How Design Agencies Are Using AI to Expedite the Engineering Design Process

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Mechanical design agencies are adopting AI tools to reduce design errors, accelerate time-to-market, and help every mechanical design engineer work smarter. Here's what's actually happening in 2026.

How Design Agencies Are Using AI to Expedite the Engineering Design Process

Mechanical design agencies are adopting AI tools to reduce design errors, accelerate time-to-market, and help every mechanical design engineer work smarter. Here's what's actually happening in 2026.

How Design Agencies Are Using AI to Expedite the Engineering Design Process

Mechanical design agencies are adopting AI tools to reduce design errors, accelerate time-to-market, and help every mechanical design engineer work smarter. Here's what's actually happening in 2026.

Team Leo

Jan 19, 2026

Mechanical design consultancies face mounting pressure: clients want faster turnarounds, margins are tight, and experienced engineers are increasingly hard to find. AI is emerging as a practical solution. McKinsey research shows AI can improve engineering efficiency by 20-40%, while agencies using AI effectively report cutting production timelines from 6 weeks to 2 weeks. For mechanical design engineers at consultancies, the value isn't replacing expertise but amplifying it: faster technical research, instant access to verified calculations, and more time for the creative problem-solving that clients actually pay for.

What Mechanical Design Agencies Actually Do

Before diving into AI adoption, it's worth understanding what these firms handle daily. Agencies or similar firms serve as external engineering departments for companies that either lack in-house mechanical design capabilities or need extra capacity for specific projects.

Their services typically span the entire engineering design process: concept development and feasibility studies, detailed CAD modeling in tools like SOLIDWORKS, Onshape, CATIA, and Inventor, technical drawings, tolerance analysis and design validation, prototyping through 3D printing and traditional methods, design for manufacture (DFM) and design for assembly (DFA), and project management through initial production.

As Sketch Design Consultancy describes their approach: "We offer the agility of a skilled contractor, allowing us to seamlessly integrate within your project team, whilst also delivering the extensive knowledge and unwavering support that are typically associated with a larger and more established design consultancy."

The challenge? These agencies operate in an environment where every hour matters. A 2025 aPriori survey found that 40-60% of engineering design projects experience delays due to late-stage manufacturability issues. Gartner research indicates only 55% of product launches occur on time. For consultancies billing by the hour or working on fixed-price contracts, inefficiency directly erodes margins.

The Real Pressures Facing Mechanical Design Engineers at Agencies

Mechanical design engineers at consultancies face a unique set of pressures that don't exist in the same way for in-house teams.

Time-to-market expectations keep shrinking. Clients increasingly expect faster turnarounds without sacrificing quality. In automotive, time-to-market can still stretch to seven years for complex vehicles, but clients push for every possible acceleration.

The cost of a design change increases dramatically the later it occurs in the development lifecycle and many times tribal knowledge walks out the door. When senior engineers leave, they take decades of institutional knowledge with them. For agencies, where the average project might involve different team members each time, maintaining consistency and capturing best practices is particularly challenging.

Research and technical validation consume disproportionate time. A mechanical design engineer might spend hours hunting through textbooks, standards documents, and manufacturer specifications to validate a single design decision. This research is necessary but doesn't directly create billable output.

How AI Is Actually Being Used in the Engineering Design Process

The hype around AI in engineering is considerable, but what's actually working? McKinsey's 2025 State of AI research found that 78% of engineering firm leaders believe AI will positively impact operations. More telling: Arup's 2025 survey found that 36% of engineers, architects, and city planners already use AI tools daily.

Here's where AI is delivering real results in the mechanical design workflow.

Technical Q&A and Knowledge Retrieval: Rather than searching through multiple sources, engineers are using AI assistants to get answers to technical questions with cited sources. This accelerates the research phase of the engineering design process without sacrificing accuracy. Tools like Leo AI are built specifically for mechanical engineering, drawing from over 1 million trusted engineering references to provide answers that engineers can verify before using.

Engineering Calculations: From stress analysis to material selection, AI tools can identify appropriate formulas, run calculations, and cite the trusted sources they're using. This doesn't replace engineering judgment but eliminates the grunt work of hunting through handbooks and maintaining complex spreadsheets.

Part Search and Design Reuse: One of the biggest inefficiencies in mechanical design is reinventing components that already exist. AI-powered part search can scan PLM systems alongside millions of vendor parts to find existing solutions before engineers design custom. This directly impacts margins: reusing proven designs reduces testing requirements and manufacturing risk.

Concept Visualization: AI can generate 3D mesh for rapid concept visualization in minutes instead of hours. This is for conceptualization rather than production CAD files. The mesh exports to CAD tools for refinement, allowing faster iteration during early-stage client discussions.

Documentation Generation: Bills of materials, scope of work documents, and manufacturing method documentation can be drafted faster with AI assistance, freeing engineers to focus on design work.

Real-World Results from Design Agencies

The numbers from agencies actively using AI in their engineering design process, not just pilots or demos, tell a compelling story.

Sketch Design Consultancy, a Cardiff-based mechanical design firm specializing in Design for Manufacture and Design for Assembly, adopted a human-in-the-loop philosophy where AI complements engineering intuition with verified technical data. Co-Director Oliver Diebel describes the transformation: "Leo is just like another engineer in the room now... It's days, weeks, to minutes. It has paid off massively for us."

The consultancy reported 10x innovation gains through rapid iteration, with research tasks that previously took days or weeks now completing in minutes. When tasked with designing a cryogenic system for transporting liquid hydrogen, a domain outside their core expertise, every mechanical design engineer on the team used Leo AI to instantly access material specs, equations, and vacuum limitations. On another project where a client delivered incomplete components creating a two-week delay, the team iterated fast enough with AI assistance to recover the lost time.

Oberman Industrial & Product Design, a full-service design firm handling everything from initial concepts to final manufacturing, faced a common bottleneck in their engineering design process: the expertise gap. While the team excelled at design and innovation, they frequently encountered mechanical and technical questions requiring specialist knowledge, particularly when working across domains like medical equipment and consumer electronics.

Before AI adoption, the studio relied on external consultants, creating slow turnaround times (single questions could take days to answer), eroding profit margins (thousands of dollars in engineering fees per project), and interrupted momentum. After integrating Leo AI as an on-demand technical resource, the results were significant: 50-70% faster planning for component requirements and client presentations, 30-40% reduction in time spent on technical drawings, manufacturing guidelines, and assembly instructions, and over $2,000 saved per project by reducing dependence on outsourced engineers.

As Harel Oberman described it: "With Leo it feels like the world was opened and has opened our minds to different fields which weren't there before." For any mechanical design engineer working at a consultancy, this ability to confidently venture into new technical domains represents a significant competitive advantage.

What This Means for Mechanical Design Agencies

The agencies best positioned to benefit from AI share certain characteristics.

They're investing in domain-specific tools. Generic AI assistants lack the engineering-specific training to provide reliable technical guidance. Tools built specifically for mechanical engineering, trained on actual mechanical parts, assemblies, and engineering standards, deliver more trustworthy results.

They're maintaining human oversight. AI excels at accelerating research, finding existing designs, and handling repetitive calculations. The engineering judgment about whether a design actually meets client requirements, will work in the real world, and is manufacturable still requires experienced engineers. McKinsey's research found that high-performing organizations are more likely to have defined processes determining how and when AI outputs need human validation.

They're measuring results, not just adoption. The Stanford research emphasizes that tracking AI usage alone doesn't tell you if it's working. Agencies need to measure actual outcomes: time-to-completion, error rates, client satisfaction, and margin performance.

They're addressing the context problem. AI tools work better when they understand the specific client's design history, standards, and preferences. Agencies that systematically capture and provide this context see better results than those treating AI as a generic assistant.

The Security Question

For agencies handling client intellectual property, AI security is non-negotiable. When evaluating AI tools for the engineering design process, mechanical design engineers and agency leadership should verify SOC 2 certification for enterprise-grade security, GDPR compliance for regulatory requirements, and explicit policies on whether data is used to train AI models. The standard should be zero training on customer data.

FAQ

Will AI replace the mechanical design engineer at agencies?

No. AI accelerates research, calculations, and routine tasks within the engineering design process. The engineering judgment, creative problem-solving, and client relationships that make agencies valuable still require human expertise. What changes is that every mechanical design engineer can spend more time on high-value design work and less on hunting for information.

What's the difference between generic AI and engineering-specific AI tools?

Generic AI assistants are trained on internet-wide data and may provide technically inaccurate information about engineering topics. Engineering-specific tools like Leo AI are trained on verified mechanical engineering sources and designed to cite their references, allowing any mechanical design engineer to validate answers before using them in the engineering design process.

How long does it take to see ROI from AI adoption?

Agencies using purpose-built engineering AI tools report seeing meaningful time savings within weeks. The key is starting with high-impact use cases, such as technical Q&A, part search, and calculations, rather than attempting to apply AI across every workflow simultaneously. Oberman Industrial & Product Design reported saving over $2,000 per project after adoption.

Does AI work with all CAD platforms?

CAD-agnostic AI tools work alongside whatever software your team uses, including SOLIDWORKS, Onshape, Inventor, CATIA, and others. The AI assists with engineering knowledge and decisions rather than replacing your existing CAD workflows, making it accessible to any mechanical design engineer regardless of their preferred platform.

What about accuracy? Can we trust AI for engineering decisions?

The best engineering AI tools provide citations to their sources so engineers can verify answers. This is fundamentally different from generic AI that may confidently state incorrect information. The workflow is: AI accelerates research, the mechanical design engineer validates the answer, then makes the final decision. Sketch Design Consultancy specifically highlighted that Leo's linked sources and academic references gave them "the solidity needed to back up their claims during design reviews."

How does AI change the engineering design process at consultancies?

AI compresses the research and validation phases of the engineering design process from days or weeks to minutes. This allows agencies to take on more complex projects, enter unfamiliar technical domains with confidence, and deliver faster without sacrificing quality. The core engineering work remains human-driven, but the supporting research happens at machine speed.