Information Technology Intern - System Operations/AI

Discount Tire•Scottsdale, AZ

53d•Onsite

About The Position

Discount Tire Systems Operations Intern Location: Scottsdale, AZ Duration: 10-12 weeks (Summer 2026) Discount Tire is the world's largest independent tire and wheel retailer, serving 38 states across the United States with more than 1,200 stores. The company is dedicated to providing the best tire and wheel buying experience in the industry, with a focus on providing excellent customer service and high-quality products. The intern will be responsible for a significant project during their internship. The project aims to demonstrate how Agentic AI can be integrated with ServiceNow to automate core IT Service Management (ITSM) processes and deliver actionable insights. The intern will design and prototype an AI-driven framework that automatically creates Change records, Problem tickets, Knowledge Management articles and Incident reports based on system alerts, monitoring tools, or user inputs. As a Discount Tire Intern, you will have the opportunity to work with a team of experienced professionals and gain practical experience in a fast-paced, fun, and challenging environment. You will have access to senior leadership and visibility to multiple facets of the business of Information Technology, with a program mission of "providing high potential students' knowledge and experience to guide them in future career decisions." Additional program specific activities will provide team building opportunities and the requirement to present to senior leaders within the IT business segments.

Requirements

Currently enrolled in an accredited college or university, pursuing a degree in Computer Science, Information Technology, or a related field.
Must be a currently enrolled undergraduate student with a GPA of 3.5 or higher.
Only Junior or Senior-level students or May 2026 graduates are eligible.
Strong knowledge of computer hardware and software systems.
Ability to work independently and within a team environment.
Excellent communication and interpersonal skills.
Ability to multitask and manage multiple priorities.
Willingness to learn and take on new challenges.
ServiceNow Fundamentals - basic understanding of ITSM modules (Incident, Problem, Change) and familiarity with ServiceNow APIs or scripting (JavaScript, Flow Designer, IntegrationHub).
Programming Skills - proficiency in Python or JavaScript for AI integration, data handling, and automation scripts.
AI/ML Exposure - awareness of Agentic AI concepts, ability to work with APIs from AI platforms (OpenAI, Azure, etc.), and some experience building or consuming AI-driven workflows.
Data Analysis & Visualization - skills in analyzing ticket data, identifying patterns, and creating dashboards (ServiceNow Performance Analytics, Power BI, or Tableau).
System Integration Knowledge - understanding of REST APIs, JSON, and connecting external AI services with ServiceNow.
Problem-Solving Mindset - ability to analyze ITSM workflows and identify where automation adds value.
Analytical Thinking - skill in drawing insights from data, not just automating processes.
Collaboration & Communication - comfortable working with IT teams, explaining AI-driven recommendations, and documenting processes.
Adaptability & Curiosity - willingness to learn new ServiceNow features, AI tools, and enterprise IT processes quickly.

Responsibilities

Research & Design: Assess ServiceNow capabilities, APIs, and current ITSM workflows to identify automation opportunities.
AI Agent Integration: Enable AI agents to analyze event data, determine whether a Change, Problem, or Incident is required, and create the appropriate ServiceNow record.
Analytical Insights: Develop reporting and dashboards that use AI-driven data to identify patterns, root causes, and trends across tickets.
Proof of Concept: Deliver a working prototype within ServiceNow that demonstrates reduced manual effort, faster response times, and improved ticket accuracy.
Documentation & Recommendations: Provide technical documentation, lessons learned, and recommendations for scaling to enterprise-wide adoption.