AI in Manufacturing

AI is transforming many industries, and manufacturing is no exception. Yet for many manufacturers, AI might seem like a distant aspiration rather than a near-term capability. Concerns about cost, complexity, and the accuracy of AI-driven actions often stall progress before it even starts.

That hesitation is understandable. AI is evolving rapidly, and the ecosystem of tools, platforms, and frameworks can be overwhelming. But waiting for the perfect moment or solution is no longer an option. Manufacturers that delay AI adoption risk falling behind more agile competitors. Fortunately, embedding AI into a digital transformation road map doesn’t require a massive leap. The most successful manufacturers start small and achieve early wins by focusing on accessible tools and practical use cases.

Start with the right foundation

Before jumping into complex AI models or large-scale automation, manufacturers need to establish the building blocks for successful AI adoption. It begins with understanding the role of AI: It’s not meant to replace the workforce but to augment it. AI can handle repetitive, time-consuming tasks and free up teams to focus on higher-value work. But it needs fuel to work effectively, and that fuel is clean, well-organized data.

Many manufacturers struggle with data that is siloed, outdated, or unstructured. Modernizing data architecture doesn’t have to be expensive or disruptive, but it does need to be intentional. Companies can start by identifying the data that supports their most critical processes. Is data stored in a way AI can use? Is it accessible across systems? Cleaning and structuring data might not sound like AI, but it’s the first step in any AI journey.

Put AI-enhanced tools into employees’ hands

One of the most effective ways to introduce AI into an organization is by empowering teams with tools they can use immediately. AI doesn’t always need to be custom-built or deeply technical. Microsoft™ Copilot AI and generative AI tools such as ChatGPT can dramatically improve productivity and decision-making across departments.

For example:

• Customer service representatives can summarize customer histories or draft follow-up communications.
• Finance teams can analyze variances, create visualizations, and prepare draft reports more quickly.
• Human resources teams can draft job descriptions or summarize resumes.

Giving employees access to these tools helps them understand AI’s potential in their own roles. It also builds comfort and trust, two key ingredients for wider adoption.

Build AI fluency before scaling

Deploying AI tools is only the first step. The manufacturers that see sustained adoption and measurable return on investment take steps to build AI fluency across their workforce.

AI fluency means understanding when to delegate work to AI, how to communicate tasks effectively, how to evaluate outputs critically, and how to use AI responsibly within the organization’s governance framework.

Without this foundation, even powerful tools sit unused. Manufacturers should consider:

• Hands-on training. Conduct interactive sessions in which employees solve real work problems using AI, not just presentations.
• Champion networks. Identify early adopters who can coach peers and share wins.
• Ongoing support. Provide templates, use-case libraries, and coaching to sustain momentum.

Building fluency takes time, but it’s the critical enabler for everything that follows. Organizations that skip this step often struggle to govern, maintain, or scale their AI initiatives effectively.

Identify high-impact use cases with low barriers

Once teams are comfortable using AI tools, the next step is to identify business processes where AI can make a real impact with relatively low effort. Focusing on repetitive, rules-based, and time-consuming tasks – especially those involving unstructured data – provides a good springboard.

AI excels at extracting data from sources, such as emails, PDFs, or scanned documents, and turning it into usable inputs for enterprise resource planning systems or dashboards. Whether parsing customer orders, turning supplier documents into structured formats, or organizing production logs, these high-frequency tasks are ideal starting points.

Prepare for scaled AI use with low-code solutions

As organizations gain comfort and begin to see the benefits of AI, they then can explore custom or integrated solutions. For manufacturers with in-house capabilities or a strong innovation mindset, low- and no-code platforms offer a way to experiment without deep technical expertise.

Claude.ai and Microsoft Copilot Studio platforms allow teams to build tailored workflows, bots, and mini applications using natural language or visual interfaces. These tools can bridge gaps between systems, automate approval chains, and simulate decision-making logic.

While low-code tools empower teams to move quickly, without clear governance, these tools sometimes can create shadow IT or data inconsistencies. Structured guidance helps innovation align with broader business goals.

Look ahead: Smarter, scalable AI in manufacturing

Once the fundamentals are in place, manufacturers can begin exploring higher-impact AI use cases, such as:

• Autonomous production flow adjustment. AI agents can monitor production schedules, customer request dates, inventory levels, and quality data to make smart recommendations.
• Demand forecasting that thinks ahead. Modern AI models can integrate internal sales data with external signals to create more accurate, forward-looking forecasts.
• AI-driven visual inspection. Computer vision systems can spot defects or anomalies in real time and improve as more visual data is collected.
• Intelligent quoting and pricing guidance. AI can analyze quote data, customer behavior, and win-loss trends to recommend dynamic pricing and product configurations.

These applications often require a higher level of data maturity and change management but offer the potential for transformative results.

Build on what you have

Manufacturers that build on their existing strengths – clean data, motivated teams, and scalable platforms – are better positioned to turn AI from a pilot project into a long-term differentiator.

If you’re not sure where to begin or want a clearer view of how AI could support your goals, Crowe can help. We work with manufacturers to evaluate opportunities, prioritize use cases, and build a road map that fits your business, from simple automation to advanced AI solutions.

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