RFQs can be a competitive advantage. Or, they can continue to be a bottleneck

Regardless of when tariffs stabilize, one thing is clear: some suppliers will see an influx of RFQs as companies look to either reshore or diversify supply chains. This should be a massive supplier win. But for most suppliers this will mean an increase in engineering work for teams that are already capacity-constrained.

Why? Because the RFQ process today is riddled with inefficiencies. These inefficiencies are already a bottleneck to win rates, quote accuracy and overall project profitability. This means higher RFQ volume will exacerbate the engineering capacity bottleneck and suppliers will miss a key opportunity to grow revenue.

To combat RFQ process problems and prepare for an influx of new RFQs, suppliers must adapt a dual strategy:

1. Detail the RFQ process problems that exist today and identify the root cause issues
2. Adopt an AI technology plan to both solve root cause process problems and prepare for the influx of new RFQs

Because if supplier engineering teams can quote faster and more accurately while also freeing up engineering capacity (all possible with AI), then these companies don’t just win more business. They turn their internal technology infrastructure and RFQ process into a competitive advantage on future bids.

The RFQ process itself is the limiting factor to engineering capacity

Quoting a highly competitive bid is fast-paced and chaotic. Engineering teams must manage broad cross-functional team input while simultaneously confirming requirements with the customer. All with as little as a couple weeks to prepare and deliver the quote.

The process itself is why RFQs feel chaotic. Engineers must:

• Create a design that meets customer requirements
• Gather feedback from cross-functional teams on that design
• Manage the hundreds of emails, chats, meetings and documents going back and forth between internal teams and the customer.
• Synthesize that information into a final design.

All with the ultimate goal of arriving on a quote that is both technically feasible and commercially viable. Then, because engineers spend so much time managing feedback and documentation, they have little room for offering the in-depth technical expertise OEMs have come to expect.

This is the engineering capacity bottleneck. Engineers are so bogged down in the rigamarole of the RFQ process that they can’t proactively provide expertise that might offer outsized value to the OEM.

RFQ errors hurt suppliers: both short and long term

The RFQ process is a classic representation of the manufacturing product problem: balancing speed, quality and cost. Because the quoting process is iterative and time is a critical factor, suppliers often sacrifice quality and/or cost unnecessarily.

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Internally, changes happen quickly. Continuous feedback from cross-functional teams comes in at different times and through different mediums. Maybe the team finalized the design, but then tooling surfaces a critical change. This adds another iteration cycle and then it’s easy for issues to get lost in the commotion. 

So, what happens? Engineering teams cut corners. They might submit a quote with a few minor errors to meet the time requirements. These errors sacrifice both quality and cost in order to meet the strict OEM deadlines.

This seems small, but even an issue like a misquoted part price or improper materials specification can mean huge losses if the supplier wins that award. Because once an RFQ is sent and then accepted, the supplier must meet the terms regardless of how thin the profit margins.

Suppliers in highly regulated industries face more complexity

Automotive, aerospace and medical device suppliers require added due diligence during the RFQ process. Couple this with the long-term nature of these projects – if a supplier wins a quote, they’re locked into a multi-year contract with the OEM – a small issue amplifies over the life of the project. Suppliers in these industries will also have relatively high volume production runs. This means that small quotation error could completely erode profit margins.  

Put simply, quotes in these industries must be highly accurate.

In the figure here, we see the trap these suppliers get caught in. They must operate at the complexity level of ETO companies, but at the production volume of BTS manufacturers. Needless to say, the margin for error is extremely low and the pressure to win extremely high. 

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Yet, suppliers in these industries follow the same processes and operate in the same conditions as those discussed earlier. Meaning, AI strategies will make an even bigger impact on the RFQ process. for suppliers in highly regulated industries.

Why AI Will Reshape Supplier RFQs

If you're thinking, “We already have PLM, ERP, and MES—why do we need anything else?”—you're not wrong. These are proven, foundational systems. Most manufacturers couldn't operate without them.

But when it comes to RFQs, these systems were never designed to manage the rapid iteration, cross-functional input, and fast-paced decision-making that quoting demands.

That’s why, even at companies with full enterprise system stacks, engineers still fall back on:

• Emails to discuss design changes
• PowerPoint to communicate requirements
• Spreadsheets to track open issues

This patchwork is more than inefficient, it’s risky. Important context gets lost, errors slip through the cracks, and valuable engineering time gets eaten up by admin.

AI doesn’t replace your core systems. It fills the gaps they were never built to handle. And no, this doesn’t mean giving engineers “another tool” to juggle. It means using AI to reduce the noise by connecting the dots across teams and surfacing the right information at the right time.

A modern, AI-enabled RFQ process could:

• Surface similar models to avoid redesigning parts that already exist
• Autofill common checks using design standards and past reviews
• Predict risks and flag gaps using contextual feedback and connected systems
• Track feedback automatically, so teams stay aligned without chasing action items

The result? Engineers spend less time chasing emails and updating trackers and more time doing the technical work that wins the deal.

Developing a technology plan to address the gaps

So, what’s the right solution then?

An appropriate solution should meet the following requirements:

• Fast, secure file sharing. Many of the RFQ process inefficiencies center around secure file sharing. Engineers need a fast, secure way to share CAD, PMI and other product data with cross-functional teams and OEMs.
• Automatic issue tracking. Similarly, too much engineering capacity is dedicated to tracking, following up on and implementing design feedback. A reliable technology solution should allow RFQ participants to comment on design files, while the technology automatically tracks these issues in the background.
• Appropriate AI application. The right RFQ solution should also apply AI to:
  
  • Surface similar models. Using AI, supplier engineering teams could automatically finds similar files and related reviews. This eliminates wasted time redesigning parts and simplifies product platforming and standardization.
  • Custom product datasets and alerts. Supplier engineering teams could import design best practices, standards, and old reviews to train AI algorithms. Then, engineers can integrate knowledge from downstream systems (ERP/PLM/etc) to surface alerts and insights during the RFQ process. Then, they can bring cross-functional teams in for more specialized expertise and insights.
  • Predictive feedback and auto reviews. Leveraging all the knowledge from past RFQs, AI can auto-review a 2D or 3D file and provide feedback in context. Leveraging a supplier’s existing checklists, they can then train AI to complete mundane checks, so engineers can focus efforts on nuanced reviews.

AI will become the competitive differentiator for supplier RFQs

The benefits of AI have thus far proven to be exponential as opposed to linear or logarithmic. This means the faster suppliers adopt AI technology and the sooner they feed the right model the right data, the faster they can leverage the benefits. And then the more profitable those benefits become over time.

As the U.S. experiences a possible reshoring, and at the very least a supply chain diversification movement, suppliers will see an influx in RFQs. 

This means suppliers have two choices: 

1. Keep doing RFQs the same way. More RFQs will squeeze the current process bottlenecks even tighter. Supplier engineering teams will cut more corners to deliver RFQs on time. More errors will slip through the cracks. And suppliers will just not be able to deliver a high volume of high-quality RFQs on time. Meaning, they will miss out on a huge potential revenue driver.
2. Adopt AI-driven technology. The last thing engineers need is another specialized tool. What they need is a tool purpose-built for the task at hand that strategically applies AI to process RFQs faster and more accurately. This positions suppliers to take advantage of an RFQ influx rather than suffer for it. 

RFQs are an administrative nightmare for engineering teams today. AI will completely shift this paradigm. The only question is: will suppliers move fast and innovate or accept the status quo and get left behind?