What Is AX? From Concept to Industry-Specific Use Cases
🧑💻 Summary
This article first explores the concept of AX (AI Transformation)—which goes beyond using AI as a tool to redesigning the way work gets done—and how it differs from DX. Next, it introduces industry-specific AX use cases across manufacturing (Samsung Electronics, Tesla), retail (Amazon, Walmart), and finance (Morgan Stanley, KB Financial). Finally, it covers the concept of 'AI orchestration,' a key requirement for AX success, and Hancom's role as it declares its shift to becoming an 'AI orchestrator.'
These days, you cannot go anywhere—recent news, management reports, or conferences—without hearing the term AX.
If companies have been driving DX (digital transformation) over the past few years, the entire industry is now rapidly shifting toward an AI-centered structure.
According to NVIDIA’s “State of AI by Industry 2026” survey, 88% of companies worldwide said AI helps increase revenue. However, the McKinsey report finds that only about 6% of companies have achieved meaningful results by actually redesigning business processes around AI. In other words, AX capabilities—connecting AI adoption to real changes in how an organization works—are emerging as a core competitive advantage.
So what is the difference between simply adopting AI and realizing AX?
In this article, we look at the concept of AX, how it differs from DX, real-world examples by industry, and “AI orchestration,” a key requirement for implementing AX.
What Is AX (AI Transformation)?
AX, or AI Transformation, goes beyond using AI as a tool. It is a transformation in which AI supports decision-making across corporate strategy and operations, and the way work is done is redesigned from the ground up.
AX is not limited to automating simple repetitive tasks; the key is using AI to create new ways of doing business and new growth strategies.
For example, using an AI tool only to polish emails or create PowerPoint slides is difficult to call AX. It becomes AX only when the structure changes so that AI evaluates and executes across the entire workflow.
Differences Between AX and DX
| Item | AX (AI Transformation) | DX (Digital Transformation) |
| Definition | AI transformation that uses AI to reorganize overall business ways of working, operating structures, and decision-making systems | Digital transformation that digitizes data and uses technology to improve work efficiency and operating methods |
| Key characteristics | Centered on AI-driven analysis, prediction, decision support, and autonomy | Centered on data digitization and operational efficiency |
| Example | AI analyzes and predicts inventory flows and recommends required purchase orders | Managing inventory data that was handled manually in Excel |
| Meaning | A transformation that redesigns the way work is done itself | A change that enables work to be done faster and more efficiently |
AX can also be seen as a concept that goes one step beyond DX (Digital Transformation), which has long been viewed as a corporate survival strategy.
In the DX era, moving inventory data managed manually into Excel was considered innovation. In the AX era, it has advanced to the level where AI analyzes and predicts inventory flows and recommends the necessary purchase orders. In other words, if DX was a change that helped people do work better, AX is a transformation that redesigns the way work is done itself.
AX Use Cases by Industry
In major industries such as manufacturing, retail, and finance, AX strategies tailored to each industry’s characteristics are accelerating.
AX in Manufacturing: A Factory Where AI Decides and Executes
- Samsung Electronics: Promoting an “AI autonomous factory” initiative that simulates the entire process in a virtual space in advance through 2030 and connects AI agents to each stage of quality, production, and logistics
- Tesla: Conducting pilot tests of the humanoid robot Optimus at U.S. factories
- BMW: Expanding pilot tests to European factories based on results from U.S. plants
- Hyundai Motor: Running proof-of-concept trials for the humanoid robot “Atlas,” with plans to begin mass production in 2028 and deploy it in earnest across manufacturing processes
AX in Retail: Shopping Where AI Knows Before You Speak
- Amazon: Generating over 30% of total revenue through a sophisticated AI personalization recommendation engine, and connecting internal operations end-to-end with AI—from customer orders to inventory adjustments and supplier negotiations
- Sephora: Using a single skin photo for AI to analyze skin type and recommend suitable products, bringing the in-store consultant experience online
- Walmart: Connecting not only customer experience but also inventory adjustments and supplier negotiations with AI, enabling AI to handle everything from product recommendations to ordering and returns
AX in Finance: AI That Deepens Trust
- Morgan Stanley: After adopting an AI-based solution in 2023, document accessibility rose from 20% to 80%, and work time per meeting was reduced by 30 minutes; today, 98% of the investment advisory division uses it in day-to-day work
- KB Financial Group: Prioritizing the introduction of AI agents in PB (private banker) and RM (corporate relationship manager) areas to advance wealth management consulting
- Woori Financial Group: Accelerating the adoption of AI agents across five core business areas, including wealth management consulting, corporate lending, and internal controls
A Key Requirement for AX Success: “AI Orchestration”
The key to AX success is not indiscriminately adopting AI as fast as possible.
Start with core areas where return on investment (ROI) is clear—such as supply chain management, sales, and R&D—while also designing “AI orchestration” that connects multiple AIs into a single flow.
What is AI Orchestration?
AI Orchestration involves connecting multiple AI models and systems with different functions to align with workflows, ensuring they operate as a single, integrated process.
Rather than each AI operating independently, they divide tasks according to defined roles, share results, and complete the overall work together. In line with this trend, Hancom has also officially declared its role as an “AI orchestrator” that goes beyond being a “document solutions company” to connect and coordinate multiple AI agents to carry out enterprise-wide work.
AX: A Must-Have Strategy, Not an Option
AI agents are rapidly taking root in the workplace, and AX is no longer an experiment limited to a few companies.
Forbes Korea analyzes that the performance gap in AI transformation will increasingly solidify into a structural gap that is difficult to catch up with in a short period of time. The key is not “which AI you have,” but “how multiple agents are designed to divide roles and collaborate.”
Therefore, AX is not a one-off project; it is a journey in which an organization continuously learns and evolves—and the companies that design that journey first will secure the competitiveness of the next era.
Key Summary Q&A
Q. What is the biggest difference between AX and DX?
DX is a change that digitizes data and business processes to improve operational efficiency and connectivity, while AX is a transformation that redesigns the way work is done itself so that AI can perform analysis, decision-making, generation, and execution. If DX is “a change that makes work more efficient,” AX is “a transformation that redesigns the way work is done itself.”
Q. If we adopt AI tools, does that mean we are doing AX?
No. Simply “adopting tools” is different from “fundamentally transforming capabilities.”
According to a McKinsey report from a global consulting firm, 88% of companies are already using AI, but most still remain in the experiment or pilot stage. True AX is completed only when you go beyond using convenient tools and redesign the entire way of working and business processes around AI.
Q. Why is “AI orchestration” important in AX?
AX is not completed by introducing multiple AI tools individually. To achieve tangible AX outcomes, it must be supported by “AI orchestration”—a structure that connects different AIs so they divide roles, share results, and move toward a single goal.
📄 References
- NVIDIA, “State of AI Report 2026”
- McKinsey & Company, “The State of AI 2025: Agents, innovation, and transformation”
- Forbes Korea, “Forbes Korea’s Top 7 Picks: 〈2026 AI Trends〉”
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