"Employees should use artificial intelligence (AI) to take on higher value-added work, and leaders should focus more on redesigning work than on introducing AI. Organizations need to adopt a learning system that reflects on-the-ground learning in operations." (Cho Won-woo, head of Microsoft Korea)
"In a Korea survey, 78% of respondents said they felt a sense of crisis that they would fall behind in using AI, but only 16% said their management's AI strategy was clear. That shows a split between individuals and organizations in how they use AI." (Oh Sung-mi, AI workforce director at Microsoft Korea)
Microsoft (MS) on the 15th released its annual report, "2026 work trend index," laying out a new work-redesign roadmap that expands human agency with the spread of AI agents. The report defined the gap between the pace of AI use and organizational systems as "the transformation paradox." It refers to a gap that arises when individuals are ready but the environment—organizational culture, manager support, talent, and performance practices—does not sufficiently support it.
In Korea, the gap was even more pronounced. In the global survey, 65% said they would fall behind if they did not adapt quickly to AI transformation, and 26% said alignment with management on AI direction was clear and consistent. Organizational metrics, incentives, and norms also tended to stick to legacy ways. In the global survey, 45% said it was safer to maintain existing goals than to redesign work. Only 13% said that even if redesign did not immediately lead to performance, the attempt itself could lead to rewards. In Korea, 43% of respondents wanted to maintain existing goals, and just 7% thought attempts would lead to rewards.
The report drew on a survey of 20,000 workers in 10 markets using AI at work, analysis of anonymized Microsoft 365 productivity data, and insights from experts in AI, work, and organizational psychology.
Cho Won-woo, head of Microsoft Korea, said, "An organization's competitiveness depends not simply on the speed of AI adoption, but on its ability to turn on-the-ground learning into shareable routines and embed AI in actual operations," adding, "The more AI executes work, the more important the human role becomes."
◇ "Leaders need to redesign work, evaluation, and rewards"… embedding AI is key
The report stressed that what the most advanced corporations have in common is that they move beyond simply using AI to focus on embedding it in work processes. Leading corporations are redesigning how they work and building structures that capture and share signals from agent-based work and reflect them in how they operate.
In particular, the factors that determine AI performance were closer to the organizational environment than to individuals. Organizational factors such as culture, manager support, and talent management practices accounted for 67% of AI's tangible impact—more than twice that of individual mindsets and behaviors at 32%. As a result, work redesign was presented as a core task for leaders. The report noted that for AI use to consolidation into real performance, leaders must fundamentally redesign operating models and processes, beyond simply introducing technology.
Oh Sung-mi, AI workforce GTM director, said, "This virtuous cycle is explained by a learning system," adding, "The owned intelligence that forms as learning accumulates is a core differentiator that other corporations find hard to imitate."
◇ The more AI is used, the more time is spent on higher value-added work
For employees, the report found that as AI maturity rose, AI use increasingly concentrated on cognitive tasks. An analysis by Microsoft of more than 100,000 instances of 365 Copilot usage data and patterns found that 49% of all conversations supported tasks such as information analysis, problem-solving, alternative evaluation, and creative thinking. The rest were collaboration and communication (19%), information search (15%), and document and deliverable creation (17%). In particular, 66% of global AI users said they spent more time on higher value-added work thanks to AI. Another 58% said they were producing results that would have been hard to create a year ago.
Distinctly human judgment has become even more important. Half of global respondents said quality control of AI outputs is important, and 46% cited critical thinking as a core competency. Also, 86% saw AI outputs as a starting point, not a final answer, and thought humans are responsible for the results. In Korea, 48% cited the importance of quality control of AI outputs and 40% cited the importance of critical thinking. Also, 82% said humans are responsible for the results.
To maintain capabilities, 30% said they intentionally perform some tasks without AI. Another 33% said they distinguish the roles of AI and humans before starting work. The report stressed that intentional effort is needed to find the balance of what work to assign to AI and how much, and what work humans should perform. Korea showed similar patterns. In Korea, 22% said they perform some tasks without AI, and 34% said they distinguish the roles of AI and humans.
The way humans and AI collaborate is also changing. Depending on how directly humans are involved in work and how much they use agents, collaboration falls into four types: delegation, collaboration, asking, and exploration.
Delegation is a method where humans set direction and agents execute, making it suitable for structured work such as repeated execution, research, and summarization. Collaboration is where humans and AI interact multiple times to refine outputs, making it effective for judgment-heavy tasks such as drafting proposals or establishing analyses and strategies. Asking is used for tasks requiring quick responses, such as fact checks, schedule or definition lookups, and sentence edits. Lastly, exploration is the stage of testing the scope and limits of what AI can do before applying it to new tasks or unfamiliar workflows.