Al App Development: A Complete Step-by-Step Guide

Artificial intelligence (AI) is quickly reshaping the way we live and work, integrating into everything from online searches to email interactions. As AI becomes more ingrained in our daily lives, customer expectations are evolving — seeking faster, more intuitive and highly personalised experiences.

To keep up, 97% of executives feel a pressing need to adopt and integrate AI tools. And as the benefits of AI become increasingly clear, more companies are not only developing AI-powered applications, but also using AI in the application development process itself.

In this article, we’ll dive into what AI development involves, best practices for building AI-powered applications and what the future holds for AI in application development.

AI app development is the process of building applications that integrate artificial intelligence to perform tasks traditionally requiring human intelligence. Development teams leverage pro-code, low-code and no-code tools — often incorporating AI itself — to streamline and accelerate the process.

These AI-powered applications rely on advanced algorithms, machine learning models, large language models (LLMs) and data analytics to automate workflows, enhance user experiences and deliver intelligent, data-driven insights.

Some of the key features of AI apps include:

• AI integration: AI apps incorporate capabilities such as natural language processing (NLP), computer vision, predictive analytics and/or machine learning to create smarter and more efficient applications.
• Automation and efficiency: By automating repetitive tasks, AI apps can significantly improve productivity and operational efficiency.
• Personalisation: AI applications can analyse user data to provide personalised recommendations, content and interactions.
• Data-based decision making: These apps use large datasets to identify patterns, predict outcomes and assist in making more informed business decisions.

You’ve likely already come across some AI applications like:

• Voice assistants: Apps like Siri, Alexa and Google Assistant use NLP to understand and respond to user queries.
• Image recognition tools: Apps including Google Photos or facial recognition software use computer vision for identification and organisation.
• Generative AI: Apps such as ChatGPT use large language models to write content, respond to prompts and interact with users.
• Predictive analytics: AI-powered financial apps provide forecasts and investment recommendations based on user data.

The key steps in AI app development include:

• Identify the problem: Start by pinpointing the desired outcome of the app.
• Get your data in order: Determine where data is coming from and how you’ll train the AI if you’re training it yourself.
• Build in data privacy and security guardrails: Have security in mind from the start and build the app to protect your customers’ data.
• Use the right tools: Use pro-code, low-code, no-code or AI tools to help you to create the app.
• Deploy and monitor: Test and then deploy your app. From there, monitor how it’s performing and adjust as necessary.

Building an AI app can take anywhere from a few hours to a few weeks, months or more, depending on several factors (including data quality and availability, whether you're using new or customised code and app complexity). Other key factors that influence development time include:

• Level of AI model customisation
• Team's expertise and experience
• Number of developers working on the project
• Required testing for quality assurance
• Security, privacy and ethical considerations
• App's features and functionality

The cost of developing an AI-powered app can range can vary widely, depending on various factors such as:

• The complexity of the AI features (e.g., simple automation vs. advanced machine learning models)
• Development tools and platforms used
• Customisation and integrations required
• The size of the development team

Developing AI apps can help your organisation stay competitive and at the forefront of your industry. On top of that, AI apps can:

AI-powered applications deliver personalised and intuitive experiences by analysing user behaviour, preferences and past interactions. These insights allow apps to anticipate user needs and adapt dynamically, creating a more seamless and engaging experience.

For example, recommendation engines in platforms like Netflix and Amazon suggest content or products based on individual tastes, boosting user engagement and satisfaction. Similarly, customer support AI agents provide instant responses to common queries, reducing wait times and improving overall user convenience.

Having AI-powered internal applications can transform your workplace efficiency by automating repetitive, time-consuming tasks. With those out of the way, your employees can focus on high-value work — like making strategic decisions or designing a new product. Automating these tasks can take the burden off of your employees and reduce the risk of human error.

Additionally, AI-driven analytics tools provide real-time insights to support better decision-making. From predictive analytics for sales teams to AI service agents, these tools instantly deliver actionable intelligence, improving overall productivity.

AI communication tools further simplify team co-ordination with features like automated meeting summaries, intelligent task prioritisation and smart workflow suggestions. By minimising time spent on administrative tasks, employees can collaborate more effectively and concentrate on meaningful, strategic work.

AI excels at transforming raw data into meaningful insights. By integrating AI into the apps they are developing, businesses can leverage advanced analytics to identify trends, detect anomalies and make data-driven predictions.

For example, in retail, AI can analyse purchasing patterns to optimise inventory management and forecast future trends, helping businesses decide which products to continue or phase out. In healthcare, AI-powered apps analyse patient records to recommend personalised treatments, while in finance, they detect fraud by spotting unusual transaction patterns.

By harnessing AI, organisations can turn existing data into actionable insights, driving smarter decision-making and improving operational efficiency.

If you’re creating an AI app, these are some of the general steps you should take:

The goal is to create something where AI would provide your customers or employees with unique benefits. Determine how AI features like personalisation, prediction or automation could do this and meet your business goals.

Starting out with a clear purpose helps your development process stay focused and makes sure that spending the time creating AI models will bring value to your company.

AI thrives on data, so collecting relevant and high-quality datasets is an important part of the development process. For example, if you’re building a recommendation engine, gather as much user behaviour data as you can to train the model on and to use.

Organise the data, clean it and ensure it’s representative of the problem you’re solving. Consider connecting your internal proprietary data with your AI apps, so you’re able to use that data to provide your customers with personalised experiences.

Protecting the data you’re using to build an AI app is vital — and adding protection from the start can help you to ensure data is safe at all levels. You might have concerns about data privacy and security (such as permissions and access controls) or even AI accuracy with your data and building guardrails can help you to avoid these problems. Here are a few ways to add security into your development process:

• Define the ethics your team will use as you build the AI app
• Implement a diverse team that can check for AI bias

Add multi-factor authentication and zero trust access to prevent potential breaches into your data and make sure only those with permission can access it

Select an AI model that fits your use case, such as natural language processing (NLP) for chatbots or computer vision for image recognition. You might be able to use some pre-built AI features depending on your tools as well.

Decide whether to train your own model or use a pre-trained one. Training an AI model in-house can be resource-intensive, requiring substantial data, time and expertise to ensure accuracy and minimise bias. If your company lacks these resources, opting for a pre-trained model with a built-in data platform may be a more practical solution.

If you choose to train your own AI model, you'll need to adjust parameters, test accuracy and fine-tune it to meet performance expectations. This process often involves working with data scientists to ensure the model is effectively implemented and optimised.

Selecting the right tools is crucial for building your AI app efficiently. Depending on your needs and expertise, you can use a combination of the following:

• Low-code or no-code platforms: Speed up development by using drag-and-drop interfaces instead of manual coding. These tools allow non-developers to contribute, making the process more accessible and collaborative.
• AI-powered development tools: AI can assist developers by generating, completing or debugging code in seconds, improving efficiency and supporting complex coding tasks.
• Pro-code solutions: Traditional programming languages offer full customisation and integration with existing systems, making them ideal for building robust and scalable applications.

Once the AI model is ready, integrate it with the app’s front-end and back-end systems. This can be done with APIs like how OpenAI does with ChatGPT, so you don’t have to start from scratch.

Conduct rigorous testing to evaluate both the AI output and the overall user experience. Ensure that the AI behaves as intended and adapts to real-world scenarios. Continuous testing and feedback loops help you to create something that will meet your users’ needs.

Once your AI app is live, continuous monitoring is essential to ensure optimal performance and user satisfaction. Gather analytics and customer feedback to identify areas for improvement, making ongoing refinements to enhance the user experience.

Additionally, prioritise security monitoring to protect sensitive data and maintain compliance with industry regulations. AI-driven security tools can detect anomalies, flag threats and automate responses to potential risks. Regular security audits and updates should be part of your monitoring strategy to keep the app resilient against evolving cyber threats.

Using an AI enterprise application development platform simplifies the complexities of integrating AI into your app. These platforms often provide access to pre-built AI models, user-friendly interfaces and deployment support, significantly reducing the time and resources required.

Robust AI enterprise app development platforms also democratise the ability to build AI apps by eliminating the need for in-depth expertise in machine learning to be able to create something functional. With pre-trained models and plug-and-play options, these platforms make it easier for individuals and teams across the business to create AI apps.

Following these best practices will help you to create a high-quality AI app that is user-friendly, scalable and secure.

AI should enhance usability, not complicate it. Keep the interface intuitive and make AI features transparent. Conduct UI tests to ensure smooth interactions and consider adding explanations about how AI functions — such as how recommendations are generated or how users can control AI-powered automation.

Before full-scale development, create prototypes to test functionality and gather user feedback. Early validation helps identify flaws and improve the product, saving time and resources in later stages.

Extensive testing — including unit, integration and user acceptance testing — is essential for reliability and performance. Detecting and fixing bugs, security vulnerabilities and performance issues early reduces downtime and ensures a smoother user experience.

Security should be a priority from the start. Implement encryption, secure APIs, continuous monitoring and regular audits to protect user data. Ensure compliance with regulations like General Data Protection Regulation (GDPR) and California Consumer Privacy Act (CCPA) and adopt a privacy-by-design approach to prioritise user consent and transparency.

AI demands grow as your user base expands. Choose cloud-based solutions and scalable frameworks that can handle increasing data loads and interactions without requiring major infrastructure changes.

For mobile apps, use lightweight AI models or cloud processing to prevent excessive battery and resource consumption. Frameworks like TensorFlow Lite and ONNX can help to ensure efficient AI performance on mobile devices.

Security should be a fundamental aspect of AI app development, given the vast amounts of sensitive data involved. Adopting a DevSecOps approach — integrating security into every phase of the development lifecycle — ensures that security is built into your AI app from the start. Here are key ways to incorporate this approach:

• Shift security left: Embed security measures early in the development process to identify vulnerabilities before they become major risks. Automated security testing, code scanning and AI-driven threat detection should be integrated into the CI/CD pipeline to continuously assess and address security concerns throughout development.
• Implement continuous monitoring and compliance: Given the sensitive nature of data processed by AI applications, real-time security monitoring is crucial. AI-powered security tools can help to detect anomalies, prevent data breaches and ensure your app complies with industry regulations such as GDPR or CCPA.
• Automate security controls: AI-powered security solutions can automate critical tasks like vulnerability assessments, identity verification and access controls. Automation reduces human error, enhances security and ensures consistent protection across all layers of the app.
• Foster cross-team collaboration: DevSecOps promotes collaboration between development, security and operations teams. This ensures that security is considered in the design of AI models, APIs and data handling processes from the beginning, rather than being added on later.
• Invest in ongoing security training: AI development teams should stay updated on evolving threats, ethical AI practices and secure coding standards. Regular training sessions help foster a proactive security culture and ensure your team is equipped to handle emerging risks.

AI can also revolutionise the way you build your apps and help you to create new products quickly and with less human error. By integrating AI-driven tools and automation into the development process, your development teams can accelerate workflows, improve code quality and reduce manual effort:

• AI-powered code generation: AI app development enhances efficiency by automating complex processes. AI coding tools can predict the next line of code, generate boilerplate code and refactor existing code for better performance.
• Automated testing and debugging: AI speeds up testing by detecting bugs, generating test cases and predicting potential defects based on past data.
• Enhanced DevOps with AI: AI-driven automation optimises CI/CD pipelines by predicting build failures, automating deployments and dynamically allocating resources. This leads to faster release cycles and more stable applications.
• Natural language processing (NLP) for requirements analysis: AI-powered NLP extracts insights from user stories, feedback and documentation to ensure the app is in line with business goals. It can also automate documentation tasks.
• AI-driven security enhancements: AI enhances security by scanning for vulnerabilities, performing automated security audits and reducing human oversight errors.
• Predictive analytics for project management: AI analyses past project data to estimate timelines, identify risks and optimise resource allocation, keeping development teams on track.

A common concern is that AI will eventually replace software developers, making app creation fully automated, like something out of a sci-fi film. However, the reality is far more balanced — AI is transforming, not replacing, the role of developers.

Many organisations now look to AI to serve as a powerful assistant, automating repetitive coding tasks, suggesting optimisations and speeding up development. Meanwhile, human developers continue to provide creativity, problem-solving skills and strategic decision-making — areas where AI still falls short.

By combining human expertise with AI’s automation capabilities, developers can work more efficiently, focus on innovation and build better applications faster. Rather than replacing developers, AI will enhance their productivity and expand what’s possible in software development.

AI has revolutionised multiple industries, driving innovation, improving efficiency and enhancing user experiences. Here are some standout success stories that highlight the impact of AI-powered applications:

AI apps like IBM Watson Health have redefined healthcare by analysing vast amounts of medical data to assist doctors in diagnosing diseases. Watson’s ability to process unstructured data has helped hospitals improve diagnostic accuracy and patient outcomes.

In fact, Watson clients reduced their medical code searches by over 70% with AI-powered tools , streamlining workflows and saving time. AI-driven diagnostic tools have been especially successful in detecting early-stage cancers and rare conditions.

Netflix’s AI recommendation engine is a prime example of AI-driven personalisation. By analysing users’ viewing habits, preferences and past interactions, the platform suggests content tailored to each individual. This approach has increased user engagement, retention rates and overall satisfaction, proving the effectiveness of AI in curating personalised entertainment experiences.

Companies like Uber and Lyft use AI to optimise their ride-sharing platforms. AI algorithms predict rider demand, determine the most efficient routes and optimise driver assignments in real time. These advancements have significantly reduced wait times, improved operational efficiency and contributed to global service expansion.

AI has transformed real estate platforms like Zillow, which uses machine learning to generate highly accurate property valuations. AI also powers virtual home tours, allowing prospective buyers to explore properties remotely. This innovation has made home searches more data-driven and convenient, giving buyers a better understanding of properties before visiting in person.

As AI continues to evolve, more apps will integrate AI-driven features to create smarter, more personalised user experiences. Here are key trends shaping the future of AI app development:

Developers will focus on AI models that analyse user interactions across apps and the web to deliver deeply relevant and tailored experiences. Advanced AI algorithms will personalise content feeds, search results and recommendations based on browsing behaviours, past interactions and usage trends, making every user’s experience more intuitive and engaging.

No-code and low-code development platforms will become more mainstream, allowing individuals with little to no technical expertise to develop AI-powered applications. These platforms simplify AI integration by providing drag-and-drop tools, making AI app development more accessible to businesses and non-developers.

More companies will integrate AI agents into their workflows to automate processes, assist development teams and improve system efficiency. AI agents, like Agentforce , are designed to scale across enterprise applications, helping businesses streamline development, enhance customer interactions and improve decision-making.

If you’re looking to build AI-powered applications and agents or improve your current development process, Salesforce’s AI app and agent development platform provides a robust, enterprise-ready solution. With Salesforce, your team can:

• Collaborate across departments: Allow seamless teamwork across your organisation with built-in collaboration tools
• Set up access and governance controls: Ensure security, compliance and proper permissions for AI applications and data
• Connect with customers: Personalise customer interactions and enhance engagement with AI-driven insights
• Integrate with external systems and data: Easily connect AI applications to third-party platforms and data sources
• Accelerate development: Use customised objects, Lightning Flow automation and the Lightning App Builder to speed up the AI app development process

With this solution, your company can stay up to date with AI and ahead of the competition as you develop apps quickly and effectively. If you opt for Salesforce’s AI customised application development solutions, pricing will vary based on the plan you select and the number of users.

Learn more about developing AI agents and applications on the Salesforce Platform and get started with AI app development with this guide.