Rising costs are forcing manufacturers to make tougher decisions about how they price. Materials, energy, and labor expenses continue to climb, with limited room to pass those increases on. And when pricing execution breaks down, it puts deals and margins at risk. 

A strong pricing strategy—how you set, adjust, and communicate your prices to customers—gives you control in an environment where very little feels predictable. By modernizing the pricing function and rewriting the rules on traditional pricing execution, you’ll deliver instant accuracy, reflect real value in every quote, and adapt pricing quickly as factors shift. 

Modern pricing capabilities help manufacturers achieve ten percent more margin expansion   

Manufacturers that invest in pricing as a core capability are strengthening margins and outpacing their competitors. Solutions like sophisticated pricing engines, solution-based pricing strategies, and deeper data visibility are putting early adopting companies ahead.  

Boston Consulting Group reports that nearly half of “pricing innovators” in industrial goods have expanded their margins by more than 10 percentage points while simultaneously gaining market share.  

Yet most manufacturers struggle to keep pace, with our 2025 State of Manufacturing survey revealing that 49% face challenges with pricing adjustments and 43% still rely on Excel spreadsheets to price and quote complex products. A lack of agile pricing processes leads to errors, workarounds, and delays that put additional cost pressures on commercial functions.  

But manufacturers can change the rules by rethinking how they conduct their pricing.  

Rule 1: Go beyond cost—price with insight 

Cost-plus pricing remains a useful foundation for many manufacturers. But in today’s market, where products are configured to order and margins are under pressure, cost alone doesn’t tell the whole story. 

Leading manufacturers are enhancing their pricing strategies by factoring in customer impact, competitive alternatives, and commercial context. That doesn’t necessarily mean reinventing pricing from scratch. It means building on your cost model with insights that reflect what your offering helps the customer achieve. 

For instance, if a particular machine configuration enables faster throughput or lowers operational costs, those benefits should inform how you frame and defend your pricing to avoid underpricing, protect profitability, and account for what your solution is worth in the customer’s world. 

Rule 2: Disconnected CPQ processes no longer compete 

In many organizations, configuration, pricing, and quoting (CPQ) are still handled in separate steps or manually across spreadsheets. This slows the sales process, creates inconsistencies, and often requires frequent collaboration between teams before a quote can be finalized. 

Manufacturers are unifying these steps into one real-time, integrated process. As sales teams or customers configure a solution, the system automatically updates the price based on selected features, business rules, and customer-specific terms. Behind the scenes, real-time pricing pulls from integrated ERP, supply chain, and cost data to ensure every quote reflects current material prices, lead times, or delivery and installation costs.  

If something changes, pricing adapts instantly, with no manual recalculations or delays. When a customer makes a configuration change, they should easily see not just the technical feasibility, but the financial impact. 

Rule 3: Dynamic pricing models are essential for complex sales 

Static pricing models cannot accommodate the complexity of today’s manufacturing sales. Leading companies are implementing multi-dimensional pricing frameworks that consider various parameters simultaneously: 

• Product configuration specifics: Base pricing that adjusts automatically with each configuration choice 
• Customer relationship factors: Different pricing tiers for new versus existing customers 
• Segmentation: Different pricing or price sensitivity for customer segment and region 
• Revenue model variations: Flexible structures incorporating one-time, recurring, and usage-based components 
• Channel considerations: Adjusted pricing for direct sales versus partner channels 

Each parameter becomes a lever for margin optimization. Instead of setting fixed prices or making after-the-fact discounts, sales teams should apply logic-based pricing models that adapt instantly within the appropriate guardrails. 

Rule 4: Instant, transparent pricing is the new standard 

Delayed pricing has become a competitive liability. Customers now expect immediate pricing feedback during the sales process, similar to their consumer buying experiences. 

Leading manufacturers are implementing systems that deliver instant pricing calculations for even the most complex product configurations. This capability dramatically accelerates sales cycles and reduces the resource drain of manual pricing processes. 

When sales teams can provide immediate pricing for any configuration scenario, they gain a significant advantage over competitors still relying on back-office calculations and delayed responses.  

Rule 5: Margin protection requires end-to-end visibility 

Many manufacturers quote based on production costs, separately pricing and quoting services, like maintenance or spare parts, rather than selling them as a full solution in one workflow. That approach can lead to margin surprises or erosion later in the deal cycle. 

End-to-end visibility solves this by ensuring that pricing reflects the full cost to deliver the configured solution, from manufacturing and shipping to field service and ongoing support. As options are added or changed, total cost and profit margin are recalculated automatically, in real time. 

This helps sales teams quote with greater confidence while protecting margin across the full lifecycle of the product and not just at the point of sale. Pricing isn’t managed in isolation. It’s connected, contextual, and controlled across the full solution, whether capital equipment, services, or spare parts. 

Rule 6: Data drives proactive pricing decisions 

Leading manufacturers are using analytics to uncover where margin is lost, how pricing performs across products and markets, and where there’s room to optimize. When pricing data is connected to configuration, customer behavior, and cost inputs, it becomes a strategic asset. 

With real-time analytics and reporting, pricing teams can: 

• Identify unprofitable deal patterns before they happen. 
• Monitor margin performance by product line, market, or sales channel.
• Simulate pricing impacts of changes in material costs or service models. 
• Align pricing strategy with evolving market dynamics and customer needs. 

Companies like Bromma are already taking advantage of sales data to improve profitability. By integrating CPQ, for example, with analytics platforms, they’ve moved from reactive pricing decisions to proactive, data-informed strategies that improve forecasting, product planning, and margin protection. 

Assess your pricing strategy  

Transforming your pricing approach doesn’t happen overnight, but even incremental improvements can deliver significant returns. Start by assessing your current pricing capabilities across these dimensions: 

• How effectively does your pricing capture the value your solutions deliver? 
• How tightly integrated are your configuration and pricing processes with your supply chain or inventory and other core systems? 
• How quickly can you provide accurate pricing for complex configurations? 
• How comprehensively do your price calculations account for all cost factors? 
• How well do your pricing models adapt to different customer scenarios? 

The answers will reveal your most promising opportunities for pricing transformation. Prioritize initiatives that directly address margin leakage and sales friction points, building toward a comprehensive approach that turns pricing from a necessary function into a strategic advantage. 

Achieve dynamic pricing with Tacton 

Tacton helps leading manufacturers protect margins, respond to rising costs, and deliver instant, accurate pricing across every product configuration. With powerful pricing capabilities built into the CPQ process, you gain full visibility, speed, and control. 

With Tacton, you can: 

• Replace spreadsheets and disconnected tools with a single system that ties product configuration directly to pricing logic. 

• Adapt pricing instantly based on real-time product selections, customer terms, aftermarket service models, and region-specific variables. 

• Reflect full cost-to-serve in your pricing, from manufacturing and shipping to installation and maintenance, so you quote with confidence and protect margin. 

• Implement needs-based configuration to complement your pricing with the business outcomes your solutions deliver. 

• Use analytics to track sales performance, forecast margins, and fine-tune your pricing strategy continuously. 

Move beyond outdated pricing models and build a scalable, profitable approach to selling complex products. Learn more about our pricing capabilities. 

Explore the P in Tacton CPQ

Artificial intelligence (AI) has been a major pillar in the rise of smart manufacturing or Industry 4.0, including its use in digital twins and intelligent robotics on the production floor. New data from over 200 manufacturers, however, shows that AI in the manufacturing industry is far from evenly leveraged, and foundational barriers make it difficult to use AI in ways that are both efficient and strategic across the value chain. However, leaders in AI adoption are getting a head start on the technology’s uses, not just in operations, but across the entire enterprise. They’re moving beyond siloed pilots to build more connected, data-driven businesses, laying the groundwork for transformation that reaches from production to sales.

Many Manufacturers Still Lag in AI Adoption 

Currently, 48% of manufacturers are exploring potential AI uses cases for their business, according to an annual State of Manufacturing survey of over 200 global manufacturing companies that Tacton conducted in conjunction with Researchscape. However, only 16% of surveyed global manufacturers are currently heavily investing in the technology.  

Where are manufacturers leading in the race to innovate, cut costs, and optimize their businesses with AI? And where are there opportunities to ensure AI is used not just as a production tool, but also as a strategic tool that brings innovation to the larger business model?  

How Manufacturers Use AI: A Quick Guide to Common Use Cases

In order to understand how manufacturers are using—and not using—AI, it’s important to first understand that AI comes in many different forms. Basic forms of artificial intelligence have been used to increase efficiency for years, while newer types of AI are reshaping how companies think about their business models. 

A few examples include:  

• Expert systems: Around since the 70s, these AI models use rules-based, if-then type logic to help solve problems, like optimizing production with best practice logic or using rules to ensure valid product configuration.  

• Machine learning and deep learning: Using deep pattern recognition and less explicit programming, these models solve much more complex problems in a way that’s similar to the human brain. Manufacturers use these models for predictive maintenance in equipment, predictive forecasting, guided selling, supply chain management, and more.  

• Generative AI: Based on their given training data, these models generate new content for different scenarios. Often used for new product design, document creation, and other applications, this form of AI is evolving quickly.  

While other forms and hybrid models exist, such as natural language processing and more visual forms of AI, today’s manufacturers still have significant room for adoption across their business functions. Despite the wide range of AI capabilities available, most manufacturers today focus their AI initiatives on fragmented sections of the value chain. 

The AI Use Cases in Manufacturing that Businesses Are Exploring Today 

With current economic constraints in the form of inflation, tariffs, and supply chain volatility, manufacturers are laser focused on how they make and deliver their product. That means limiting disruption and equipment downtime, streamlining the assembly line, and meeting demand profitably.  

According to those surveyed:

• 19% of companies see the biggest AI opportunities in production line automation. 
• 18% see major opportunities in supply chain optimization. 
• 13% see major opportunity in predictive maintenance. 

While 15% see generative AI for product design as a major opportunity, this is mostly concentrated in mid-market to enterprise businesses ($500M to $5B in revenue). Larger enterprises with over $5B in annual revenue tend to lag here, likely due to the friction of legacy systems and complex internal structures. 

The Impact of AI Adoption 

Companies already investing heavily in AI are more likely to see stronger business outcomes across the value chain from their digital transformations: 

• 80% report improved productivity 
• 66% cite improved inventory management 
• 60% have increased sales 
• 49% saw better product-market fit 

Early adopters are also more likely to prioritize market expansion, sustainability, and customer experience to stay competitive, showing a more holistic approach to transformation. 

Where Leaders Are Shifting Next: The Future of AI in Manufacturing 

Today, much of the focus on AI in manufacturing is tactical and centered on cost reduction. Even for mid-market businesses who are more heavily investing in AI, the supply chain and production process is still king. But more mid-market players are investigating beyond factory efficiency towards value-add for their customers.  

Some manufacturers are already using AI to improve how they bring products to market. They’re moving from production-centric use cases to customer-centric transformation and connecting buyer needs with production capabilities in real time.

Early adopters of AI, specifically those below $5B in annual revenue, are starting to explore more opportunities to use AI across their business and outside of the factory floor. In addition to interest in generative AI for product design and engineering, they’re also reporting more interest in AI for guided selling and smarter configuration in the sales process.  

AI in CPQ: A Missed Opportunity? 

Only seven percent of manufacturers today see AI as an opportunity in guided selling or smarter configuration, despite the clear value. AI-enhanced CPQ (Configure, Price, Quote) platforms offer a powerful path forward, helping manufacturers and their customers: 

• Automate complex configurations 
• Guide non-technical buyers in configuration
• Reduce quoting errors 
• Optimize pricing based on customer, cost, and sales data 
• Enable dynamic recommendations at the point of sale 

These aren’t just incremental improvements. They’re powered by AI models that continuously learn from quoting behavior, customer preferences, and sales outcomes. That makes AI in CPQ a major opportunity for scalable, data-driven customer engagement.

In addition, by integrating AI-driven CPQ with other operational systems, manufacturers will soon turn quote and configuration data into a strategic asset that drives predictive forecasts, adaptive product strategies, and intelligent pricing models. These capabilities are still emerging, but together, AI and CPQ can reshape how manufacturers sell, price, and innovate.

Early Adopters Have the Advantage 

AI adopters have an opportunity to get a head start on the customer experience, where they understand that there is important value in remaining competitive at both the back end and in front of customers. Expanding customer channels, delivering more value at the point of sale, and creating a seamless, data-driven buyer journey will be the next areas where innovators and early adopters lead the industry. 

Preparing for AI at Scale: What It Will Take

Manufacturers who continue to use AI only as a factory floor tool will hit diminishing returns. Sustained gains—higher margins, better customer retention—will come from connecting AI across the full customer journey. 

But achieving a more strategic future requires a foundation that manufacturers are still building: 

• Clean, structured product and customer data

• System integration across sales, engineering, and operations

• Alignment between IT, sales, product, and executive leadership

Forward-thinking manufacturers are starting to make these investments. They recognize that scaling AI means thinking beyond automation to transform how products are sold, experienced, and delivered.

Tacton Brings Smarter Selling to Manufacturing 

At Tacton, we help manufacturers move beyond traditional CPQ solutions to transform go-to-market efficiency and buyer engagement in one integrated platform. Our configuration, pricing optimization, embedded data, and guided selling solutions bring intelligence into the buyer journey, giving manufacturers the tools to sell and deliver complex products strategically.  

Ready to connect your strategy to customer outcomes? 

Learn More About Tacton CPQ