What's the point of AI?

Artificial Intelligence (AI) is now part of our daily lives. Interacting with Siri, Bixby, and Alexa on your smartphone, using Google maps to calculate the most effective way to get from point A to point B, interconnected Smart Home appliances, and so on.

ISO 2382-28 defines Artificial Intelligence as a branch of computer science dealing with models and systems for the performance of functions generally associated with human intelligence, such as reasoning and learning. In other words, we are speaking about computer programs and algorithms aiming to analyze data, automate tasks, optimize systems, and make predictions based on historical data.

For large enterprises, simply said, AI helps to amplify the value of your company.

Fears of AI

Despite the urban legend, the goal of AI is not to replace humans with computer programs. The goal is to automate simple and trivial tasks to relieve employees of busywork so they can devote their attention to more important tasks that best make use of their skills.

Keep in mind that AI is only as good as the creators of the algorithms. There are AI usages such as facial recognition, resume screening, etc., where the author of the original algorithm could unintentionally introduce some bias. While this is an understood challenge to IT professionals, the general fear of AI has much deeper roots; it stems from the unknown, and... from the film industry!People are often anxious about AI because they see scary things in movies. For example:

• The Terminator: We are afraid that machines will become "intelligent", take over, and we won’t be able to control them.
• The Master of the World: We fear automation will drive mass unemployment of human workers.
• Robocop: We are anxious that "bad people" could be doing "bad things" with AI.
• The Matrix: We worry that AI intelligence will eventually supersede human intelligence, and humans will be placed at the rank of animals and treated as such.

AI Adoption

Artificial Intelligence is no longer science fiction. According to the survey by Accenture, "84% of C-suite executives believe they must leverage artificial intelligence (AI) to achieve their growth objectives". As we see occur with many new technologies, private sector companies are the first in line to harvest its benefits:

• Human-like interaction via Machine Learning chatbots
• Automated support system (ticket routing and automated answers to common questions)
• Access control via facial or voice recognition
• Sales enablement to identity new leads or buying patterns
• Augmented marketing to discover market insights and deploy them at scale
• Assisted recruitment to digest larger numbers of CVs and better match talents

AI to better at understanding humans (Contextual Intelligence)

AI is made by humans, and interestingly sometimes aims to better understand human behaviors and decision-making factors. Contextual Intelligence refers to the ability to adapt and apply what has been learned in real world scenarios. In a sense, it helps enterprises to develop their corporate emotional intelligence to create new products or improve existing business services adapted to their target customer base. It is used by enterprises to deepen customer engagement, using data collected from customer surveys, and interactions with customer-facing applications or customer-facing employees.

AI to optimize IT

Another aspect of AI is to improve IT systems operations. Machine learning can help find the root cause of an outage, suggest possible solutions based on IT specialists' historical actions, and thus help resolve the issue in a timely manner. With enough historical data upon which to train the algorithms, it can even predict problems before they occur, based on patterns of events that might otherwise seem normal behavior to IT specialists. If the prediction is accurate, actions can be taken to prevent the problem from occurring in the first place.

AI accuracy

Rome was not built in one day, and Machine Learning will not be perfect by tomorrow. All current AI systems produce false positives, which result in time wasted chasing nonexistent problems. When false positives are too frequent, AI may not deliver on time and money savings promises. You can mitigate this risk by using a more sophisticated approach that considers the confidence level of a ML result set. Humans can review low confidence predictions manually, and the result of their analyses can be used to train the model in order to decrease the number of false positives over time. This implies short term additional labor costs, but consider this a necessary investment to achieve your longer-term business goal.