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2026 State of Manufacturing: Building Toward the Connected Factory
Each year, Tacton surveys manufacturing leaders across North America and Europe to track how the industry is evolving. In 2026, rising complexity and digital maturity is making the gaps between systems, teams, and data harder to ignore.
About the manufacturing trends report
Who was surveyed
manufacturing leaders across departments
countries
industry sectors
recommendations for success
The State of Manufacturing has surveyed 1084 respondents since 2022.
Key takeaways
6 manufacturing trends you need to know in 2026
Product complexity hits a 4-year high.
67% of manufacturers report very or extremely complex products — the sharpest single-year jump in four years of research.
Customization challenges persist.
Customization challenges aren't going away. The #1 quoting challenge, even as more manufacturers invest in CPQ tools.
Margin erosion lives across the lifecycle.
62% experience at least moderate margin loss from quote to delivery, and data siloed teams carry nearly twice the critical risk.
Most engineers are maintaining manually what few have automated.
Only 7% define product configuration rules once and reuse them everywhere. Everyone else must re-sync and translate at handoffs.
Most manufacturers track outcomes but not what's driving them.
Only a small portion have visibility into sales demand at the option or feature: the layer that makes everything else actionable.
AI investment nearly doubled, but quick wins are missed.
79% are investing in or exploring AI, but quick win applications are underutilized for certain significant challenges.
01. Confront
What are the biggest challenges facing manufacturers in 2026?
Manufacturers in 2026 are struggling to scale mass customization as product complexity reaches a four-year high. Nearly 70% now operate in hybrid CTO environments, balancing standardized product models with engineer-to-order (ETO) demands that still rely heavily on manual engineering effort and downstream adjustments.
As product variation grows, so do the configuration rules, dependencies, and exceptions that industrial machinery manufacturers must manage, increasing operational complexity and risk across the lifecycle.
Product complexity is not slowing down.
67% of manufacturers report very or extremely complex products in 2026 — up 20 points in a single year. As a result, manufacturers must deliver faster and easier buying experiences with even more complex, ETO portfolios.
Skepticism for CTO stalls sales scalability for complex products.
Nearly one in four manufacturers doubts the ROI of expanding configure-to-order coverage. Without confidence in the model, or the data to know where to standardize components, complexity stays unresolved and the sales process stays convoluted.
02. Quote
Is CPQ software solving the quoting problem for complex manufacturers?
CPQ adoption trends in 2026 show rapid rise, but faster quoting doesn’t solve the complexity problem. Accurate quoting for highly configurable products depends on whether CPQ configuration logic stays aligned with engineering constraints, production capacity, pricing, and parts availability.
As manufacturers scale CTO and hybrid ETO models, disconnected product data and manual engineering adjustments continue to drive quoting errors, rework, change orders, and delivery risk.
CPQ adoption is up.
46% of manufacturers now use third-party CPQ, up 19 points since 2022. Response times are faster. But speed alone isn't solving the underlying problem, as finding the best valid solution is still difficult.
Customization is the #1 challenge for sales.
43% cite customization as their top quoting challenge in 2026, up from 36% in 2022. When configuration logic doesn't reflect what engineering can build or supply chain can source, faster quotes just means faster assumptions.
03. Own
Why are manufacturers struggling to protect profitability on complex orders?
In 2026, 62% of manufacturers report moderate to severe margin erosion from quote to delivery. Commercial, engineering, supply chain, and production teams tend to see the impact at their own stage without shared visibility across the full lifecycle. As product complexity and customization demands grow, the compounding effect of multiple small gaps and inconsistencies between handoffs is difficult for industry leaders to quantify or address.
Production quality is the leading contributor for perceived margin issues, but 20% cite quoting errors as a direct cause before a product is ever built.
Shared data threads surface problems earlier.
When data is a shared, single source of truth, margin problems appear at production or quoting, where they're cheaper to fix. Manufacturers with a single shared system are least likely to cite delivery issues as a margin cause. The same problems don't disappear, they just move upstream.
Siloed data doubles critical margin risk for manufacturers.
Manufacturers working from a single shared system report a 12% critical margin erosion rate. Those where each team works from its own data report 23% — nearly double. The spread holds across sectors and company sizes.
04. Connect
How are manufacturers managing product configuration across sales, engineering, and production?
Most manufacturers have invested in digital tools: CPQ for sales, PLM or ERP for engineering, MES for the shop floor. Each system often maintains its own language regarding product rules, engineering constraints, and manufacturing logic. Alignment requires manual updates across quotes, change orders, and product revisions, increasing inconsistencies between what’s sold and delivered.
As a result, more than one-third of manufacturers report frequent change orders and ongoing challenges generating valid engineering and manufacturing Bills of Material directly from configured quotes.
Only 7% define configuration rules once and reuse them everywhere.
7% of manufacturers define configuration rules once and reuse them across all systems. The other 93% manually re-synchronize across systems and processes, adding engineering effort and introducing risks. Every duplicate rule set is a divergence waiting to happen.
Engineering change propagation remains mostly manual.
Only 21% of manufacturers automatically propagate engineering changes across sales and production systems, and 63% describe mostly consistent change management that still requires manual updates. As portfolios grow more complex, that manual tax compounds.
05. See
How are manufacturers using data to improve product sales in 2026?
Most manufacturers still lack visibility into how decisions made throughout the configuration and quoting process impact quoting accuracy, margins, change orders, delivery performance, and sales outcomes. While many track high-level revenue metrics, only 45% track configuration-level data tied to product combinations, customer requirements, and buying behavior.
This requires a single source of truth in their data. Manufacturers with this level of visibility and data connectivity are better positioned to optimize product portfolios, reduce operational complexity, and scale AI adoption with a stronger data foundation.
Option-level data at configuration is the least collected.
80% of manufacturers track revenue and business outcomes, but only 27% of manufacturers track demand at the product feature and option level. Most visibility exists at the product-family level, but not where configuration decisions are made.
Data readiness drives AI performance.
Manufacturers heavily investing in AI report 80% visibility into product and configuration performance data, compared to 56% among those still exploring AI. The trend suggests that manufacturers with stronger configuration data, analytics maturity, and operational visibility are also further along in AI adoption.
06. Optimize
How are manufacturers investing in and using AI in 2026?
AI in manufacturing has grown substantially, though European manufactures lag slightly behind their U.S. counterparts. But across nearly every segment, AI appears as a subsequent priority, not a starting point. The manufacturers seeing the strongest results have built a single source of truth across their manufacturing lifecycle. AI doesn’t fix a broken digital thread. It automates a working one.
Automation is still a top use case, with analytics and decision intelligence to follow. In 2026, manufacturers are seeing potential value in AI for automating configurations and reducing time and errors associated with quoting and product sales.
AI adoption in manufacturing nearly doubled in 2026.
79% of manufacturers are investing in or exploring AI in 2026, up from 64% in 2025. Skepticism is at an all-time low. The momentum is real, but the return correlates with data readiness.
AI's biggest opportunity: speed and accuracy at configuration.
Manufacturers expect the most value in using AI for: automating complex configurations (56%), reducing quoting errors (48%), and accelerating quote response times (47%). Yet, 81% of manufacturers find CPQ model maintenance moderate to very high effort, and it's the most underestimated AI use case in the survey.
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