What are manufacturing leaders prioritizing for digital transformation in 2026?

Manufacturing leaders are leaning into three fronts simultaneously: move faster on complex deals, make smarter decisions about where to grow, and protect margins that cost pressure is squeezing from every direction.  

Digital transformation was supposed to help with all three, and for many manufacturers, it has. Manufacturing digital transformation in 2026, however, looks different from the last wave. The first wave of transformation automated processes within functions, but automation across functions in the order-to-delivery lifecycle is lacking. Many of the systems manufacturers implemented to get there weren’t built to share data and important product logic across sales, engineering, and production, let alone support the speed and scalability that selling highly customized products demands. 

Here’s what the data says about where the real leverage is in 2026, based on survey data from 280 global manufacturing leaders.  

1. The trend toward mass customization grows as leaders look for speed 

Speed is the first casualty of high product complexity, and 67% of manufacturers in the U.S. and Europe are producing very to extremely complex products.  

New and existing configuration rules across thousands of growing product variants and options create a new place for something to slow down. Buyers disengage due to overwhelming product portfolios. Quotes require more engineering input and downstream adjustment.  

More manufacturing leaders are increasing configure-to-order coverage as a direct path to faster, more scalable sales. In 2026, 39% of manufacturers have 20–39% of their portfolio in configure-to-order, while another 30% cover up to 59%, meaning nearly 7 in 10 are running hybrid ETO-CTO models simultaneously. The goal is to reduce the manual, case-by-case effort that engineer-to-order demands by standardizing more of the portfolio into repeatable, configurable modules, but that model only delivers speed if the configuration logic behind it is consistent across sales, engineering, and production. 

Leaders moving fastest on complex deals have one thing in common: they’ve connected the configuration logic of their modular or configurable product options that governs what can be sold to what can be built, thereby removing the back-and-forth that kills cycle time. 

2. Legacy quoting tools aren’t solving for customization  

Although nearly half of manufacturers have adopted third-party CPQ software, with others using homegrown solutions, organizations are still struggling to deliver quotes that are correct and optimized to the customer’s needs. Most can respond quickly to RFQs (within 48 hours for most survey respondents), but legacy quoting and CPQ tools aren’t addressing the need for more intelligent customization.  

Customization is now the number one challenge for sales teams. Manufacturers now need CPQ tools that don’t just address the first problem of speed, but that also address the problem of product complexity.  

Faster quotes that require downstream correction aren’t faster at all: the time lost to engineering review, pricing adjustments, and post-signature changes erases the front-end speed gain Those who invest in guided selling capabilities, AI-driven configuration matching, and CPQ tools that can keep up with configuration rule maintenance see speed and accuracy improve. 

3. Leading IT and the C-suite teams are viewing margin at a bird’s eye  

Dwindling margins won’t be fixed by any single department. They’ll be fixed by having a digital thread that accurate translates one definition of product configuration across PLM, CPQ, ERP, MES, and other systems.  

With 62% of manufacturers experiencing moderate to severe margin erosion across quote to delivery, each part of the value chain contributes due to a lack of people, data, and processes that can work from the same source of truth.  

Margin declines at the handoffs between teams. That means optimizing one department’s process doesn’t fix the total cost. Under severe cost pressure, the instinct is to cut within functions by reducing headcount, tightening procurement, and speeding up production. But the biggest margin leak is the coordination failure between functions that no budget line captures.  

The manufacturers protecting margins most effectively have built shared visibility across the lifecycle, so the cost of a bad configuration decision at quote time is visible before it becomes a change order, a delay, or a reputational problem at delivery. 

4. Strategic leaders know which products and configurations are actually driving growth

Most manufacturers can track revenue and win rates, but less than half (45%) have visibility into which specific configurations, variants, or product options are driving profitability versus quietly adding engineering cost without closing deals.  

This data gap makes strategic growth planning harder than it needs to be. To build the most defensible growth roadmaps, successful leaders can answer questions like: which variants always require manual engineering review? Which configurations correlate with the fastest sales cycles? Which options are specified frequently but rarely convert? 

Having configuration-level data requires a working digital thread so that your organization has the full context of every quote decision. Digital transformation efforts that focus on a connected source of truth will have deeper insights into the configuration behavior that drives portfolio performance.  

5. AI will accelerate growth with engineering teams positioned for immediate gains 

AI—especially generative AI—is becoming ubiquitous. In 2026, 79% of manufacturers are investing in or exploring AI, up from 64% in 2025. The top priorities are practical: automating complex configurations, reducing quoting errors, guided selling, faster RFQ responses.   

But the manufacturers getting the most from AI aren’t the ones who invested in it first. They’re the ones who built the data and workflow foundation that gives AI something useful to work on 

Under intense cost pressures, AI’s most immediate value isn’t automation for its own sake. It’s reducing the maintenance burden that consumes engineering capacity, catching configuration errors before they become margin problems, and surfacing the product intelligence needed to make smarter growth decisions faster.  

The three highest-priority AI use cases tell that story directly: automating complex product configurations tops the list at 56% (the single biggest lever for reducing the engineering validation bottleneck that slows every complex deal). That’s followed by real-time pricing optimization (removing the manual pricing adjustments that pull engineering into commercial decisions they shouldn’t own) and reducing quoting errors and approval delays before they reach engineering. For sales, the gains are equally direct: fewer deals that unravel downstream, faster quote cycles, and commitments that production can keep. 

While only 41% of manufacturers currently see AI-assisted CPQ model maintenance as a potential priority, it’s where engineering teams stand to gain the most by freeing capacity from upkeep and putting it back into innovation.  

How to align manufacturing digital transformation for greater speed, growth, and profitability 

The manufacturers who move faster, grow more strategically, and protect profitability under cost pressure in 2026 aren’t doing it with the most expensive tools. Their foundation is built on a single source of truth that automatically aligns each part of the lifecycle. That looks like connected configuration logic, shared data across the lifecycle, and emerging technology built on top of something solid.  

The full data behind these trends comes from 280 manufacturing leaders across 8 countries.  

Download the 2026 State of Manufacturing report.