Generative AI Use Case Library for the Non-Durable/Consumer Packaged Goods Manufacturing Industry
Identify value-driven generative AI use cases to transform your organization.
Gen AI holds tremendous potential in resolving these challenges and unlocking new opportunities. However, several barriers to adoption exist:
- Fluid and unfamiliar solutions ecosystem and unknown cost implications
- Concerns about impacts on workforce
- Quantifying the benefits of using Gen AI and making a case for implementation
- Partnering with the right vendor and navigating complex implementation options
- Policing the use of confidential, proprietary, and sensitive data with these platforms
Our Advice
Critical Insight
Manufacturers can leverage Generative AI, with its ability to analyze vast amounts of data, generate optimized solutions, and enhance decision-making processes, to unlock new opportunities for growth, efficiency, innovation, and ultimately, competitive differentiation.
Impact and Result
Info-Tech’s use case library provides practical guidance to help manufacturers accelerate value-driven Gen AI use case adoption.
Generative AI Use Case Library for the Non-Durable/Consumer-Packaged Goods Manufacturing Industry
Identify value-driven generative AI use cases to transform your organization.
Analyst Perspective
Artificial intelligence is disrupting every industry, but you can get ahead of the shift.
The world moves quickly. Earlier this year, supply chain executives were still pondering what it means to be “agile” and “resilient,” and CIOs finally had the confidence to initiate enterprise resource planning (ERP) upgrades and other large transformation programs, when ChatGPT was launched. Like a trending meme on social media, ChatGPT’s rise in popularity was meteoric. The large cloud providers wasted no time in jumping on the bandwagon. ChatGPT’s launch was soon followed by a multiyear, multibillion-dollar investment by Microsoft in OpenAI and its integration with the Azure cloud platform. In quick succession, AWS shook hands with Hugging Face, and Google invested in Anthropic in a bid not to cede ground to Microsoft in the generative AI (Gen AI) space. With this, a new “space race” began, and the Gen AI solutions ecosystem exploded in both options and investments.
Today, manufacturing leaders are striving to make sense of the new realm of Gen AI, with its endless acronyms and complex jargon, while seeking ways to stay ahead of their competition. Gen AI is a game changer – a powerful tool and partner in innovation that can amplify human capabilities and free us from mundane tasks, enabling us to focus on more strategic pursuits. Although some worry that technologies like Gen AI are accelerating humans toward a dystopian future in which machines have the power to take over the world, we see the many benefits that Gen AI brings to the table. In this research paper, we explore a myriad of Gen AI applications that can breathe new life into the manufacturing industry. From product design to process optimization, Gen AI is poised to unleash a wave of efficiency and creativity. Imagine a world where a simple sketch can be transformed into a detailed 3D model within seconds, or where a production line can adjust itself on the fly to maximize output while minimizing waste. The possibilities are endless.
So, join us and, together, we will demystify the technology behind Gen AI and explore its practical applications in the manufacturing industry.
Shreyas Shukla
Principal Research Director, Manufacturing Industry
Info-Tech Research Group
Executive Summary
| Your Challenge | Common Obstacles | Info-Tech’s Approach |
Manufacturers face many challenges in today’s competitive landscape:
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Gen AI holds tremendous potential to resolve these challenges and unlock new opportunities. However, several barriers to adoption exist:
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Info-Tech recognizes the potential of Gen AI for application in the manufacturing industry.
Understanding the abilities of Gen AI and identifying capability areas that can benefit from this technology will help accelerate its adoption. In this research paper, Info-Tech will provide:
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Info-Tech Insight
Manufacturers can leverage generative AI, with its ability to analyze vast amounts of data, generate optimized solutions, and enhance decision-making processes, to unlock new opportunities for growth, efficiency, innovation, and ultimately competitive differentiation.
Gen AI is an innovation in machine learning
Many vendors have jumped on “Gen AI” as the latest marketing buzzword. When vendors proclaim to offer Gen AI functionality, you need to pin down exactly what is “generative” about it. The solution must be able to induce new outputs from inputted data via self-supervision – not trained to produce certain outputs based on certain inputs.
Generative AI (Gen AI)
Gen AI is a form of machine learning. In response to prompts, a Gen AI platform can generate new outputs based on the data it has been trained on. Depending on its foundational model, a Gen AI platform will provide different modalities and thereby different use case applications.
Machine Learning (ML)
ML is an approach to implementing AI, whereby the AI system is instructed to search for patterns in a dataset and then make predictions based on that set. In this way, the system “learns” to provide accurate content over time. (Think of Google’s search recommendations.)
Artificial Intelligence (AI)
AI is a field of computer science that focuses on building systems to imitate human behavior. Not all AI systems have learning behavior; many systems, such as customer service chatbots, operate on pre-set rules.
“Generative AI has the potential to revolutionize many industries, from entertainment to healthcare to finance, by enabling machines to create new and unique data that was previously only possible for humans to produce.”
Source: ChatGPT in Supply Chain Today, 2023
Other industries are already using AI, whether or not manufacturers are prepared
“The next 5-10 years will see significant adoption of AI technologies in the nonprofit sector. The low hanging fruit will be around making routine tasks more efficient to allow people to work smarter, not harder.”
- Nathan Chappell, Futurus Group, 2019