AI-Powered Analytics for Maintenance: A Buyer’s Guide

This article is part of our series on The Role of AI-Powered Decision Intelligence in Modern Maintenance Strategies. It’s a deep dive into why AI, decision intelligence, and advanced analytics are vital parts of modern maintenance strategies and how to successfully implement these technologies in maintenance operations.

AI-powered analytics platforms significantly enhance maintenance strategies. These solutions uncover actionable insights, streamline operations, and help maintenance analysts make smarter, faster decisions. However, selecting the right software can be a complex process that requires carefully considering business needs, vendors, and long-term value. 

This guide walks you through how to build the business case for AI-powered analytics and how to find the right one for your maintenance toolset.

Make Your Business Case For AI-Powered Analytics

Maintenance operations today are more intricate and mission-critical than ever. In both business and military environments, the ability to predict equipment failures, optimize resource allocation, and streamline operations is essential to success. This means that traditional maintenance practices, which rely heavily on reactive approaches, are no longer sufficient in the face of escalating operational demands and constrained budgets.

Investing in an AI-powered analytics and decision intelligence platform isn’t just a technology upgrade—it’s a strategic decision that can transform your organization’s decision-making processes. Use these steps to create a strong business case to your executive leaders:

1. Align With Strategic Goals

Connecting your maintenance goals and challenges back to broader business objectives is key to showing executives what’s at stake if things don’t change. For example, is the inability to lower downtime incidents leading to delayed deliveries or services, which in turn negatively impacts customer satisfaction and sales? 

By articulating how your team’s pain points roll up to business outcomes, you can paint a clearer picture of why AI-powered analytics will lift up the entire organization—not just your maintenance operations.

2. Quantify the ROI

Demonstrating potential return on investment (ROI) is crucial to proving value. You want to use vendor case studies and industry benchmarks to estimate how the platform will:

  • Reduce your costs by automating data analysis of all your disparate systems
  • Improve your revenue through better and more proactive decision-making
  • Enhance your efficiency by streamlining workflows and communication across maintenance teams

It’s also important to balance the short-term and long-term value when presenting your case. Short-term wins include quick improvements in operational efficiency, such as reducing downtime or streamlining routine maintenance tasks. Early victories will help justify the investment and build momentum within the organization. 

In contrast, long-term gains should focus on strategic advantages, such as improving mission readiness or enabling continuous learning through AI-driven insights. Over time, the organization gains deeper decision-making capabilities that can reshape operations on a larger scale. Organizations should ensure that their software investment aligns with both their immediate operational needs and future strategic objectives.

3. Involve Key Stakeholders Early

Engage leaders from departments like IT, finance, and operations to ensure alignment on the platform’s value and its integration into existing processes. Having cross-departmental buy-in strengthens your case, while also easing potential implementation challenges later.

Once you’ve made your case and gotten approval to start selecting a vendor, your next task is to ask the right questions. 

6 Questions to Ask AI-Powered Analytics Vendors

Selecting the right AI-driven analytics platform requires a thorough evaluation of its capabilities. Leverage these questions to help you better understand vendor offerings and avoid costly mistakes:

1. What Specific Use Cases Does Your Software Address?

Understanding whether the vendor’s solution aligns with your needs is the first step. Can it make a difference in high impact areas, such as proactive maintenance for equipment or streamlining resource allocation? By focusing your vendor conversations on your critical areas, maintenance teams will be better positioned to maximize the value of the AI investment from the outset.

2. How Does Your Software Handle Data Integration?

Ask how easily the software integrates with your existing systems, such as EAM, ERP or FSM platforms, and whether it can handle data from diverse sources. In order to support data-driven decision making, AI-powered analytics must be able to do the heavy lifting of ingesting immense volumes of complex and disparate data, and then analyzing it all to provide you with accurate insights that help improve overall operations.

3. How Scalable Is the Solution?

Scalability is a key factor, as the platform must be able to grow with your organization’s needs. This also means asking about integration since you want the solution to work seamlessly with your existing systems to avoid operational disruptions. If your industry has unique requirements—such as compliance with specific regulations or specialized analytics models—ensure the solution can also accommodate them.

The user experience must also be considered, ensuring that the interface is intuitive and accessible to even for team members without analytics or technical backgrounds. Again, early engagement from stakeholders in IT, operations, and leadership will help ensure that you select the solution that meets the diverse needs of your business and users.

4. What Are the AI Capabilities?

Dive into the specifics of the AI engine. Does it use machine learning, natural language processing, or other advanced techniques? How accurate are its predictions and recommendations? 

Find out if the platform has Explainable AI (XAI) capabilities. XAI is a framework that is interpretable by humans, enabling maintenance professionals to understand and trust the insights and recommendations created by their AI models. This increases the likelihood that decisionmakers will become more comfortable using the platform to build their decision intelligence muscle.

5. What Security Measures Are in Place?

Data security is paramount. Ask about encryption standards, compliance with regulations like GDPR, and the vendor’s disaster recovery plan. Virtualitics, for example, adheres to the strict security standards and frameworks of the U.S. Department of Defense, including meeting certifications like NIST SP 800-171, CMMC, and SOC 2 Type 2.

6. What Support and Training Do You Offer?

Vendor relationships play a crucial role in the successful adoption of an AI-analytics platform. When evaluating vendors, it’s essential to assess their commitment to training your team and providing ongoing support. This includes implementation support, such as guidance on data preparation and system configuration, and executive-level training if needed. 

It’s Time To Modernize Maintenance—For Everyone

Adopting an AI-powered decision intelligence solution is as much about people as technology. Once you’ve made your business case and found the right platform, it’s important to develop a change management plan alongside your vendor. 

It’s common for users to feel like the old way of doing things might be more comfortable, especially at the beginning, which is why it’s critical to take the time before and during implementation to champion the great potential that AI offers. Consider a pilot program with your top maintainers, where they’re trained to leverage the full capabilities of the solution and evangelize its analytics and decision-making tools to the rest of the team. 

With a thoughtful approach like this, you’ll be in the best position to maximize the benefits of AI-powered analytics and realize value from your investment.