

[Corporate Challenge] Special Lecture on AI-Driven Problem Solving, Persuasion, and Research Strategy by Jae-sung Kim (Ex-McKinsey)
PROFILE: An expert with a top-tier career in strategy, AI, digital marketing, and presentation. CEO Jaesung Kim graduated from Seoul National University's Department of Computer Science and Engineering and worked as a consultant at the world-renowned management consulting firm McKinsey & Company, and as the first CEO staff member of the Strategy Office at Kakao Headquarters. He has been actively involved in AI-related activities, including business strategy, global expansion, leading the joining of the AI Alliance, and discussing collaborations with OpenAI. He currently serves as the CEO of J curve partners. His publications include: 『Perfect Presentation 3』 (2024), 『Perfect Slide Clinic with PowerPoint』 (Acorn Publishing, 2020), 『Why Does That Person Succeed in Everything They Do?』 (2024), 『The Completion of Action』 (Annapurna, 2019), and 『Perfect Presentation』 (2012). 『Perfect Presentation』 has been adopted and utilized as a presentation textbook for employees at Samsung Electronics and Cheil Worldwide, as well as a primary presentation textbook at numerous universities in the Seoul metropolitan area. He is a strategy and marketing expert, and the most sought-after communication/presentation specialist by corporations. He consistently delivers lectures to various major corporations, including Samsung Electronics, Cheil Worldwide, SK Networks, GS Caltex, Hana Securities, LG Uplus, AhnLab, and Kyobo Book Centre. He participated as a main speaker at the 2024 Albatross Conference alongside Nobel laureates and Wharton School professors. https://biz.newdaily.co.kr/site/data/html/2024/03/27/2024032700272.html He also participated in the YouTube channel "AND Studio," recording the highest view count in the channel's history, and continues to strive to spread knowledge to many people. https://www.youtube.com/watch?v=Zxcj0avyWhQ Inquiries for Lectures/Collaboration/Partnership: jaesung_kim@jcurvepartners.ai


[Jaesung Kim's Negotiation Practice - Utilizing MESO in Real-World Negotiations]
[Jae-sung Kim's Negotiation Practice - Utilizing MESO in Real-world Negotiations]
Hello, today I received a proposal to create new online content. There were no major issues with the other details, but in order to get better terms, I tried negotiating once again!
Company A (Client) Proposal: OOO won paid as a lump sum
My proposal
1. Lump-sum payment: Requested 2.5 times the original proposal
2. Revenue Share Payment (Presented two options)
2.1. Partial lump-sum payment (1/2 of the original proposal) + a certain percentage of RS
2.2. No lump-sum payment + a certain percentage of RS (a rate 50% higher than option 2.1.)
Those who haven't taken a negotiation course would likely try to negotiate for a higher amount when they feel the initial offer is too low, or depending on the situation, they might end up accepting the proposal reluctantly.
In my case, I already had a history of collaborating with this client multiple times, and the RS (Revenue Share) terms I proposed were at the same percentage level as those previously agreed upon, so
I stated, "I am proposing an RS (Revenue Share) plan based on our past history and for the sake of a long-term partnership."
Meanwhile, the initial amount proposed by the client was at a similar level to what is typically offered for similar content production. By proposing an option 2.5 times higher than that, I threw out a choice in advance so that they would avoid choosing Option 1 if possible. However, even if they were to choose the 2.5x option, I would have no major complaints. (This specific point is related to MESO, which you can learn about in the course.)
Whether the negotiation results turn out well or not, I will make sure to share them with you again later :)
If there are those who wish to take a systematic course on negotiation strategy, I recommend checking out the following lecture.
McKinsey alum Jae-sung Kim's AI-driven Persuasion & Negotiation Strategy: https://inf.run/Sg1TB




