In diverse industries—from technology startups, Cloud providers, software developers, and cybersecurity firms, to financial services, gaming, retail, energy, electronics and manufacturing, and more— virtually any sector where system reliability is non-negotiable, managing changes effectively is critical to success. Tools like ServiceNow (News - Alert)®, Jira®, or similar applications are the backbone of project management, incident tracking, and release cycles, enabling teams to coordinate updates, patches, and deployments across complex ecosystems. However, a single poorly executed change—lacking thorough testing, clear validation, or a robust rollback plan—can lead to costly downtime or performance degradation. This risk is amplified during peak traffic periods, such as holiday shopping seasons, major retail sales events, or global gaming tournaments, when systems must perform flawlessly under intense demand. Based on my significant experience in software engineering, and particularly financial services and video gaming, I propose an AI-powered solution to transform change management, streamline reviews, and ensure reliability during these high-stakes periods.
The challenge of change management in high-vigilance periods
Change management is a structured process in which organizational Change Advisory Boards (CABs) or similar teams review tickets to assess scope, impact, and risks. In large companies, with cross-functional teams and interconnected applications, this process becomes increasingly complex. During peak seasons—holiday transaction surges in financial services, Prime Day sales in retail, or gaming tournaments—teams enforce strict oversight, banning non-critical changes to prioritize system stability. Critical changes, such as fixes for downed applications or network security updates, still require CAB approval, creating bottlenecks.
While working in the financial services industry, I participated with teams that meticulously planned for peak seasons to handle high transaction volumes. Video gaming companies similarly prioritize server uptime during global events, while retail firms focus on e-commerce stability during sales. However, the current process has significant limitations, including:
- Manual incident linking: tools like ServiceNow or similar platforms allow linking incidents to changes, but this manual process is often skipped, missing opportunities to learn from past issues.
- Limited dependency analysis: reviews typically focus on direct dependencies, overlooking indirect ones or historical patterns, increasing the risk of unforeseen impacts.
- Time-intensive CAB meetings: pre-peak CAB meetings, spanning multiple time zones, are highly inefficient. For instance, over a two-week period, daily 90–120-minute meetings with 75–100 participants (leadership, CAB members, change owners) can consume approximately 1,531 hours, with teams waiting to present even low-risk changes.
- Information overload: static reports and cluttered User Interfaces (UIs) hinder prioritization of high-risk changes.
- Last-minute chaos: late change submissions, absent from downloaded reports, disrupt reviews, adding to the chaos.
These inefficiencies waste valuable time, divert teams from critical development tasks, and heighten the risk of disruptions, particularly during high-vigilance periods when reliability is paramount.
A transformative AI-powered solution
To address these challenges, I propose an AI-driven change management solution that leverages historical data, streamlines reviews, and enhances system reliability. Designed for companies using ServiceNow or similar tools, this solution comprises the following five key components.
- A user-friendly, component-based, fast and scalable User Interface (UI) web portal for real-time data: an internally hosted portal—built with appropriate tools based on the company’s user requirements—integrates with ServiceNow’s (or similar tools)’ APIs to fetch real-time change data. Displaying changes in a customizable table—rows for each change, columns for description, impacted systems, and status—the portal allows reviewers to filter by risk score, department, or application. A real-time analytics dashboard highlights trends (e.g., percentage of high-risk changes), and asynchronous review capabilities enable global teams to collaborate across time zones without live calls, replacing static reports and handling last-minute submissions seamlessly.
- Python®-based AI scoring model: A Python script, using AI libraries like GPT-4 or company-preferred alternatives, trains a model on historical change tickets, incidents, requests, and dependency mappings. The model scores changes from 10 to 100 based on defined criteria: changes with successful histories, robust rollback plans, and clear validation steps score higher (e.g., 90), while those with unknown impacts or multiple dependencies score lower (e.g., 30). It predicts optimal change windows (e.g., low-traffic periods) and auto-generates draft rollback plans for risky changes, ensuring comprehensive risk assessment.
- Intuitive UI with scoring and notes: the portal’s UI includes a column for each change’s AI-generated risk score, detailing its calculation (e.g., “Score: 88; based on six prior successes, no incidents”) and referencing related changes or incidents. A notes section allows CAB teams to add comments directly, such as “Auto-approve; score > 80” or “Needs testing; risky dependency.” Auto-approval rules for high-scoring changes (e.g., >75) free CABs to focus on high-risk cases. Decisions and notes can be exported as CSV or PDF reports for sharing or audits.
- Automated notifications: an AI communication agent, integrated with Slack® or email, sends targeted notifications. Change owners are alerted only when their change enters the top 10 in the review queue or requires a CAB call, reducing idle meeting time. For low-scoring changes, the agent suggests improvements (e.g., “Add latency checks; past changes caused delays”). CABs can request specific stakeholders to join discussions, keeping meetings efficient.
- Incident analysis and prevention: the solution extends to incident analysis, linking CAB notes and mitigation measures to build a knowledge base. The AI identifies patterns (e.g., “Database changes often spike latency”) and recommends preventive actions for future changes, fostering continuous improvement and system resilience.
Benefits across industries
This solution delivers transformative benefits that impact multiple stakeholders and teams across various enterprises and functional departments, particularly during high-vigilance periods. For example:
- For credit card companies and banks, holiday transaction peaks demand reliability. The AI prioritizes critical fixes (e.g., security updates), reduces CAB time, and ensures uptime, while exported reports support regulatory compliance.
- As of 2024, the global e-sports tournament industry market size is estimated to exceed $2 billion. Major international video gaming events require stable servers. Low-risk changes (e.g., UI updates) are fast-tracked, and risky ones are scrutinized, maintaining player experience. Reports aid post-event analysis.
- In the retail industry, big sales events like Amazon Prime Day™ and Target (News - Alert) Circle™ Week need e-commerce stability. The AI prevents risky changes, the portal manages late submissions, and reports align teams.
By saving ~1,531 CAB hours per peak period, reducing downtime, and leveraging historical insights, the AI solution enhances operational efficiency and compliance across these industries.
Implementation considerations
Organizations seeking to deploy this solution can follow this implementation outline to achieve the desired outcomes:
- Train the AI model: use historical data from ServiceNow or similar tools, retraining quarterly to stay current.
- Develop the portal: build a secure web portal with API integration, export functionality, and cloud hosting for scalability.
- Integrate additional tools: support platforms like Datadog or Azure DevOps for monitoring and project data.
- Configure notifications: set up the AI agent for Slack or email with queue and CAB call alerts.
- Pilot and scale: test in one department, refine based on feedback, and roll out company-wide.
- Ensure security: implement role-based access and encryption for sensitive data.
- Define cost and value proposition.
It is important to note that implementing this solution will require significant investment, but the return on investment (ROI) is substantial and measurable. A reasonable estimate of necessary expenditures includes: developing the web portal ($60,000–$120,000 over 3–6 months); training the AI model ($25,000–$60,000), integrating with tools ($15,000–$25,000); and annual maintenance ($15,000–$25,000). Yet the employee labor savings that can be achieved by eliminating 1,531 CAB hours per peak period at an average $50/hour employee rate equates to approximately $76,550. Preventing downtime—which has the potential to cost $1 million per hour in financial services or millions in lost sales for retail—further amplifies the value. Enhanced compliance through exportable reports and proactive incident prevention add long-term benefits, making this a strategic investment that scales across teams and industries.
In industries where peak-season reliability is critical, change management inefficiencies can jeopardize system performance. This AI-powered solution—combining a real-time web portal, a predictive AI scoring model, automated notifications, and incident analysis—transforms the process. By saving thousands of hours, reducing downtime, and building a framework for continuous improvement, it ensures systems remain robust when demand is highest. The investment is a fraction of the cost of outages or wasted time, delivering unparalleled efficiency and reliability. It’s time to harness AI to redefine change management and keep our systems ready for the spotlight.
Author:
Prem Kumar is Senior Principal Engineer at SambaNova Systems, a technology company designing purpose-built enterprise-scale AI platforms that was named #4 on Fast Company’s “Most Innovative Companies of 2025” List. For the past decade, Prem has specialized in cloud infrastructure provisioning, Kubernetes orchestration, incident management, change management, product management, and automation. As a Site Reliability Engineer, he has applied his significant expertise deploying leading-edge tools and cloud providers to support Fortune 500 companies worldwide, most recently for the top global brand in the financial services industry. Prem earned an M.S. in Electrical and Computer Engineering with a focus on Security from Tennessee Technological University, and postgraduate diplomas in AI & ML Business Applications and Data Science and Business Analytics from the University of Texas. His professional certifications include RHCSA and RHCE 6 licenses. Prem is a Senior Member of IEEE (News - Alert).