How AI Enables Smarter Service for Manufacturers

Despite AI's rapid evolution across industries, many manufacturers remain behind the curve and still rely on outdated systems that limit their ability to achieve meaningful business outcomes and scale technology efforts.

Interestingly, 80 percent of manufacturers say AI is essential to grow or maintain their business by 2030, according to a survey conducted by the National Association of Manufacturers last year.

Getting manufacturers on board with AI doesn’t have to be a struggle. What it does require is the right foundation. A well-structured data strategy is the critical first step and will enable AI capabilities regardless of where data is stored.

Even manufacturers still running legacy systems are discovering that modernizing field service operations can open the door to AI use cases they never originally anticipated and introduce a host of benefits to their organization. Let’s take a closer look at the opportunities.

Hidden Revenue

For decades, field service has been treated as a necessary cost; technicians are dispatched to fix problems, log their hours in broad strokes, and move on to the next call. But that framing has always undersold the potential sitting right there in the field. Instead, manufacturers should be repositioning their service technicians as active revenue generators.

The shift requires a combination of process reform and technology enablement. One of the most persistent issues is revenue leakage: billable work that goes uncaptured, contract terms that go unread, and technician time that gets logged in vague eight-hour blocks rather than tied to specific client activities.

AI is now playing a direct role in closing that gap. From increasing productivity and efficiency by surfacing relevant contract details in real time to helping technicians understand exactly what's covered and what’s billable, AI tools are turning every service visit into a more precise, more profitable interaction. The latest technology can even help organizations avoid revenue leakage with better contract management.

Just as important is time capture and utilization. Traditional manufacturers have long relied on outdated time-tracking methods that obscure how technicians are actually spending their hours. Granular, accurate time data tied to specific clients and tasks is definitely an operational improvement, but more importantly, it’s a foundation for understanding productivity, pricing services more competitively, and demonstrating value to customers. Process and technology, working together, make this possible.

Smarter Leads, Lower Costs

The opportunity doesn’t stop at field service. For original equipment manufacturers (OEMs) and their channel partner networks, AI is also reshaping how service leads are identified, qualified, and converted.

Consider the challenge a local contractor might face when a piece of equipment breaks down and they need parts or service quickly. Historically, the process of finding a qualified local provider has been slow or inconsistent. By applying AI-driven lead scoring and qualification, manufacturers can dramatically improve the odds. Instead of a 50-50 chance that a contractor will engage with a service provider, a well-qualified lead pipeline can push that conversion rate to 70 or 80 percent because the right business has been matched to the right opportunity at the right time.

For OEMs, this represents a meaningful shift in economics. The cost of absorbing a smarter, AI-powered lead qualification process is far lower than the revenue lost to poor conversion and wasted outreach. It’s clearly better for the bottom line, but even more importantly it’s a better experience for everyone in the channel.

Breaking Down the Silos

Behind all of these opportunities lies a more fundamental challenge: data fragmentation. Most traditional manufacturers don't suffer from a lack of data; they suffer from having too much of it scattered across too many disconnected systems. Legacy platforms, homegrown databases, and siloed ERPs make it nearly impossible to get a unified view of inventory, warranty information, parts location, or pipeline forecasting (among many other issues).

This is where modern data cloud strategies are changing the game. Rather than requiring manufacturers to completely replace legacy systems, a process that historically could take a year or more and consume enormous resources, today's AI-enabled approaches allow companies to surface and connect data across existing systems without a considerable migration.

Think of it as the ability to make the right information accessible to the right people at the right moment, whether that’s a technician in the field checking warranty status or a planner trying to forecast parts availability across the supply chain.

The old assumption that fixing data problems meant a massive, painful ERP overhaul no longer holds. Manufacturers can take incremental steps, layer in AI capabilities as they go, and start seeing results in weeks or months rather than years.

The B2B Commerce Play

By now, it’s clear that AI is reshaping how manufacturers handle B2B selling, particularly in commodity-driven industries where market fluctuations make fixed-price transactions complicated.

Historically, a sales rep quoting aluminum or other materials would need to navigate a slow, manual quotation process while market prices shifted. Today, technology is automating that process.

A prospective buyer can move through the full product selection and configuration workflow online, then request a formal quote that accounts for real-time market conditions and hedging strategies. What once took days can now be turned around quickly and give sales teams a sharper edge and buyers the confidence to move forward faster.

A Bold Path Forward

Manufacturers need to know: AI isn’t a distant transformation project. It’s a practical, near-term lever for turning service operations, channel relationships, data infrastructure, and sales processes into competitive advantages.

The good news is that getting started doesn't require a multi-year implementation or a total overhaul of existing systems. Today’s AI-powered tools are purpose-built for speed and can help manufacturers go from strategy to live deployment in a matter of weeks, not years.

Embracing strategic advisory experts can help get organizations started. Then, once the benefits are realized, continue to build and iterate upon. Now is the time to embrace this pace of change and drive real business value.

Roneel Naidu is executive vice president, manufacturing market and portfolio leader at Astound Digital. Naidu collaborates with C-suite executives to bridge the gap between bold vision and disciplined execution, with a strategic focus on refining market-facing plays and prioritizing portfolio investments that deliver compounding growth for global clients.