As we delve further into the world of technology and digitalization, it’s important to understand the benefits of what’s in front of us. Artificial intelligence (AI) machine learning (ML)and are the foundation of an entirely new approach to how we run our businesses. We now have the tools to embrace this digital frontier—from fighting off cyber threats to enhancing the way we market to customers.

According to a McKinsey & Company study, 50% of companies have adopted AI in at least one business function.[i] AI and machine learning have the power to put hours back into your day, if you know how to harness them.

How AI and machine learning work together.

We know that AI and machine learning are inextricably linked, but how? Artificial intelligence refers to the science of training machines to perform human tasks. Defined sometime during the 90’s, this evolving technology aims to mimic the way our human brains interact and gather information from the world around us. Where AI is the broader science, machine learning refers to the specific subset of AI that trains a machine how to learn. By looking for patterns and drawing conclusions on data, machine learning models can artificially establish a point of view. So instead of writing code that tells the machine exactly how to think, we can now simply ask the right questions and let the computer calculate. Once your machine learning algorithm understands all the available data, it’s able to apply that knowledge to new sets of data—increasing accuracy and performance.

Other subfields of AI.

While machine learning uses analytical models to surface data insights without being prescribed what to think, it’s not the only technology being used. Here are other subfields of AI being leveraged today:

Deep learning.

As a subset of machine learning, this AI technology changes how we think about the relationship between problem solving and analytics. Instead of training the computer how to think, deep learning lets the data train the computer—leading to predictive models that become stronger with each set of data its fed. Most commonly used in features using speech recognition or image identification today, deep learning doesn’t need us to facilitate how it organizes data. For example, if a deep learning machine was designed to tell the difference between a rock and a baseball, the machine would use neural networks to identify the stitching characteristic as a sign it’s a baseball—as opposed to being programmed to look for that detail.

Neural networks.

The most effective way to explain neural networks is to think of them as a human brain. Instead of neurons working together, they use interconnected nodes to identify correlations within raw data. Located between an “input layer” and “output layer,” these nodes form a network of connections that interact with one another to calculate an output. The more information the network receives, the deeper the computational power becomes—similar to the way a human brain develops.

Computer vision.

Upon the introduction of digital cameras and images, this AI subfield became an inevitability. Computer vision refers to the ability to accurately identify and process objects in the visual world. The computer can acquire the image in several ways—through real-time pictures or video, most commonly seen in facial recognition software. The computer then uses deep learning models to process properties within the image, based on a robust collection of pre-labeled images in its memory. From there, computer vision can identify the object.

Natural language processing.

Like computer vision, natural language processing (NLP) explores the auditory side of AI technology. NLP allows computers to process, understand and produce human language—bridging the gap between human communication and machine understanding. As a pioneer technology in the world of computational linguistics, NLP raises the capability ceiling by intaking larger sets of data in the form of language variability, accents, or slang. In fact, approximately 3.25 billion people used voice-activated search and assistants worldwide in 2021—almost half of the world’s population.[ii] 

Where AI & machine learning are helping businesses.

It’s common to think about AI as something to resist or be worried about, but when leveraged correctly there are many benefits. Businesses can optimize operations, shed manual processes, and move faster. According to Forbes, 76% of enterprises prioritize AI and machine learning over other IT initiatives in 2021.[iii] Here are the top benefits:

Improved customer experience.

There might not be anyone who benefits more from AI technology than customers. Eliminating the lag between customer needs and business responses has become possible with automated chatbots, triggered emails, and other personalized messaging systems. Using deep learning and NPL, it’s never been easier to provide timely, tailored experiences for customers. Additionally, it takes the strain off your customer support teams—increasing efficiencies while eliminating manual workflows.

Reducing errors.

Once the foundation of your AI and automation models are established, you’ll notice manual errors starting to disappear. Remedial tasks like data processing or onboarding become background processes—not because they’re no longer important, because there’s no longer a need for thorough oversight. Small errors simply disappear because the machine only understands accuracy.


You can’t talk about the speed that comes with AI and machine learning without mentioning automation. The most common output of AI, there’s not a business process that automation can’t positively impact. From communications and marketing to internal onboarding and support, the technology feature can remove inefficiencies from every corner of your business. For example, automation in sales boosts productivity within the department by 14.5%—while bringing down marketing costs by 12.2%.[iv]

Additionally, taking manual workflows out of your organization frees up resources for ideas and projects that were seemingly unavailable. With automation, businesses replace the minutia of small tasks with the freedom to think strategically about the bigger picture.

Decision making.

The goal of AI has always been to generate smarter decision making. It’s not that we’re not able to think critically as humans, we’re just limited in how quickly we can process and coordinate mountains of data. AI takes the job of delivering data, analyzing trends, and forecasting results—while taking the human emotion out of it. It’s able to take raw data and translate it into an objective decision.

Tackling complex problems.

Introducing deep learning and machine learning into business strategy allows you to take on more complex problems. These technologies make it possible to not only find solutions, but at scale. From problems with customer support operations to cybersecurity threats—implementing AI into your solution gives you a foundational approach that saves time, money, and resources.

Increase operational efficiencies.

Between automating your most repetitive tasks and expanding your operations with AI, your business will see an immediate increase in efficiencies. Want to open your support lines for additional hours? You don’t have to worry about AI chatbots working overtime. Worried about the overwhelming amount of data entering your system? Automation never burns out. In fact, the estimated improvement in business productivity by using AI is 54%.[v]

The future of AI & machine learning.

As companies look to scale and expand their businesses, AI and machine learning are powerful tools that can help get them there faster. Moreover, AI and machine learning are becoming table stakes for companies looking to remain competitive within their industries. With the right tools in place, your company will improve customer satisfaction, reduce errors, and increase operational efficiencies. And as deep learning technologies continue to develop, the future of these tools will only become more powerful.

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