Can a Good AI Model Alone Deliver AX?
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Korea’s government-backed AI model competition has recently drawn attention to an important question: Does a high-performing AI model automatically translate into real business value?
In the second-round evaluation announced on August 18, Upstage, SK Telecom, and LG AI Research advanced to the next stage. Notably, strong technical performance alone did not determine the outcome.
The evaluation allocated 40 points to benchmark performance, 35 to expert assessment, and 25 to user evaluation. Compared with the first round, greater emphasis was placed on practical AX applications, while members of the public were also invited to test the models directly.
This reflects a broader shift in AI: performance still matters, but usability and real-world deployment are becoming just as important.
Different Models, Different Applications
Source: Superb Associates
The three selected teams demonstrated different approaches.
SK Telecom focused on commercial deployment and use cases across defense, manufacturing, legal, and tax services. Upstage tested its model in real service environments while working with domestic AI semiconductor partners. LG AI Research stood out in agentic AI, safety, and reliability.
The takeaway is simple: the value of AI depends not only on the model itself, but on what it is designed to do.
In Business, AI Must Go Beyond Answers
Source: 프라임 커리어
For companies, summarizing a contract is only the first step.
A useful enterprise AI system may also need to compare the contract with previous versions, identify missing information, check business rules, and pass the result to the next stage of the workflow.
This is where AI adoption becomes AI Transformation, or AX.
AX is not simply about using a more capable model. It is about connecting AI with company data, systems, and operating processes so that it can perform real work.
Why This Matters in Reinsurance
Source: MIT Sloan
Reinsurance operations involve documents such as slips, treaties, endorsements, cover notes, and claims.
When a new document arrives, the task does not end with reading it. Information must be extracted, compared with existing records, checked for inconsistencies, and prepared for review or the next action.
For reinsurance AX, the key question is therefore not only which AI model is used, but what the AI can actually do across the workflow.
Treasurer INS and Reinsurance AX
Treasurer INS supports reinsurance workflows through Document Intelligence, document comparison and validation, Slip Automation, News Insight, and AI Agents.
Its role is to move beyond document reading by structuring data, validating information, and connecting each step of the process.
A strong AI model is only the starting point.
AX begins when AI starts changing how work is actually done.