Pros and Cons of Automation and Machine Learning: How it Will Impact the Workforce

Automation has been proven to extremely important for lowering production cost and driving revenue growth. However, as a result of the nature of automation, the customization and personalization of services and products has taken a hit. Considering that customization is driving force for customer loyalty, finding how to efficiently customize a company’s offerings can be a big hurdle for companies using automation. Machine learning on the other hand is specifically designed to help companies more efficiently analyze data to help with the customer experience across multiple industries. However, the ML process is still prone to errors and is not really suited for smaller companies that cannot generate enough data to make the algorithm efficient.

Pros of Automation

1. Lower production costs

  • One of the main advantages of automation is that it naturally lowers production cost, as the maintenance of machines costs less than manual labor and are able to work without break.
  • Machines also tend to be more precise and efficient in their work, which boosts productivity.
  • Automation further allows for shorter lead times, quicker delivery and a more efficient use of stock and cash flow.

2. Higher revenue generation

  • The purpose of automation is to increase production and lower overhead costs, which would lead to higher revenue opportunities.
  • As the efficiency of work increases due to automation, a company can solely focus on implementing an economy of scale in order to stimulate its growth.
  • Moreover, as the routing tasks are handled by a machine, the employees of the company have more time to focus on how to increase the revenue of the company.
  • Research has even shown that jobs that are routine in nature serve to suck up profit rather than generate it.

3. Can potentially create more jobs

  • While it is undeniable that automation will most-likely reduce the availability of some positions, it has also inspired a greater demand in other tech-related sectors.
  • For instance, the rising demand for AI and machine learning has inspired people to pursue careers related to those fields more and more.
  • Research has also shown that there has been a rise in the number of students in STEM-related fields.
  • Over 33% of new jobs in the United States were created for occupations that didn’t exist 25 years ago.

4. Higher convenience for customers

  • Automation allows for a firm to become more creative with its products.
  • Firms that use automation can provide a higher variety of choices to their customers, allowing them to choose the size, look and function of the product they want to buy.
  • Moreover, automation allows for a company to constantly produce and maintain a higher quality process, which is crucial in delivering a consistent brand experience and building trust.

Cons of Automation

1. No Customization

  • While automation may allow for a greater variance in the product creation, personalization and customization of specific offers and products through automation is not possible.
  • A machine can only do a limited scope of tasks, as it was designed to do a single task over and over again.
  • This means that for something to be personalized or tailored to the specific needs of a customer, the system needs to be reprogrammed.
  • Unfortunately, as automation is solely focused on efficiency, reprogramming the process to create something unique for a few customers would most likely not sit well with companies that are trying to maximize their profits.

2. Higher unemployment rates

  • Automation has contributed greatly to the rise in unemployment rates, especially in the US, Western Europe, and China.
  • Over 25% of the jobs in the United States will be at risk of automation job displacement by 2030.
  • It is estimated that by 2022, 42% of total task hours will be completed by machines.
  • The sectors most at risk of losing jobs to automation are the transportation, storage and manufacturing sectors.

3. Lower quality of life

  • While automation does increase the profit margins of a company, it does not necessarily mean that it can also increase the quality of life of its employees.
  • It has actually been shown that, while the profits of companies that use automation has increased, median wages have stagnated in the US.
  • Automation may also lead to the loss of human contact, increasing the sense of loneliness and separation.
  • Automation has also had an impact on the segregation of employees by creating an environment of winners and losers in companies that have employed the use of automation.

Pros of Machine Learning

1. Easily identifies trends and patterns

  • One of the biggest advantages of machine learning is that can easily identify patterns in different data sets.
  • This means that the algorithm is great for finding trends and customizing the work experience to the customer’s needs.
  • For instance, machine learning has been able to help insurance companies to calculate risk more accurately and it has allowed many companies using e-commerce to build their recommendation engines.

2. Efficient at handling data

3. Wide range of applications

Cons of Machine Learning

1. High chance of errors

  • The machine learning process has been proven to be susceptible to errors, as there are multiple variables at play that need to align together for it to function properly.
  • As ML usually operates with big data sets, errors will inevitable accumulate. The problem is that since the data set is so big, finding those errors is nearly impossible.
  • Unfortunately, if there are any errors in the data, all the subsequent analysis will be flawed.
  • This can be especially devastating for the marketing of products as a small mistake in the predictive algorithm can negatively impact an entire marketing campaign.

2. Takes a lot of time, space and resources to set-up properly

  • While machine learning improves its efficiency and time of processing over time, it still takes time and resources until the algorithm is fully operational because the training process needs to be run on all available algorithms to determine the best outcome.
  • The usual process of ML learning takes several repetitions of data learning and algorithm selection for the process to become efficient and error-free.
  • As ML handles large amount of data, its computing requirements are also considerable.
  • It is entirely possible that the operating system runs out of space during an ML analysis or it consumes more CPU power than predicted, which can lead to crashes or other problems.

3. Needs a huge amount of data to be successful

  • Machine learning is designed to work with a big amount of data to be efficient.
  • If the data generation by the company is too small, then there is a high chance that the algorithm will be biased in its analysis.
  • This means that smaller companies that do not generate or use a lot of data would not be able to use the service.
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