AI-Powered Manufacturing Transformation Using Right-Sized Solutions

Digital transformation in manufacturing is often discussed as a technology problem. In reality, it’s a trust problem. Many factories are stuck in a holding pattern, weighed down by aging systems, unclear goals, and a deep disconnect between top-floor ambitions and shopfloor realities. AI promises to revolutionize production, but too often, it arrives as a top-down mandate with no traction among the people who actually need to use it.

Jeevan Mulgund has lived every angle of this challenge. From his early days on the factory floor at Tata Motors and Eaton, to leading global transformation initiatives at Honeywell, to investing in small and midsize manufacturers at Ignova Ventures, he brings a rare blend of operational experience and strategic vision. From this unique vantage point, he has observed that real transformation only begins when people, process, and technology are fully aligned. AI needs to be introduced, not as a silver bullet, but as a right-sized tool built to serve the factory, not the other way around.

In this article, we explore what AI-powered manufacturing transformation actually looks like when it works, drawing on key lessons from Jeevan’s journey and practical insights from the factory floor that he shared on a recent episode of the Manufacturing Intelligence Podcast.

Most manufacturers do not fail at transformation because of technology. They fail because they underestimate what it takes to change behavior on the shop floor. For companies with legacy systems, change often feels like disruption. Teams that have been doing things the same way for decades are not likely to embrace something new unless they trust that it will make their jobs easier.

Mulgund learned this early in his career, not in a strategy workshop, but at the controls of a CNC machine. In one of his first roles at Eaton, he was tasked with modernizing a machining process. Instead of jumping into a technical solution, he spent weeks listening to operators, running parts himself, and learning how the process worked from the inside. That hands-on experience built credibility, and more importantly, it created the trust that made real change possible.

This is where many digital transformation efforts go wrong. Leaders invest in systems and tools before they earn the support of the people who are expected to use them. 

Transformation begins when the people closest to the process feel seen, heard, and respected. When that happens, new technology becomes a tool for empowerment, not a source of resistance.

The biggest myth in digital transformation is that technology leads the way. In reality, it should follow. Every successful manufacturing transformation rests on a foundation of three interconnected pillars: people, process, and technology. When these elements are aligned, transformation becomes sustainable. When they are not, even the best tools fall flat.

Mulgund describes this relationship as a triangle that only works when all three points are stable. People are the starting point. They carry the tacit knowledge, the routines, and the instincts that keep production lines running. Process comes next. It provides the structure for consistent decisions and cross-functional alignment. Technology should serve these first two, not disrupt them.

While walking the floor in a factory in Romania, Mulgund asked a team about their efforts to reduce setup times on an SMT line. Instead of deferring to a presentation or calling in a report, the line supervisor walked him to a touchscreen dashboard near the machines. With just a few taps, he pulled up three years of setup time data, showed the current performance trend, and opened a live A3 project plan detailing the corrective actions in progress.

This was not a flashy analytics tool. It was a living system embedded in the daily operations of the plant. It empowered frontline teams to diagnose issues, make informed decisions, and drive improvement without relying on outside intervention.

That kind of visibility is not about dashboards. It is about accountability. When the right data is placed directly in the hands of the people doing the work, technology becomes part of the process itself rather than a layer that sits above it.

Manufacturers who design their systems around this reality unlock the full value of their data. The goal is not more software. The goal is better decisions, made faster, by the people closest to the work.

Manufacturers often assume that transformation requires a full system overhaul. In practice, that approach creates friction, delays, and resistance—especially in environments where teams are already under pressure. The better path is to start with what works and build from there, using technology not to replace systems but to enhance performance at the edges.

Jeevan Mulgund describes this as an “edge-in transformation” strategy. Rather than beginning with complex integrations or top-down software mandates, he recommends identifying small, high-impact opportunities and using lightweight tools to solve them. This might mean working from a basic CSV file instead of waiting for an ERP integration, or deploying a simple AI tool to monitor machine performance before automating entire workflows.

This same philosophy applies to AI. Right-sized AI does not mean scaled-back innovation. It means solving specific problems with tools that match the environment. In some factories, the best application might be a chatbot that gives planners instant access to process documentation. In others, it might be a scheduling assistant that reduces manual effort and helps the team respond more quickly to changes in demand.

Jeevan shared a compelling example on the podcast. One of the companies he invested in struggled with quoting speed. Customers expected responses in 24 hours, but the company’s process often took days or even weeks. Instead of launching a broad digital transformation, they focused on streamlining the quotation workflow. That targeted improvement created measurable impact, built internal momentum, and opened the door for future projects.

This kind of prioritization requires discipline. It means resisting the urge to roll out large-scale AI initiatives or treat every site the same. Different factories, even within the same organization, will have different needs. Some may benefit from advanced MES systems. Others may simply need visibility tools or basic automation that reduces friction in daily work.

Ultimately, the smartest technologies are the ones that fit the problem, support the people using them, and move at the pace the organization can sustain. When manufacturers focus on delivering value at the edges, they unlock trust, build traction, and create the conditions for lasting transformation.

Real transformation in manufacturing is not about ripping out legacy systems or chasing the latest technologies. It is about meeting people where they are, solving real problems, and earning trust through practical results. Right-sized solutions are not watered-down versions of enterprise tools. They are purpose-built, focused, and designed to deliver value where it matters most.

Successful transformation requires a deep understanding of how factories actually run. It begins with the people on the floor, is guided by proven processes, and is powered by technology that fits the environment. When these elements come together, manufacturers can modernize without disruption and achieve meaningful change, even in the most complex legacy environments.

To go deeper into Jeevan’s insights, listen to the full episode of The Manufacturing Intelligence Podcast.