AI Data Transparency Is Rewriting Brand–Manufacturer Dynamics

An electronics assembly line.

Instrumental Inc.

Talk to the people building the world’s favorite devices—from smartphones to wireless mice—and they’ll tell you the real revolution happening in electronics manufacturing right now is in the relationships – not the technology. AI is changing the information that brands demand from their manufacturing partners, information that could give brands powerful ammunition to put downward cost pressure on manufacturers. The technology can provide powerful benefits to both sides, and is forcing leaders to rethink what manufacturing partnerships look like in the AI Age.

The Relationship Between Brands and Their Manufacturing Partners

Most electronics brands contract with manufacturing partners to support the manufacture of their devices – some even get engineering support. Electronics manufacturing is considered a commodity, with relatively low margins. Andrew Scheuermann, CEO of Arch Systems, which provides software to electronics manufacturers, notes, “Margins can be as low as two percent. If [factories] were to share the whole [production] dataset carte blanche, it could be used to cut down cost—and that’s existential to their business.”

Many contracts are negotiated based on a “costs plus” model, which reflects the actual costs of producing a device. This creates an incentive not to share information that might reveal hidden cost savings, which a brand might want to renegotiate. Other incentives are also misaligned – a brand wants to deliver the best possible customer experience, while the manufacturer wants to maximize their margins without triggering returns.

Scheuermann’s company, Arch Systems, is used by tier-one electronics factories worldwide – including Flex, Jabil, Plexus, and Sanmina – to provide both brands and factory teams with real-time production visibility. For many manufacturers, sharing that kind of data with their customers makes them uneasy. Scheuermann explains, “They think, I’m doing a great job, but if I share this dataset, you might pick on one or two things that aren’t the full picture—and use it to negotiate against me. There can be fears of liability or recalls. That makes people cautious.”

From Manufacturers to Manufacturing Partners

AI is a catalyst for changing these relationships. Electronics brands have been investing in building large repositories of their design, manufacturing process, and returns data with the intention of identifying opportunities to build better products for their customers. A significant portion of that data relies on manufacturing partners to be more transparent in sharing all their data, rather than providing reports for individual queries.

Martin Hess Pedersen, a longtime hardware executive now leading global quality and manufacturing at Logitech, has been at the forefront of this shift. His 20-year career has spanned three very different manufacturing cultures: the precision of Nokia, the process discipline of Microsoft, and the scale of Foxconn. Having sat on both sides of the table between brands and manufacturers gives him a unique perspective.

At Nokia, Pedersen says, “we built five million phones a month out of a single [Nokia-owned] plant,” something that was exceptional at the time. Nokia’s data-driven culture made transparency a competitive advantage. “When you own both the process and the data,” he recalls, “trust is the default.”

But that model changed as more and more electronics brands shifted to contract manufacturing in the early aughts. When he later worked at Foxconn, Pedersen saw how data could also become a source of friction. “When margins are narrow, fear [to share too much] is natural,” he explains. “Suppliers worry that full transparency means losing leverage.”

Pedersen believes the answer lies in rewriting the terms of the relationship – instead of focusing solely on cost-plus or who can offer the cheapest bid. He shares, “When both sides share accountability for the outcomes, data becomes an engine for improvement.” At Logitech, Pedersen has institutionalized this mindset. “We don’t have suppliers,” he says. “We have partners. We grow together and learn together.”

The company’s contracts explicitly share risk. That legal structure reinforces cultural trust: engineers on both sides can share real data—good or bad—without fear. Transparency isn’t just expected from factories—it’s reciprocated. Logitech’s partners see anonymized performance dashboards, consumer sentiment data, and even early innovation roadmaps. “They want to know the happiness of the consumer, star ratings, return rates, reviews,” Pedersen says. “That’s their achievement, too.”

This creates a powerful feedback loop: factories learn from the field, and brands learn from the floor.

Results From AI In Manufacturing Are Driving Changes In Approach

Scheuermann’s vision for the use of AI in manufacturing oversight addresses this tension between fear of sharing and improved performance head-on. “There may be this ability for brands and [manufacturers] to work together where you use some AI versus only humans to compute a task,” Scheuermann explains. “[Factories] could give the data to that AI to solve a quality problem—and then the data is gone. Almost like your Snapchat message that disappears.” It’s a small but profound shift: data can be used collaboratively without threatening margins or intellectual property.

Pedersen’s vision for AI is different – he believes that the next wave of manufacturing excellence will come from blending consumer data with factory data—creating what Pedersen calls “closed-loop quality.”

“AI is already helping us mine call transcripts, repair logs, and manufacturing records for root causes,” he says. “You can get to an 80 or 90 percent correct hypothesis in minutes. That’s game-changing.”

The implications extend far beyond the production line. When brands and their manufacturing partners collaborate more effectively, new products launch more quickly, quality improves, and consumer satisfaction increases. The very devices people hold in their hands become evidence of the invisible partnerships that produced them.

As Scheuermann puts it, “I’ve never seen a top [manufacturer] say no when the business case is clear.” The same can be said of the future itself: when the value of collaboration is undeniable, the hesitation to share fades away.