Accelerating your AI journey A practical guide to making progress with AI without disrupting your business

Accelerating your AI journey

Table of contents

What an AI journey actually looks like

02 How Autodesk helps05

The three stages of AI maturity in manufacturing

03 Your next step06

01 The reality check 04 What AI leaders do differently

Accelerating your AI journey

+34% outperformance

+29% greater

resilience

+26% stronger

growth intent

01. The reality check Why AI momentum matters now

AI is no longer experimental in manufacturing. Organizations that are embedding AI into how work actually gets done—across product development, production planning, and operations—are outperforming their peers1.

The difference isn’t ambition—it’s approach. AI leaders focus on incremental progress, not disruption. They build confidence step by step, using AI to remove friction and improve decisions where it matters most.

This guide shows what that journey looks like in practice and how to move forward without overhauling your business.

1. The blueprint of an AI leader (based on Autodesk State of Design & Make data)

What the data shows (AI leaders vs others)

https://damassets.autodesk.net/content/dam/autodesk/www/pdfs/the-blueprint-of-an-ai-leader-dm.pdf

Accelerating your AI journey

02. What an AI journey actually looks like Understanding the path to AI advantage Many organizations delay AI adoption because they assume it requires perfect data, radical change, or large upfront investment.

In reality, AI maturity in manufacturing grows inside everyday workflows—design reviews, engineering change processes, production planning, and quality management. AI value increases as data becomes more connected across teams, stages, and decisions.

Leaders progress through three practical stages, each building confidence, capability, and value.

On the next page, we look at what each stage entails— including actions you can take and pitfalls to avoid.

The three stages of AI maturity in Manufacturing

Stage 3: Connected intelligence AI connects data across the lifecycle to improve speed, cost, and resilience at scale.

Stage 1: Assisted workflows AI reduces manual effort and improves consistency in everyday design and engineering work.

Stage 2: Predictive insights AI helps teams anticipate issues earlier and make better design, planning, and production decisions.

Accelerating your AI journey

Stage 1

Build consistency before intelligence AI at this stage is about removing friction from everyday work. The goal is to make engineering and production workflows more repeatable and reliable.

Focus on • Automating repetitive manual tasks • Eliminating avoidable errors • Creating a stable digital foundation

Approach Start with workflows, not tools. Standardize core processes before optimizing, and prove value in a small, high-volume use case.

Next steps • Identify where teams lose the most time • Establish shared environments for

product and process data • Expand only after early success

Move from reacting to anticipating With consistent workflows in place, AI can help you see issues earlier—when they are easier and cheaper to address.

Focus on • Catching risks before they escalate • Improving confidence in planning and decision-making • Reducing late-stage surprises

Approach Apply AI where earlier insight changes outcomes. Keep insights close to engineering and production decisions, and measure success in avoided rework, scrap, and delays.

Next steps • Identify decisions that benefit most from early signals • Pilot predictive use cases within existing workflows • Assign clear ownership for acting on insights

Scale what works across products and plants At this stage, AI becomes part of how products, factories, and portfolios are run. Connected data enables predictability and continuous improvement at scale.

Focus on • Consistent performance across products, lines, and plants • Portfolio-level visibility • Continuous improvement

Approach Treat data and governance as strategic assets. Scale proven practices deliberately and embed intelligence into daily decision-making.

Next steps • Establish standards and ownership across lifecycle stages • Connect design, manufacturing, and operational data • Expand AI where it consistently improves outcomes

03. The three stages of AI maturity in manufacturing

Assisted workflows

Predictive insights

Stage 2 Connected intelligence

Stage 3

Accelerating your AI journey

Manual effort still dominates engineering and production tasks

Issues are often discovered late

Productivity depends heavily on individuals

Many issues are identified earlier

Data informs planning and design decisions

Fewer surprises, but not eliminated

Data flows across design, manufacturing, and operations

Decisions are based on connected information

AI is embedded into standard ways of working

Where do you stand? A self-assessment for manufacturing leaders

Use these indicators to identify which stage best reflects your current workflows:

Stage 1 Assisted workflows

Predictive insights

Stage 2 Connected intelligence

Stage 3

You may find you’re further along in some areas than others. That’s normal—the priority now is using that clarity to decide where to move next.

Accelerating your AI journey

04. What AI leaders do differently Five characteristics of organizations succeeding with AI

They prioritize workflow readiness over technology acquisition AI amplifies existing capabilities — it is not a substitute for them.

01 They balance speed with governance Data standards and guardrails are established early, enabling faster scale later.

02 They measure what matters to the business Time saved, costs avoided, risks reduced. Not just “AI features deployed.”

03 They expand deliberately as confidence builds Start focused. Prove impact. Learn. Then scale.

04 They invest in people alongside technology Skills, trust, and change management are core to success, not afterthoughts.

05

“Generative AI has helped us move from long, repetitive ideation cycles to rapid iteration — enabling our designers to explore more concepts faster and focus on innovation.”

Senior Designer, Kia Global Design

Accelerating your AI journey

05. How Autodesk helps Accelerating your journey— starting where you are

Autodesk embeds AI directly into manufacturing workflows— across design, engineering, production, and operations.

What this enables: • Faster product development and iteration • Earlier insight into quality, cost, and production risk • Better decisions across the product lifecycle

Why this matters: • No separate AI platform to implement • Minimal disruption to how teams work • Value increases as workflows become more connected

Accelerating your AI journey

06. Your next step See what accelerating your AI journey looks like

This guide is built on three simple ideas:

1. AI progress is incremental—not disruptive You don’t need perfect data or a large transformation. Value builds as workflows become more consistent and connected.

2. Confidence comes before scale Organizations that succeed with AI start small, focus on real work, and expand only after proving impact.

3. You’re likely closer than you think Most manufacturers are already on the path. The opportunity is taking the next deliberate step forward.

Continue your AI journey, at your pace

Explore practical resources, use cases, and perspectives from Autodesk specialists to see where AI could deliver value fastest.

Explore AI resources

https://boards.autodesk.com/ai-for-dm?mktvar004=7508293002&internalc=true