Transitioning from engineer-to-order (ETO) to configure-to-order (CTO) means standardizing repeatable configurations, so you can scale sales without overwhelming engineering. It’s not about eliminating customization but shifting toward mass customization with a more balanced hybrid model that reduces bottlenecks while preserving flexibility. 

Done well, increasing CTO leads to faster quoting, fewer errors, stronger margins, and growth that isn’t limited by headcount. This roadmap outlines the key steps and common pitfalls to help you make that shift successfully. 

What does an ETO to CTO transition actually mean? 

ETO manufacturing models are defined by complete customization, where no components are standard. CTO manufacturing models use standardized modules that can be combined to create flexible, yet validated solutions that don’t require heavy engineering and design support.  

Many manufacturers operate in a hybrid model, but increasing CTO adoption is a goal for companies that are tasked with increasing sales without increasing engineering headcount.  

Why manufacturers are increasing CTO coverage 

Greater CTO coverage means more control over your sales and production functions. When you have modular products whose configurations can be constrained by automated, validated engineering rules, you open the door to multiple opportunities.  

• Shorter quote cycles and competitive response times with fewer engineering bottlenecks.  

• More time for engineering teams to handle exceptional projects and high-value innovation. 

• Scalable sales that don’t require excessive product knowledge across your sales teams.  

• The ability to expand sales channels to include ecommerce or self-service buying where customers can configure valid, standardized components independently.  

• Fewer surprises and rework downstream, resulting in less margin erosion. 

Step-by-step implementation roadmap for moving from ETO to more CTO 

Fundamental transitions like these should happen in phases to reduce risk and ease your organization into the new process. These implementation steps for CTO manufacturing help you transition without major disruption to your business.  

Before taking on this project, create your business case: Answer the following questions:  

• How many hours are spent on non-winning quotes? 

• What percentage of quotes require engineering input? 

• What’s the average engineering time per quote? 

• What’s the conversion rate of ETO quotes? 

Phase 1: Assessment and readiness 

Identify your high-volume product families. Define repeatable configurations from true, custom work, and look at the products that have the most quoting effort or engineering involvement to alleviate some of that burden. Use configuration-level quote data to identify which components, options, and combinations are repeatedly configured and ordered. Patterns often reveal that more is repeatable than teams believe.  

Then, evaluate PLM, ERP, and pricing data quality. Is there integration complexity that makes it difficult for teams across regions to work from the same data or configuration logic? Document inconsistencies and ownership gaps.  

Finally, define your business objectives. What is your target quote turnaround time? What is an acceptable CTO/ETO split? Metrics like margin, win rate, or even tool adoption will best measure the success of increasing CTO coverage.  

Phase 2: Pilot and modeling 

Don’t move all at once. Begin with a single product line that your organization feels will have the highest business impact. Choose a product line with moderate complexity first, strong internal ownership, and good data availability. Cleaning up data and product values is crucial for the next step: product modeling.  

When modeling in CPQ, translate engineering logic into rules or constraints. Define valid combinations and dependencies. Embed pricing logic. Maintain controlled exception pathways or alerts when a configuration requires an engineering override or an ETO exception.  

In addition, approvals (e.g., discount approvals or business approvals) and margin governance should be built into your quoting tool for the best line of control.  

Phase 3: Integration and commercial alignment 

Your team can’t scale sales if they’re working from different data and systems. Configuration logic should be your single source of truth across the organization, with strong integration between PLM (product data), ERP (pricing, orders), CRM (opportunities), and CPQ (quoting).  

Phase 4: Change management and adoption 

One of the biggest obstacles to increasing CTO coverage is objection from internal stakeholders—especially engineering teams.  

Position CTO as scaling expertise rather than replacing it. Provide communication early in the process by explaining the business reasons and value of a hybrid manufacturing model. Create champions within your teams and bring engineering and sales into the transition process to own new processes, product logic, and workflows.  

Structured training is key. Phasing your transition also reduces disruption to your business and proves value one product, region, or channel at a time, so your internal success stories fuel further buy-in. 

Phase 5: Formalize the “exception to standard” conversion loop 

Continue to track repeated ETO requests. After X occurrences, evaluate for potential CTO inclusion and assign ownership for rule addition and portfolio documentation.  

What modular CTO and mass customization looks like at scale 

For many manufacturers, the objective of increasing CTO coverage is to reduce engineering bottlenecks, shorten quote cycles, expand sales channels, and create tighter control across systems. Piab’s experience illustrates what that looks like when modular design is structured for scale. 

Piab, a global manufacturer of vacuum automation and lifting solutions, already established a modular manufacturing model and wanted to scale. By centralizing and standardizing its modular logic within an integrated CPQ connected to ERP, pricing, and documentation, Piab turned product flexibility into scalable commercial capability across its company with great results: 

• 40,000+ monthly self-service configurations, enabling customers and partners to generate validated solutions independently. 

• 58,000+ configured ERP items, converting repeatable configurations into modular order components. 

• 6,000+ automatically generated documents per month, accelerating validation and reducing manual engineering effort. 

The 10 most common mistakes in expanding CTO coverage 

Avoid these common pitfalls when shifting from ETO to CTO:

1. Automating a broken quoting process

Digitizing an unclear or inconsistent ETO workflow only scales inefficiency. Redesign poorly governed approvals, exception handling, ERP consistencies, or sales–engineering collaboration before automating.

2. Trying to standardize 100% of products

Eliminating ETO entirely creates resistance and removes strategic differentiation. Structure exceptions with controlled approvals instead of eliminating them. 

Build approved alternatives into your configuration logic so flexibility doesn’t require full engineering involvement. For example, allow pre-approved component or control system brands (e.g., ABB vs. Siemens) within defined rules rather than triggering custom design.

3. Skipping product modularization

“Our product structures are not mature enough.” 

If platforms, options, and dependencies aren’t clearly defined, rule complexity explodes. Clean up product architecture before modeling configuration logic.

4. Underestimating data preparation

Poor product, pricing, or ERP data delays modeling, complicates system maintenance, and erodes trust. Define governance and ownership early. 

5. Failing to define rule ownership 

Eventually, configuration rules become outdated, and without ownership, teams blame each other and sales bypasses the system completely.  

Assign responsibility for rule maintenance, pricing logic, and lifecycle updates.

6. Ignoring exception governance

Unchecked overrides and manual workarounds quickly erode margins. Define escalation workflows and convert repeated exceptions into standard options.

7. Scaling before stabilizing

Expanding across regions or systems too quickly multiplies inconsistencies. Prove and stabilize one product line before scaling.

8. Underestimating integration complexity

PLM, ERP, CRM, and CPQ alignment is foundational. Scope integrations deliberately. Don’t assume they’ll “work themselves out.”

9. Treating CTO as an IT project

This is a commercial transformation. Executive sponsorship and cross-functional alignment are critical.

10. Measuring adoption instead of business impact

Go-live date is not the impact metric. Track engineering hours saved, quote turnaround time, margin performance, and CTO/ETO ratio over time. 

Move from reactive engineering to scalable configuration 

Scaling modular CTO requires structured configuration logic, integrated systems, and governance that supports growth over time. It also requires visibility. 

Tacton CPQ helps manufacturers not only translate complex product architecture into scalable, constraint-based configuration, but also analyze configuration-level quote data to identify repeatable patterns, recurring exceptions, and components that are strong candidates for CTO expansion. 

See how we help manufacturers increase CTO coverage strategically.  

Request a Demo 

 

Frequently asked questions about CTO 

Is expanding CTO coverage right for everyone? These questions or objections may sound familiar.  

Q: Can we move to CTO with highly complex products? 

If you manufacture highly configurable products, your pride yourself in the complexity and uniqueness of your product. But complexity does not eliminate all repeatable patterns.  

Repeatable configurations are always possible, and regulatory and performance variations can often be modularized.  

Q: Will moving to CTO reduce our ability to handle custom requirements? 

Entirely ETO or CTO manufacturing models are unsustainable. A hybrid CTO-ETO model that uses controlled escalations for true edge cases is the best model for complex manufacturers. CTO handles volume, while ETO handles differentiation.  

Q: How long does an ETO to CTO transition take? 

The transition requires standardized configuration logic to be digitized in a quoting tool, such as CPQ. That implementation can be longer for homegrown tools or as little as four to six months for a purpose-built CPQ software.  

However, the full transition could take much longer, depending on how many products are piloted and how much of the portfolio is phased in over that journey.  

Q: What resources are required? 

Besides quoting tools to scale and streamline sales, your greatest resource is people. The following stakeholders are essential for making the transition from ETO to CTO manufacturing.  

• Business process owner(s) 

• Product modeling experts 

• Sales and engineering key users 

• Integration and IT support 

• Executive sponsorship 

Q: What ROI can we expect? 

By streamlining sales and quote validation, you can reduce quoting time from weeks to hours, with additional margin uplift through pricing control and reduced order errors or rework.  

Faster response times also yield higher win rates against slower competitors. 

 

The line between B2C and B2B sales continues to blur. Even buyers of highly configurable products want the same independent, immediate, and simplified online buying experience they get on Amazon or Uber. Yet, while 67% of buyers want a rep-free, self-service buying experience, only 24% of manufacturers prioritize self-service as a sales channel. 

Manufacturers of complex products have an opportunity to gain advantage against competitors with faster sales cycles, fewer human errors, and an easier way to explore your products. Tacton Self-Service Channels (SSC) takes customer self-service ordering to the next level, removing the need for advanced API setup with an easily embeddable website quoting experience. Enhance buyer engagement and sales-buyer collaboration across the sales cycle. 

What is Tacton Self-Service Channels?  

Tacton Self-Service Channels (SSC) allows you to embed powerful quoting capabilities directly into your website, powered by the exact same product rules, pricing logic, and validation engine used in your internal Tacton CPQ. 

Using ready-to-deploy HTML components, you can embed this experience without building a custom solution from scratch. While customers can continue using the original API-based Customer Self-Service, SSC makes it significantly easier to launch a fully branded online buying interface and, soon, will support sales-buyer collaboration earlier in the process. 

Tacton SSC includes: 

• A quick, basic setup once your product model is ready. 

• HTML components you can drop directly into your site. 

• One product model, one pricing logic, one source of truth across all channels (OEM, dealer, partner) directly from your Tacton CPQ configurator. 

Earlier collaboration across the sales cycle 

Unlike the first-generation Tacton Customer Self-Service tool, SSC is designed for collaboration. The original Customer Self-Service tool keeps the process isolated until the buyer completes configuration and submits it. Sales can only engage at the end. 

With the new Self-Service Channels, sales staff and support will soon be able to step in at any point, including reviewing configurations, adjusting pricing, collaborating on proposals, or guiding complex deals before a final quote is issued. Buyers can start independently, and your team can join when it adds value.  

Rather than replacing sales reps through independent buying, this tool gives sales the opportunity to close faster and exert control over the sales experience.  

Tacton SSC scales revenue with control 

A streamlined self-service tool gives your buyers a consistent, accurate experience of your products. It also gives internal teams the ability to scale sales sans the additional costs.  

• Ensure consistency everywhere: Power internal sales, distributors, retailers, and web buyers from the same product catalog. Product and pricing changes propagate across all channels automatically. 

• Offer flexible access: Enable anonymous browsing or require login for personalized experiences. 

• Protect pricing integrity: Use role-based pricing so distributors see their pricing, not your margins. 

• Maintain approval control: Launch with quote-request-only workflows and require manual approval for sensitive or high-value quotes. 

• Control product exposure: Decide which products are available externally and which remain internal-only. 

• Sell faster: Sales teams spend less time on basic quoting and more time closing strategic deals. 

• Guarantee valid quotes with fewer engineering hours: Engineering stays focused on innovation instead of validating configurations. Every configuration follows the same rules used by your internal CPQ. 

• Reduce tool complexity: IT extends CPQ securely without adding system complexity. 

• Scale revenue: Executives scale revenue without scaling operational costs. 

 

The shift toward self-service is already happening across manufacturing. As business analyst at Meyn Food Processing Technology, Sicco Saft, recently shared when asked about the future of digital sales tools: 

“Today everybody has e-commerce. We get more and more people retiring. We have less workforce and a lot of customer demand. So self-service is the way forward.” 

How to get started with Tacton SSC 

Tacton SSC does not automatically replace the first-generation customer self-service tool for current users. Likewise, new customers will not be able to purchase the old self-service tool.  

Speak with a Tacton representative to learn more about how Tacton Self-Service Channels integrates into your larger channel strategy.  

Request a Demo 

Frequently asked questions about Self-Service Channels

Can we control what pricing customers see?
Yes. You control pricing visibility (including no pricing visibility) through login requirements, role-based pricing, and optional approval workflows before quotes are finalized. 

Do we have to allow customers to place orders automatically?
No. You can limit the experience to quote requests only and require manual review before issuing firm quotes or accepting orders. 

Can customers save their progress in the self-service experience?
Yes. If you enable login functionality, users can save configurations and return later to complete or revise them. 

Does Tacton track the end user directly?
No. User logins and customer data remain controlled by you, not Tacton. 

Will Self-Service Channels support system configuration?
Yes. Self-Service Channels supports full configuration setup with assistance during implementation. 

How is pricing structured for Self-Service Channels?
Pricing is consumption-based. 

How much of your business is truly engineer-to-order, and how much should be? 

Engineer-to-Order (ETO) and Configure-to-Order (CTO) are two manufacturing models that determine how products are designed, quoted, and produced. 

In true ETO manufacturing, every order requires new engineering. Designs start from scratch with each order, and capacity is limited by available engineering headcount. That level of customization can be strategic, but it’s also expensive and hard to scale. 

In configure-to-order (CTO), products are built from predefined, standardized modules and rules. Sales can move faster and growth isn’t constrained by engineering bandwidth. But pushing too far can limit flexibility where it actually matters. 

Many manufacturers overestimate how much of their portfolio needs to be ETO. As customers reward faster response times and sales volumes increase, having the right hybrid between ETO and CTO manufacturing allows manufacturers to balance innovation with operational predictability.  

ETO vs CTO manufacturing: strengths and limitations 

Manufacturers often overestimate their current level of customization due to a misunderstanding of what constitutes ETO and CTO manufacturing.  

What is engineer-to-order (ETO) manufacturing? 

In true ETO manufacturing, each project is completely unique. Each component is designed or heavily engineered after order intake.  

Due to their high customization, unique configurations are often driven by site-specific conditions (e.g., wind loads, snow loads, earthquake loads), regulatory variables (e.g., electrical compliance, pressure vessel compliance, emissions), and customer-specific performance needs (e.g., precision tolerances, torque).  

Why adopt ETO?

• Customers have the ultimate flexibility in their solution.  
• ETO supports unique or emerging use cases not supported by standard components.  
• ETO enables and encourages deep engineering differentiation and innovation.  

What are the limitations of ETO?

• ETO manufacturing models have long sales cycles and lead times (up to months or years) due to heavier engineering input and customer scope creep. 
• Heavy customization requires more product expertise, which strains engineering and product teams.  
• Manufactures face more manual processes and rework as components are not digitally standardized. 
• Reliance on engineering makes it difficult to scale sales without adding headcount. 
• Lack of predictability in configurations, pricing, and production leads to inconsistent margins. 

What is configure-to-order (CTO) manufacturing? 

The CTO manufacturing model is based on building products from repeatable, predefined components or assemblies that can be combined in different ways to meet customer needs.

Many manufacturers that believe they operate fully in an ETO model often discover that large portions of their products are actually repeatable. Core components or assemblies are standardized, while only certain elements require customization.

In CTO, engineering knowledge is captured and structured into rules, enabling sales teams—or even customers—to configure valid solutions without requiring engineering involvement for every deal.

It’s important to note that not all CTO models are fully modular in the same way.

• In more advanced, modular CTO models, products are built on a structured architecture (often a 150% Bill of Materials) that consists of a product with an assembly structure where each piece can be switched out with interchangeable components. Larger, more complex products may include multiple layers of assemblies with interchangeable components.
• In other cases, CTO may be less formalized, where teams define what the configuration will be within certain boundaries, without a fully modular structure.
• Many manufacturers operate in a hybrid CTO + ETO model, where standard configurations handle the majority of demand, and true customization is managed through controlled engineering exceptions.

Configure-to-order models versus SKU models

CTO is not the same as a SKU model or standard consumer product.  

A SKU model sells fully predefined products — each variant already exists and is ordered as-is. There’s no configuration at the time of sale. 

Configure-to-order (CTO) uses standardized components and rules to create a valid, customer-specific solution during the sales process. 

If you try to manage high product complexity with pure SKUs, you end up with SKU explosion. If you try to manage it with pure ETO, you overload engineering. CTO sits in the middle. 

Why adopt CTO?

• CTO allows faster quoting, because customization affects selection, not core design.  
• When components are already validated and established within CPQ, there are fewer configuration and quote errors.  
• CTO enables more customer self-service and guided buying, which scales sales processes by reducing the need for always-on sales teams and engineers. 
• Manufacturers who have repeatable, standard components have higher data visibility into which variants are actually sold and how often.  

What are the limitations of CTO?

• Engineers and product teams often respond with fear of reduced flexibility. 
• Standardizing and codifying product and configuration logic requires upfront modeling effort in CPQ. 
• CTO requires structured data and teams to be working from the same PLM data. 
• Organizational change and change management are trickier when sales and engineering processes must fundamentally change.  

For most manufacturers, the real opportunity is not replacing ETO, but shifting more business into structured CTO where possible. 

ETO vs CTO: what changes for your business? 

When ETO is strategically necessary 

It’s common to believe that you “never sell the same thing twice.” While this is on the far side of the spectrum, not all customizations should be eliminated. Engineer-to-order models can be optimized with great success. 

ETO remains critical when: 

• Truly unique customer requirements drive value.  

• Unique requirements lead to new product development or market entry.  

• Economically, a specialty project is low-volume but high-margin.  

The goal is to reserve engineering effort for high-value differentiation, which requires some transition to CTO manufacturing models.  

The business case for increasing CTO manufacturing

Quoting cycles can range from days to months, and buyers expect rapid pricing and quoting, even if price isn’t the final decision factor.  

CTO manufacturing brings speed to manufacturers bogged down by engineering or pricing back-and-forth.  

In addition to speed and response times—which often determine win rates and competitive positioning—there are several strategic benefits to increasing CTO products.  

8 reasons to expand CTO manufacturing

• Sales volumes are rising, but headcount isn’t scaling with demand. 

• Distributors, partners, and customers can’t generate accurate quotes independently. 

• Revenue growth remains unpredictable and tied to engineering capacity. 

• Rework and order errors consume time and erode customer trust. 

• Critical product knowledge walks out the door with retiring experts. 

• Sales onboarding takes months—sometimes over a year—to reach proficiency. 

• Sales execution and customer experience vary across regions and channels. 

• SKU sprawl and redundant parts create supply chain inefficiencies you can’t easily untangle. 

Common objections to CTO—and how to rethink them 

Recalibrating your mix of ETO and CTO products is not a simple switch. It’s no surprise that internal stakeholders will have objections.  

“We’ll lose flexibility.” 

When you dig deeper into your orders, how many are truly unique?  

Focus on identifying which products or features truly require engineering. Moving just a fraction of orders from ad hoc engineering to standardized configuration can improve margins, reduce lead times, and free up engineering capacity. Edge cases can still be managed through controlled engineering escalation, supported by clear approval workflows for exceptions that fall outside standard rules. 

“Our products are too complex.” 

Complexity is the reason to adopt CTO, not avoid it. 

CTO makes complexity manageable through rules, constraints, and guided selling. Without structure, complexity drives longer sales cycles and inconsistent margins. By translating engineering logic into rules and constraints, you enforce valid combinations automatically. The complexity stays behind the scenes; the sales experience becomes guided and reliable.  

“Our data isn’t ready.” 

Is data messy or missing completely? Is data scattered across multiple PLM systems or are entire data fields missing? 

Transition is iterative, not a one-time overhaul. Start with one product line, such as a core product line. Clean and structure data incrementally and treat cleaning data as part of an essential journey toward data maturity.  

“Implementation could take years.” 

Yes. It’s a journey, but one that has long-term benefits on your top and bottom line. 

Remember, value can be realized in phases. Pilots reduce risk, and the ROI from faster quoting and error reduction can offset the investment.  

“Engineering will resist this.”  

There is always concern about the fear of losing control over product integrity, or that sales will misconfigure complex solutions. Engineers don’t want their expertise undermined or their innovations to be dumbed down.  

CTO captures and scales engineering expertise through rules that protect product integrity. Instead of reacting to sales support requests, engineers focus on architecture, innovation, and optimization. 

Ready to shift the balance to hybrid ETO-CTO manufacturing? 

Shifting from ETO to a more balanced CTO model doesn’t mean sacrificing flexibility or engineering expertise. It means applying structure where it creates scale and protecting customization where it truly adds value. If you’re evaluating how to move in that direction, the next step is a strategy conversation.  

Tacton CPQ helps complex manufacturers master ETO and CTO workflows through a constraint-based configuration engine and guardrails that preserve product integrity while giving your customers the flexibility they want.  

Learn more

Product portfolio management in complex manufacturing is the process of deciding which products, variants, and configurations to maintain, simplify, or retire based on profitability and demand. As manufacturers move from engineer-to-order to configure-to-order models, product portfolio complexity becomes harder to manage and optimize.  

Your current product portfolio decisions may rely on aggregated ERP or CRM data. But average margin or total revenue rarely reveal which configurations actually drive profitability and when.  

These product portfolio metrics within CPQ will help you understand how to simplify and rationalize configurable products to prioritize your most profitable opportunities.  

How to go deeper with portfolio optimization  

Many manufacturers manage product portfolios at the SKU level. In configurable environments, that approach can oversimplify the commercial impact of certain configurations or customer requirements.  

Profitability and sales performance are also determined by configuration choices, rather than simply product families. Two variants under the same type of product can perform dramatically differently across margin, win rate, and regional demand. 

In configurable manufacturing environments, portfolio management must go beyond final product analysis and examine configuration-level performance metrics as they relate to margin, win rate, and even regional performance. 

Where do configuration-level product portfolio metrics come from? 

These metrics are generated from configuration and sales data captured during the quoting and ordering process. 

By analyzing configuration-level data, including selected attributes, variants, win/loss outcomes, margin rates, and territory performance, manufacturers can connect engineering decisions directly to commercial results. 

This requires analytics that sit within CPQ and configuration systems. 

Who should use product portfolio metrics from CPQ data? 

Product portfolio metrics are not owned by one department. In complex manufacturing, they require cross-functional visibility: 

• Engineering leaders use them to rationalize variants and reduce unnecessary complexity. 

• Product managers evaluate performance across markets and segments. 

• Sales leaders assess win rates and margin performance by territory. 

• Executives use portfolio data to align profitability with long-term strategy. 

How to measure product portfolio performance in complex manufacturing: 5 insightful metrics  

By simplifying product portfolios and eliminating unnecessary options for your customers, you create greater bandwidth for innovation and engineering focus, faster sales cycles, and prioritization of higher value opportunities. 

What are the performance metrics that every manufacturer needs to know for deeper product portfolio management? 

Metric: Win or loss rate by product attribute  

In CPQ (Configure, Price, Quote), a product attribute is a specific characteristic or property of a product that can be selected, configured, validated, or used in pricing logic during the quoting process. For example, a product attribute may be an engine type, a body length in a heavy vehicle, or even a wall color for an elevator.  

When analyzing win rates on the attribute level, you may find that in an industrial machine, selection of Spanish or French for control panel language is associated with a substantially high win rate of 50%. Or, heavy vehicles with vinyl seats selected have a very high win rate. Now, you know how to increase the likelihood of a configuration being successful.  

On the other hand, loss rate by product attribute is equally informational. Perhaps medical trays with 60 instruments or more have very low win rates—too complex. Now you know whether you should refine the product or move it from configure-to-order (CTO) to an engineer-to-order (ETO) process for special approval and processing only.  

Metric: Sales by product attribute or value  

Analyzing sales by specific product attribute, such as installation site country, application type (e.g., home, commercial, office), or sales channel (e.g., direct vs. self-service) gives product managers and engineering teams a much clearer view of real functional demand.  

Instead of relying on assumptions in market needs, product stakeholders see which configurations actually convert in which contexts. For example, if a certain feature set consistently wins in commercial applications but underperforms in residential, that signals where to focus roadmap investment, standardization, or simplification. Similarly, strong performance in one country may justify regional variants. 

This kind of functional needs analysis supports smarter portfolio optimization. Engineering can prioritize high-revenue configurations and eliminate low-performing combinations that add complexity without driving revenue. Product managers can also tailor packaging, for example, by channel and simplify offers for self-service while preserving advanced options for direct sales. The result is fewer SKUs, lower operational complexity, and a portfolio shaped by real market demand. 

Metric: Average margin rate per deal  

Looking at average margin by territory can reveal important performance gaps. For example, Brazil may show both a strong win rate and healthy average margins of 20%, while Canada closes a similar number of deals but at significantly lower margins. 

This insight helps you determine whether pricing adjustments are needed, whether discounting behavior differs by region, or whether market conditions justify a different margin target. Instead of focusing only on win rate, you can balance growth with profitability and make deliberate decisions about where and how to protect margin. 

Metric: Cross-analysis by product attribute or configuration parameter  

Cross-analyzing attributes against defined configuration parameters — such as installation site country, application type, building size, or sales channel — uncovers meaningful patterns in buyer behavior. For example, you may discover that customers in Germany consistently select lower energy output components than those in the U.S., or that high-rise construction projects are more likely to go through direct sales rather than self-service. You might also find that certain cabinet locations correlate with stricter noise requirements. 

These insights allow you to adjust pricing, refine packaging, guide sales conversations, or even regionalize your product strategy. But to unlock this value, the relevant parameters must be clearly defined and structured within your CPQ process. Without consistent data capture at the configuration level, these patterns stay hidden.  

Metric: Number of solutions by category  

Tracking the number of solutions sold by category helps identify what is and isn’t moving. For example, if your “High-Performance 6000 RPM Motor” configuration has seen little to no sales over multiple quarters, it may be adding unnecessary complexity to engineering, pricing, or inventory management.

Low- or zero-volume categories that have been included in zero solutions can often be consolidated or removed from the portfolio entirely to reduce SKU sprawl and operational overhead. While ERP shows what ultimately shipped, CPQ data provides visibility into configured solution categories and demand patterns that may not be obvious from finished goods data alone. 

What product performance metrics enable 

Together, these product portfolio performance metrics allow manufacturers to: 

• Identify underperforming variants 

• Compare margin performance across markets 

• Rationalize product complexity using commercial data 

• Align engineering decisions with real-world demand 

• Move from anecdotal portfolio management to measurable optimization 

Proactively optimize your portfolio of highly configurable products 

Portfolio optimization requires designing products and configurations that support profitable, repeatable decisions. Tacton’s CPQ-embedded analytics gives product managers and engineering leaders direct visibility into how configuration choices impact win rates, margins, sales velocity, and portfolio complexity. 

Engineering-led metrics connect product design decisions to commercial performance, helping teams identify which variants to scale, which to simplify, and where complexity slows growth. Configuration-level data equips stakeholders to strengthen both portfolio strategy and execution. 

Discover our analytics capabilities

From model hallucinations to cybersecurity breaches, AI adoption is a risky endeavor if your company doesn’t have AI security best practices in place. According to Accenture, 77% of companies are failing at AI data security as they continue to rush AI adoption without foundational security practices.  

Of over 200 surveyed manufacturers, low confidence in AI and its data is a core barrier to AI adoption. And as CPQ software—which continues to be a crucial part of the sales process—integrates AI into its capabilities, companies can expect to have more questions for vendors.  

These questions and AI security best practices make it easier to set up the guardrails you need to safely and securely reap the benefits of AI-driven CPQ.  

What is AI data security and why is it so important? 

AI is capable of processing vast amounts of data, from publicly available data to confidential, internal information. That may include personal identification details, product details, financial data, and other sensitive information. With so much data comes the risk of AI security vulnerabilities, including breaches, non-compliance, and errored outputs.  

As you adopt AI tools and integrate them across your digital systems and software, you are putting confidential information in the hands of your vendors. Your customers and your internal teams rely on IT leaders to evaluate and choose trusted partners that keep their information safe.  

10 questions and best practices for AI data security in CPQ software  

In addition to understanding available AI use cases in a vendor’s CPQ platform, it’s crucial to know a vendor’s policies around data security, especially if the company is using generative AI or LLMs.  

Start conversations around AI security with these essential questions. 

1. How is customer data handled when processed by AI features within the CPQ tool?

Start with what customer data is used by the AI algorithms. Understand the flow of that data through the system, as well as any governance practices that limit user access to sensitive data and clearly define where that data exists to prevent exposure. 

2. Does the CPQ vendor use any customer data to train AI models—either their own or third-party models?

Buyers should clarify whether their data contributes to model improvement and whether opt-out mechanisms exist to prevent data leaks. Your data should never be used to train public AI models. Consider what contractual guarantees exist to prevent data sharing with third parties, including model providers. 

3. Are AI models hosted in secure, enterprise-controlled environments or in public cloud AI systems?

When evaluating CPQ that uses generative AI and LLMs like OpenAI (ChatGPT), vendors should clearly state whether sensitive configuration, pricing, and product data is exposed to external, public environments. Work with CPQ partners that process data using enterprise-grade level environments or AI tools that keep all data within their internal environment.  

4. How does the vendor ensure compliance with relevant data protection standards?

AI tools must align with both global privacy requirements and internal corporate governance. AI data security regulations are often more stringent in the European Union, for example, than in North America. Different divisions and regions may have different data security regulations, such as the AI Act in the EU, which are important to be aware of if you are a global company.  

Helpful follow-up questions:  

• How is it ensured that my data remains within the EU (for global or European businesses)? 

• Are all AI interactions logged for audit and compliance purposes? 

• Are your AI services compliant with GDPR / CCPA / ISO 27001 / SOC 2? 

 

These certifications signal whether a vendor has independently audited, repeatable security processes in place. Buyers should confirm that compliance applies not only to the core CPQ platform but also to all AI components, including sub-processors and model providers. Ask whether the vendor supports data residency requirements (e.g., EU-only processing), maintains audit logs, enforces role-based access controls, and provides documentation for security reviews. 

5. What mechanisms are in place to ensure human oversight of AI-generated outputs?

Garbage in equals garbage out, no matter how secure your AI CPQ software data may be. First, verify with your internal experts that your data used by the AI is clean and consistent—an important step for all companies adopting AI. Then, ensure that AI suggestions, configurations, or automated content are validated before being used in sales or engineering processes. All outputs generated by the CPQ vendor should be subject to human review for internal quality control to prevent hallucinations and other errors.  

Helpful follow up question:  

• Who owns outputs created by AI, the customer or the vendor?  

 

AI is not a person, so who owns its outputs? AI output can be considered intellectual property, which means that suggestions, documentation, or configuration assets may be property of the CPQ vendor rather than the customer. Ensure you are the owner of AI outputs, especially if those outputs are to be patented or commercialized.  

6. What encryption and security practices protect data during storage, transmission, and AI processing?

Buyers should look for a vendor that can clearly describe how data flows, where it is encrypted, who can access it, and how the environment is monitored. Is data encrypted in both transit and at rest? Vendors should also monitor AI environments for anomalies, unauthorized access attempts, or unusual model behavior. If vendors have appropriate role-based access and segregated environments, there is less probability of misuse or external access.  

7. What level of transparency does the vendor provide about the AI system’s behavior, limitations, and decision logic?

AI-enabled CPQ shouldn’t be a black box. Can your CPQ vendor easily explain the types of AI algorithms and logic rules that are used? Does it use a mix of symbolic AI and generative AI to improve trustworthiness? Understanding how the AI arrives at recommendations is critical for trust and auditability. 

8. How does the vendor manage model updates, patching, and lifecycle governance to ensure ongoing security?

AI models evolve, so companies should know how updates are applied and validated. Equally, if your vendor is not consistently investing in modernizations and improvements to its capabilities and platform, it may be time to look elsewhere.  

Helpful follow-up questions:  

• How are updates to the AI model made, and how are customers notified? 

• Are the AI functionalities AI-native or AI-enabled? 

 

AI-native capabilities are built directly into the core CPQ architecture, ensuring unified governance, security, and consistent use of product logic. AI-enabled (“bolt-on”) features often rely on external components, which can introduce additional data transfers, limited governance, and higher security risks. 

Ask whether: 

• AI runs within the same rules engine 

• It depends on external orchestration tools 

• Product logic is governed centrally or duplicated across service 

• Safeguards are built into your architecture to switch models or rebalance cost/performance without disruption 

 

These questions address fears about model vendor lock-in, AI regulation changes, and tech bubble volatility for long-term data security.  

9. What technical safeguards exist to prevent inaccurate, biased, or non-compliant AI outputs from entering customer-facing quotes?

Buyers should confirm that the CPQ vendor has technical, automated safeguards that prevent the AI from generating outputs that violate or override product rules, pricing policies, compliance requirements, or accuracy standards. Unlike human oversight, these safeguards are built directly into the system and ensure that AI-generated recommendations are always validated against the product model, pricing structures, and regulatory logic before they appear in a quote. This avoids, for example, a potential regulatory issue or an unauthorized discount. 

10. How is the CPQ vendor using AI in its own internal operations?

This question often goes overlooked. But it’s a major red flag if an AI-driven CPQ company is not operationalizing AI within its own teams. When a company uses AI to drive its own efficiencies and innovations—especially operationalizing an AI use case internally before marketing it externally—then you know they have full confidence and trust in their AI capabilities.  

Keep your data secure in AI-enabled CPQ 

Tacton CPQ AI capabilities protect your product, pricing, and customer information across tools like AI Product Modeling and configuration assistance. We combine enterprise-grade security with human oversight, so you can innovate and sell highly configurable products with confidence. 

Learn more about AI-driven selling 

How do global manufacturers scale product portfolios, regions, and sales channels in a controlled and repeatable way?  

The challenge often comes from inheriting dozens of pricing and quoting tools across regions—both manual and digital—when the goal is to keep everyone working from the same information. The best CPQ for enterprise manufacturers standardizes information into a single platform that is built to both, 1) handle growing interdependencies across products and options, and 2) reduce efforts to maintain and govern. Manufacturers that scale successfully at the enterprise level focus on the following CPQ capabilities and practices that support phased growth, governance, and ongoing change. 

What is enterprise CPQ and what makes it different? 

Enterprise CPQ is not simply CPQ used by large companies. It reflects a different set of requirements that emerge when manufacturing organizations operate across multiple product lines, regions, and sales channels. 

In enterprise manufacturing, CPQ must support more than fast quoting. It becomes a shared system of record for product configuration, pricing, and quoting logic across the business. That means accuracy, governance, and reuse matter as much as usability. 

What distinguishes enterprise CPQ is the scale of products, users, and change. Product portfolios evolve continuously. Pricing varies by customer, region, and contract. Quotes must work for internal sales teams, dealers, and engineers alike. At this level, CPQ must function as long-term infrastructure rather than a point solution. 

Enterprise CPQ also introduces operating-model questions that simpler implementations don’t face. Teams must decide where product logic lives, how changes are governed, how new regions or acquisitions are onboarded, and how to scale without duplicating rules or creating regional silos.  

For manufacturers evaluating CPQ for enterprise manufacturing, the real differentiator is whether the platform is designed to support phased rollout, shared ownership, and continuous expansion without losing control. 

What are the benefits of enterprise CPQ for manufacturers? 

At the enterprise level, CPQ delivers value well beyond faster quotes. When implemented and scaled effectively, enterprise CPQ helps manufacturers: 

• Standardize configuration and pricing across the business while allowing regional and customer-specific variation. 

• Scale products, regions, and channels without duplicating logic or creating silos. 

• Reduce dependency on engineering and IT by separating technical constraints from commercial logic. 

• Protect margin and accuracy as complexity and change increase. 

• Create a foundation for long-term growth, rather than a tool that needs to be replaced as the business evolves. 

3 critical capabilities for scaling CPQ and product sales 

Homegrown and simple CPQ platforms need substantial support from IT and consultants to keep up with growing product rule sets, fragile workarounds, and vendor interventions for changes. When selling highly configurable products, updating product models should be straight forward. When investing in long-term tools, it should be clear what investments are needed each year to keep up with technological innovation and new buyer needs.  

Yet, these tools often fail to meet the requirements of scaling companies with increasing sales model and channel complexity. The core requirements for CPQ for enterprise manufacturing then change.  

1. Governance: who can change, update, or maintain the system safely and easily?  

1. Reuse: How can modular models across product lines and divisions be reused in a standardized way, especially as possible configurations continue to grow?  

1. Channel control: How can internal, partner, and customer channels work from a single operating model to ensure a consistent brand and sales experience?  

These questions are vital to scaling commercially, but there’s one more requirement that easily goes overlooked: ongoing innovation.  

Manufacturers who work with outdated and legacy tools eventually find that their original solution can’t adapt to new buyer needs or technology, and investing in constant innovation for their platform is costly and resource consuming.  

How to scale enterprise CPQ: best practices and what to look for 

As products, regions, and sales channels expand, CPQ must shift from a one-time project to a long-term operating model. 

The questions below reflect what enterprise teams ask when they think seriously about scaling CPQ, and how they can solve for these challenges.  

Can CPQ be implemented without a massive, multi-year project? 

Enterprise manufacturers often worry that CPQ will require a long, all-at-once rollout across every product line and region. In practice, that approach creates more risk, not less. 

The most successful enterprise CPQ programs start with a focused pilot. Teams choose one product line or region—often with medium complexity—to prove value quickly. This pilot establishes product models, pricing logic, integrations, and workflows that can be reused as the solution expands. Once the foundation is in place, additional product lines, regions, and channels can be added in parallel rather than treated as new implementations. 

What to look for in CPQ: 

• Support for phased implementation and proof of concept pilots  

• Constraint-based configuration rather than rules-based configuration for easy reusable product and pricing logic that doesn’t require hundreds more lines of rules for each new product 

• The ability to expand without rebuilding core models 

How will CPQ fit into our existing CRM, ERP, and PLM landscape? 

Enterprise environments are rarely clean or static. Most manufacturers operate with multiple systems that vary by region and change over time. CPQ must work within that reality. 

Rather than relying on brittle, point-to-point integrations, CPQ should support a decoupled architecture. This allows enterprises to evolve their CRM, ERP, or PLM systems without breaking configuration or quoting processes. In practice, teams often prioritize the most critical integrations first—typically CRM and ERP—then extend to PLM, analytics, and other systems as the solution matures. 

What to look for in CPQ: 

• Flexible, API-based integration  

• Compatibility with middleware and enterprise integration strategies 

• The ability to add or change systems without disruption 

Where should product and pricing logic live? 

As CPQ scales, unclear data ownership becomes a major source of friction. Enterprise teams need a clear separation of responsibility. 

A common best practice is to keep technical constraints (i.e., what can be built) in PLM, while managing commercial logic (i.e., what should be sold) in CPQ. This separation allows engineering teams to protect product integrity while sales and pricing teams control market-specific offerings, pricing structures, and approvals. It also reduces dependency on engineering for everyday commercial changes. 

What to look for in CPQ: 

• Clear separation between technical and commercial logic 

• Centralized governance with role-based control 

• A single source of truth for configuration and pricing 

What happens when products, pricing, or rules change? 

Change is constant in enterprise manufacturing. New options are introduced, pricing updates occur, and components are phased out. CPQ must handle change without disrupting active sales processes. 

Enterprise-ready CPQ platforms provide version control, release management, and traceability. Teams should test changes in isolation, release them deliberately, and decide when existing quotes need to be revalidated. Updates should not automatically break open quotes or regional processes unless that is explicitly intended. 

What to look for in CPQ: 

• Versioned product models 

• Controlled release workflows 

• Clear visibility into what changed and why 

How much effort does it take to maintain CPQ after go-live? 

Enterprise CPQ should be maintainable by a small, trained internal team. Product modelers, pricing administrators, and CPQ admins should handle most changes without heavy IT involvement or ongoing reliance on consultants. When every update requires external support, scalability suffers and costs rise. 

What to look for in CPQ: 

• Low-code or no-code configuration tools 

• Training that builds internal ownership 

• An operating model designed for continuous growth 

How a global manufacturer approached CPQ standardization and scalability 

Xylem, a pump and flow technology manufacturer, operates globally with diverse products, regions, and sales motions. Like many enterprise manufacturers, they faced fragmentation across quoting and configuration tools—over 40 disparate tools, making it hard to deliver consistency and scale. 

In an effort to create a scalable and consistent CPQ process, Xylem created Solver, which uses CPQ as a single source of truth for its products across regions.  

Today, after consolidating their tools into one powerful selector and CPQ platform, they have: 

• The ability to standardize core logic while allowing local flexibility. 

• Support for multiple users, regions, and channels without rebuilding models. 

• A foundation that could evolve with their business, not just solve today’s problems. 

 

Xylem’s experience reflects a common enterprise reality: CPQ is less about features and more about creating a long-term operating model for growth. 

Scale confidently with Tacton CPQ 

Enterprise CPQ success isn’t about speed and automation alone. It’s equally important to prioritize governance, reuse, and long-term ownership. 

Tacton CPQ is built specifically for complex manufacturers of highly configurable products. Our constraint-based foundation, phased rollout approach, and ability to evolve with your business needs allow you to expand across products, regions, and channels while keeping governance intact. That focus on long-term ownership—not just initial implementation—is why global manufacturers rely on Tacton as complexity and scale increase. 

Explore Tacton CPQ 

Manufacturers are keen to embrace generative AI, including large language models (LLMs), despite the many lingering questions surrounding how it works and how to safeguard inherent risks. Will AI fix your problems? Can you trust it? How much can you automate safely?

In manufacturing, many of these questions surface first in sales and quote-to-order processes, where AI is increasingly used to speed up configuration, pricing, and decision-making. However, the implications reach beyond sales.

Generative AI in manufacturing is powerful, but not limitless. It accelerates strong systems and clean data, but it doesn’t repair broken processes or replace the contextual experience of human beings.

These generative AI best practices, illustrated through the manufacturing quote-to-order and CPQ process, will help you avoid common pitfalls and clarify what AI can and can’t do across your broader manufacturing organization.

Data quality best practices: accelerating structure with AI

AI, especially generative AI and natural language processing, process a large amount of information to develop outputs. It can do things like: 

• Accelerate model building when structured data already exists (and, in some cases, it can turn unstructured data into structured data for you).  

• Extract patterns from clean, consistent product documentation. 

• Reduce manual effort once data foundations are in place. 

However, it can’t fix poor data quality or missing product logic. It also can’t create a reliable output, such as a product model, from fragmented, conflicting, or inconsistent data.  

Do 

• Know where your data comes from: Identify which systems (PLM, ERP, PIM, spreadsheets, documents) are the source for product, pricing, and rules so AI isn’t trained on conflicting or unofficial data. 

• Establish a single source of truth: Decide which system owns each type of data and ensure AI outputs map back to that authoritative model instead of creating parallel versions. 

• Define the target structure before using AI: Use AI to extract and organize unstructured data only when attributes, allowed values, units, and rule logic are already defined. 

• Use AI to surface gaps, not hide them: Treat missing attributes, unclear rules, and low-confidence outputs as signals that data needs refinement before scaling. 

• Validate AI outputs against existing rules: Ensure AI-generated models, configurations, or quotes always pass configuration, compatibility, and pricing rules before customer use. 

• Consider unit and regional consistency: Inconsistent units, currencies, or regional standards (metric vs imperial, local certifications) are common failure points. 

Don’t 

• Assume AI will resolve data ownership or consistency issues: AI cannot decide which source is correct when systems disagree. 

• Treat extracted data as final: Information pulled from RFQs or documents must be normalized and mapped, not used verbatim. 

• Skip validation because results look reasonable: Plausible outputs can still violate hidden constraints or edge cases. 

• Don’t use edge cases and exceptions: AI performs best on standard cases. Rare exceptions should be clearly flagged or excluded. 

If your data foundation is incomplete or poor, AI will only amplify the problem.  

Transparency & oversight: generative AI can only assist

AI tools are incredibly powerful for suggesting models, product configurations, pricing, or predictive demand, but it can’t understand business context the way humans do. Though there is an opportunity for AI to eliminate unnecessary work, it shouldn’t be a replacement for experienced talent, but rather a tool to give them more time for strategy.  

Do 

• Make AI-generated outputs clearly visible: Users should always know what was generated or suggested by AI so they can review, question, and correct it. 

• Keep humans in the loop, especially early on: Use expert review during initial rollouts to catch errors, build trust, and train the system before scaling. 

• Provide confidence or accuracy indicators: Show how reliable an AI output is so users understand when extra scrutiny is needed. 

• Surface where AI struggled or made assumptions: Flag missing data, ambiguities, or inferred values instead of hiding uncertainty. 

Don’t 

• Fully automate customer-facing decisions: Avoid letting AI finalize quotes, configurations, or recommendations without human review. 

• Hide AI involvement: Presenting AI-generated outputs as human-created undermines trust and adoption. 

• Treat AI outputs as authoritative without review: AI suggestions should be treated as drafts, not final answers. 

AI can recommend, but only humans can take responsibility. 

Implementation approach: Enabling focus

Generative AI in manufacturing speeds up repetitive, time-consuming tasks and supports internal teams by taking on the manual lift, so the business can scale. But it’s not something that can be globally adopted and scaled right away. It’s crucial to follow AI best practices that ensure your organization doesn’t jump the gun or act on unrealistic expectations.  

Do 

• Start with internal use cases first: Use AI internally (e.g., engineering support, RFQ processing, model preparation) to validate accuracy and workflows before exposing it to customers. 

• Focus on narrow, well-defined problems: Apply AI to very specific tasks like data extraction, draft model creation, or parameter identification rather than broad, end-to-end processes. 

• Pilot, measure, then expand: Test AI on a limited set of products, track accuracy and exceptions, and refine before scaling. 

• Standardize inputs to improve results: Consistent formats, terminology, and schemas reduce ambiguity and increase AI reliability. 

Don’t 

• Treat Gen AI as a silver bullet: AI enhances existing processes but does not replace clear product logic, governance, or expertise. It can’t replace the knowledge that your teams possess around business context or commercial impact. 

• Rush to customer-facing deployment: Exposing immature AI features risks errors, loss of trust, and rework. 

• Expect near-perfect accuracy from day one: AI performance improves through iteration and refinement over time.  

Security & governance: generative AI can be safe if used correctly

 How do you process proprietary data without exposing it? Whether you’re using AI models you’ve built internally or using a third-party vendor, reading the fine print is one of the first steps in evaluating AI tools.  

Do 

• Use AI vendors with explicit data protection guarantees: Confirm contractually that your data remains private, isolated, and protected within your environment. 

• Ensure data is not used to train public models: Verify that proprietary product, pricing, and customer data is excluded from any shared or external model training. 

• Understand data flow and access points: Know exactly where data is ingested, processed, stored, and accessed across systems and AI components. 

• Involve legal and IT teams early: Align security, compliance, and governance requirements before AI is deployed, not after. 

Don’t 

• Upload proprietary data into public AI tools: Public or consumer-grade AI platforms lack the controls required for sensitive manufacturing data. 

• Assume all AI platforms follow the same standards: Security, isolation, and compliance vary widely between vendors and deployments. 

• Skip governance discussions to move faster: Weak governance increases long-term risk and slows adoption when issues surface. 

• Don’t treat AI-generated insights as exempt from compliance requirements: Outputs derived from regulated data may still fall under local laws, IP protection, or industry regulations. 

Use case selection: prioritizing for early wins

There are hundreds of ways to use AI in manufacturing, but not every use case is necessary for your business. AI can solve specific problems, but it shouldn’t be adopted for the sake of building a technologically advanced operation.  

Generative AI excels in a number of areas, including supporting guided selling workflows, building and defining products, analyzing performance data, etc. It can, however, be difficult to implement in high-risk processes.  

Do 

• Use Gen AI for acceleration, screening, and preparation: Let AI handle early-stage work like data extraction, draft models, or RFQ triage so experts can focus on decisions. 

• Apply AI where expert knowledge already exists: AI performs best when it’s reinforcing documented rules or proven processes. 

• Treat AI outputs as starting points, not final answers: Use AI to generate drafts that engineers, sales, or product experts refine and approve. 

Don’t 

• Deploy AI where errors have serious consequences: High-risk scenarios (safety, compliance, contractual commitments) require strict controls and review. 

• Use AI without expert validation: AI cannot replace engineering or product judgment, especially in complex configurations. 

• Don’t choose use cases with unclear ownership: If it’s unclear who reviews, approves, or corrects AI outputs, adoption will stall. 

• Don’t pick use cases just because they sound impressive: Prioritize practical value over novelty. 

Change management: augmenting your people

AI can be a powerful tool for faster onboarding and workflow acceleration. When your teams have the resources, training, and confidence they need, they’ll be more likely to adopt these tools.  

Do 

• Set realistic expectations about maturity and accuracy: Position Gen AI as an evolving capability that improves over time, not a finished or flawless solution. 

• Communicate clearly what AI can and can’t do: Be explicit about where AI assists, what data and logic it’s working from, and where validation is required. 

• Provide training and onboarding: Help users understand how to work with AI outputs and review them effectively. 

• Address workforce concerns about replacement directly: Position AI as a tool that reduces manual effort and scales expertise, not one that replaces engineers, sales, or product experts. 

• Listen to user feedback: Use real-world input to refine AI behavior, workflows, and trust over time. 

Don’t 

• Oversell AI capabilities: Overpromising erodes trust and increases resistance when reality doesn’t match expectations. 

• Expect immediate perfection: Early errors are part of the learning process and should be planned for. 

• Ignore adoption and change challenges: Successful AI deployment depends on people, incentives, and workflows, not just technology. 

Continuous improvement: iterating through your evolution

AI will only continue to evolve, and if you’re working with AI-enabled tools, keeping up with new capabilities, regulations, and options is essential.  

Do 

• Monitor AI outputs continuously: Track accuracy, exceptions, and confidence levels to ensure AI behavior stays aligned with business and product rules. 

• Analyze recurring errors: Look for patterns in mistakes to identify gaps in data, rules, or prompts that need refinement. 

• Refine prompts, data, and rules over time: Adjust inputs and guardrails based on real usage rather than one-time setup. 

• Iterate based on real-world usage: Let how users work with AI guide improvements and prioritization. 

• Stay informed on evolving AI capabilities and regulations: Regularly reassess tools and practices as models, compliance requirements, and enterprise standards change. 

Don’t 

• Don’t position AI capabilities as static or “finished”: Messaging should reflect that AI evolves and improves, not that it’s fully complete. 

• Don’t promise long-term behavior based on early results: Early performance does not guarantee future accuracy without ongoing refinement. 

• Don’t reuse outdated AI messaging as capabilities change: Keep customer-facing and internal messaging aligned with current functionality. 

• Don’t ignore regulatory or policy shifts: Compliance and governance expectations around AI will continue to evolve and must be reflected in both product behavior and messaging. 

The most successful manufacturers know AI’s limits

Generative AI delivers real value when your data is ready, your use cases are focused, and humans remain accountable. Following AI best practices helps manufacturers avoid unnecessary risk while unlocking meaningful efficiency gains. 

At Tacton, AI is built to strengthen your quote-to-order process. We integrate AI into our CPQ to help you accelerate RFQ-to-quote workflows, support product modeling, and validate configuration of highly complex products. AI works alongside your existing product logic and expert knowledge, not around it. 

If you’re exploring AI, consider how AI can elevate your CPQ strategy, and how it fundamentally shifts how your teams deliver value.  

Download the AI ebook 

The competitor that makes it easiest to buy their product is the one who wins the customer. Speed and accuracy are key to making an RFQ response competitive, but this isn’t easy to accomplish as buyers seek more information and RFQs become more complex. Greater product variance, sustainability needs, lead times, services, and other configuration factors make it difficult for your sales team to manually respond to hundreds of RFQs while still recommending the best product for the customer’s needs.  

It’s no longer simply a test of price and product. RFQ response time matters as a key differentiator for your customer that, in turn, speeds up your sales cycle. And with generative AI in the configure, price, quote (CPQ) process, sales teams can process large RFQs and find manufacturable, validated solutions within minutes or hours.  

What buyers want in a “winning” RFQ response vs. what sales can deliver 

Detail makes for a good RFQ response, but this is difficult to do quickly. The manual RFQ-to-quote process requires your team to sift through and interpret large documents, then collaborate with engineering to find a manufacturable solution. This can take weeks in some cases.  

While CPQ software can accelerate quoting by digitalizing engineering rules and constraints and validating configurations for sales teams, much of the data in an RFQ is unstructured and still requires interpretation.  

Meanwhile, customers are looking for the following to evaluate RFQ responses:  

• Speed to first credible response  

• Configuration accuracy and solution completeness—no ambiguity  

• Feasibility and compliance for highly configurable products in highly complex industries 

• Clear assumptions and constraints  

• Accurate pricing and lead times  

• Visualization and other differentiating items, such as sustainability estimates 

 

In order to achieve this, sales teams need to automate the RFQ interpretation and configuration process.  

Can AI automate RFQ responses? How generative AI is accelerating winning RFQ responses  

Generative AI for RFQ response automation is an emerging tool that helps you interpret RFQ documents and turn that into a configurable solution. Think of it like a sales assistant rather than a full automation tool.  

Generative AI in CPQ can process unstructured data from documents like PDFs, emails, and more. Imagine a 50-page RFP that includes a mix of long-form technical descriptions and operating conditions, such as cold weather or high pressure. In that mix are some tables, some notes, and then a sales discovery transcript contains additional business context. The AI sales assistant then interprets that conditional language and translates it into configuration logic that is already embedded in your CPQ platform.  

What the AI outputs cannot do, however, is provide a final solution. An RFQ-to-Quote tool provides an early-stage, validated configuration and some alternatives, perhaps with a match percentage to help you gauge confidence in the solution. It cannot, however, provide the final solution. This is where your sales team provides control and contextual industry expertise.  

AI does not: 

• Make final legal or contractual decisions 

• Replace engineering authority 

• Remove the need for final review and approval processes 

 

AI does: 

• Reduce manual interpretation 

• Increase consistency and speed 

• Support better-informed human decisions 

Optimizing Your Solution with AI  

How do manufacturers propose better RFQ solutions while still maintaining efficiency?  

AI helps sales representatives move past simple requirement discussions and move towards solution-based discussions. Using AI assistance, sales can collaborate to come up with:  

• Multiple viable options  

• Faster delivery alternatives  

• Cost-optimized configurations  

• Options aligned to highest customer priorities  

 

Because AI helps you create early configurations aligned to constraints, price, and other factors within your CPQ, your sales teams now have more opportunity and time to speak to the business outcomes and value of your solution.  

The business benefits of AI-powered RFP-to-quote

AI delivers the greatest advantage in RFQ-to-quote automation for manufacturers seeing high RFQ volume, limited engineering capacity, and a strongly competitive market. For manufacturers with modular and CTO offerings, this is especially powerful.  

• Less variability across RFQ responses: AI applies the same interpretation and configuration logic across RFQs, regardless of seller, region, or customer (with region and customer configuration logic still intact). In turn, you create a stronger, consistent brand experience.  

• Faster sales cycles without cutting corners: AI reduces the time it takes to move from RFQ document to a validated, sales-ready quote. As a result, you can improve pipeline velocity and engage buyers earlier in solution discussions to increase your win rate.  

• Faster reuse of logic, assumptions, and best practices: AI makes it easier to reuse proven configuration logic, pricing structures, and response patterns across similar RFQs, so teams benefit from this knowledge automatically.  

• Reduced reliance on individual experts: Instead of depending on a few highly experienced engineers or sellers, AI helps standardize how RFQs are interpreted and configured to help onboard sales teams faster and increase engineering capacity when key people are unavailable.  

• Built-in traceability from RFQ to quote AI supports clearer traceability between RFQ requirements, configuration decisions, and the final quote. This allows you to see how requirements were interpreted and provide stronger documentation for audits, internal approvals, or customer questions.  

• Identified mandatory requirements for formal RFQ processes: AI helps surface mandatory, non-negotiable requirements and constraints early. This lowers the risk of disqualification in public tenders or formal RFQ processes and ensures compliance.  

What manufacturers need to successfully accelerate RFQ responses with AI 

AI can be a powerful advantage in RFQ-to-quote when it’s applied on the right foundation. Standalone AI tools are not equipped with the product logic and training as purpose-built, CPQ-embedded AI tools for manufacturing.  

Additionally, AI should not be a standalone quoting tool. Understanding how or why decisions are made is crucial in answering customer questions and instilling trust.  

To be successful, manufacturers should first have the following in place:  

• A credible CPQ with digitalized product and manufacturing logic: AI works best when it builds on existing configuration rules, constraints, and validation logic. 

• Validation of AI outputs: AI should accelerate interpretation and setup, while CPQ ensures the final configuration and quote are manufacturable and compliant. 

• Secure handling of RFQ data. RFQs often contain sensitive commercial, technical, or regulatory information. AI must operate within enterprise-grade security and data governance frameworks. 

• Clear ownership between sales and engineering: AI enables faster collaboration, but roles and approval steps still need to be defined, especially for exceptions.

Create winning RFQ responses faster with Tacton 

Tacton is an end-to-end manufacturing lifecycle platform and leader in CPQ, purpose-built for manufacturers selling complex, high configurable products. Our AI capabilities are grounded in proven CPQ foundations, using your existing product logic, constraints, and manufacturing rules to ensure every quote is feasible and defensible. That’s why leading manufacturers trust Tacton to sell smarter while maintaining accuracy, governance, and engineering confidence. 

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Highly configurable products are difficult to communicate, whether they’re being communicated from engineering to sales or from sales to the buyer. Buyers want convenience and ample information before they speak to sales, but more information upfront also makes the experience more complicated. Visual configurators and 3D product configurators create an intuitive experience, so that buyers can be sure they’re making the right decision despite your product’s high configurability.  

Visual configuration in Tacton CPQ (configure, price, quote) makes it easier to remove friction in the sales process and differentiate your buyer experience from your competitors. Learn how to tackle your common sales challenges with CPQ visualization tools.  

What Is Visual Configuration in Manufacturing? 

Visual configuration is an interactive product tool that is often part of the configure, price, quote process and software. Sometimes called a 3D product viewer or online product builder, it uses technology that allows sales and end-buyers to see the product they are customizing in real-time, including the variants, accessories, and even colors they choose. 

Tacton’s visualization and 3D product configurator is natively integrated within Tacton CPQ’s configuration engine, unlike third-party tools that require separate connections. The visualization tool uses augmented reality, scaling models placed in real environments via a QR code or mobile link. The AR changes dynamically, in real time as the configuration changes. 

Buyers can be sure they’re creating the right product with the right fit in their operational workspace, and they can manipulate the product (e.g., rotate, zoom, etc.) for more visual context. 

What is layout planning visualization? 

For space planning scenarios, visual configuration can also support layout planning, which allows users to upload floor plans, place multiple components in a space, and validate fit without involving engineering. Tacton’s layout planning capabilities, for example, are especially helpful for manufacturers selling construction or building materials or industrial equipment for factories, where procurement requires strong visualization.  

How 3D product configurators work  

A 3D configurator isn’t a standalone visualization tool. It renders a visual representation based on the same product logic, constraints, and configuration rules in the CPQ system.  

The system pulls parametric values and updates the 3D model using pre-prepared visual assets. Visualization supports simple, pre-defined parametric changes (such as length or height) when modeled in advance. CAD assets are used initially and converted to lightweight FBX models for Tacton ahead of time, and the visualization updates from these prepared assets. 

When a user selects an option, the CPQ’s constraint engine validates the choice and sends back: 

• Allowed options 

• Blocked/invalid options 

• Updated price 

• Updated BOM or configuration state 

Static images generated from predefined 3D camera angles can be automatically included in the proposal. 

It’s important to remember that visual configuration supports guided, rules-based visualization for quoting and sales, but it’s not a replacement for CAD. It doesn’t provide the depth of engineering-level precision or fully dynamic parametric modeling. 

The manufacturing challenges that visual configuration helps solve  

Visual product configurators reduce sales friction and streamline the buyer’s decision making by solving for these common bottlenecks: 

Buyers don’t understand their options 

Many B2B buyers today are not technical buyers. They need to see the solution in order to understand what it looks like, how it fits, and whether it’s compliant in their space. Without seeing it, they may delay their choice or create scope creep as changes and add-ons are requested post-quote.  

A 2D or 3D product viewer removes ambiguity. It builds confidence in your buyer and empowers them with more information without overwhelming them.  

Sales struggles to communicate complex variants 

Not all sales reps are technical experts, either. It’s much harder to effectively sell capital equipment when relying on PDFs, static images, or CAD screenshots that may be difficult to understand. Work happens in motion, and being able to use 3D or augmented reality in your onsite meetings or sales conversations makes you look like an expert.  

Engineering is pulled into every quote early in the sales cycle 

Selling highly configurable products means that engineering is tightly involved, especially in custom, engineer-to-order products. That means engineers spend a large portion of their hours supporting sales with feasibility checks. Visual configuration that’s embedded in CPQ ensures only buildable configurations are visualized, and buyers, sellers, and engineers are not navigating unbuildable solutions. Instead, engineering can focus on CAD and design work for special projects and new models.  

Quotes are slow and error-prone 

The longer it takes for your team to provide a quote, the more opportunity a faster competitor has to win their business. Between lack of accuracy and long delays while sales and engineering communicate, a buyer can become uninterested.  

Visual configuration eliminates interpretation errors between sales and engineering as well. Engineering often has to interpret what the customer wanted. 

Visual configurators eliminate this by providing: 

• A shared, visual “source of truth” 

• Dimensions and components 

• Automatically generated, validated BOMs and configuration data alongside the CPQ 

Buyers are not engaged 

Buyers want an easy, Amazon-like experience when they buy. Visual configuration is a valuable feature of an e-commerce or online sales experience, presenting products in a similar way that B2C buyers explore products online.

3D product configurators embedded in CPQ also give you the ability to take screenshots of visualizations and export them into engaging, visual proposals that make your business stand out and empower your customers with the confidence to buy.  

Sales and approvals require multiple stakeholders 

Visualization helps align diverse stakeholders early in the buying process. 

Decision-making and sales cycles becomes faster when all stakeholders can see the same configured product rather than relying on static descriptions or assumptions.  

This is especially valuable when procurement requires input from operations, engineering, finance, facilities, safety teams, surgeons or medical staff in medtech equipment, dealer networks, or IT.  

Customization comes at the cost of efficiency 

Manufacturers want to sell customizable products without becoming engineer-to-order on every deal. With modular product models, you can standardize configurable options, present them visually, and keep customization and variability under control. This supports mass customization at scale.  

Sales onboarding takes too much time 

Visual configuration equips your sales reps, dealer reps, and new hires, so they can provide more information with less knowledge. 3D visualization, for example, dramatically reduces onboarding time, as valid configurations are automated, and ensures quoting consistency.  

Dealers and partners don’t have strong customer-facing tools 

Dealers are often stuck with outdated tools that don’t highlight your brand the way you need. A customer-facing product demo with interactive visuals is a major differentiator for your sales partners. Tacton’s visual configurator works in dealer portals and self-service omnichannel sales, so your distribution network can streamline sales of your products.  

How to implement visual configuration: what manufacturers need to know 

• Prepare CAD data: Ensure clean, simplified visual assets and CAD models for the products you want to visualize. Tacton reuses your existing CAD, rather than requiring rebuilds.  

• Connect CAD to CPQ rules: Link CAD parameters to your product logic so the 3D model always reflects a valid configuration. Tacton keeps everything aligned in one product model. 

• Convert and optimize 3D assets: CAD files are transformed into lightweight, web-friendly geometry for fast loading and real-time updates. 

• Configure the visual experience: Define how the model behaves during configuration, such as what updates dynamically, what moves, and what customers can interact with. 

• Deploy in the cloud: Tacton’s SaaS platform manages hosting, scaling, and asset storage automatically, with optional PLM/PDM integration to keep models up to date. 

Visual product configuration is becoming a standard 

With automated drawings, error-free quotes, and a buying experience that sets you apart, Tacton helps you accelerate sales while removing friction across engineering and operations. If you’re ready to reduce rework, protect margins, and bring your complex products to life for your buyers, learn more about our buyer engagement capabilities.  

Learn More About Tacton Buyer Engagement