the problem or challenge that the AI product aims to solve. Understand the pain points of the target audience and stakeholders.

2. Resource Planning. Next, we need to assess the technical, financial, and resource-related feasibility of the project. Determine if the proposed AI solution is viable and achievable within the given constraints.

3. Business Analysis. Another important step here is to perform deep business analysis to identify potential risks and challenges associated with the development of the AI product. Develop mitigation strategies to address these risks and ensure project success.

4. Regulatory Compliance. The next step is to understand and comply with relevant regulations, and ethical considerations related to AI development and deployment. Ensure that the AI product adheres to security policies.

5. Planning and SRS creation. The final step of the discovery phase is to create a detailed project plan (SRS document) with all feature descriptions outlining timelines, milestones, and deliverables for each phase of the development process. Allocate resources and define roles and responsibilities within the project team.

The technical Discovery Phase sets the core of further AI product development, ensuring alignment with stakeholder expectations and project goals.

AI Model Design

The first step in AI app development is the designing of an AI model that will be used in our application or system. Before starting, we recommend choosing only experienced and skilled AI/ML engineers. It will help you to save tons of time and money in the future. Feel free to use our guide on how to hire AI/ML engineers.

The process of designing an AI model involves several key steps:

1. Data Collection and Processing. Next, we need to gather relevant data that will be used to train and validate the AI model. Ensure the data is representative, diverse, and of high quality. After that, we clean, preprocess, and transform the raw data to make it suitable for model training. This may involve tasks such as handling missing values, encoding categorical variables, and scaling numerical features.

2. Feature Engineering. The next step is to extract relevant features from the data that will be used as inputs to the AI model. Feature engineering plays a crucial role in determining the model's performance and generalization ability.

3. Model Selection. Next, we have to choose an appropriate AI model or algorithm that is well-suited to the problem. Try to examine all possible alternatives before using the well-known models. Consider factors such as the type of data, the complexity of the problem, and computational resources.

4. Model Training. After that, you should train the selected AI model using the training data. During training, the model learns from the input data and adjusts its parameters to minimize a predefined loss function.

5. Model Evaluation. The next step is to evaluate the performance of the trained model using validation data that was not seen during training. Assess metrics such as accuracy, precision, recall, and F1 score to gauge how well the model generalizes to new data.

6. Fine-Tuning. Next, we need to fine-tune the hyperparameters of the AI model to optimize its performance. Hyperparameters control aspects such as the learning rate, regularization strength, and model complexity.

7. Model Validation. After that, please, validate the final trained model using a separate test dataset to ensure its robustness and generalization ability. This step helps assess how well the model will perform in real-world scenarios.

8. Deployment. The final step is to deploy the trained AI model into production where it can be used to make predictions or decisions on new data. Ensure proper monitoring and maintenance of the deployed model to keep it up-to-date.

The following visualized workflow will help you to understand the process better. Let’s have a look.

Iteration Development

The third phase in our “How to build an app with AI” guide is the development of the app itself. Before moving to the process of the AI app building, we need to define at least the following:

1. What AI programming language to use?
2. What kind of general and vector databases do we need? 
3. What cloud hosting shall we use?
4. What will be the development approach?

At Springs, AI development projects follow the Agile methodology, conducting iterative cycles of development services to identify gaps early and ensure high-quality, market-relevant products. 

Transitioning from a minimum viable product (MVP) to a final solution, we prioritize user feedback and continuous improvement.  When implementing an MVP, we present a basic product version to evaluate essential functionality and assess the overall viability of the app's concept. 

In the development of an AI-driven application, similar to a typical software development life cycle, the following steps are essential:

• Solution Architecture. We carefully plan the architecture of the solution to ensure scalability, efficiency, and compatibility with AI components.
• UI/UX Design. Designing a user-friendly and intuitive interface is crucial for enhancing user experience and engagement.
• Frontend and Backend Development. We develop both the frontend (user interface) and backend (server-side) components, focusing on functionality, performance, and security.
• AI Software Testing. We make sure your product is fully tested by both manual QA engineers and AI testing.

During the development stage, our efforts are concentrated on:

1. Optimizing App Performance. We fine-tune the application to ensure optimal performance and responsiveness across different devices and operating systems.
2. Expanding Functionality. We continuously improve and expand the app's features to meet evolving user needs and market demands.
3. Adapting to updates. We ensure that the app remains compatible with the latest operating system updates, providing a seamless user experience.
4. Ongoing Deployment. Springs team always tries to make sure your product is supported with the newest features on an ongoing basis.

Developing an AI-powered application necessitates a custom tech stack comprising programming languages like Python or NodeJS, SDKs, and toolkits essential for the application's functionality.

Model Integration

In this phase of developing an AI app, the focus shifts to integrating the trained AI model into the app's architecture and user interface. This involves:

• Designing and Implementing Software Components. Develop components that interact with the AI model, including data input/output, preprocessing, and postprocessing.
• Optimization for Deployment. Ensure the model is optimized for deployment by addressing factors such as size and memory requirements, to ensure efficient operation on the target platform.
• Integrate User Input and Feedback. Enable user input and feedback mechanisms to enhance user interaction with the AI model, allowing for creative exploration and experimentation with its outputs.
• Design Interactive Interfaces. Create interactive and adaptive interfaces that enable users to customize app behavior and preferences, enhancing user experience.
• Reinforcement Learning. Implement techniques such as reinforcement learning to enable the app to learn from user interactions and adapt its behavior over time, enhancing its functionality and usability.

Reinforcement learning is a type of ML technique where an agent learns to make decisions by interacting with an environment. The agent takes actions in the environment and receives feedback in the form of rewards or penalties, depending on the outcomes of those actions. The goal of reinforcement learning is for the agent to learn the optimal sequence of actions that maximize cumulative rewards over time.

Overall, the AI model integration, like the ChatGPT integration process stands as incorporating the trained AI model seamlessly into the app's infrastructure and UI. It involves developing components to facilitate data input and output, preprocessing, and postprocessing. Additionally, optimizing the model for deployment is crucial, ensuring efficient operation on the intended platform by addressing factors like size and memory requirements.

Finally, integrating mechanisms for user input and feedback enriches user interaction, fostering exploration and customization of the model's outputs.

QA Testing and Support

The final step in our guide called “How to Create an App with AI” is quality assurance testing, maintenance, and support. QA testing of an AI application involves several steps to ensure its functionality, reliability, and performance:

1. Unit Testing. QA testing of individual components or modules of the AI application to verify their correctness and functionality in isolation.
2. Integration Testing. Manual testing of the integration of the components to ensure they work together as expected.
3. End-to-End Testing. Testing the entire AI application to verify its behavior and functionality from end to end, simulating real-world usage scenarios.
4. Performance Testing. Assessing the performance of the AI application under various conditions, such as different loads or data volumes, to ensure it meets performance requirements.
5. Security Testing. Identifying and addressing potential security vulnerabilities in the AI application to protect against threats and breaches.
6. User Acceptance Testing (UAT). Involving end users to validate whether the AI application meets their requirements and expectations.

Using AI software while testing is a great point for successful product development. According to Zipdo research, 44% of organizations admit that they have already implemented AI in their QA process, while 19% plan to implement AI in QA within the next two years.

Maintenance and support of an AI application involve ongoing activities to ensure its continued operation, stability, and improvement:

1. Bug Fixing. Finding, addressing, and resolving any issues or bugs identified during testing or reported by users to maintain the application's reliability and functionality.
2. Performance Optimization. Continuously optimizing the performance of the AI application to ensure it meets performance requirements and delivers optimal user experience.
3. Software Updates. Updating the AI application to incorporate new features, enhancements, or bug fixes, and to address compatibility issues with new operating system versions or dependencies.
4. Monitoring and Logging. Implementing monitoring and logging mechanisms to track the AI application's performance, usage patterns, and errors for proactive maintenance and troubleshooting.
5. Support for Users. Providing assistance and support to users, addressing their queries, issues, and feedback to ensure a positive user experience.
6. Continuous Improvement. Iteratively enhancing the AI application based on user feedback, changing requirements, and technological advancements to stay relevant and competitive in the market.

Overall, testing, maintenance, and support are essential phases in the lifecycle of an AI application to ensure its success, longevity, and continued value to users.

Fails in AI app development and how to avoid them

Now, as we already know how to build AI applications, let’s focus on the fails and problems that may appear while building an AI application. Moreover, we will show you how to avoid them and how to make your application stable and fully functional.

Fais in AI app development can occur due to the different reasons, starting from technical challenges to misaligned expectations. Let’s dive deeper into each of them.

• Lack of Objectives and Understanding of the Project. Failure to define clear objectives and requirements for the AI application can lead to misalignment between the developed solution and the actual needs of users or stakeholders. For example, if you want to create a healthcare chatbot, you need to understand the industry and the needed pharma goals and objectives that you wish to achieve.

How to avoid. Make a deep business analysis, discovery phase, and document the project requirements, create professional SRS documentation, set goals, and success criteria before starting product development. 

• Insufficient Data Quality or Quantity. Poor quality or insufficient quantity of training data can negatively impact the performance of the AI model and your future product may suffer a lot from this.

How to avoid. Make sure that you have access to high-quality, relevant data in sufficient quantities to train the model effectively. Top-notch data preprocessing techniques can also be used to improve data quality.

• Overfitting or Underfitting. Overfitting occurs when the model learns to memorize the training data instead of generalizing to new, unseen data, while underfitting occurs when the model is too simple to capture the underlying patterns in the data. 

How to avoid. Using modern regulation techniques, cross-validation, appropriate model architecture, correct chatbot intent, and other settings, can help to avoid these issues.

• Bias Issues. ML models may perpetuate biases present in the training data, leading to unfair or discriminatory outcomes. It may become a huge problem, especially for companies that deal with sensitive data.

How to avoid. You need to carefully curate training data to mitigate biases, use diverse datasets, and implement modern evaluation metrics.

• Low Performance in Production. AI models may perform differently in production environments compared to development or testing environments due to factors such as data drift, changes in user behavior, or infrastructure limitations. 

How to avoid. Conduct thorough QA testing and validation in staging environments, and monitor model performance continuously to detect and address issues proactively.

• Lack of User Feedback. Developing an AI application, especially custom chatbot development,  without ongoing marketing A/B testing from real users can lead to a disconnect between the application and user needs. 

How to avoid. Try to involve users in the early stages of product development (POC, MVP, MMP), gather feedback iteratively, and incorporate user insights to improve the application's usability and effectiveness.

By addressing these common failures and adopting best practices throughout the AI app development lifecycle, AI development companies can increase the likelihood of success and deliver AI applications that meet user needs, perform reliably, and adhere to ethical and legal standards.

Conclusion 

The artificial intelligence integration in your application brings forth a realm of advanced capabilities, offering many users a great experience while simultaneously moving business revenue to new heights and ROIs.

As we see the rise of AI-powered applications, set to continue gaining momentum across diverse industries in the years ahead, we at Springs have remained steadfast in our commitment to closely tracking and embracing this transformative trend. With our finger on the pulse of technological evolution, we recognize the true importance of staying ahead of the curve in this dynamic landscape.

As a forward-thinking AI development company, we are fueled by the anticipation of what lies ahead. We invite you to join us on this exhilarating journey as we explore the boundless possibilities that AI has to offer. Stay tuned for more updates as we embark on this transformative endeavor, and prepare to witness the dawn of a new era in technology-driven innovation. Together, let us shape the future of AI applications and redefine what is possible in the digital realm.