AI disruption is coming for manufacturing. How should your firm adapt?
- To remain competitive in an era of AI disruption and rapidly changing business models, manufacturing firms should develop a holistic AI strategy to become more adaptable and profitable.
- Manufacturers can leverage AI tools built into their existing tech, but should also explore using AI to create custom software or apps that fit their specific business needs.
- To implement your AI strategy, embrace a big overall vision but start with small pilot programs before attempting to reimagine your core processes or workflows.
Many manufacturing companies have already begun using AI tools within their businesses. But with the pace of AI disruption seeming to only accelerate, manufacturers will face growing pressure to do more than just experiment with individual AI solutions or risk being relegated to a bygone era.
Now is the moment to get out ahead of that pressure. From quote to cash, you have opportunities to develop and deploy a cohesive AI strategy to make your business more flexible, efficient and profitable before your competitors beat you to it.
Keep reading to learn more.
For manufacturers, AI is becoming urgent
The tech sector has already experienced AI layoffs, albeit with unpredictable results, as tech companies attempt to rethink their businesses to adapt to AI’s possibilities. But manufacturers should not assume that this disruption will remain confined to tech.
To be blunt, AI is changing how companies operate. Your current operational structure may not require the same overhead costs, as certain tasks become automated. If you don’t want to face competitors suddenly offering prices you can’t compete with, you need to start thinking about how your own business could evolve.
However, this isn’t a call for layoffs. Instead, evaluate how you can use automation to redeploy your current team to do higher-value work. For example, if you begin using AI to create faster quotes during your sales process, this frees up your salespeople to spend more time actually talking to potential customers.
Lack of unified data is hampering manufacturers’ AI efforts
Many manufacturers already using AI are doing so piecemeal. This could look like office teams leveraging generative AI to help write sales proposals, experimenting with basic automation or exploring AI features that are already built into their enterprise resource planning (ERP) platforms.
However, manufacturers typically face a big hurdle in implementing AI more deeply: disconnected systems. The typical manufacturing company may rely on more than a dozen different systems to operate and far too often, those systems still don’t share data with each other.
AI generally needs a clean, unified data foundation to draw on in order to actually deliver results. Take AI agents, which allow you to automate certain repeatable tasks — these valuable tools are essentially worthless without good data.
If your disconnected systems can’t communicate, you can’t give your AI the data and business context it needs, throwing up a formidable roadblock to implementing your AI strategy.
What are the top AI tools or solutions for manufacturing?
Manufacturers looking to make better use of AI should evaluate the AI tools already included in their existing tech stack. For example, your ERP may already have significant agentic features that you can make use of.
However, the more transformative opportunity may be in using AI to make custom apps to fit your specific business needs. You may have heard the term vibe coding, which means building your own software by using AI. This approach essentially opens up software development to non-developers who have no idea how to write code.
As you look for tech solutions to problems within your business, don’t immediately defer to buying an off-the-shelf solution. That’s not only expensive, but requires you to shift your workflows or processes to accommodate it.
Instead, explore whether it could make sense for your team to lean on vibe coding to make your own software that’s much more flexible and tailored to exactly what you need — while potentially saving you tens of thousands or more in licensing fees.
What AI use cases make sense for manufacturing?
AI can help make both your production and your office operations more flexible and efficient. Here are three anonymized real-life examples of how our manufacturing clients are using AI tools:
1. Mobile warehouse app developed in-house with Claude Code
One steel distributor and components manufacturer that does about $250 million in annual revenue built a custom mobile warehouse app to complement decades-old existing systems with new functions that had previously been too costly to achieve with legacy technology.
- By vibe coding with Claude, the company was able to create this app entirely in-house. The operations team led initial development, then handed the project to IT for additional refinement.
- The entire development process took three weeks, including refinement and hardening, at which point the company then tested the app in its largest warehouse facility for two months. Less than six months after development began, the company was using the app in all six of its warehouses.
- Because this is an in-house solution, it not only fits the company’s exact needs but also avoids tens of thousands of dollars in annual licensing fees.
- The company also now has the confidence to explore developing more vibe-coded software solutions in the future.
2. Automation platform to retain knowledge, support sales and avoid labor backfill
Faced with the impending retirement of a design engineer who played a key role in the sales process, a $30 million industrial engineering firm developed an automation platform to preserve his knowledge and facilitate future sales.
- This process began with 2-3 months of knowledge transfer from the retiring employee, who had 30 years of experience that was largely undocumented, to a junior staff member.
- The firm then worked with Wipfli’s team for several months to design a future-state automation platform incorporating generative AI and other automation tools to make this engineer’s knowledge more accessible to other stakeholders.
- The automation platform has reusable components that make it easier to scale to other product lines.
- After he retired, the company did not need to backfill the engineer’s position.
- Using this project as a first step, the company estimates it will be able to reduce the turnaround time on a new sales proposal from 2-3 weeks to an hour by the end of this year.
3. AI agent to track tariffs
Its business roiled by ever-changing tariffs, one manufacturer and distributor that imports both raw materials and finished goods worked with Wipfli to develop a custom AI agent to automatically track tariff rates and speed up HTS classification.
- This manufacturer needed to classify products using HTS codes to determine tariff obligations.
- Any errors meant penalties and delays, but tracking rapid tariff changes using manual, spreadsheet-driven processes was slow and cumbersome at best.
- The development process embedded an AI agent within Microsoft Copilot, allowing users to get HTS codes and other essential tariff details by asking natural language questions that led to clearly understandable outputs.
- The company was able to reduce its risk of over- or underpaying tariffs while also significantly cutting down on the amount of time it needed to spend on tariff research and compliance efforts.
How Wipfli can help
We advise manufacturers on performance, technology, tax strategy and growth. Let’s talk about your challenges and how we can help you overcome them. Start a conversation.
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