Negotiation Automation Platform
coordination relies on human resources , with significant resources spent on interactions with counterparties and gathering information inside and outside the company . By replacing these tasks with AI , it is possible to significantly reduce the amounts of human resources required . We present the quantitative evaluation results of these case studies in Section 5.1 Electronic Component Procurement . Additionally , losses due to human error can also be minimized . While it is difficult to obtain precise statistics substantial losses , or numerous minor losses due to inappropriate coordination caused by operational mistakes , are undoubtedly occurring in the business . Automated Negotiation through AI can significantly reduce these losses .
The second benefit of AI-driven negotiation is the ability to form agreements under favorable conditions through fast and precise coordination . In negotiation situations , it is often challenging to reach better agreements , even when both parties can feasibly agree , due to limited interactions before the deadline or a lack of detailed evaluation when making offers and responses in a rush . As the negotiation AI can generate offers and decide on acceptance quickly and accurately , it increases the possibility of reaching better agreements .
The third benefit is the opportunity for business expansion or risk reduction , as the time it takes to reach an agreement is shortened . For example , in sales negotiations , presenting a favorable offer before rivals can increase the chance of winning contracts , while procurement negotiations can secure scarce resources in constrained environments or avoid excess inventory in surplus environments by determining the necessary amount just before the deadline .
The fourth , albeit indirect , benefit is the expansion of product / service lineups and the shortening of delivery times due to reduced overhead . By efficiently procuring necessary components / services and selling / providing them on time and at appropriate prices , the potential to reduce overhead , such as safety stock levels and safety lead times in manufacturing and logistics execution , increases . By simultaneously achieving these reductions , companies can improve their competitiveness .
It is important to keep in mind that there are also challenges to AI-driven Negotiation Automation which need to be addressed . Ensuring compatibility with current practices is essential . Presently , external coordination relies on human resources , utilizing communication channels such as phone calls and emails and input / output systems designed for human interaction . Introducing a NAP solution requires reforming internal business processes .
Companies implementing the solution will experience new tasks , such as selecting negotiations to automate based on their preferred level of automation and verifying negotiation progress and outcomes . Moreover , even if a company does not implement Automated Negotiation by itself , it must still adjust its business processes when dealing with companies using NAP , adapting to changes in communication channels , or utilizing faster responses from NAP users .
There may also be challenges in integrating with some existing internal systems . As discussed in Section 3 Technology , various internal information is required for negotiation AI to perform its
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