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The impact of technology on our lives over the past decade has been so profound that it’s hard to imagine a world without it. Thanks to innovations like Generative AI, developed by forward-thinking companies, we won’t have to.
Generative AI is making waves across various sectors, including healthcare, manufacturing, banking, entertainment, education, energy, and agriculture.
In healthcare, Generative AI assists radiologists in analyzing medical images to detect conditions like cancer, heart disease, and neurological disorders.
In the entertainment industry, it helps create new content, personalize user experiences, and optimize production processes.
In the consumer-packaged goods industry, companies like Alloy.ai are leveraging machine learning, predictive analytics, and Generative AI to help consumer goods companies manage their supply chains, inventory, and sales. Alloy.ai works with strategic partners such as Westernacher in the digital supply chain space to deliver AI-driven solutions to 100s of retailers and e-commerce partners.
GenAI 101: What is it and what matters when AI & digital supply chains meet
Generative AI also enhances supply chain operations by automating, augmenting, and accelerating processes. It enables faster and more accurate decision-making through data analysis and insights.
How does it all work?
Generative AI refers to artificial intelligence models designed to create new content, such as text, audio, images, or videos. Generally, it incorporates four main workflow stages – data collection, model training, content generation and refinement. Overall, training and refinement are the components that matter most and are the toughest to augment. Once the model is trained, it can generate new content by sampling from the latent space or through a generator network. The generated content is a synthesis of what the model has learned from the training data. Depending on the task and application, the generated content may undergo further refinement or post-processing to improve its quality or to meet specific requirements. This technology is useful for developing virtual assistants, creating dynamic video game content, and generating synthetic data for training other AI models.
Generative AI is significant in the supply chain world as it can drastically improve efficiency and decision-making by analyzing vast amounts of data.
The Generative AI models require a significant amount of high-quality, relevant data to train effectively. Acquiring such data can be challenging, particularly in domains where data is not available, sensitive, or protected such as in healthcare or finance.
Training Complexity
Training these models is resource-intensive, time-consuming, and expensive, posing a barrier for smaller organizations or those new to AI. Due to the vast and intricate nature of supply chain data, including the diverse sources, formats, and dependencies across different stages, it is difficult to develop accurate and robust AI algorithms that can optimize complex supply chain operations separately.
Controlling the Output
Ensuring the output of generative AI is relevant and appropriate can be difficult. Models might generate content that is incorrect, offensive, or biased. Additionally, deepfakes and misleading text can be misused for misinformation or fraud, leading to uncertainties and potential legal disputes.
Bringing out the best in AI – Solutions that work
Top tech companies are rapidly integrating AI tools into their sophisticated products to elevate user experiences. A prime example of this innovation is the generative AI copilot—a virtual, conversational assistant powered by artificial intelligence, specifically large language models (LLMs). These AI copilots are designed to assist users with an array of tasks and decision-making processes, harnessing the power of LLMs to interpret, analyze, and process enormous volumes of data. This enables them to deliver personalized, real-time insights, helping users navigate complex challenges with ease and efficiency.
SAP has designed one such capability that will save consultants up to 1.5 hours per day with fast answers on SAP system implementations. Joule is an advanced, generative AI copilot embedded throughout SAP’s cloud enterprise portfolio to deliver proactive and contextualized insights from across SAP’s applications and third-party sources. It is developed by SAP, leverages generative AI to enhance business process efficiency. Unlike tools like ChatGPT, Joule primarily relies on data from SAP software, ensuring security and privacy. Joule is built on a language model created by SAP for business data applications.
To learn more about other SAP tools, artificial intelligence, and its use cases in transportation management, check out the White Paper: Artificial Intelligence in Transportation Management.
