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Everyone has heard of ChatGPT and Gemini. Most have also used it for fun or for work. These are Large Language Models (LLMs). They have
But many have often wondered, how does one go beyond the basic use-case of LLMs like ChatGPT and deploy them for commercial purposes.
Read here: Harnessing ChatGPT for Supply Chain Efficiency
Ready to unlock the power of Large Action Models in procurement? Let’s dive in!
We have heard of GenAI being able to do a lot or most of the work that is currently being done by humans. i.e GenAI being table to take real-world actions. How is that actually possible?
Watch this Webinar: How Generative AI Can Set Procurement Leaders up for Success
Introducing Large Action Models (LAMs), the next frontier in AI that promises to transform the way work is done.
What are Large Action Models (LAMs) and How are they Different from LLMs?
LLMs
Masters of language, adept at generating text, translating languages, and providing informative responses.
LAMs
Go a step further, understanding and executing actions in the real world. Think of LAMs as ‘AI Agents’ tasked with executing a specific process or activity.
For example, an LLM could search flight options for you. It could even refine or sort those options based on your criteria. But an LLM connected to your procurement system – LAMs could find flight tickets, compare prices, and even initiate a purchase order and possibly book the ticket using your saved card credentials depending on how the LAM has been set.
Key Differences Between LAMs and LLMs in Procurement
Let’s see how LAMs differ from LLMs in transforming procurement
FeaturesLarge Language Models (LLMs)Large Action Models (LAMs)
| Focus | Text generation, translation, information retrieval | Executing actions in the real world |
| Capabilities in Procurement | Analyze data, generate reports, answer questions | Automate tasks, manage workflows, complete transactions |
| Impact on Work | Improve information access and analysis | Reduce manual effort, increase efficiency, improve decision-making |
| Example in Procurement | Identify potential suppliers based on keywords, summarize contract terms | Negotiate price with a supplier, generate and send purchase orders |
Key Characteristics of LAMs
- Real-world Interaction: Connect to external systems and interfaces to perform actions like controlling devices, retrieving data, or manipulating information.
- Task Completion: Designed to complete specific tasks based on your instructions, unlike LLMs focused on responses.
- Integration and Automation: Integrate with various applications and automate workflows, streamlining processes and saving time.
How LAMs Work and How are they Trained
LAMs leverage a combination of techniques to bridge the gap between language and action. At their core, they often utilize neuro-symbolic programming, which blends neural networks (inspired by the brain’s structure) with symbolic AI (dealing with logic and rules). This allows LAMs to understand not just the words in a request, but also the underlying intent and sequence of actions needed to complete a task.
Training LAMs involves a multi-pronged approach. One method is imitation through demonstration, where LAMs observe user interactions with interfaces (like clicking buttons or filling forms) and learn to replicate these actions.
Additionally, supervised learning with labeled data helps LAMs understand the relationship between language and the actions they need to perform. This data might include examples of user instructions paired with the corresponding sequence of actions in a specific application. Through these techniques, LAMs become adept at interpreting user intent and translating it into real-world actions within various software systems.
The Power of LAMs in Procurement
LAMs hold immense potential to revolutionize the entire Source-to-Pay (S2P) process. Here are some exciting use cases:
1. Automated Supplier Discovery and Sourcing
LAMs can crawl the web, analyze market data, and identify qualified suppliers based on your specific needs. This streamlines the sourcing process and reduces reliance on manual searches. The LAM or AI agent could even issue RFPs to these suppliers based on pre-existing RFP templates.
2. Smart Price Negotiation and Contract Management
LAMs can analyze historical data, market trends, and competitor pricing to negotiate the best possible terms. They can also automate contract creation and management, ensuring accuracy and compliance.
3. Streamlined Order Processing and Invoice Management
Eliminate manual data entry and errors. LAMs can process purchase orders, send them to suppliers, and track deliveries. They can also extract data from invoices and automate approvals for faster payments.
4. Real-time Spend Analysis and Risk Management
Gain deeper insights into your spending patterns. LAMs can analyze procurement data to identify potential cost savings and predict risks like supplier performance issues.
Read here: Retrieval-Augmented Generation (RAG) in S2P: Enhance Contextual Understanding in Source-to-Pay
Real-World Application: SIRVA Achieves Sourcing Efficiency with GenAI
As we delve into the transformative impact of Generative AI, it’s crucial to highlight real-world examples of organizations leveraging these advanced technologies for significant benefits. SIRVA, a leading global moving and relocation services provider, is one such example.
Discover how SIRVA transformed its procurement processes and achieved remarkable efficiency gains by adopting Zycus’ GenAI-based solutions. Watch the video below to learn more about their journey and the significant improvements they experienced.
The Future of LAMs in Source-to-Pay
As LAM technology matures, we can expect even more transformative applications in procurement:
- Predictive Procurement: LAMs can analyze historical data and market trends to predict future needs and automate purchasing decisions.
- Cognitive Collaboration: Imagine AI assistants that support negotiation strategies, suggest alternative suppliers, and even predict procurement outcomes.
- Enhanced Risk Management: LAMs can monitor for potential disruptions in the supply chain and recommend proactive mitigation strategies.
Explore Zycus’ Procurement Generative AI Platform: Gen AI Software for S2P
Conclusion
LAMs are poised to become a game-changer in procurement. By automating tasks, streamlining processes, and providing valuable insights, LAMs can empower procurement teams to work smarter, faster, and more efficiently. As this technology continues to evolve, the future of procurement promises to be more intelligent, automated, and ultimately, more successful.
FAQs
1. What are the benefits of using LAMs in procurement?
Increased efficiency, cost savings, improved decision-making, reduced risk, and faster turnaround times.
2. Are LAMs ready to replace human procurement professionals?
No, LAMs are tools to augment human capabilities, not replace them. They will handle repetitive tasks, allowing professionals to focus on strategic initiatives.
3. How can I prepare my procurement team for the adoption of LAMs?
Invest in training to educate your team on LAM capabilities and how to integrate them into existing workflows.
Related Reads:
- Beyond Knowledge Retrieval: How RAG Enhances Contextual Understanding in Source-to-Pay
- Why RAG is the Lynchpin for GenAI-powered S2P Success
- Beyond the Hype: A CPO’s Guide to Selecting the Right Multi-Agent GenAI for Procurement
- Leveraging Conversational AI in Procurement
- Harnessing ChatGPT for Supply Chain Efficiency
- ChatGPT and Procurement: A Match Made in AI Heaven?
- White Paper: Transforming Procurement through Conversational AI
- eBook: Harnessing Generative AI for Source to Pay
- On Demand Webinar: Transforming Business Performance with Digital and Generative AI
- eBook: Mastering Modern Procurement: Your Guide to Efficiency & Innovation
