In a time when companies of all sizes, from tech behemoths like Meta and Google to start-ups, rush to implement AI in their existing services, artificial intelligence has become a present-day necessity to stay competitive. Yet, AI integration into the existing business infrastructure can pose several challenges. 

In this article, we shall discuss how to overcome the challenges of AI integration and describe the step-by-step process for doing so successfully. 

Understanding AI Integration

AI integration means injecting specific AI features into your existing application. 

The list of AI features you may add is almost infinite, so we’ll narrow it down to several examples by sphere of application:

• Enhanced search: Autosuggestions, search predictions, and autocorrections help users find the desired content faster. This feature is especially useful for mobile apps, as typing on a tiny screen is not convenient. 
• Recommendations: Netflix uses AI algorithms to track users’ viewing history, scores, and likes to offer relevant content. This helps the company keep users engaged and reduce churn.  
• Sentiment analysis: AI allows scraping mentions of your brand from multiple sources, like press, social media, comments on the website, and communications with customer support to check the attitude of your audience toward your brand. 
• Predictive analytics: Services like Kayak employ enhanced ML algorithms to study seasonal trends in flight demand and prompt their users to decide whether it’s sound to buy a ticket now or wait for a better offer. 
• Face recognition: applications use images of a person’s face to authorize access. Some companies, like BNP Paribas, take a step further and authorize customers by videos. 
• Image generation: The Virtual Artist app from the beauty retailer Sephora allows users to try their products on virtually by “applying” cosmetics to them through live video sessions. 
• Voice assistants and chatbots: This technology is widely used in conversational commerce, which helps customers to purchase through conversations, similar to Iron Man’s talks with Jarvis. Another example is the Timesheets app, created for busy workers wishing to log hours via a voice assistant.
• Text-to-speech: TTS apps like Listening simplify study and research by allowing students and researchers to listen to science papers on the go. 

Enhancing your application with AI-based functionality can benefit both your client service and your business management. 

Lufthansa Group, for example, uses AI prompts in 90% of decision-making tasks regarding passenger flows, staff rotations, and route plans. They say they fly better by using data. 

Luxury automotive brands like Toyota or BMW use AI-based car configurators to increase client engagement. Some brands offer virtual showrooms and even virtual test drives, and a surprising 64% of shoppers are ready to buy a car after such sessions without a real test drive. 

The data discovered by McKinsey show that AI-based personalization prevents customer churn, as 67% of customers get frustrated when a business offer is not aligned with their needs. 

So, from the successful AI integrations across industries, we see that AI-based features become the demand of your time. Yet, to start with AI, one should prepare a business accordingly. 

Overcoming Pre-Integration Challenges

To avoid disruptions on the AI integration track, it’s better to ensure that your business is ready for both the novel technology and the increased workloads it will bring. 

Here are the main things to consider to make your business prepared for AI integration: 

• System compatibility. Outdated technologies may not be powerful enough to sustain AI-based functionality. If you are using a legacy infrastructure, you may have to modernize or even completely rebuild it before you start with AI integration. 
• Data readiness. Most businesses keep data unstructured. It is not an issue if you plan to integrate an open-source AI solution, like ChatGPT. But if you want a custom solution, like a prediction mechanism based on your historical data, be ready to bring them in order, as most machine learning mechanisms that will power your AI require highly structured and labeled data for training. 
• Scalability. AI development may and will lead to increased workloads. If the system cannot grow alongside project demands, there will be performance issues, unexpected bottlenecks, and delays. To avoid these, some modernization steps are required. Different app modernization approaches are available based on your application’s structure and age. Some will require minor changes, while others will presuppose a complete rebuild. Yet, it is necessary to do everything needed to ensure successful future AI integration. 
• Security and compliance. AI solutions (especially those in healthcare or finance) work with sensitive data and, therefore, require the highest standards of compliance and security not to compromise the privacy of people using your services. For example, if you work with protected health information (PHI), the integration with APIs will be risky from the security and compliance standpoint.  

Once you have settled these points and ensured your business is AI-ready, you can start the integration process. 

Step-by-Step Integration Process

To successfully integrate AI into the existing application, you have to take the following actions: 

1. Define the AI Feature Scope

In the first step, you must decide what an AI feature will do and how this will help improve your business. This will be further translated into project documentation with in-detail actionable steps. 

At this stage, you have to evaluate your resources. It is important to determine whether your in-house IT team has the necessary skills and experience or whether you’ll need some AI consulting by experts. 

2. Choose the Integration Approach: API-based, built-in libraries, or custom-built solutions

In the second step, you will have to decide what AI integration approach you will use: APIs, built-in libraries, or custom-made solutions. 

The choice of the model will depend on the type of solution you need.

Numerous AI APIs exist for text-to-speech tasks, image and video generation, translation, language analysis, predictions, and more. These APIs give access to AI models that have already been trained and may be customized to fit your needs.

 Yet, if you need a solution tailored specifically to your business, such as a defect detection tool for aviation fuselage inspection, you might need custom AI development.  

3. Data Preparation: Clean, organize, and prepare data for AI use

Your AI model is as good as the data you put inside, so the next step is to make sure your data is ready for AI use. 

This will include checking if the data contains accurate information to be inherited by the future AI model. The flaws present in data lead to erroneous results provided by the model in the end. 

For example, if you train the AI model, that a black cat is white, it will identify all black cats as white in the future. In practice, the results might be catastrophic. For example, if you don’t clear a dataset for an image detection model used for predictive maintenance of equipment, it won’t be able to detect defects effectively, which may affect both business and human lives. 

Also, it is important to ensure that the data is relevant to the training task. For example, if you are building a credit score tool that has to detect whether a person will return a loan within a certain timeframe, information about a person’s hair color is irrelevant.

To understand which data is relevant to every particular task, the development team performs exploratory data analysis (EDA), which helps to understand which data will be relevant to a certain ML task, and which is not. 

Once you have ensured that all the data have correct and relevant information, you can move on to model training. 

4.  Model Training and Testing: Train the AI model using relevant data and test to meet evaluation metrics

Different types of AI problems require different metrics to evaluate the model. Accuracy is just one of them that can be used for classification problems. Even if you try your best to ensure data accuracy, the trained model may reveal bias, overfitting bugs, or unexpected outputs. 

To make sure the model you’ve trained is reliable for your application, you should test for bias, performance, robustness, or correctness. Every of these criteria has a specific set of tools and methods for their evaluation.  

Also, it is important to note that testing approaches vary as different ML tasks (object detection, text generation, sentiment analysis, etc.) are considered. 

5. Deployment: How to deploy the AI model into the existing application

Deploying the ready made AI model into the existing application requires exposing it through APIs or microservices. APIs enable the model’s interaction with the application, like sending data and receiving predictions. Microservices allow the deployment of the model in separate modules when each microservice can handle a specific part of the model. 

6. Monitoring and Optimization: Continuous monitoring and refining of AI performance

To ensure the ready made model performs as expected, you must track its performance, resource usage, and operation to detect and address issues proactively. 

Conclusion

Although implementing AI features is a complex, time-consuming, and cost-consuming process, it is often a necessary step to stay competitive in your business niche and meet rising customers’ expectations. Yet, sticking to the right integration plan will help you reap the most of your AI solution’s benefits while minimizing risks and expenses.