7 min read

How to Integrate AI Into an App: A Practical Guide and Essential Considerations

Integrating AI into a released product is one of the most working ways to increase its competitiveness. If you don’t want to lag behind, here’s how to upgrade your app with AI in 7 steps, from defining the right goals to choosing a suitable AI model.
Written by
David Schoch
Published on
April 11, 2024
Read time
7 min read

Around 42% of enterprise-scale organizations actively use AI in their businesses, while 59% plan to increase investment in the technology, as stated by the IBM Global Index.

AI integration is a complex project and this is the reason why many companies are hesitating to implement the technology. We’ve already written about the best strategy to integrate generative AI for business. In this article, we would like to share some key practical steps on how you add AI to your existing solution.

But first, let’s discuss why you shouldn’t stay aside when everyone is trying to get the most out of using AI.

AI transforms operations and drives revenue growth

While integrating AI won’t revolutionize your business overnight, it can significantly enhance your operations and increase revenue, setting you apart from the competition.

For example, the implementation of AI technology within the popular no-code website builder Wix has notably bolstered the conversion rates towards its premium subscriptions, which are crucial for the company’s revenue flow. The company used OpenAI’s GPT-4 to develop AI Text Creator. This tool generates compelling website content in response to user prompts, saving Wix users a lot of time.

One more example of successful AI integration is Salesforce. The company teamed up with OpenAI to integrate generative AI into its Einstein 1 Platform. This collaboration enabled Salesforce to offer out-of-the-box generative AI capabilities for various use cases. By leveraging AI, Salesforce’s applications streamline workflows, such as generating personalized service replies, leading to faster and more personalized customer support experiences.

Today, every other company is striving to adopt AI, and many have already done it successfully. You can check out our article about real-life applications of AI.

And now, let’s focus on the central question of this guide – how to incorporate AI into your project.

How to integrate AI into your app

AI integration is a huge and difficult project, but you don’t have to take a big-bang approach. Instead, we recommend doing it gradually. Start by defining project objectives and iteratively move step by step to ensure none of your business processes are disrupted. As a result, you will be able to minimize risks and optimize costs along the way.

With that said, let’s take a closer look at how you can successfully integrate AI.

Define clear objectives

Start by outlining your business goals and what you’re planning to achieve by integrating AI into your app. Avoid broad general goals like “improve the customer experience” or “accelerate sales”. Instead, focus on narrow objectives such as “reduce the waiting time” or “recommend products that a customer might like”.

In the context of optimizing manufacturing processes, a specific goal might sound like this: “Reduce machine downtime by 20% through predictive maintenance”. In this case, we’re focusing on a particular issue (machine downtime) and specify the desired outcome (a 20% reduction) achieved through a specific method (predictive maintenance powered by AI).

Assess data requirements

Any AI system heavily relies on the data it is trained on, so you should prepare a strong data foundation for your project. Here is a quick checklist on how to do that:

  • Identify relevant data sources like CRM, ERP, or third-party datasets.
  • Evaluate the quality of the data and cleanse it by removing duplicates, and correcting errors, inconsistencies, and irrelevant information.
  • Ensure compliance with data governance policies, industry standards, and privacy regulations, such as GDPR or HIPAA.
  • Implement measures to protect sensitive data and maintain data integrity throughout the integration process.
  • Finally, integrate data from multiple sources into a unified dataset using a loosely coupled tech architecture. With this approach, you can seamlessly deploy new AI models without causing any disruptions to the overall system.

Choose the right AI model

An AI model itself is a program pre-trained with specific data sets to make predictions and come up with decisions. When picking out an AI model for your specific case, you should consider such factors as the type and characteristics of your data, the complexity of the problem you’re trying to solve, the availability of labeled data, and the computational resources required.

For example, if you want to build an AI-powered chatbot, you should use an NLP model. Computer vision models are well-suited for medical imaging applications. If you’re working on autonomous driving systems, employing deep learning models specialized in sensor fusion and perception tasks can significantly improve the vehicle’s ability to understand its environment and make informed decisions in real time.

Here are some of the most popular AI models/algorithms:

Selecting the right model is crucial to ensure that model outputs are relevant, reliable, and usable for your organization’s needs.

Select a technology stack

When considering technologies for your AI project, Python emerges as the most popular choice due to its extensive support for implementing core AI capabilities such as NLP, image recognition, and more. Python’s rich ecosystem of libraries and frameworks, including TensorFlow, PyTorch, and scikit-learn, provides a wide range of resources for training sophisticated AI models.

When it comes to databases, MySQL and PostgreSQL are the most fitting options for storing and managing data essential for AI integration projects.

Train AI models

As mentioned, any AI-related project relies on the quality of data the AI model is trained on. The more relevant data you feed the model, the more accurate the results will be. Fine-tuning the AI model to ensure better performance and accuracy is crucial at this step.

Your development team must design robust mechanisms for feeding data into AI models that ensure compatibility and effective communication. Typically, the model shows low accuracy at first, so you should repetitively expose it to rounds of tests to improve accuracy and rectify any issues. When conducting tests, it’s important to use data sets distinct from those used during the model’s training.

Mind ethical considerations

While AI can help streamline business operations, we shouldn’t forget about the potential risks it raises, like algorithmic bias, explainability and transparency, reliability, regulatory compliance, and more. For instance, when trained with biased data, an AI model can provide biased output leading to unfair outcomes for certain individuals or groups.

One more issue is related to integrating “black-box” AI models which presents challenges related to accountability and transparency. While being highly accurate, black-box AI models lack explainability, which makes it hard to understand how they make decisions. Additionally, human control is crucial for ensuring that AI systems align with human values and ethical standards. People should remain in the loop to make sure AI systems follow ethical guidelines and societal norms.

The steps covered will help you streamline AI integration into your solution, but the project’s success also depends on the software development team responsible for it. Here are some tips on how to find a reliable tech partner.

Partner with a trusted AI implementation partner

Before launching a project, assess the expertise within your internal team and identify areas where additional support from external specialists may be needed. When shortlisting tech companies, pay attention to the following aspects:

  • Expertise. Look for partners with a proven track record in AI-related projects and technical capabilities required to build an AI-powered solution, such as expertise in machine learning, natural language processing, computer vision, and deep learning. Explore the company’s case studies and client testimonials specific to your industry or domain.
  • Technical resources. Verify that your partner possesses the necessary computational resources, like CPUs, GPUs, or TPUs to adequately support your project requirements.
  • Pricing. Make sure your potential partner provides transparent pricing, considering factors such as upfront costs, ongoing expenses, and potential return on investment.
  • Project timelines. Discuss project timelines with a potential partner to ensure alignment with your schedule and deadlines.

After choosing a tech company, assemble a team that includes both business and AI experts, and concentrate on achieving the specific objectives you’ve outlined. Modeso can help you with that. With our proficiency in custom software development, we can help you streamline the implementation of AI-powered projects that align closely with your business goals.

Reach out to us today to explore how we can seamlessly integrate AI into your business operations.

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