Artificial Intelligence: Where We are Today and Its Impact on Tech Support Roles

A Brief Introduction David Tan – Chief Technology Officer Started my first IT Company in 1993 Embraced Managed Services around 2004 Cloud Services around 2008 Member of the CompTIA Emerging Technology Executive Council Started CrushBank in 2016

Life At A Help Desk Retention and Recollection Triage and Prioritization Proprietary Processes Breadth of Products Intimacy and Familiarity Problem Analysis

Understanding The Terms

3 Types of Machine Learning Algorithms Understanding The Foundation Supervised Learning Unsupervised Learning Reinforcement Learning 3 Types of Machine Learning Algorithms

Understanding The Foundation Decision Trees Naïve Bayes Classification Ordinary Least Squares Regression Logistic Regression Support Vector Machines Ensemble Methods Clustering Algorithms Prinicipal Component Analysis Singular Value Decomposition Independent Component Analysis

Understanding The Foundation Decision Trees Naïve Bayes Classification Ordinary Least Squares Regression Logistic Regression Support Vector Machines Ensemble Methods Clustering Algorithms Prinicipal Component Analysis Singular Value Decomposition Independent Component Analysis Some of real world examples are: To mark an email as spam or not spam Classify a news article about technology, politics, or sports Check a piece of text expressing positive emotions, or negative emotions? Used for face recognition software.

Understanding The Foundation Decision Trees Naïve Bayes Classification Ordinary Least Squares Regression Logistic Regression Support Vector Machines Ensemble Methods Clustering Algorithms Prinicipal Component Analysis Singular Value Decomposition Independent Component Analysis Regressions can be used in real-world applications such as: Credit Scoring Measuring the success rates of marketing campaigns Predicting the revenues of a certain product Is there going to be an earthquake on a particular day?

Understanding The Foundation Decision Trees Naïve Bayes Classification Ordinary Least Squares Regression Logistic Regression Support Vector Machines Ensemble Methods Clustering Algorithms Prinicipal Component Analysis Singular Value Decomposition Independent Component Analysis Identifying Fake News Targeted Marketing Classifying Network Traffic Identifying Fraud Fantasy Football

Getting Started

What Do I Need To Start? Foundation Skills Team Data Data Data Processing Power (GPU) Massive Storage Capacity Security Skills Team Mathematics / Algorithms Probability & Statistics Development – Python / R Distributed Computing Unix Advanced Signal Processing Translator Data Scientist Data Engineer Visualization Analyst Workflow Integrator Delivery Manager

Internal Use of AI in Tech Support Cognitive Search Chatbot / Automated Support Ticket Routing / Triage Customer Service Predictive Analytics “Self-Healing” Networks

What To Ask Question 1: How does it work? What data is used/how is it used? How is artificial intelligence used? What is the integration/installation process? Question 2: “What have been the results?” Looking for proof Beware of being the guineapig Examining AI case studies

What To Look For Outside Data vs. Inside Data Inside Data – you supply from your business Outside Data – pre-trained / useful data from the vendor Is the data accessible? AI Expertise vs. Buzzwords and Hype Case Studies with tangible benefits / ROI / Value

Case Study – Service Ticket Classification Challenge Client Background Solution & Benefits The process of handling support requests to a help desk can very cumbersome and expensive for any IT department or MSP. Tickets need to be categorized, prioritized, and then assigned to a technician quickly to remain within pre-defined Service Level Agreements time, all the while working to make sure technicians aren't overloaded, and that items do not get missed.  This process must be performed quickly so as to not negatively impact performance on a help desk, and traditionally, it has been a person (or group of people) assigned or dedicated to meeting these requirements. Often times tickets also need to be shuffled around and reassigned to meet changing needs. This is a process that is wrought with issues of bias towards certain resources, lack or understanding on the operator’s part, and widespread inefficiency. This was a process screaming to leverage the power of AI and machine learning.

Case Study – Attorney Time Entry Review Challenge Client Background Solution & Benefits Our client, a national law firm that does work for large insurance companies was struggling with getting paid in a timely fashion for work performed. The issue is the insurance firms put a sophisticated series of rules around how and why time can be entered and billed, and attorneys didn’t do a good job of following the rules. To counter this, we build a middle tier application that evaluated all time entries based on the models we created from millions of accepted and rejected time entries. This application allows the attorneys to fix flagged entries that would likely have been rejected. As a result, we went from an acceptance rate of about 60% to close to 90%.

Case Study – Document Release / Redaction Challenge Client Background Solution & Benefits This client does document retrieval and release for auto insurance companies. The issue is that there are several types of documents and or data points that can’t be released. Including some internal communication and certain health details. As a result, the process was very manual and time consuming. We built a bolt-on to their workflow application that we trained on the types of documents and data that can be released legally. As a result, this manual process became completely automated and allowed them to repurpose all the employees doing the review to much more important job functions.

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