Crystal Ball 2025: The opportunity for AI-powered digital transformation

As we look toward 2025, we anticipate the manufacturing industry will continue to face many of the same challenges seen over the past few years—unstable supply chains, skilled labor shortages, sustainability concerns, and economic uncertainty. Industry trends indicate manufacturers are increasingly turning to data, AI, and machine learning to overcome these challenges.

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The push toward digital transformation remains strong, with data-driven decision-making and predictive maintenance taking center stage. Businesses will continue prioritizing efforts to collect more data and use the stream of data from IoT solutions to inform their decision-making.

One method by which manufacturers are transforming is adopting smart tools for their day-to-day processes. In addition to being used to execute the immediate task at hand, these tools support the collection and aggregation of data used to perform the task.

This data can then be used with AI to make recommendations for the next steps and enable deeper insights into decisions made not just on the plant floor but across the organization.

However, this transformation cannot depend solely on the integration of new smart tools. It hinges on the combination of all collected data from the plant floor, which includes information from smart tools, condition monitoring, maintenance records, production data, and other sources.

This comprehensive data collection empowers organizations to ask and answer critical questions: Repair or replace? What replacement parts are needed for this problem? How much will labor cost? What inventory do we need to replenish? When should we order it?

A continued focus on smart tools will revolutionize manufacturing for frontline workers. The future of industrial plant maintenance is evolving, and it’s clear that electricians and plant technicians are experiencing a fundamental shift in how they interact with machines.

Smart tools are critical for driving efficiency, safety, and success. Supported by edge computing, cloud connectivity, and AI, they are more than just measurement devices—they analyze and provide actionable insights, evolving from basic tools to intelligent agents.

For example, customers are using thermal imagers to scan hundreds of drywall panels in just minutes, pinpointing defects before they lead to costly recalls or legal issues, ultimately protecting businesses from significant financial risks and empowering workers on the frontline.

Investing now in the future workforce

Filling skilled labor roles right now is challenging, but we have the tools to take steps that will alleviate the challenge in coming years.

As an industry, we need to continue focusing on what we can control: automating processes through AI to relieve teams, adopting new technology, training to upskill existing employees, and ensuring knowledge transfer from those set to retire.

Most critically, we need to keep investing in the next generation of trades persons by partnering with schools, universities, and development programs.

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Organizations are continuing to make significant investments in continuous learning and upskilling to keep their teams abreast of emerging technology trends. Companies are also establishing partnerships with trade schools and universities to create a pipeline of skilled employees.

We’re losing institutional knowledge along with our retiring skilled workforce, leaving behind a major experience gap. But with AI, organizations can capture that knowledge in an information storehouse.

With this information stored and accessible, organizations can use it to help upskill less experienced workers, document and share procedures across worksites, and retain experienced insights for the next generation of employees.

Stabilizing supply chains with PM and connected reliability

When we talk with customers, there’s a continued emphasis on building resilient supply chains, driven by concerns over the disruptions of the past few years. Building adaptability and transparency into supply chains has become crucial. There's also uncertainty around trade headwinds, raising questions about the reliability of overseas suppliers.

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As a result, businesses are exploring ways to integrate adaptability and transparency by reshoring or seeking alternate suppliers outside of potentially problematic regions and adopting dual-source strategies for critical equipment.

Technologies such as AI and predictive maintenance are being integrated into supply chain planning to predict spare parts needs well in advance, which also reduces stress from reactive fixes.

We are already seeing AI transforming predictive maintenance and process optimization, enabling manufacturers to improve uptime and efficiency while reducing costs. The ability to plan things like inventory, resources, and production is game-changing to not just efficiency but the bottom line.

Sustainability initiatives: economically prudent, environmentally conscious, and AI-driven

The current economic climate has amplified the focus on efficiency and maintaining equipment rather than replacing it. As this trend gains momentum, more businesses are looking to drive sustainable value through condition monitoring and connected reliability.

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Advances in technology, such as wireless sensors, have made continuous monitoring more affordable, allowing for broader asset coverage and improved efficiency. This approach cuts energy use, reduces waste, and lowers costs, demonstrating that sustainability-focused maintenance can drive both environmental and economic benefits.

Government commitments to net-zero emissions, coupled with rising consumer demands, are significantly advancing sustainability and electrification efforts.

The need for new infrastructure—such as electric vehicle (EV) charging stations, renewable energy facilities, and modernized grid components—is being driven by the demand for more efficient, resilient, and sustainable energy distribution systems.

As organizations work to comply with stricter environmental standards, achieve sustainability goals, and adhere to external regulations, digital transformation is becoming a critical strategy for success.

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For example, the International Energy Agency (IEA) reported in its 2024 World Energy Investment Report that global investments in solar energy now exceed those in all other energy types combined. This emphasis on renewable energy and electrification is expected to increase the demand for advanced technologies to support these initiatives.

Here, predictive maintenance has emerged as a key focus and is increasingly being talked about in conversations around sustainable operations and the importance of optimizing equipment for both reliability and environmental impact.

By enhancing renewable energy asset availability, extending equipment lifespans, and lowering emissions, predictive maintenance supports companies’ environmental, social and governance goals. With customers placing more emphasis on sustainability, this strategy aligns operational reliability with global environmental priorities.

Deloitte reported that predictive maintenance can also result in a 5% to 15% reduction in facility downtime and a 5% to 20% increase in labor productivity. Given the skills challenges right now and the cost of downtime per minute, this is a compelling proposition for operators.

A positive vision for the future

This year, we will see significant benefits arise from the integration of AI in manufacturing.

We have observed numerous compelling pilot projects that effectively utilize these technologies, successfully identifying their optimal applications within operations.

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By adopting these advancements, manufacturers are enhancing efficiency, product quality, and significantly reducing their operational costs on a larger scale.

Yet, this represents only a small fraction of a much broader opportunity for manufacturers. The competitive advantage lies in the ability to scale these solutions to navigate ongoing disruptions and foster resilience.

By holistically integrating AI into their operations, organizations can improve their responsiveness to market demands and promote sustainable growth. As we look toward next year, the outlook is promising for manufacturers that harness the power of AI and data-driven insights. Leaders who commit to innovation will create agile and adaptive environments capable of thriving in the face of uncertainty.