How to Respond to Manufacturing RFQs Faster with AI Sales Assistance

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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