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AI for Good Innovate for Impact

procurement solutions that help businesses streamline online transactions and automate B2B
negotiations.

2 Use Case Description 4.6: Finance

2�1 Description

AI Price Negotiation Solution, Nego-Wiz has been developed to revolutionize the process of
corporate price negotiations [7]. Traditionally, e-commerce negotiations were time-consuming
and inefficient, placing significant pressure on the MD (Merchandise Director) staff. The AI
solution aims to address key challenges such as high transaction time, inefficiency in price
negotiations, and the overburdening of MDs with manual tasks.

AI Price negotiation in this solution means using AI to automatically discuss and agree on the
best price between buyers and sellers, making the process faster, fairer, and less work for
people who used to do it manually.

Problem Overview:

1. Transaction Time: Time spent on a single transaction was restricted by MD work schedules
and labor-intensive negotiation processes. MDs would spend significant hours on negotiations,
affecting their overall productivity.

2. Negotiation Efficiency: The manual process led to repetitive, inefficient negotiations,
which were highly prone to errors due to human biases and emotional factors.

3. Negotiation Outcomes: MDs were unable to achieve optimal negotiation outcomes
consistently, especially in complex or bulk transactions, as they were burdened by time and a
lack of automated support.

4. Limited Data Utilization & Individually Skewed Insights: Negotiations often relied on
fragmented or incomplete data, and information or insights were frequently biased toward
individual perspectives, limiting organization-wide optimization and reducing fairness and
transparency.

Solution Overview: The AI-powered solution automates price negotiation, reducing time and
increasing efficiency while optimizing the negotiation outcome. By using AI, the solution:

1. Provides real-time AI-powered price negotiation that operates 24/7, automating
repetitive tasks such as renegotiations and price adjustments based on real-time data.

2. AI personas in Nego Wiz adjust their negotiation tactics in real time by continuously
ingesting and analyzing incoming negotiation data such as the partner’s last offer, current
market prices, historical deal outcomes, and defined target margins. Specifically, a “Self-
Reflection” agent evaluates the short-term memory (STM) (i.e., the live negotiation context)
against long-term memory (LTM) (i.e., past partner behavior and results) to select or modify
a persona (e.g., cooperative, competitive, or neutral). That persona then drives the “Actor”
agent to generate tailored responses (e.g., concession patterns or firm counteroffers). As new
data (e.g., a sudden price cut by the supplier, changes in order volume, or shifts in partner
responsiveness) enters STM, the Self-Reflection agent re-scores which persona and strategy
combination is most likely to succeed, and the Actor agent updates its messaging accordingly

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