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August 21, 2025
Businesses often err by pursuing the latest AI trends without a defined objective. The most effective AI strategies do not originate from the question, “What can we do with AI?” They begin with, “What are our biggest business challenges, and can we measure them?”
Before examining a single AI tool or model, consider the specific operations it supports. The objective is to identify practical challenges that AI can address.
What goals are achievable in the next quarter or year? Be specific. Instead of “reduce maintenance costs,” aim for goals like “reduce maintenance costs by 15%” or “reduce equipment downtime by 12%.” Clear goals will help keep the focus on the expectations for the AI project.
To get the ideas flowing, ask questions like these:
Upon compiling a list of potential issues to address, the subsequent step involves identifying the initial project. The key lies in avoiding an overly ambitious undertaking. For this inaugural initiative, swift success is paramount—one that is both attainable and capable of yielding demonstrable value.
A great way to visualize this is with a simple Impact vs. Effort Matrix. Draw a four-quadrant grid like the one below. Label one axis “Business Impact” (low to high), and the other “Implementation Effort” (low to high).
The first AI project should come directly from the “High Impact, Low Effort” quadrant. These are the “low-hanging fruit” opportunities that can build momentum and get further buy-in from teams and management.
What does this look like in practice? Here are a few examples of starter projects that can significantly enhance operations for industries such as utilities, water treatment, telecommunications and the oil and gas sector.
Instead of reacting to equipment failures after they happen, this project uses AI to predict them. AI models train on historical data from sensors on industrial equipment, such as pumps, turbines, or pipeline sections, as well as maintenance records and repair logs. These models quickly identify the early warning signs of a breakdown.
This capability enables companies to achieve operational improvements by transitioning from reactive or scheduled maintenance to predictive maintenance. Technicians dispatched to fix equipment before it breaks down reduce costly unplanned downtime, extend asset life and improve worker safety.
This project focuses on utilizing AI to more accurately predict future demand for resources, such as electricity, water, or network bandwidth. AI algorithms analyze historical consumption data, along with external factors such as weather patterns, economic indicators and time of day, to create highly accurate demand forecasts.
For utilities, better forecasting enables the optimization of energy generation and grid load balancing, thereby preventing outages and reducing waste. For telecoms, it ensures network capacity can handle peak usage times, improving service reliability and customer satisfaction.
For industries managing vast networks of pipes, this project utilizes AI to pinpoint leaks and other operational issues quickly. AI systems monitor real-time data from acoustic sensors, pressure monitors and flow meters installed along pipelines. The models utilize the data to identify specific data signatures associated with events such as leaks, pressure drops, or equipment malfunctions.
This analysis enables the rapid detection and precise location of leaks, which is critical for conserving resources (such as water or gas), preventing environmental damage and ensuring regulatory compliance. The operational improvements include drastically reducing the time and labor required for manual inspections of miles of infrastructure.
An idea to utilize AI is just an idea until a plan accompanies it. A project roadmap transforms the chosen use case into an actionable project, detailing what to do, when to do it and how to determine if it’s working.
The first roadmap doesn’t need to be a 50-page document. It can be a simple outline covering these four key areas:
Creating an AI strategy may sound intimidating, but it ultimately boils down to a practical and logical process. By focusing on business needs first, companies can ensure that all efforts are grounded in creating real value.
The AI journey begins with a single step. A well-defined, conquerable first step provides the tangible evidence and momentum needed to champion broader AI adoption across the organization. By selecting an attainable project with clear, measurable metrics, companies are not only aiming for technical success; they are also building a case for future investment. Choose wisely, measure meticulously, learn, deliver value and start building a culture of innovation.
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