Straight talk about AI

French author Victor Hugo famously wrote that nothing in the world is more powerful than an idea whose time has come. That idiom definitely applies to artificial intelligence (AI), which is now an unstoppable force. For manufacturers, AI is rapidly becoming part of the operating environment, and is already embedded in product design, production monitoring, quality inspection, demand forecasting, predictive maintenance, worker safety, and sustainability analytics. For design engineers in specific, AI has the power to completely transform the field, accelerating development by automating repetitive CAD/CAE tasks, generating topology-optimized geometries, and predicting physical simulation results instantly.

But there are right and wrong ways for these firms to use it, especially for first-time integrators – while AI is incredibly powerful at accelerating ideation, industry experts caution against trusting it blindly without human verification, and stress that human oversight remains essential to ensure designs are manufacturable. Design Engineering recently spoke with Mike Hagedorn, vice president of professional services at IMAGINiT Technologies, to dig deeper into AI integration. With more than 25 years of experience in consulting, software, and data science, Littleton, Colo.-based Hagedorn is a recognized leader in AI, digital transformation, and enterprise technology innovation.

Design Engineering (DE): How does a manufacturing firm prepare for using AI?

Mike Hagedorn (MH): AI is an enabling technology, but you have to get beyond the hype – many AI vendors stress that you need it, but they don’t tell you what to do with it. So you need to understand how to fundamentally and realistically use AI, and the answer will vary with each shop. But all users need to satisfy the same two criteria. First, they have to identify where in the business AI can create the most measurable value. This involves understanding which AI capabilities are needed for each business problem. Generative AI can create text, code, concepts, summaries, and design alternatives; predictive AI can forecast outcomes, risks, demand, failures, and performance; and agentic AI can execute defined tasks with less human intervention, provided the organization has strong rules, governance, and monitoring in place.

Second, make sure you have the data quality, standards, integration, and governance required for reliable AI outputs – in other words, are your people ready for it, and are you mature enough as a business to handle the changes that AI is going to create? Using AI to simulate line switches and manage raw material inventory and scrap is very different from using it to create a 3D model of an entire manufacturing plant, and you have to understand that from the outset. As I tell my customers, your data and process readiness have to be at the level of at least a “four” on a scale of one to five for AI to be effective. If you have those things, and your people are ready, your chances of success are good.

Many organizations began their AI journey with copilots, chatbots, and content generation tools like Claude that assist individual productivity. Those tools are valuable, but they’re only the first step. The larger opportunity is to use AI to improve business performance: forecasting outcomes, recommending actions, triggering workflows, and supporting autonomous execution within defined guardrails. AI isn’t a silver bullet, but it is a trigger event that forces users to make better people decisions around process and better data decisions around the data they have, because the tech won’t work without that. It’s triggering the mindset that we call digital transformation. Digital transformation involves your process, your business, and your data, and preparing yourself for using this enabling tech properly.

Users of chatbots such as Claude and Gemini may run the risk of inadvertently sharing confidential data. Image credit: 91/Adobe Stock

DE: What are the big challenges to a successful AI integration?

MH: AI initiatives often stall when organizations underestimate the operational foundation required to scale. A manufacturer may have promising tools and strong executive interest, but value can be limited by fragmented data, inconsistent standards, unclear process ownership, weak integration across systems, or employee resistance.

AI can accelerate work, but it can’t compensate for a foundation that lacks trust, so – first – you have to be careful where the data’s coming from and understand how the language model works. AI can learn, but you need to make sure that what you’re producing aligns with what you expect. If you take blind faith in an AI answer, you’ll fall off track quickly. It’s about combining human experience with AI assistance and making sure your data foundation is solid.

Second, there’s the “employee resistance” problem that I mentioned earlier – some people are blocking the adoption of AI because of what we call “FOBO”: fear of becoming obsolete. So employees have to be trained, supported, and reassured as AI changes how the work gets done. If you tell your employees – both design engineers and others – how they’re going to be a part of this new paradigm, you’ll break down this resistance. Don’t be afraid of losing your job to AI; be afraid of losing your job to someone else who knows how to use AI.

DE: What’s the biggest mistake that first-time AI users make?

MH: Trying to do too much too soon. I highly recommend following the “KISS” principle of keeping it simple. You don’t need to pursue every AI possibility at once. For a manufacturing firm, at the macrolevel, this means that the right starting point is the intersection of business value, data readiness, process clarity, and organizational sponsorship.

For individual part designers who are new to AI, start small: don’t give it the hardest, most complicated job that you have, such as designing a high-rise building. Instead, ask it to validate something, or do some research – for example, ask it to show the difference between “X” and “Y” or determine why one production line on the shop floor is more efficient than another similar line. Let it sift through all the critical knowledge that’s trapped in spreadsheets, local files, and disconnected systems; and then start teaching it, so that it learns how you talk and how you want to ask questions and understands the rules that go along with the results you get. Once the tool understands how you want to use it, and what information you want it to pull, then you can expand the tasks that you assign.

DE: Are concerns about AI security vulnerabilities valid?

MH: Absolutely! Using public AI tools without understanding their privacy settings can expose proprietary information or allow it to be used to improve public models, which is every user’s worst nightmare. That’s why there have to be clear policies that define acceptable use, privacy, intellectual property, data access, and risk controls. Specifically, governance should define how AI can be used, what data is permitted, where human review is required, how outputs are validated, and how performance is monitored. This is particularly important when AI touches intellectual property, customer data, supplier information, regulated processes, or safety-critical decisions.

Part of protecting yourself involves using common sense: Read the “Terms of use” small print before clicking on anything, for example, and be careful when getting data from websites – a government site, a conference site, or an industry site – that you don’t accidentally punch a hole in your firewall and allow someone to query your results. And we encourage people to be careful when introducing AI technologies such as Claude, Copilot, and Gemini. You don’t want to inadvertently train a public domain language model and share confidential data, because once that information is out there you can’t get it back.

These privacy protections should already be in place from using connected Industry 4.0 technologies to internal policies to standard IT data protection like SOC, but people don’t always remember that when a new technology like AI comes into play.

DE: How does a firm choose the right AI solution for its needs?

MH: With some of the bigger vendors – such as Autodesk, SAP, Oracle, Infor, and Microsoft – the AI is embedded into their solutions, so when you buy their products, the AI is already there, working behind the scenes. Sometimes this is beneficial, because you get AI specifically tied to what you want to accomplish; and sometimes it’s not, because you’re getting that vendor’s interpretation of how AI should work. It’s a decision that varies from company to company. If you buy AI independently, you can use your own language model and rules and integrate it yourself. IMAGINiT Technologies can help assess current-state maturity, identify practical AI opportunities, define a roadmap, prepare data and processes, support pilot execution, and build the change management foundation needed for adoption at scale.

DE: What are the benefits of AI for design engineers?

MH: Currently, they can use AI to help them find standards, summarize requirements, generate documentation, identify design inconsistencies, and support repetitive CAD or product data tasks. This is especially valuable when expertise is concentrated among senior staff or when onboarding new engineers is slow. When properly implemented, AI can identify issues with noncompliance, or differences between as-built versus as-designed parts, or engineering change management. AI can start interpreting those things and making those changes for you, predict certain outcomes, and automate updates. An example that I use is light fixtures: they can have a hundred different components, and if one component changes, you have to find every drawing where that sits; AI can do that for you, instead of a human engineer opening up hundreds of engineering documents manually.

In future, the value will be in creating autonomous design-to-manufacture pipelines, with the ultimate goal being faster release cycles, reduced rework, and improved design quality.

That said, keep a human hand on the steering wheel. As a rule of thumb, I suggest letting AI take the work to roughly 85 per cent, with human quality control checks along the way.

The promise of AI is off the charts provided you’re ready for it and don’t get distracted by the bright, shiny objects. Some of the biggest productivity gains come simply by standardizing workflows and cleaning up how information flows between teams. The design engineers who create value from AI will be the ones that focus less on experimentation for its own sake and more on readiness, prioritization, and measurable results.