AI (or artificial intelligence) is a topic that has been making headlines for several years now. The technology has been applied in a variety of industries and use cases, but the most common ones are in marketing and customer service. In this article, we will explore how to use AI-powered apps to improve your organization’s operations. We’ll outline why you should consider using these technologies, explain some key considerations before bringing them into your company’s workflow, and show how you can integrate them into your app architecture.
To delve deeper into building AI-powered apps and enhancing your organization’s capabilities, read more.
Software developer’s workplace – illustrative photo. Image credit: Farzad via Unsplash, free license
Understanding AI and Its Applications
The term “AI” refers to a variety of technologies that allow machines to “think” and make decisions. This can include everything from simple algorithms that predict your next move while playing chess, all the way up to systems that can identify objects in images and understand human speech.
AI has many applications today, including:
- Speech recognition: The ability for computers to convert audio signals into text (and vice versa). This can be used in dictation software or as part of chatbots like Alexa or Siri on your smartphone.
- Image recognition: The ability for computers to classify images based on what they see within them (such as faces) and then use this information for applications such as facial security authentication or tagging photos on social media platforms like Facebook or Instagram with hashtags like #dogsofinstagram.
Identifying Appropriate Use Cases for AI
The first step to building an AI-enabled application is identifying the right use cases for AI. This is a challenging task because there are so many potential uses for machine learning and deep learning algorithms, but not all of them will be appropriate for your business.
The most common use cases include:
- Image recognition (e.g., identifying objects in photos or videos)
- Speech recognition (e.g., converting speech into text)
- Natural language processing (e.g., understanding what someone means when they say something)
Planning and Defining Objectives
Before you start building your AI-enabled app, it’s important to define your objectives and goals. What do you want to achieve? What problem are you trying to solve? Is there an existing solution that could be improved upon, or does someone need this problem solved in order for them to be able to do something they’ve never been able to do before?
If the answer is yes (the latter), then congratulations! Your next step is defining some tangible fitness goals that will motivate and inspire people who have never had them before. These might be small steps at first, but once they’re achieved they can help lead towards bigger ones over time: maybe one day someone will use your app as part of their daily routine because they know it’ll help them reach their long-term goal of living life without limitations due solely through technology (and not just because “AI” sounds cool).
Choosing the Right AI Technologies
- Choose the right AI technology. AI technology is a tool, and as with any tool, it has its strengths and weaknesses. Choosing the right AI technology for the job can mean the difference between an app that works well for your users and one that fails miserably.
- Choose the right AI technology for your data. Every data set has unique characteristics that define how it should be processed by machine learning algorithms and some approaches work better than others depending on what kind of information you’re trying to extract from them (or “train” into) an algorithm. For example: If you have a lot of personal photos with faces in them but no other personal information attached (like names), then face recognition software would probably be a good fit; however if those same photographs include captions or titles indicating who’s pictured within each picture frame then perhaps text classification would be better suited (because they contain both visual cues as well as textual ones).
Integrating AI into App Architecture
The next step is to integrate AI into your app’s architecture. As you’ve probably guessed, this involves deciding which kinds of data should be processed by AI and which ones should be left for humans. The first thing to keep in mind when deciding whether or not an actionable item should be automated is whether or not it falls within one of these three categories:
- Improves user experience (UX)
- Improves efficiency/effectiveness/ease-of-use for customers/users/employees
- Protects against security threats
Developing AI Models
AI models are software programs that use machine learning to make predictions. They can be trained on large amounts of data and then used to make predictions in a variety of applications, including fraud detection and image recognition. For example, if you have an AI model for predicting whether someone is at risk for heart disease based on their medical records, you could use that information to offer them preventative care or even recommend lifestyle changes based on their individual risk factors.
In order for an AI model to work properly, it must be trained with relevant data; otherwise its predictions will not be accurate enough for your needs (or even worse: they could lead you astray).
Ensuring Data Privacy and Security
Data privacy and security are two of the most important issues to consider when building AI-enabled apps. Ensuring that your data is not compromised, used in ways that are not intended, or used for other purposes requires careful planning on your part. These considerations include:
- How will you collect and store user information?
- Who has access to what types of data?
- Are there any potential security vulnerabilities with how you store this information (e.g., if someone were able to access it)?
We’ve covered a lot of ground in this article, but we hope you have a better understanding of how to integrate AI into your app. AI is a powerful technology that can help make your app more engaging and useful for users, but it’s important to choose the right use cases and build them correctly so they don’t negatively impact performance or user experience.