Machine learning and artificial intelligence approaches: how they can be game-changing for businesses

What do you need to do first when embracing machine learning and artificial intelligence approaches? Why should you adopt them? Find the answers to those and more questions in our new article.

From enhancing supply chain efficiency and improving customer service to helping healthcare providers deliver better care. The uses of machine learning (ML) and artificial intelligence (AI) approaches are as broad as handy.

Maybe that’s why Gartner says that, by the end of 2024, 75% of the companies will change piloting AI technologies for operationalizing AI. Plus, the consulting organization forecasts that those companies will enjoy a 5X increase in streaming data and analytics infrastructures.

If you’re considering being one of the companies that jump into the ML and IA approaches, this article will help you learn a little more about them. If you aren’t sure about embracing them, here will give you the reasons why you should, with some good examples of what can be done and the benefits those uses can bring to businesses.

Tidy up your data first

Let’s explain a little bit more about how ML and AI approaches work, so we can dive deep into why organizing your data first is key. AI and ML are transforming the way data is being processed and analyzed.

For example, think about the power of ML: it lies in its ability to find patterns in historical data and even predict outcomes for new input. This can not only save a lot of time if we compare it to doing that manually or by using traditional tools but can also help companies be ahead of risks and potentially improve their operations.

It sounds tempting, doesn’t it? Even more when you realize that the tools you use at your company can produce more and more high-quality data to be harnessed. But here’s the thing: all processes, metrics, and data infrastructure must be arranged to implement AI and ML approaches correctly.

Data is the fuel for those models, so it must be set up to keep them running smoothly. Make sure that data governance, analytics, and quality control are being handled in the right way. Another thing to keep in mind is that your team must be empowered with data skills. As you can see, driving ML and AI approaches is more than just incorporating technology into your processes.

5 Uses and benefits of driving ML and AI approaches in businesses 

Making sure that data management is on track and that your data is high-quality and worth enough to be used as input for ML and AI, can have pretty good benefits. We’re going to explore some common uses and their advantages.

1 – Helping healthcare providers save lives

Utilizing AI for healthcare is maybe one of the most game-changing uses because it can genuinely make a difference in profits and in saving lives. Previously, healthcare providers used data to diagnose, treat, or manage medical conditions. Nowadays, they still do but with a major difference.

When they incorporate AI, this technology can provide them with real-time alerts to make faster decisions. In healthcare, responding on time can be life-saving, for instance, in situations like a stroke.

But AI can help them beyond the urgencies. Healthcare providers can get help from this technology to diagnose diseases and find treatments faster when AI is fueled with patient data.

2 – Hi, I’m a chatbot! How can I help you?

You must have heard about chatbots in businesses. Especially when it comes to customer service. If we combine a chatbot with AI capabilities, the result will be enhanced customer support. A chatbot empowered with AI can have voice-activated interfaces and instant messages settled to quickly and successfully answer customer questions.

Plus, because keeping your clients happy seems like it’s not all, chatbots can even help you in data collection and analysis, improving customer insights so you can make data-driven decisions faster to improve the customer journey.

3 – Lending more than a hand to e-commerce organizations

E-commerce companies are currently relying on AI to tailor the buying experience. For instance, this technology can show customers products that meet their needs according to their preferences and even to what they’ve previously added to their shopping cart.

ML has a nice role to play here too. When it’s used to enable computer vision, for instance, analyzing uploaded images can pave the way for tagging and organizing products under categories, brands, and sizes.

It can also help e-commerce companies deal with a vast frenemy: inventory. ML has the power to make inventory forecasts taking internal and external variables into account.

4 – Keeping the supply chain optimized

The supply chain can be a headache for many manufacturers, retailers, and e-commerce companies. That’s why AI raises a hand to say “I can help”. Many of those players are using an AI solution to improve their supply chain efficiency because it can do everything ranging from recommending inventory, transport, and dispatch to reducing delays and human mistakes that can result in losses for the company. 

5 – Creating a better workplace

AI can help organizations better their workplace and HR management. On the one hand, it can be used to support collaborators in prioritizing and planning their must-do lists. How? By taking into account urgency, effort, and the amount of time, the collaborator might need to spend to finish the task. This is an interesting use to improve productivity in times of remote work.

On the other hand, AI can be empowered to identify patterns of disengagement before a collaborator quits. If we see this in-depth, it can translate into an improved work environment because collaborators’ issues and concerns will be addressed proactively before they become a solid reason to resign.

Bonus track: AI and ML match with DevOps

Another trend in this industry is DevOps. The good news is that both AI and ML can take DevOps to the next level. For instance, you may think about how AI and ML approaches can help DevOps relax while automating repeatable and daily tasks.

Plus, AI can support DevOps in making data more accessible for teams. While AI and ML together can even ease and make application development more efficient. These are just two of the many benefits AI and ML can deliver to the DevOps environment.

AI and ML matter to all the companies that want to keep their processes streamlined and deliver better services to their customers. This is just the tip of the iceberg of a digital transformation that organizations are already embracing. The heart of it all is data and staying up to date with the latest technologies. We have our blog for that. Explore here the latest tech trends.