Autonomous negotiations are an evolving field within artificial intelligence (AI) and procurement technology, leveraging advanced algorithms to perform negotiations between multiple parties without human intervention, for example between procurement organizations and their suppliers. This interaction is about negotiating a deal between these two parties, and can focus on such diverse objects such as a business code of conduct, a general agreement, different commercial terms such as pricing, volume, delivery date, payment terms, lump sums, etc. while the system can inquire about the other party’s preferences and can generate new offers based on such inputs as well as react to received offers. Consequently, an autonomous negotiation system is differs for example from a sourcing optimizer, an eAuction or an autonomous sourcing solution.This blog post will provide you with a general explanation of autonomous negotiations (not regarding Pactum AI only), the underlying technologies, and potential applications across various industries and domains.
Negotiating is an essential skill in diverse aspects of life, including business transactions, international diplomacy, and conflict resolution. Traditionally, negotiations have been conducted by humans, requiring expertise, experience, and intuition. However, advances in AI have paved the way for the development of autonomous negotiation systems over the past few years that can negotiate with one or multiple parties, sequentially or in parallel. These systems are capable of performing complex negotiations independently with a human representing the other party via a conversational interface, i.e. a chatbot. Enterprises such as Walmart (Harvard Business Review article), Amazon, Deutsche Telekom and others are using Autonomous Negotiation Systems at scale.
Autonomous negotiations result from the integration of AI and multi-agent systems (MAS), where multiple intelligent agents interact to negotiate and reach a consensus. These systems can utilize machine learning, natural language processing, and decision-making algorithms to analyze data, predict outcomes, and adapt to changing circumstances during a negotiation. Today, several key technologies enable autonomous negotiations.
Machine learning algorithms, such as reinforcement learning and deep learning, enable autonomous negotiation solutions to learn from experience and improve their performance over time. These algorithms are essential for modeling the behavior of other parties, predicting their preferences, and optimizing negotiation strategies. These systems can explore various negotiation tactics, assess their effectiveness, adapt their strategies based on feedback from the negotiation environment and can learn complex representations of the negotiation domain.
Natural Language Processing (NLP)
NLP is vital for understanding and generating human-like language during negotiations. It enables autonomous negotiation systems to process and interpret textual information, recognize the intent of the negotiating parties, and generate contextually appropriate responses. Key NLP techniques today used in autonomous negotiations include: Sentiment Analysis allows autonomous negotiation systems to gauge the emotional tone of messages exchanged between parties, enabling them to adapt their strategies accordingly. Entity recognition identifies key entities (e.g., names, dates, quantities) within text, providing valuable information for the negotiation process.
Decision-making algorithms help autonomous negotiation solutions determine the best course of action by considering available options, potential consequences, and desired outcomes. These algorithms can incorporate game theory, which provides a mathematical framework for modeling strategic interactions between parties. Game theory is a branch of mathematics that models strategic interactions between rational decision-makers. In autonomous negotiations, game theory for example can help to predict the behavior of other parties and identify optimal negotiation strategies. Multi-criteria decision-making techniques enable autonomous negotiation systems to evaluate various aspects of the negotiation process simultaneously, balancing conflicting objectives to reach a satisfactory outcome for all involved parties.
Autonomous negotiations have the potential to revolutionize various industries and domains – below are three rather examples:
Procurement / Supply Chain Management
Autonomous negotiation platforms can help organizations optimize their supply chain operations by negotiating with suppliers, distributors, and other stakeholders to secure the best possible terms, optimize costs, and ensure timely delivery. Today, the application of autonomous negotiations along the supply chain ranges across direct as well as indirect procurement organizations, including for example, (1) Telcos who procure items from their electronic device suppliers, (2) retailers who negotiate the purchases of indirect (Goods-not-For-Resale) as well as direct (Goods-For-Resale) items or (3) Manufacturers who leverage the technology to regularly negotiate agreements for the materials they use to produce the goods they sell, ranging from tail spend to multi-million dollar deals.
Within Human Resources, autonomous negotiation systems can conduct salary negotiations, benefits packages, and other employee-related agreements, streamlining the process and improving overall satisfaction as well as transparency. Salary negotiations often involve complex interactions between employees and employers, where both parties aim to reach an agreement that satisfies their respective needs and expectations. Autonomous negotiation systems can analyze vast amounts of data, such as market salary trends, employee performance metrics, and company budgets, to determine a fair and competitive compensation package. By automating this process, HR professionals can ensure more objective and data-driven negotiations, reducing the likelihood of individual bias and promoting fair and equitable outcomes.
Autonomous negotiation systems can be used in smart grid applications to facilitate dynamic pricing and resource allocation, ensuring optimal energy distribution and minimizing the overall cost of energy production and consumption. Dynamic pricing is a crucial aspect of modern energy markets, where the cost of energy varies based on factors such as supply and demand, time of day, and grid conditions. AI can analyze real-time data from various sources, such as energy production facilities, weather forecasts, and consumer demand patterns, to determine and negotiate optimal pricing structures that promote efficient energy consumption and minimize costs for both consumers and producers.
The derivatives industry is increasingly seeing the importance of standardizing, automating, and digitizing every step of the trade process. This spans from the initial drafting and discussion of legal documents to the post-trade follow-up. By standardizing documents and transitioning to digital creation and discussion of legal paperwork, the negotiation process for objects such as Custody Agreements, IM Documentation or Benchmark Reform Amendment Agreements is in the process of being digitized and thus becomes automated, and ergo more efficient for everyone involved.
Despite the potential benefits, a few critical issues must be addressed as the use of autonomous negotiations are more widely adopted. Namely, ethics, trust, and interoperability are critical considerations as autonomous negotiations gain prominence in replacing human decision-makers. Ensuring that these systems operate ethically and maintain user trust requires transparency in decision-making processes and adherence to privacy regulations (example: rule based AI versus open ended AI). Simultaneously, the ongoing development of standardized protocols and interfaces is crucial for facilitating communication between different parties, allowing seamless integration with existing infrastructures as well as new ones.
Consequently, autonomous negotiations are a growing field that has already started to fundamentally transform various industries and will continue to do so by automating simple to complex negotiation processes. In supply chain management, when one wants to conduct a negotiation with suppliers, there is now an additional option available aside from calling, emailing or meeting the other parties face-to-face or running some form of a traditional (e)auction event. The effect of this new option is much more than just another simple, digital process automation, but actual process innovation. As the technology continues to advance, autonomous negotiations will become more and more sophisticated and capable of addressing a wide range of challenges across different domains. By ensuring ethical and interoperable systems, autonomous negotiations are going to continue to revolutionize the way humans negotiate and reach agreements with each other on a global scale.