How to Build AI-Powered Mobile Apps?

Artificial intelligence (AI) has been the buzzword for quite some time in the world. It is everywhere—from entertainment, business, healthcare, travel, and education to governance, there is not a single domain that AI has not influenced.

To make it even more interesting, large corporations like Google, Facebook, Microsoft, etc., are also pouring millions of dollars into AI-powered app development projects. Along with these large tech conglomerates, smaller businesses are also developing AL-powered apps.

However, developing a high-performing and efficient application is a challenge. You must work with an established AI mobile app development agency to achieve the desired results.

If you are wondering how to build an AI-powered mobile app, this blog will help you understand the details.

Let’s start.

What is artificial intelligence or AI?

Artificial intelligence (AI) involves developing computer systems capable of performing tasks using human-like intelligence. These tasks include learning, reasoning, problem-solving, perception, etc.

At its core, AI aims to create intelligent machines that can mimic and surpass human cognitive abilities.

Here are some key characteristics of AI systems.

1. Learning and adaptation

AI systems can learn from experiences, interactions, data, etc. They can recognize patterns, analyze data, and interpret information to make decisions based on their inferences. And they can do all these without being programmed for it explicitly.

2. Perception and recognition

AI can understand and interpret sensory data, such as images, speech, and text, like humans do. This is one of the key aspects of developing generative AI.

3. Reasoning and problem-solving

AI algorithms can solve complex problems by analyzing data and applying logic. This characteristic makes AI suitable for complex calculations that would be impossible for humans.

4. Natural interaction

AI technologies like NLP and speech recognition enable machines to understand and use natural language. This helps these AI systems communicate with humans.

What are the components of AI?

AI has six different components. Let’s understand each of them here.

1. Machine learning

Machine learning is a chief aspect of artificial intelligence that makes computer systems work without being programmed to do the task. These systems are trained on different sets of data to learn from them. After training, based on their learning systems, they respond to various commands.

2. Neural networks

The neural network is a collection of various algorithms that interact with each other to find relationships among and between diverse data sets provided to the system. These algorithms use various statistical techniques like regression to perform tasks.

3. Robotics

This is another element of artificial intelligence where machine learning and neural networks are used to create robots. They are programmed to do certain tasks that are impossible or hard to do for humans.

4. Expert systems

Expert systems, which have been under work for decades, are systems that are adept at decision-making. They can model human-like decision-making capabilities based on the data fed into the system.

5. Fuzzy logic

This is a mathematical logic technique where the rules of reasoning are open and not closed. This means that it takes into consideration diverse uncontrollable, unpredictable, and uncertain elements of decision-making, just like real world, when making decisions.

6. Natural language processing

Humans use natural language to communicate with each other and understand each other. However, computer systems and technologies do not understand this. Natural language processing can overcome this limitation. This technique allows computer systems to understand, decipher, and mimic how humans talk and interact with each other.

What are the applications of AI in business?

The applications of AI in business are limitless. There’s no business where AI cannot be leveraged.

Here are some of the top applications of AI in business.

1. Top-notch customer service

Chatbots have become a game-changer in customer service. They leverage machine learning to interpret and respond to human queries. These AI-powered assistants keep the conversation flowing, providing a seamless experience for users.

2. Personalized services

With facial recognition, voice recognition, and neural networks – deep learning brings cutting-edge technologies together to elevate security and personalization. By analyzing vast datasets, it can tailor content to each user’s preferences, creating a truly personalized experience.

3. Efficient virtual agents

Virtual agents that automate ticket routing tap into knowledge bases, offer routine solutions, etc., are transforming IT service management. These AI helpers enhance efficiency, allowing IT teams to focus on more complex tasks.

4. Supply chain optimization

AI is revolutionizing supply chain management, from predictive maintenance to risk management and enhanced procurement processes. Its ability to rapidly analyze data automates decision-making, ensuring logistics and operational efficiency.

5. AI-powered sales enablement

By leveraging customer data, AI helps identify and nurture potential leads. Using this, you can build guided selling techniques. This not only boosts sales execution but also increases revenue through personalized customer engagements.

6. Marketing creatives and optimization

AI is a marketer’s best friend. It enables real-time personalization, optimization of content and media strategies, campaign development, etc. All these help deliver relevant customer insights that accelerate marketing campaigns.

7. HR support

AI can streamline recruitment by matching job descriptions with candidate skills using natural language processing (NLP). It also enhances skill development through recommendation engines and supports career paths. HR professionals can carry out performance analysis with AI-powered assessment tools.

8. Finance management and support

Thanks to AI’s analytical abilities, it can simplify financial processes. It helps businesses carry out expense report audits, vendor invoice processing, ensure accounting compliance, etc. with ease. With AI, you can significantly reduce manual workloads and improve accuracy.

How to build an AI app?

Building an AI-powered mobile app is a lengthy process that requires extensive research, planning, and execution.

Here is a detailed breakdown of AI app development for mobile phones and devices.

1. Define the app’s goals and purpose.

Before you build the app, you need to establish what your app is going to be? Will it be an entertainment app or an education app? Will it be a functional app? Or will the app be a business app?

You may be wondering why you need to establish the goals in advance. It helps you understand:

  1. Who is the target audience?
  2. What are the pain points of the audience?
  3. The features you need to add.
  4. Who is your competition?
  5. What are the top industry trends?
  6. How is the industry?
  7. What type of AI should you integrate?

The answers to these questions will help you build an app that can stand apart from the competition and resolve the issues of the audience.

2. Find the right app development agency.

After finalizing the idea, the next step is to find a reliable AI-powered app development agency. Look for an agency that can help you choose the right technologies, language, tools, and frameworks for your project.

As we are developing an AI-powered app, we also need to consider a variety of other AI technologies. Some of the often-used technologies are natural language processing, predictive analysis, machine learning, etc.

If you check with these agencies, they can also help you improve and perfect your app idea. Their experience and insights in the field enable them to do it.

Some agencies use AI tools for AI app development, which helps the project move forward faster.

3. Designing and developing the app.

This is the next important stage. You need to finalize the architecture of the app along with its design and development. Before you start the development process, you must finalize the app’s look, feel, and architecture. This helps you get into the next stages faster.

There are several ways you can design the app, like creating prototypes, mock-ups, wireframes, etc. You can use any wireframing app like Figma, Adobe XD, etc., to get the look of the app.

An important aspect to check is to ensure that the design aligns with your business persona and the expectations of the market. Look at the design trends in the industry and adopt a design that ensures the best user experience for the users.

The same goes for the development process, as well. Every feature, functionality, etc., must cater to solving the larger pain points of the users.

4. Test the application and deploy it.

After designing the app, the next crucial step is to test it. Testing enables you to understand whether the app performs as expected. This is vital before deployment. The testing process also lets you know if the AI element of the app also works as expected.

While testing, you assess a range of elements of the app from its features, AI capabilities, performance, design, and more.

More than a process to see whether the app is ready for deployment, testing must be looked at as the opportunity to improve its look, feel, features, and performance. A thorough testing process must empower the testers to carry out extensive testing and offer suggestions for improvement.

Once the app is tested and made changes based on the test insights, it can then be deployed and made live for users.

5. Assess the app and improve it.

Deployment is just the end of the first phase of any development process. After deployment, you must stay alert to gather information and feedback from your end-users to see how they like it.

Most of the time, the end-users will provide insightful feedback and reviews about the performance and overall experience. The feedback offers numerous opportunities for your business to understand where your app performs well and where it is falling behind.

Based on the user experience and feedback, you can make changes to the app, as app updates. You can add new features that customers are asking for, as well.

Future trends in AI app development

Like everything, AI also is undergoing various changes. The trends in the industry are changing the way businesses use AI and how it will be used in the future for mobile application development.

Here are some of the trends that are currently happening in AI app development.

1. More apps will adopt generative AI

Of course, generative AI has made crucial strides in the last few years. Especially in areas such as content generation, image generation, natural language processing, music competition, etc. This trend is going to get more momentum as more apps will integrate Gen AI for more features.

2. Autonomous decision-making systems

Self-driving vehicles, automated trading, and healthcare diagnostics will use autonomous decision-making systems more and more in the future. Yes, businesses already use these technologies to advance these industries. However, it will be more prevalent in the coming years.

3. AI ethics and regulation

AI-related ethics and regulations will take a more concrete role in the coming years when building AI-powered systems. AI is now limited by issues like AI bias, data privacy and storage, etc. Hence, statutory regulations and ethical standards will become more stringent.

4. Edge AI

As the need for the Internet of Things is on the rise, so is the need for systems that work in real-time for smart homes, wearables, and industrial IoT. As Edge AI reduces the dependency on cloud servers by local processing, this demand accelerates the growth of Edge AI.

5. Convergence of AI and IoT 

The developments in AI and IoT are leading these to converge in a few years. This combination will provide faster decision-making and data generation capabilities for autonomous and other AI-powered systems. It will also improve their efficiency and expand their applications.

6. No-code and low-code AI development

Yes, there are many popular no-code and low-code platforms due to generative AI. However, they will become more widespread. Anyone without design or coding skills can use these platforms to create user-friendly, aesthetic, and high-performing apps and systems. This will also speed up AI adoption across various industries.

7. AI-driven cybersecurity

You can prevent cyber-attacks with AI in a few years. AI systems have the ability to detect anomalies, analyze patterns, and even predict security compromises faster. As AI can understand and respond to threats faster than traditional systems, more businesses will adopt AI-driven cyber security systems.

Challenges in AI development: How to solve them

While AI is revolutionizing the field of technology, the challenges associated with AI development are not to be taken lightly. Businesses need to be on the look for such challenges. And address them as they happen.

Let’s discuss some of the top challenges in AI development and how businesses can solve them.

1. Data privacy, security, and storage

AI systems are trained on huge data sets. When developing AI systems, storing these huge data sets is a challenge for businesses. And this also creates data security, as well. Businesses need to invest more in data protection and safety.

How to solve this issue?

Building a better and more efficient data management system with stringent access is the best solution. Stringent data access and management policies can also prevent data security and theft.

2. Quality data section and adoption

Finding the right data set to train AI is another challenge that businesses face. The data should be top-notch and must be available freely for businesses to use. However, acquiring these data sets is expensive and sometimes nearly impossible.

How to solve this issue?

Working with partners who can provide high-quality data is the only way to obtain quality AI training data. You can also implement data cleaning systems to improve the data quality after collecting them.

3. The challenges of AI bias

The issue of AI bias can be tricky. Using compromised or biased data for training leads to biases in AI systems. Bad data often leads to biases in terms of gender, race, location, etc.

How to solve the issue?

Using high-quality data without inherent biases and prejudices is key to addressing this issue. Implementing systems and frameworks to ensure transparency and reduce biases also helps.

4. AI integration challenges

AI integration is another challenge that businesses face. You need experts who can integrate AI successfully into both existing and new business systems. There’s no doubt that AI integration is more sophisticated than adding new plugins to new websites or apps.

How to solve this issue?

Businesses must assess the extent of AI integration and hire experts who can help with it. You also need to have adequate systems and platforms that support the integration process.

5. Regulatory and legal challenges

Legal issues are another challenge for businesses using artificial intelligence. AI models may use data from sensitive data sets or provide wrong predictions, which can lead to business losses. Potential violations of privacy and other data regulations by AI can also put businesses in tricky situations.

How to solve this issue?

Building stringent privacy and data regulation protocols while developing AI systems and their applications can help businesses avoid such legal challenges.

6. Hiring AI adoption experts

Hiring the right skill sets to develop AI apps and adopt AI is a challenge that most businesses face. You need to build a team that can understand the possibilities of AI and ensure innovative AI development. As the field has only grown in recent years, finding AI experts with extensive exposure is a challenge you must face.

How to solve the issue?

Investing more in in-house teams to develop AI skill sets can help businesses mostly address these issues. Until that is possible, outsourcing AI development to AI development agencies is the best strategy.

How can AI be implemented in mobile apps?

You can use a variety of AI models to power your mobile app based on your app’s features and functions.

Here are a few ways AI can be implemented in mobile apps.

  1. You can implement AI models with face detection capabilities in your app. This unique feature can ensure the security and privacy of the app. There are several apps that already use face-detection technologies in banking, finance, healthcare, etc.
  2. Another way to use AI in your mobile app is for search prediction. In your app, when users search for a certain thing, using predictive search to suggest to them what they are looking for can improve user experience.
  3. The app can offer unique suggestions to the users using AI’s analytical capabilities. Based on users’ preferences, tastes, and historical searches, AI apps can offer them suggestions for products, entertainment, vacation destinations, dating profiles, etc.
  4. Sentiment analysis is another feature you can use in your app to determine the sentiment and emotions of a user. This can be used in dating apps, customer support apps, messaging apps, etc.
  5. You can add image-generation AI models to various apps to enable users to create stickers, emojis, personalized memes, etc.
  6. These are only a few top ways AI can be integrated into an app. More sophisticated apps for businesses and enterprise businesses can have even intricate uses of AI, like data analytics, cybersecurity, etc.

How much does it cost to build an AI app?

The cost of developing an AI app depends on the type of app, its functionalities, and who you want the app to serve. A smaller or lighter app will have significantly less development cost while a large or extensive app for businesses and enterprises will obviously have more development cost.

Here are a few factors that affect the cost of AI app development.

  1. Acquiring large datasets and cleaning, labeling, and preprocessing them are a significant cost factor. It can be even higher with proprietary data.
  2. Training advanced AI models requires powerful GPUs, TPUs, or cloud computing resources. These resources also add to the overall cost of the app.
  3. Hiring and retaining skilled AI researchers, data scientists, and engineers with expertise in the specific domain is another cost element.
  4. Projects with longer development cycles cost more as the human and technological resources need to be paid for these extended periods.
  5. Testing, collecting feedback, and implementing the required changes also add up to the overall AI app development cost.

Here is a table of the estimated cost of apps based on the complexity of the app.

Sl. No Type of App  Development Cost 
1 Basic AI App  USD 30,000 – 50,000
2 Medium AI App  USD 50,000 – 80,000
3 Advanced AI App  USD 80,000+


Developing an AI-powered mobile app is not easy. With extensive research, analysis, and market study, there are a lot of things to do. And then comes the development and deployment part.

These processes take a lot of time and effort for the AI app development agency. However, working with an established AI-powered app development company ensures that you can save time, effort, and money. These companies already have processes and systems in place to save you time and effort.

Apart from these elements, they also have experienced professionals who can offer the best AI app development services. And if you are looking for such a reliable agency to work with on your AI app development Fullestop is the best.

Speak to our client service team and learn more about us.

Frequently Asked Questions

An AI-powered app is a solution that uses AI capabilities to function or improve its functionalities. Several such apps are in the market, such as ChatGPT, CoPilot, Midjourney, etc.

Developers use AI techniques like machine learning, natural language processing, etc., to build intelligent apps. These features include personalized recommendations, voice assistants, image recognition, and predictive analytics.

There are several no-code or low-code app development platforms for building apps without coding. If you check, you will understand that all these platforms have visual interfaces and drag-and-drop tools that let you gradually build the apps. If you have an idea of how the app works and its performance, it is effortless to create apps without extensive coding knowledge using these platforms.

Building an app with AI is not very complicated if you know what you are doing. First, you need to choose a pre-trained AI model that matches your app's functionalities. After that, you need to integrate the AI model with the app and ensure that both the app and the AI communicate ideally using an API. You need to test the app and its AI capabilities to make sure that they both perform as expected. After making the app live, you also need to monitor the app for its performance and improve it with constant release of updates.

Creating an AI app involves various elements. You need to first understand the problem you want to solve. After that, pick the AI model and train the same with the required data. Once you do that, optimize the AI model to deliver accurate responses. The next step is to integrate the AI models into the app. Now comes the deployment part. After this, the development team will continue to evaluate its performance to understand areas of improvement and work on the same.