Benefits of Machine Learning to Optimizing Your Team’s Productivity

Benefits of Machine Learning to Optimizing Your Team’s Productivity
Article by DesignRush DesignRush
Last Updated: October 11, 2022

There’s an incredible demand for technology to be smarter than ever. In the US, the machine learning market is on trend to increase from $100 million in 2018 to $935 million in 2025.

The algorithm needs to predict what show you want to stream next, find a similar product to what you’re searching for, or recommend places to stay for your next vacation. Though it’s a highly convenient function, it simplifies processes so small you may be surprised when it doesn’t work the same.

In this article, learn the benefits of machine learning, how to develop automation for your work processes, and how you can apply the same to your operations and products.

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A Brief Guide to Machine Learning

You may be surprised at all the tools machine learning has optimized. It’s present in most, if not all, of the platforms you use daily.

It began in 1962 when self-proclaimed checkers master Robert Nealey played checkers against a computer and lost. The game software, which was the first known application of machine learning, was developed by IBM executive Arthur Samuel.

With this, we learned that a machine can learn and predict patterns following probability. How, then, can we benefit from this function and improve efficiency?

What is Machine Learning?

First, let’s learn more about machine learning. Also known as AI training data, it's a branch of artificial intelligence (AI) and computer science focused on imitating the way humans learn and improve their functions through data and probability algorithms.

There are different models of machine learning, such as the following:

  • Semi-Supervised Machine Learning – Your team actively works to support your machine learning systems, with certain tasks managed by humans and other tasks handled by machines.
  • Supervised Machine Learning – The majority of your systems are managed by your machine systems, with your team overseeing that everything is operating as required.
  • Unsupervised Machine Learning – The advantage of setting up a comprehensive machine learning system is that it can manage itself and operate as well as can be expected, with little to no corrections needed.

How Does Machine Learning Work?

Machine learning can be used for several operations. This is why integrating it into your workflow can be a significant boost, no matter what industry your organization is focused on.

The following are the three main operations of machine learning, as established by UC Berkeley:

  • Data Processing After Data Input – This step establishes a baseline that your system can use to set up a course of action for each step or stage in the process. The system “learns” when it knows what action to take for each one, along with the ability to organize and improve the workflow accordingly. This is how your system processes information added to the platform.
  • An Established Error Function – The system compares newly acquired information to previous data to determine if there are errors in the function. This can inform you about your system’s accuracy, how well it can predict trends and how you can improve.
  • System Optimization Process – Building a process that reads, evaluates, and optimizes the information is at the core of machine learning. The system is functional when it processes data, corrects errors, and adjusts to a faster and more accurate model.

Where is Machine Learning Used Today?

Below are some examples of the tasks and operations that allow your organization to reap the benefits of machine learning and AI:

  • Audio Recognition – The Internet of Things is closely intermixed with our regular peripherals and household items, many of which have vocal command functions. This involves receiving vocal prompts and processing voice-to-text commands.
  • Automated Functions – With machine learning, you can automate many of your team’s workflow processes. This includes internal communications, external marketing content, customer query management, operations and admin tools, and more.
  • Chatbots and Moderators – Receive and manage customer inquiries more efficiently with a chatbot to moderate your channels. You can also set up quick replies for frequently asked questions, your operations schedule, and an option to talk to an online chat agent.
  • Detecting Potential Fraud – Intelligent machine learning can predict risk potentials through an established pattern. This can be integrated into your chat moderation, security and anti-virus program, and blocking dangerous links and files shared within the organization.
  • Feedback Analysis – Language machine learning can assist you in receiving suggestions and recommendations from your audience. This can be an arduous task for big organizations, so automating the analysis process can help your team focus on practical tasks.
  • Image Processing – Image recognition software can be used across many industries. Applied in the medical field, machine learning and AI can detect familiar imagery and assist medical professionals in diagnosing illnesses. In smartphone technology, it can enhance and stabilize photos taken on your mobile device.
  • Precision Medicine – Chemical compounds and pharmaceutical solutions are science, and the data and information can be digitized. Installing these into a platform and programming with an algorithm to understand and predict diseases can significantly impact the creation of effective treatment options.
  • Search Engine Results – Perhaps the most everyday result of machine learning, search engine optimization (SEO) allows people easy access to the most relevant information. This is thanks to a programmed algorithm that is constantly updated and improved, largely automated by machine learning.

Uses and Benefits of Machine Learning for Your Enterprise

Machine learning is applicable across all industries. Making systems smarter supports your team in their day-to-day operations, automating repetitive and tedious tasks that tend to slow them down. This makes machine learning an essential step in optimizing your team’s productivity.

Think of the many different tasks and processes you manage every day – how many of them could you automate? Moreover, which ones require analytical judgement? How can your organization realistically integrate machine learning into your workflow?

We break down various machine learning benefits for your business below:

Benefit #1: Processing Information Is Faster and More Efficient

39% of leading businesses have been able to free up workers for more creative functions thanks to task automation.

Optimization and data processing is at the heart of machine learning. This makes data collection and analysis a core function that you can expect to improve and streamline when you integrate machine learning into your operations.

For example, email management is a common workplace task that may need to be analyzed before going to the next step. This is especially critical to receiving customer queries that include private information.

With machine learning systems, you can program your platforms to automatically gather data to inform your other applications. This allows your team to focus on building and developing tools.

Benefit #2: More Accurate Market Forecasts and Data Analytics

Working with AI has resulted in a reduction of 20% in forecast errors. In lowering margins of error in prediction data, companies effectively plan and allocate resources, which results in increased productivity.

In machine learning, once you integrate the data collected into the system, this enables machine learning processes to analyze and predict trends on behalf of your team. This eases your team's workload and significantly lowers the probability of critical errors.

Benefit #3: Personalizing Social Media Feeds and Marketing Channels

Social media is programmed to encourage continuous use by viewers. Platforms keep this up by personalizing users’ social media feeds and email marketing content.

With AI marketing and machine learning, you can set up an algorithm that analyzes people’s preferences and predicts what their preferred content would be. This system can also consider the values your customers appreciate the most about your brand, so be sure to create content that reflects them.

Benefit #4: Effective in Reducing Operating Costs

Automating operations will come with an upfront cost, especially when you’re setting it up for the first time.

That said, it will save your company on future costs as your team becomes more efficient and productive. In fact, 80% of professionals have attested to AI helping to increase their revenue.

This is potentially why 84% of C-suite executives are eager to invest in AI to boost their operations and achieve their growth objectives.

How To Start Integrating Machine Learning for Business

Before you can make the most of machine learning benefits for your operations, you have to prepare your process and see how best to integrate the automation system appropriately.

What does your current business model comprise? For companies just starting out, it may be easier to shift gears and set up machine learning systems as part of your infrastructure right away. It may be more challenging for established corporations with a long-standing process, but it isn’t impossible.

Similar to the software development process, planning strategically is essential and will inform your decisions moving forward. What are the basic steps to machine learning you should start with?

Below are some points to guide you in setting up machine learning systems:

  • Take inventory of your organization’s workflow and collate the operations you want to improve.
  • Make a list of tasks that slow you down and how you can streamline them.
  • Keep in mind your short-term and long-term goals.

Here are some questions you can use to self-check and help you decide in your future machine learning operations:

  • Is your database built on a public or a private cloud?
  • How open or restricted will controls be for your machine?
  • What is your process for contributing new information?
  • Will this be monitored by a data scientist team or a limited administrator?
  • What’s a feasible schedule for evaluating your machine learning efficiency and how you can improve?

Answering these questions will inform how you can set up machine learning for your workplace, as well as any infrastructure or tools you should prepare before the AI process.

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