Page 571 - AI for Good Innovate for Impact

AI for Good Innovate for Impact

2�2 Benefits of the use case

• Industry, Innovation, and Infrastructure The solution uses AI-driven negotiation to
modernize the way KT Commerce operates, promoting innovation in the e-commerce
industry. The introduction of advanced AI technology creates a more efficient and resilient 4.6: Finance
business infrastructure, helping to drive growth and sustainability across the organization
[6].

2�3 Future Work

Planned Enhancements

1. Implementation of Multi-AI Agent System for Dynamic Strategy Adjustments:
Future versions will enhance the Multi-AI Agent System, refining how the AI negotiates by
leveraging multiple agents working in parallel to evaluate and execute various strategies in
real time. This system will use a combination of specialized agents, each focusing on different
aspects of the negotiation, such as price adjustments, competitor analysis, and partner
response modeling. The Multi-AI Agent System will continuously adapt its approach based
on the evolving negotiation dynamics, ensuring that each decision made aligns with both
short-term goals and long-term strategy [5].

2. Advanced Goal Setting Mechanism: The Goal Setting function will be enhanced to allow
the AI to establish dynamic, real-time target prices based on market conditions, competitor
pricing, and transaction history. The AI will be able to automatically calculate the optimal target
price, considering factors such as product value, negotiation history, and the negotiation
goals set by the user. This will improve the precision of AI's pricing strategies and ensure that
negotiation targets are always aligned with market realities.

3. Advanced Indicator System for Real-Time Strategy Assessment: The Indicator system
will be refined to provide more granular real-time analysis of negotiation success probabilities.
This will include visualizing not just the success rate but also dynamic factors like time, partner
strength, and competitor pricing. The indicators will provide actionable insights, helping both
the AI and human negotiators make informed decisions.

4. Integration of Real-Time Data Sources for Improved Price Prediction: The Lowest Price
Search feature will be enhanced to incorporate real-time updates from a wider range of
suppliers and marketplaces, ensuring that the AI can always propose the most competitive
pricing during negotiations. This will also include expanding the use of big data and machine
learning algorithms to predict price trends and adjust negotiations accordingly.

5. Expansion of Dynamic Negotiation Scenarios: As the AI learns from ongoing negotiations,
it will expand its capability to handle more complex and diverse negotiation scenarios. Future
updates will introduce additional negotiation variables like multiple negotiation rounds, bulk
discounting strategies, and contract term negotiations. This will allow the AI to handle more
varied and sophisticated negotiation processes.

Resource Needs

1. Data Expansion and Integration: The AI will require access to an expanded dataset for
improved learning and model training. This includes integrating data from additional market
sources, transaction histories, and real-time competitor pricing. A stronger focus will be placed

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