Developing AI Applications: Challenges and Best Practices

Artificial Intelligence is one of the most significant digital trends of recent years, with the potential to transform various industries and sectors. AI has also become an essential component of many modern types of software, like web, mobile, and SaaS applications, websites, and online marketplaces. AI offers numerous benefits for virtually any software by streamlining processes, assisting with customer experience, and providing data for better business decisions. 

Even though AI sounds incredible, it’s not that easy to implement and train for an inexperienced developer. So, in this blog post, we will introduce the benefits of AI in applications, the challenges when creating AI apps, and some tips for those planning an application development.

Benefits of AI in Applications

Given that AI can provide a range of functions to software, apps that feature AI are on a proven way to outrunning any other types of applications. The thing is, the nature of artificial intelligence makes it possible to analyze and process large amounts of data, detect patterns and trends, and make predictions based on that data. A machine learning development companies can greatly facilitate this process.

AI is capable of replacing humans in doing certain operations, which undoubtedly end up providing the following business benefits: 

  1. AI can easily personalize all the tasks that are assigned to it, for example, improving customer satisfaction and engagement by providing timely responses and relevant solutions in a chatbot. But that’s not the limit! AI can even recommend customized content or help you build tailored user interfaces based on their behavior and preferences. For example, Amazon’s recommendation engine uses AI to recommend products based on a user’s browsing history and purchase behavior.
  2. Following up on the topic of human replacement, AI can automate repetitive tasks, like answering frequently asked questions, thus reducing human error and freeing up time for more complex tasks.
  3. As mentioned before, AI can analyze and, thus, train on large chunks of historical data to identify patterns and trends and make predictions, e.g., anticipate customer behavior, detect fraud, forecast demand, or optimize a company’s pricing. Google Analytics uses AI for this reason as well: AI provides insights and predictions about user behavior on websites.
  4. AI is undoubtedly a huge competitive advantage on both the internal processes side and the customer side. It’s always better to have AI on your side than not, so if you expect the effort spent on implementing AI will pay off, don’t hesitate to add it to the feature list. 
  5. Last but not least, a sum of all benefits listed above gives us an ultimate business perk – you can reduce costs by automating processes and improving efficiency. Plus, by improving the shopping experience for your customers based on behavior analysis, you can make more money. 

When it comes to the benefits of AI in SaaS applications, there is one unique factor to consider. SaaS is widely recognized as subscription-based cloud software, meaning you can make use of AI to optimize subscription management. For example, AI can be used to predict when customers are likely to churn and offer targeted promotions to retain them.

Challenges of Implementing AI in Applications

While AI can provide many benefits to applications, there are also significant challenges to implementing AI, including:

  1. AI should rely on high-quality data, and if the data used to train AI models is inaccurate, biased, or incomplete, it can lead to the same types of results. So, there’s a lot of manual work needed to clean up the historical data and make sure it is good enough to base your decisions on. 
  2. As mentioned before, implementing AI is a technically complex task requiring deep expertise in machine learning, data science, and software development.
  3. It is worth remembering that AI is raising ethical concerns more and more often. For example, bias and privacy violations need to be addressed to ensure the ethical and responsible use of AI.
  4. Integrating AI into existing applications can be challenging and require significant changes to the application’s architecture and infrastructure.
  5. Implementing AI can be costly, requiring significant investment in infrastructure, personnel, and technology. So before making a decision on building an AI application, make sure it will pay off in the long run. 

The Process of Building AI Applications 

The process of building an AI application is a lengthy one and requires thorough preparation. It typically involves the following steps:

1. Define the Problem 

The first step is to clearly define the problem you want to solve with your app and how AI can help it. This involves identifying the business problem, the desired outcome, and the data sources that can be used to train the AI model.

2. Collect and Prepare Data

The next step is to collect and prepare the data that will be used to train the AI model. This involves identifying relevant data sources, cleaning and preprocessing the data, and transforming it into a format suitable for AI training.

3. Choose an AI model

Once the data is ready, you need to choose the AI model that will be used to solve the problem. This depends on the type of problem and the nature of the data. There are many types of AI models, including neural networks, decision trees, and support vector machines.

4. Train the Model

After selecting the model, the next step is to train it using the prepared data. This involves feeding the data into the model and adjusting the model’s parameters until it produces accurate results.

5. Test the Model

Once the model is trained, the next step is to test it using a separate set of data that was not used during training. This helps ensure that the model is accurate and generalizes well to new data.

6. Deploy the Model

After testing the model, the next step is to deploy it in a real-world environment. This involves integrating the model into the application, setting up the infrastructure to support it, and monitoring its performance.

7. Monitor and Improve the Model

Once the model is deployed, the final step is to monitor its performance and continuously improve it over time. This involves collecting feedback from users and using it to refine the model and improve its accuracy.

Overall, building an AI application requires a multidisciplinary team with expertise in data science, machine learning, software development, and domain-specific knowledge. The process is complex and can take from several months to several years before the final launch of your app. Regardless, keep in mind the benefits AI can bring as it can make significant improvements in terms of efficiency, reducing costs, and providing a better user experience.

Best Practices and Tips for Developing AI Applications

To overcome the challenges of implementing AI, developers should follow best practices for developing AI applications, including:

  1. Ensure the quality of data used to train AI models by addressing data quality issues, identifying and mitigating bias, and collecting diverse data sets.
  2. Your development team should include programmers with expertise in machine learning, data science, and software development.
  3. Consider the ethical implications of AI, such as bias and privacy concerns, and design AI applications to address these concerns.
  4. Plan for AI integration early in the development process and consider the application’s architecture and infrastructure when designing AI solutions.
  5. Manage your costs by starting small and scaling up as necessary, leveraging cloud-based solutions, and optimizing hardware and software resources.


In general, the focus of AI is not on creating machines that mimic human behavior but on enhancing their performance as machines by augmenting human capabilities. This field presents ample opportunities for innovative ideas, as most current AI applications are tailored solutions rather than standardized ones.

Developing AI Applications: Challenges and Best Practices was last updated June 7th, 2023 by Colby Bickner