How to Automate Your Boring Tasks with Machine Learning

8 categories of less-than-exciting tasks ideal for automation.
By Claudia Virlanuta • Updated on May 18, 2023
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Today, many time and money-consuming tasks can be automated with machine learning. So if you’re a business owner or executive, now is a great time to ask yourself: what activities can be automated in my business? Could automation help my employees eliminate repetitive—and often tedious—tasks?

A 2017 study by McKinsey & Company, a global management consulting firm, estimates that 30 % of business tasks done by 60 % of occupations can be automated using artificial intelligence, machine learning, and robotics. Professional services firm PricewaterhouseCoopers (PwC) conducted a similar analysis covering twenty-nine countries in a report titled, “Will robots steal our jobs?” In their report, PwC predicts that automation could replace around 30% of jobs by the mid-2030s.

In this article, you’ll find eight examples of less-than-exciting tasks that you can automate through machine learning (and may even be handed off willingly by your employees).



How to Automate General and Administrative Work with Machine Learning

Your finance and accounting employees likely work with plenty of spreadsheets. Luckily for them, spreadsheet tasks can typically be easily automated. Check out our case study with ANZ Bank for inspiration on automating financial workflows using Python.


Other areas where many tasks can be repetitive, yet detailed, are legal and compliance and records maintenance. For example, things like copying data from one system to another or assessing risk can be done more quickly—and likely more accurately—by machine learning models. However, due to possible bias, many companies are wary of using machine learning in these areas.


How Machine Learning Can Help with Human Resources and Talent Acquisition

You can take a load off your human resources department by implementing machine learning in hiring and career planning. You can train machines on data from previous successful applicants to help recruiters make a hire and use similar data to model your employees’ ideal career paths. This way, you can build a team designed for success. By letting machine learning models use employee profile data, they can match your employee’s goals, experience, and interests with opportunities within your organization. You can also use machine learning for predictive analysis to mitigate the risk of losing employees and ease HR’s concerns about employee retention.


hr decision tree machine learningImage Source: Machine Learning in HR,


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How Machine Learning Can Help with Business Intelligence and Analytics

Businesses in any sector can use machine learning to take large quantities of data and use it to help maximize performance. Not only can machine learning models analyze your data and identify potentially profitable relationships, but they can also do so faster than your fastest human employee. Check out our Independent Health case study for some ideas on how to make your data analysis workflows more efficient.



How Machine Learning Can Help with Software Development

Writing repetitive code can try the attention span of even your most enthusiastic developer. Because code is modular, you can let machine learning tools generate code independently. Many apps have already been built that automatically produce portions of code for various uses, such as web design.

Intelligent programming assistants can be a great tool if you want to decrease the time spent on reading documentation or debugging code. Some examples of intelligent programming assistants include Kite for Python and Codota for Java. Additionally, you can use machine learning-based software tools that automatically flag errors within a system with automatic analytics and error handling.


How Machine Learning Can Help with Marketing

Many businesses already use machine learning to optimize digital ads. Machine learning models can make decisions about product pricing, ad placement on websites, and which ads to show to a particular person. Later in this article, I will tell you how you can take personalization one step further by applying machine learning to your email marketing campaign. Machine learning can also help you accurately attribute revenue to advertising because it allows you to eliminate the human bias involved in this challenging task.



How Machine Learning Can Boost Sales

Marketing teams use customer segmentation to divide customers into groups in order to target ad campaigns to the right people. Sales teams also use customer segmentation to group prospects and customers to target sales promotions, incentives, and account division within a sales team.


Previously, segmentation was a time-consuming task but now, machine learning models can quickly process customer data and find customer segments that would otherwise be complicated for a human to spot. They can also make accurate predictions about potential and current customers, which in turn can help increase the efficiency and productivity of your sales development reps.

Once it’s time to analyze your sales, you no longer need your employees to painstakingly input data into Excel. Instead, you can use machine learning tools to generate sales predictions to plan for the future of your sales.


ai used in marketingImage Source: Machine Learning used in Marketing,


How Machine Learning Can Help with Customer Support

You’ve probably already encountered conversational agents, such as digital assistants, chatbots, and autocomplete, in your day-to-day life. But have you considered implementing a conversational agent to deal with, for example, your business’s FAQs? With ChatGPT, creating a chatbot is a much simpler task than it used to be.

Another important aspect of customer support is dealing with unsatisfied customers, who can churn and abandon your brand. Afterall, your churn rate is a significant indicator of the health of your business. To help deal with this, you could, for example, use machine learning for sentiment analysis to identify potential churners so that you can take action and gain them back.


Another way to retain your most profitable clients is to know your clients’ lifetime value. While this may sound difficult, machine learning can help you predict lifetime values and identify your key customers.

Automating tasks using machine learning in your business can be a challenging but rewarding decision. In the face of potential economic slowdowns, automation can help you increase efficiency and stay competitive.


How Machine Learning Can Help with Content Creation

With the advent of generative AI such as ChatGPT, new avenues of using machine learning to create content have become available. Marketing online is an essential part of reaching new customers, and that usually includes brainstorming new ideas, creating a content calendar, writing articles, creating scripts for videos, and much more. Nowadays, you can speed up content creation by automating many of these tasks.

Contemporary AI models can also help you deliver content to potential customers through email marketing. Email marketing has always been essential for increasing outreach, but traditionally, creating an efficient email marketing campaign has been difficult. You may have used templates to make the task less repetitive in the past, but this also made the emails sound bland and uninspired. Machine learning models can ensure that your marketing emails feel personalized and unique because AI models can take a template and quickly make it better suit each customer you are trying to reach.

Machine learning can revolutionize how we approach mundane and repetitive tasks by automating them with remarkable efficiency. As we advance technologically, the demand for rapid and precise solutions will only grow, making machine learning-driven automation increasingly vital. Embracing these intelligent systems can streamline your workflows and enable your team to focus on more creative and innovative pursuits, ultimately driving progress across various industries.


Claudia Virlanuta

CEO | Data Scientist

Claudia Virlanuta

Claudia is a data scientist, consultant and trainer. She is the CEO of Edlitera, a data science and machine learning training and consulting company helping teams and businesses futureproof themselves and turn their data into profits.

Before Edlitera, Claudia taught Computer Science at Harvard, and worked in biotech (Qiagen), marketing tech (ZoomInfo), and ecommerce (Wayfair). Claudia earned her degree in Economics from Yale, with a focus on Statistics and Computer Science.