Full Screen

AI vs. Human

Start a FREE Trial View Course

More Free Lessons in

Data and Analytics View All →

Get cutting-edge digital marketing skills, know-how and strategy

This micro lesson is from one of our globally recognized digital marketing courses.

Start a FREE Course Preview Start a FREE Course Preview
Global Authority

The Global Authority

12 years delivering excellence

Members

245,000+ Members

Join a global community

Certification

Associate Certification

Globally recognised

Membership

Membership Included

Toolkits, content & more

Digital Marketing - Study Notes:

It is important to recognize when to use AI or machine learning versus human critical thinking. Machines will offer new ways to complete tedious and repetitive tasks, but there are some tasks a machine will struggle to complete, and many tasks that a machine simply won’t be able to complete as effectively as humans can. Knowing when and how to deploy machine labor and human labor is crucial to ensuring a maximum return in your strategy. Here are some examples of when to use AI and when to use human critical thinking:

AI is suitable for:

  • Repetitive Tasks
  • Data sets too large for humans to process
  • Situations subject to Human Bias

Human critical thinking is needed for:

  • Creative solutions
  • Subjective situations
  • Empathetic emotion-based scenarios
Back to Top

Digital Marketing Resources:

Jack Preston

Jack Preston is a Data Scientist working within marketing analytics, with a particular focus on strategic customer loyalty. Jack has experience working in both small-scale startups and large corporates, including dunnhumby and Notonthehighstreet. He also holds an MSc in Business Analytics from UCL where he graduated with distinction.

ABOUT THIS DIGITAL MARKETING MODULE

Analytics
Jack Preston
Skills Expert

This short course covers the principles of analytics and demonstrates techniques and useful tools that you can use to develop and refine your knowledge of data analytics.

You will learn:

  • The fundamentals of data, collecting data, and processing data, including best practices, techniques, and challenges
  • The principles of web analytics, the benefits and limitations of Google Analytics, terminology for reporting, and the legalities around consent and data privacy
  • The concepts of Big Data, the processes around data, including mining, scraping, cleansing, and de-duping, and the various languages and programs for testing your data
  • The importance of AI, Machine Learning, analysis types, the value of testing hypotheses, and forecasting based on the data available
  • How best to report and present data findings to management and the different tools available to you

Approximate learning time: 3 hours