A Step-by-Step Guide to Creating an AI Strategy for Maintenance

There’s a lot of pressure for organizations to deliver an AI strategy. Yet, just 1% of executives describe their AI rollouts as “mature,” and only 25% have a well-defined AI adoption roadmap, according to a 2025 study by McKinsey.

In this scramble to map uncharted territory, maintenance teams have a chance to stand out by delivering a strategy that connects AI uses to business outcomes. 

This article provides a framework for building a strategy that both frontline workers and executives can buy into and that you can confidently execute.

Check out the Maintenance Leader’s Quick-Start Guide to AI for more practical AI templates

Key takeaways

• Tie AI initiatives directly to business priorities and measurable outcomes to earn executive buy-in.
  
• Adopt a phased roadmap that delivers quick wins while building toward long-term AI maturity.
  
• Define clear KPIs, address risks early, and revisit the plan regularly to keep your AI strategy resilient.

How to build an AI strategy for maintenance in six steps

1. Find the best AI use case for your maintenance team

Landing on the wrong AI use case can waste time and goodwill from senior leadership and frontline workers. To avoid this fate, maintenance teams must be deliberate with their AI efforts by answering three questions:

What are the problems you need to solve?

“A lot of teams get stuck with AI they don’t use because they start with a solution instead of a problem,” says Roshan Satish, lead product manager for applied AI at MaintainX.

“The first step in building an AI solution is to identify a problem. This ensures value from the beginning.”

Before looking at any technology or workflow, list the biggest roadblocks for your maintenance team. Use this list to filter out the noise when building and executing your AI strategy.

What reliable data can you access?

“One of the biggest mistakes when adopting AI is not having a solid data foundation,” says Roshan.

Just like a car runs on gas, AI runs on data. The better the data, the more valuable (and reliable) the outputs. But you don’t need a year-long data project to kickstart your plans.

“You can start with the reporting you have and data you already collect, making sure it’s clean and well-structured first, then build from there,” explains Roshan.

Focus on AI use cases that use existing data you know is clean and can be structured easily. This might mean starting small, but your efforts will be more effective.

What existing processes and workflows can AI be integrated into?

The best AI initiatives support or optimize processes you already use.

“When thinking about how to use AI, you need a clear incentive for frontline workers,” says Roshan. 

“You need to be clear about the value it adds and the motivation to adopt it.”

Instead of designing a new way of operating that could tank adoption, look at processes that can be improved with AI.

See 7 AI use cases that maintenance teams are using right now (and how you can use them too)

2. Connect AI initiatives to business outcomes

Adopting AI is both an investment of resources and a shift in the way your team operates. This is inherently risky, which is why the best AI projects tackle something even more dangerous—a problem that’s draining resources. 

You need a framework for identifying high-level objectives, barriers, and opportunities, so you can present your plan as a solution. The first step in creating this framework is to highlight why the status quo is broken:

The second step is to present AI as part of the solution. This isn’t a deep dive into your strategy, but rather an overview of where you're investing in AI and why.

3. Build a phased plan for executing your AI strategy

The next part of your roadmap outlines how you’ll turn AI potential into AI progress using a few key elements:

• Execution: Projects you can deliver without many or any extra resources
• Phases: How you’ll take AI adoption from 0 to 1 and beyond
• Value: How you’ll deliver quick, quantifiable value

Here’s one framework you can use to showcase your strategy:

Repeat this structure for additional phases of your plan. In the example above, additional phases could include:

• Using work order data and optimized PMs from phase 1 to forecast parts usage.

• Automatically generating optimized procedures and work orders to reduce downtime.

• Implementing IIoT sensors and predictive maintenance, starting with the asset with the highest cost of failure.
  

4. Set clear goals and KPIs

Many AI projects fail because they’re expected to be silver bullets. When this isn’t the case, they’re abandoned. That’s why it’s essential to define and align on what success looks like.

There are two types of goals to set for your AI projects:

1. Outputs: What you produce with AI, like workflows, procedures, or insights.

2. Action: How you implement the outputs to improve operations.

Here’s a template for presenting your goals and KPIs in an AI roadmap:

5. Identify risks and create a mitigation plan

While there’s a lot of hype surrounding AI, companies also see risk in the technology and adoption process. Your roadmap should identify these risks, outline a plan to prevent them, and explain what you’ll do if they appear. 

This risk assessment framework can help you build confidence in your AI roadmap:

6. Highlight key decision points and immediate action

Below is a template for planning your next steps. This should be a living document that you and your team update as you make decisions or progress.

Your AI roadmap should be flexible, as technology, teams, and priorities all change. But the core of your strategy—the problems you’re solving and your approach to solving them—will create stability as you adjust. Revisit your strategy at set checkpoints to ensure your plans are on track and connected to the direction of your team and company.

Where to go from here: Building AI strategies that stick

AI adoption is still in its early phases for most organizations, but that’s exactly where maintenance leaders can create an advantage. By choosing problems that matter, tying every initiative back to business outcomes, phasing your roadmap for quick wins, and setting measurable goals, you turn AI from a buzzword into a lever for real impact.

The truth is, companies don’t need more AI experiments, they need strategies that work across the executive suite and the shop floor. Maintenance teams are uniquely positioned to deliver that. The organizations that treat AI as a disciplined, outcomes-driven roadmap, not a shiny tool, will be the ones that win.