Most people conflate artificial intelligence (AI) and machine learning (ML) as the two technologies coexist, and ML is actually a branch of AI. So, it’s not a huge mistake to say AI/ML interchangeably, as long as you mean a solution using ML.
Today, we will examine the differences between AI and ML, discuss their utility, and explain how to choose the right one for your project. Using S-PRO’s experience in creating custom AI solutions, this guide will help you navigate the world of ML easily.
Definitions and Differences: AI vs ML
AI, or artificial intelligence, refers to solutions that leverage rules, expert systems, neural networks, and other advanced algorithms. AI is used in many ways, depending on the industry or company. For example, it might help predict trends, analyze data, or even power chatbots. When someone says they’re building an AI-based MVP, it could mean almost anything.
Conversely, machine learning is a narrower term. It refers to the process of designing, training, validating, and deploying models that can learn and improve from data.
In short, ML is part of AI, so the choice between them isn’t exactly binary. Do you want to leverage intelligent systems for automating operations or running data analytics? Then AI solutions, often powered by machine learning models, are what you’re seeking. However, if your goal is to develop or customize algorithms by training models with specific datasets—ML is the answer.
When is ML a Worthwhile Investment?
ML can be an essential way to improve things if you’ve identified the problems in your enterprise as a lack of efficiency and modern products. ML also helps internal systems refine analytical outputs, uncover actionable insights, and automate complex tasks.
For example, ML is great for personalization, which is usually attributed to AI in general. This makes it invaluable for those seeking to strengthen client connections and improve the effectiveness of their marketing and support.
Similarly, ML can do wonders in the financial sector, as it’s often used for fraud prevention and training banking systems to detect dangerous and suspicious transactions. It goes without saying that this specific function can save a lot of money and preserve a company’s reputation. So, if you’re in fintech and haven’t considered ML for your operations, you’re missing out.
When to Choose AI?
AI can technically do many of the same things that ML can because, as we’ve established, ML is a subset of AI. However, AI encompasses a broader range of technologies, offering additional capabilities. For example, AI use in intelligent automation systems capable of adapting or learning is incredibly common in healthcare, supply chain management, and e-commerce.
Companies can delegate routine processes under machine control by relying on AI-powered solutions. With their help, things like shipment scheduling, email campaigns, and booking will require minimal human intervention. Considering that the market is seeing more and more pre-made solutions offering these functions, AI is becoming an increasingly affordable way to streamline operations on a budget.
Time to Make Your Choice
Now that we’ve explained the differences between ML and AI and their use cases, you can choose the right technology for your AI app. Regardless of your goal and tools, the road will be easier with a tech partner.
Choosing the right team to develop your solution can be tricky. You need a partner with tons of experience, transparent processes, and a deep familiarity with AI and ML. Thankfully, you’ve already found us.
S-PRO has spent more than a decade delivering unique software with cutting-edge technologies to companies that want innovative solutions to their problems. We offer an excellent level of polish, building products that stand the test of time. Our team is proactive and always offers expertise based on their own experience, making development smoother.
So, if you want to ensure your AI-based solution is of top-notch quality and made by professionals, get in touch today.
