3 Steps to Prepare for Generative AI in Manufacturing
While there’s been a lot of hype, generative AI is proving its worth in manufacturing. A recent survey shows that 65% of manufacturers are investing in or exploring generative AI use cases1. Meanwhile, McKinsey reports that many organizations have seen revenue increases of more than 5% in supply chain and inventory management2.
But what exactly is generative AI and how are manufacturers using it?
In essence, generative AI systems can answer questions and generate new, original content to fulfill specific tasks. This includes generating human-like text.
With tools like Copilot for Dynamics 365, manufacturers can:
- Meet customer demands faster, in full, and on time with inventory stocking recommendations.
- Uncover issues affecting orders across materials, inventory, carriers, and distribution networks (and understanding the impact of these issues).
- Identify exceptions early and address issues proactively to minimize production and customer satisfaction impacts.
Three Steps for Generative AI Readiness
As you prepare to integrate generative AI into your operations, you need a clear and systematic approach. That’s why we recommend following B.E.S.T. Practices for AI Maturity.
Not sure how to get started? Prepare for generative AI and get Copilot-ready by following these three steps.
Step 1:
Identify and Prioritize Use Cases
Before diving into generative AI, you must understand why and how your organization will use it and then develop a clear strategy. Here are a few tips for success.
Identify potential use cases
First, identify potential high-impact use cases that align with your business goals. These goals might include improving supply chain efficiency, reducing downtime through predictive maintenance, or enhancing product quality.
Involve all key stakeholders in a collaborative process
Bringing together stakeholders from different departments – including IT, operations, and finance – is crucial for success. A collaborative approach ensures the use cases you select have broad support and align with overall business objectives.
Prioritize use cases based on feasibility and impact
Once you identify potential use cases, evaluate them for feasibility and potential impact. This will help you create a focused roadmap and avoid the pitfalls of trying to do too much at once. Target use cases you can deliver in a year or less to demonstrate early success and build momentum.
“Organizations don’t fail because they lack use cases, but because they have too many options. Executives must go through a thoughtful, collaborative process, bringing together all the key stakeholders to identify, validate, value, and prioritize the use cases they want to go after — and target use cases they can deliver within 9–12 months.”
– Bill Schmarzo, Customer Advocate for Data Management Incubation at Dell Technologies3
Step 2:
Assess Data Quality and Availability
Assessing the quality and availability of your data is critical. 94% of organizations anticipate challenges in this area, emphasizing the need for careful planning4.
Here are a few tips to help you succeed.
Identify gaps and areas for improvement
Start with a comprehensive audit of your data. Identify what data you’re missing, what data you have, where it’s stored, and its current state. This exercise will help you understand what to improve and what you can use as-is to support your prioritized use cases.
At the same time, don’t wait for perfection to begin your AI projects. You can still achieve valuable insights and improvements with less-than-perfect data. Instead, start with what you have and continuously improve data quality.
Implement strong data security measures
To prevent breaches and comply with relevant regulations, implement strong data security measures. This includes setting standards for data collection, storage, and usage, and assigning responsibilities for data management. You’ll also want to ensure AI tools like chatbots don’t inadvertently share data they shouldn’t, like employee compensation or confidential legal details.
Use data management tools
Invest in tools that can help you consolidate, organize, clean, and maintain your data. Tools like MCA Connect Inspire Platform™ can help.
You can also use built-in data management and security features in Microsoft Copilot Studio to streamline data processes involved in data preparation. This makes it easier to ensure data quality and availability for your prioritized use cases. Meanwhile, Azure AI Studio provides advanced capabilities for managing and deploying AI models, enhancing your data management strategy.
“While plant data may be available for specific use cases, you need to have the systems in place to get it, format it, process it, and store it for AI to work its magic.”
– As Todd Edmunds, Global CTO for Manufacturing at Dell Technologies3
Step 3:
Pursue Your Prioritized Use Cases
Once you have identified use cases and ensured data quality, it’s time to actively pursue your prioritized use cases. Here are a few tips to help you succeed.
Set clear objectives and metrics
Define specific goals for each use case and establish metrics to measure success. This will help you evaluate performance and identify areas for improvement.
Collect and act upon feedback
Gather feedback from stakeholders and end-users to understand the impact of your AI solutions. Use this feedback to enhance and optimize implementations.
Build and leverage a strong team
Ensure your team is actively involved in all stages of AI implementation. Their expertise in validation, testing, and monitoring is crucial for identifying potential issues and optimizing performance.
Stay agile and flexible
Be open to changing your approach based on new insights and evolving business needs. Flexibility is key to successfully integrating AI into your operations. Start small, build confidence, and grow with the technology.
Get the most out of generative AI in manufacturing
Generative AI offers significant opportunities for manufacturers. By following B.E.S.T practices for AI maturity, including identifying use cases, assessing data quality, and actively pursuing prioritized projects, you can harness the power of AI to drive efficiency and innovation.
With tools for data management, AI model training, and deployment, Microsoft Copilot Studio helps manufacturers get ready for generative AI. Additionally, Azure AI Studio provides an integrated environment for building, testing, and deploying AI solutions, further enhancing your AI capabilities.
“For manufacturers, Copilot is there to help sales teams be more effective and more productive responding to customers. It’s in Dynamics 365 Customer Service to help manufacturers answer questions more effectively. It’s also down to the production process. Everything we’re doing with AI is to help people complete their work more effectively.”
– Mike Ehrenberg, CTO, Business Applications, Microsoft
Ready to learn more?
Get more insights on the new era of manufacturing from Microsoft’s Mike Ehrenberg and Dave Durham, President of MCA Connect.
Need help identifying top use cases and building an actionable plan?
Dive into your top priorities and challenges with a principal data strategist and manufacturing expert from MCA Connect. They will review your key systems, reports, and stakeholders, and help you identify the best next steps
Book a 30-minute expert consult for a Fast Track to GenAI.
