Revolutionise Decision Support with Gen AI driven Conversational systems and Augmented AI
The advancements in Conversational AI driven by GenAI are significantly reducing the human-machine gap. By enabling more natural and efficient interactions and providing real-time, personalized insights, these technologies are transforming various sectors, leading to more seamless and effective decision-making processes.
Around 40% of supply chain organisations are investing in GenAI, focusing on knowledge management applications.
The rise in Generative AI is attributed to the following trends
Gen AI driven Conversational AI (Virtual Assistant) :
Generative AI is revolutionising conversational AI by enhancing its capabilities, making interactions more natural, personalized, and contextually relevant. Gen AI systems will increasingly be able to tailor outputs to individual preferences and situational needs, enhancing user engagement and satisfaction in applications ranging from entertainment to supply chain to e-commerce and beyond. Shift from complex decision making to simple decision making through a Conversational AI will reduce the Human Machine Gap
Practical Applications of Conversational AI in supply chain
1. Warehouse Management
A warehouse supervisor can query a conversational AI Agent (Virtual Assistant/Bot) for real-time inventory levels of a specific item. The Virtual Assistant provides immediate, accurate data, enabling quick and informed decision-making without the need for complex interfaces or manual searches.
2. Demand Planning
A demand planner can ask the conversational AI Agent for product demand forecasts based on current market trends. The conversational AI Agent uses predictive analytics to deliver instant insights, simplifying the decision-making process and allowing planners to focus on strategy rather than data analysis.
3. Logistics Optimisation
A logistics manager can request the conversational AI Agent to analyze transportation costs and suggest cost-saving strategies. The Conversational Virtual Assistant can provide detailed analyses and actionable insights on reducing expenses, facilitating immediate and effective decision-making.
4. Agricultural Monitoring
Farmers can leverage GenAI applications to monitor crop health and receive alerts regarding agricultural emissions. These applications can provide real-time data and insights, helping farmers make proactive decisions to improve crop yield and reduce environmental impact.
Enhancing Augmented AI with Generative AI
GenAI can enhance AR applications that allow customers to visualize products in their real-world environment before making a purchase.
Example: An online cosmetics store can use GenAI to power an AR feature
that lets customers virtually try on makeup products, improving the shopping
experience and reducing returns.
Stay tuned for our upcoming post on the role of Large Language Models in Generative AI!
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