Artificial intelligence is the apparently intelligent behaviour by machines, rather than the natural intelligence (NI) of humans and other animals. In computer science, AI research is defined as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of success at some goal. Colloquially, the term "artificial intelligence" is applied when a machine mimics "cognitive" functions that humans associate with other human minds, such as "learning" and "problem solving".

Machine Learning (ML) is the subfield of computer science that explores the construction and study of algorithms that can learn from data. Such algorithms operate by building a model based on inputs and using that to make predictions or decisions, rather than following only explicitly programmed instructions.

ML “evolved from the study of pattern recognition and computational learning theory in artificial intelligence." It “explores the study and construction of algorithms that can learn from and make predictions on data—such algorithms overcome following strictly static program instructions by making data driven predictions or decisions, through building a model from sample inputs.”

For casinos, sports books, retailers and airlines, AI and ML can be used in the following ways:

  • Voice recognition
  • Voice search
  • Sentiment analysis
  • Flaw detection
  • Fraud detection
  • Recommendation engine
  • Facial recognition
  • Machine visions
  • Motion detection

Intelligencia has been working in the AI + ML field for several years now. We can explain all of the parameters to think about before making the substantial investment this field requires. Only one-in-three AI projects find success, so it's imperative to get honest opinions and accurate assessments before starting any AI project. 

So why should brands that aren’t software companies choose to go down the tricky and complex AI road? Well, in the article Artificial intelligence Unlocks the True Power of Analytics, Adobe explains the vast difference between doing things in a rules-based analytics way and an AI-powered way, including:

·         Provide warnings whenever a company activity falls outside the norm. The difference:

o    Rules-based analytics: You set a threshold for activity (e.g., “200–275 orders per hour”) and then manually investigate whether each alert is important.

o    AI-powered analytics: The AI analytics tool automatically determines that the event is worthy of an alert, then fires it off unaided. 

·         Conduct a root cause analysis and recommend action. The difference:

o    Rules-based analytics: You manually investigate why an event may have happened and consider possible actions.

o    AI-powered analytics: Your tool automatically evaluates what factors contributed to the event and suggests a cause and an action. 

·         Evaluate campaign effectiveness:

o    Rules-based analytics: The business manually sets rules and weights to attribute the value of each touch that led to a conversion.

o    AI-powered analytics: The AI analytics tool automatically weights and reports the factors that led to each successful outcome and attributes credit to each campaign element or step accordingly. 

·         Identify customers who are at risk of defecting:

o    Rules-based analytics: You manually study reports on groups of customers that have defected and try to see patterns.

o    AI-powered analytics: Your tool automatically Identifies which segments are at greatest risk of defection.

·         Select segments that will be the most responsive to upcoming campaigns:

o    Rules-based analytics: You manually consider and hypothesize about the attributes of customers that might prove to be predictive of their response.

o    AI-powered analytics: Your tool automatically creates segments based on attributes that currently drive the desired response. 

·         Find your best customers:

o    Rules-based analytics: You manually analyze segments in order to understand what makes high-quality customers different.

o    AI-powered analytics: Your tool automatically identifies statistically significant attributes that high-performing customers have in common and creates customer segments for you to take action on.