Upgrading your product using top-notch technologies like artificial intelligence (AI) is often considered the key to gaining a competitive advantage. Even during the pandemic in 2020, 47% of organizations left their investments in AI unchanged and 30% decided to increase their AI funding, according to Gartner.
AI can enhance processes across various industries. For instance, when applied in healthcare, AI can analyze thousands of MRIs and X-rays in minutes, helping therapists quickly identify abnormalities. In industrial production, AI solutions can improve quality control by processing a wide range of data from production lines, maintenance records and customer complaints.
But despite their promising potential, AI and other innovative technologies must be implemented with a focus on providing particular business values and meeting particular needs — not based on hype. Otherwise, you risk ending up with an over-advertised, overpriced yet under-performing project.
In this post, I’d like to share a few tips to help you decide whether your project needs artificial intelligence at all.
Four Things To Consider Before Implementing AI
Let’s be realistic: To make a reasonable decision on whether to adopt AI, you need to perform thorough research, which can take several weeks to several months. However, there are a few things you can consider right now to get a general idea of whether or not your project needs AI:
The Ethical Aspects Of Implementing AI
An AI code of ethics typically states that AI technologies should benefit and empower as many people as possible. Ask yourself whether using AI in your project will help people. Obviously, it’s not a good option to implement AI if you will have to dismiss your employees after doing so, especially since even the most advanced AI solutions aren’t as proficient, creative, and open-minded as humans.
The next issue to think over is whether you’re ready to deal with AI bias. Biased algorithms can produce inaccurate predictions, resulting in poor system performance or even unintended racial and gender prejudices. For instance, biased datasets are the reason many facial recognition solutions tend to misidentify women and people of color more often than white men. This is why IBM and Microsoft both put their facial recognition projects on hold in 2020.
Processes You’d Like To Automate
You can accelerate and simplify a lot of routine work using AI and machine learning (ML) algorithms. For example, financial institutions can leverage AI to quickly and securely offer personalized services to clients.
However, not every routine process requires AI-powered automation. In many cases, you can use rule-based automation software. Non-AI automation systems simply follow the rules you set for monotonous, repetitive tasks. In contrast, AI solutions can solve problems that require decision-making and can provide intelligent insights.
When my company worked on a project for detecting and measuring follicles in ultrasound images, we combined two approaches — one with AI and one without. To detect follicles, we developed a complex AI solution. But to measure them, we used a simple and efficient non-AI algorithm. In this way, we built a highly accurate system without exceeding the limits of our project resources.
The Volume Of Data Your Solution Should Handle
If your solution is supposed to regularly handle tons of data, adopting artificial intelligence is the right choice. AI algorithms can efficiently and quickly analyze huge quantities of image, text, video and audio files.
But keep in mind that you’ll also need to gather a lot of quality data for training an AI solution and evaluating its performance. For instance, my company worked on an AI-powered medical diagnosis solution that needed to process thousands of images to detect skin abnormalities and classify types of skin cancer. During this project, we had to pay close attention to data sufficiency to achieve a highly accurate AI model.
If your project doesn’t need to regularly handle thousands of files, look for specialized data analysis tools or services that can meet your needs. This will save you time and funds on unnecessary AI development.
To succeed in implementing AI, you must use your project resources wisely. Delivering a beneficial AI project from scratch isn’t always straightforward, and some unexpected pitfalls may appear. If you have strictly limited resources, take a look at ready-to-go AI solutions or maybe even non-AI alternatives.
For example, if you need to enhance your web application with translation functionality, you can simply implement the Google Cloud Translation API instead of developing your own online translator from scratch.
Another alternative for AI-based techniques is template matching, which allows you to find small parts of an image that match a template image. This technique can be based on either machine learning or non-AI algorithms depending on a task’s complexity. If your solution doesn’t require templates with strong features like color, shape and textures, you can use non-AI template matching. It can help you improve quality control in manufacturing, navigate robots and do many more things.
Implementing AI technologies can benefit your business so long as you clearly understand what values they bring and set realistic goals. Before rushing into building an AI solution from scratch, check whether there are ready-to-go AI solutions or efficient non-AI alternatives that can meet your needs. Not building an AI algorithm from scratch can free up resources for enhancing other parts of your project.