How to Use AI in Business Processes for Faster Quoting, Smarter Design, and Improved Manufacturing Quality

Artificial intelligence is no longer experimental—it is becoming essential for companies looking to improve speed, accuracy, and decision-making. The real opportunity lies in applying AI business processes across the value chain, particularly in quoting, design exploration, material substitution, and manufacturing quality. I have been using AI since 2023 or earlier, for a host of applications, which I will disclose below. Early on, the capabilities were interesting, even if I sometimes received what, upon closer inspection, turned out to be a fabrication. I think it is called an illusion.

From this experience, organizations that succeed are not simply adopting tools—they are transforming how decisions are made.

Why AI in Business Processes Matters

Traditional business processes are often slow and manual. This takes the time of talent, and sometimes this is the only way to accomplish the objectives. We have human talent and creativity, which you will not find in AI. An organization’s structure can affect throughput and coordination of effort across domains or functions. This is one of the reasons for project management. Dividing an organization in this way allows for specialization among team members and tools.

These limitations are evident in several areas. There can be delays in RFQ responses, finding the correct person to perform the estimates, and creating the proposal. That is just the beginning; inconsistent estimating is another failure to be expected when multiple people provide responses to the RFQ.

When it comes to design, we may consider only a few alternatives. From my product development experience, it is not unheard of, for the sake of time, to select the first design incarnation that comes to mind. We suggest reading the book The Lean Machine as an example of not selecting the first design idea that comes to mind. That may not translate to quick delivery. Evaluate a few design options before selection, run experiments, and explore.

Late discovery of quality issues, also from experience, is for sure a thing. There are plenty of examples where testing and verification activities are cut short due to time constraints.

Implementing AI business processes enables organizations to move from reactive decision-making to proactive, data-driven execution.

AI for Quoting and Estimating (RFQ Response)

Automating RFQ Intake

AI can extract, categorize, and interpret data from incoming material. I have used AI to assist and explore in a multitude of ways over the years. RFQs come in, and with other source materials and specific prompts, it is possible to create an initial outline and response to the incoming documents. Engineering drawings are automatically converted to a Bill of Materials (BoM), and, coupled with a materials and cost database, the assembly’s material costs are determined quickly.

Customer RFQs
Engineering drawings
Specifications and emails

Key information identified:

Materials
Quantities
Tolerances
Delivery requirements

This reduces missed details and speeds up response time. It should always be critically reviewed, even if the material is produced manually.

Improving Estimate Accuracy

AI models trained on historical data can help with labor estimating.

Predict labor hours and costs
Identify high-risk jobs
Recommend pricing ranges

Instead of relying solely on experience, estimators now validate AI-generated insights—improving both speed and consistency.

AI for Exploring Design Concepts

Early design decisions drive cost, manufacturability, and quality. Yet most teams default to familiar solutions.

AI expands possibilities by generating:

Alternative geometries
Simplified designs
Cost-optimized configurations

This approach helps teams:

Reduce downstream rework
Improve manufacturability early
Make better trade-off decisions

Using AI in business processes shifts thinking from “first solution” to “best solution.”

AI for Material Substitution

Material decisions are often constrained. One of my first applications of AI was working with a procurement firm to reduce the cost of a manufacturing consumable (not the product) used by one of its customers. While the rest of the team was exploring ways to reduce the acquisition costs of this material, the engineer in me decided to explore other ways to get away from that expensive material- period. After much reading and exploring old school and through prompts, I found other materials that would be suitable.

Availability
Cost pressures
Legacy specifications

As we see, AI can enable smarter material substitution quickly.

Equivalent materials
Cost-performance trade-offs
Supply chain risks

Practical Use Cases

Replacing hard-to-source materials (or geographically volatile)
Reducing cost through standardization
Improving lead times

Human validation remains critical to ensure. Though I found a solution, it would have required exploring formulations and performing test and verification activities to refine it.

Compliance with requirements
Engineering feasibility

AI for Manufacturing Process Substitution

Beyond materials, AI can recommend alternative manufacturing methods. I have used AI to write preliminary work instructions for configuring the settings and applications of production hand tools at workstations.

Machining vs casting
Additive vs traditional processes
Manual vs automated operations

AI evaluates trade-offs across:

Cost
Lead time
Quality risk

This allows better decisions during the quoting phase—not after production begins.

AI in Manufacturing Process Quality

Predictive Quality

AI can identify potential defects before they occur through analysis. I have also used AI to create an outline of the manufacturing process steps for the product and to identify common defects and their remediations for that type of assembly. Remediation in this case is proactive. We design the manufacturing process details to ensure these failure modes are greatly reduced or eliminated.

Historical production data
Process parameters
Failure patterns

Real-Time Quality Monitoring

AI-powered systems (e.g., vision inspection) can:

Detect defects instantly
Identify patterns humans may miss
Reduce scrap and rework

Closed-Loop Learning

Quality data feeds back into:

Future estimates
Design improvements
Process optimization

This creates continuous improvement across AI business processes. I have used AI to analyze data from an active manufacturing line. Through this tool, I identified specific quality hotspots (errors) and, upon further exploration, the typical causes in that type of manufacturing.

Connecting the End-to-End Process

The real value of AI comes from integration:

RFQ received → AI extracts requirements
AI generates estimates and flags risks
AI suggests design concepts and substitutions
AI evaluates manufacturing processes
AI predicts and monitors quality

Instead of siloed decisions, organizations achieve a connected decision system.

Managing Risk in AI Implementation

AI introduces new risks if not managed properly:

Over-reliance on outputs
Poor data quality
Lack of accountability

A structured approach should include:

Defined decision ownership
Validation checkpoints
Continuous model improvement

Measuring Success

Key performance indicators include:

Quote turnaround time
Estimate accuracy
Win rate improvement
Material cost reduction
First-pass yield

The goal is not just efficiency—but better business outcomes.

Common Pitfalls to Avoid

Applying AI without process standardization
Ignoring data readiness
Treating AI as a replacement for expertise
Failing to integrate across functions

AI should enhance—not bypass—good process discipline.

From Tools to Transformation

The advantage of AI is not in isolated applications, but in how it connects decisions across the organization. No matter the size of the organization, it is possible to benefit from AI. In fact, it is not necessary to even have an internal database, though there are some advantages; for sure, it always comes back to garbage-in-garbage-out (GIGO). There are options to close this gap between external and internal.

By implementing AI business processes, companies can:

Respond to RFQs faster and more accurately
Explore better design options early
Make smarter material and process choices
Improve manufacturing quality proactively

The result is a shift from fragmented execution to integrated, intelligent operations. Small organizations and individuals can accomplish much more, and faster.

If you are interested in reading more of our AI application work, use the Contact Us link below or post to us on LinkedIn (also below) to send us your questions.