Why Samsung is moving beyond AI tools to redesign how work gets done

After the event, one phrase became much clearer: work and organizational culture

Samsung Electronics held Samsung AI Forum 2026 at its Seocho office on September 30 under the theme The Agentic Shift: From Intelligence to Impact. The program focused on connecting agentic AI and Physical AI to real work and business outcomes.

Vice Chairman and CEO Young Hyun Jun said agentic AI would go beyond a technology shift and bring fundamental changes to how people work and to organizational culture. The workflow signal visible in the pre-event agenda was therefore confirmed in management language on the day of the forum.

In the afternoon DS AX Innovation track, Samsung's AX/PI Center presented examples of AI being applied across semiconductor design and manufacturing. That makes the event more useful as an operating-model signal than as another AI research conference.

The first shift is Chatbot to Workflow

A chatbot is largely a question-and-answer interface. A workflow has to decompose work, use tools, verify outputs and move to the next step.

That distinction matters for talent. Being good at prompting is not the same as being able to structure a domain workflow so that AI can execute parts of it safely.

As AI moves from personal interaction into work processes, the capability stack expands from tool usage toward workflow design and verification.

The second shift is individual productivity to organizational productivity

AWS's keynote is titled From Individual Productivity to Enterprise Wide Impact. Making one employee faster is different from changing how an enterprise operates.

Enterprise deployment requires decisions about what to automate, where AI has authority, which systems and data it can access, who checks errors, and how impact is measured.

AI capability therefore becomes partly an organizational design problem, not just an individual skill.

In June the question was which AI to use; by September it was how to divide work with AI

Samsung's DX division formally introduced ChatGPT, Gemini and Claude for employee use in June. The notable choice was not to lock employees into a single service, but to let them choose according to the task and purpose.

If that stage was about access and tool choice, the September agentic shift goes deeper. As AI receives goals and performs intermediate steps, people have to decompose the work, define authority and verify the output.

Being able to use AI is therefore not the same capability as being able to decide what AI should do, what humans must still own, and how the result will be checked.

The third shift is Digital to Physical

The morning program includes agents grounded in the physical world, while the DX AI Technology track has a dedicated Physical AI session.

Once AI moves beyond software into robots and physical products, models are only one component. Sensors, controls, hardware, safety, systems integration and real-world uncertainty all matter.

That makes it harder to describe the required talent as a single AI job family. The value of people who can connect domain expertise with AI at the system level may increase.

The fourth shift is from a finished model to a continuously learning system

Samsung Research's morning keynote focuses on a self-evolving expert agent powered by continual learning, and the final DX session is also devoted to continual learning.

The premise is not that a model is trained once and then left alone. The system has to learn, be deployed, observed, verified and improved as conditions change.

That puts more weight on capabilities that connect training, deployment, monitoring, verification and improvement into one operating loop.

The DS track ends with Toward AI-Native DS

The DS AX Innovation track moves from Dell's AI-native enterprise journey to safe workplace use of Claude and ends with Samsung's Toward AI-Native DS.

AI-native is a broader idea than adding one tool to an existing process. It implies designing work, automation, decision rights and verification with AI already assumed to be inside the system.

For a semiconductor business that spans design, process, manufacturing, quality, equipment and data, that raises the importance of combining deep domain knowledge with AI system design.

This is not only a conference agenda: multi-agent workflows are already appearing in semiconductor engineering

At NVIDIA GTC 2026, Samsung DS publicly described an Agentic AI strategy spanning semiconductor design and manufacturing.

Samsung said it had used reinforcement learning and genetic algorithms to automate parts of analog design and described a future multi-agent workflow in which schematic and layout agents continuously exchange feedback.

In manufacturing, Samsung described Agentic AI and digital twins for equipment, process and yield management, with a longer-term direction toward an autonomous AI factory. That makes the forum's agentic and AI-native language more than an abstract theme.

For candidates, the better question may be how AI fits into the job

The forum should not be read as Samsung's hiring rubric. But it does suggest useful questions for people describing their own work in an AI-heavy environment.

Which part of my workflow can AI perform? How do I verify the output? Which decisions still require human judgment? How does my domain knowledge connect to the AI system?

Evidence that links a real job problem, AI execution and verification may become more meaningful than simply listing AI tools.

BANSEOG VIEW | From AI user to AI workflow designer

The Samsung AI Forum 2026 agenda moves from chatbots to agentic workflows, enterprise impact, Physical AI, continual learning and AI-Native DS.

Banseog reads that sequence as a public capability signal showing AI moving from an individual tool into work systems and organizational architecture.

If that direction continues, the talent question may shift from whether someone can use AI to whether they can design how AI works inside their domain and verify the result.

The capability combination increasingly looks like Domain Expertise + AI Workflow Design + Verification & Judgment + System Integration.