An AI-ready operating model requires each workflow to have a clear purpose, permitted data, authority limits, checkpoints and performance measures. This article proposes three actions for the next 90 days: pilot two or three workflows, define what AI and people are authorized to do, and track output quality alongside processing time and rework.
Microsoft’s 2026 Work Trend Index draws on a survey of 20,000 AI-using knowledge workers across ten markets and privacy-protected Microsoft 365 productivity signals. The Vietnam release, based on a survey of 2,000 knowledge workers, reports that 39% are Frontier Professionals, above the global average of 16%; 48% of AI users say their organizational leadership has a clear and consistent AI direction, compared with 26% globally.
These are indicators of perception and self-reporting, not direct evidence of productivity or profit. Even so, they raise an important issue: people may be ready to test new ways of working faster than the organization can standardize processes and design an operating model.
Why are AI tools alone not enough for operational readiness?
Many enterprise AI programs still begin with a software list, the number of accounts issued, or prompt-training sessions. These steps are useful, but they do not answer the harder questions: which work will be redesigned, how managers will change their operating rhythm, and who is accountable when an AI-supported recommendation enters a decision.
The report’s global data show that 49% of Microsoft 365 Copilot conversations support cognitive work such as analysis, problem solving, evaluation, and creativity. AI is entering work closer to judgement than repetitive administration. If approval flows, data standards, and quality criteria remain unchanged, an organization may create more output without necessarily creating more value.
Management situation
A sales unit uses AI to summarize calls, suggest opportunities, and draft emails. The initial benefit is time saved. But if CRM data are incomplete, the definition of a “qualified opportunity” is inconsistent, and managers still evaluate only email volume, AI merely accelerates a process with weak signals. The first fixes are data standards, classification criteria, and the pipeline review rhythm.
How should managers change the way work is organized?
When AI supports more specialised work, managers are no longer only assigning tasks and checking progress. Their role shifts toward setting intent, defining output standards, allocating decision rights, and designing checkpoints. A good request does not simply say “prepare a report.” It specifies which decision the report supports, which data may be used, what level of certainty is needed, and who makes the final confirmation.
Humans and AI should not be treated as opposites. The valuable capability is coordination: people define goals, interpret context, and remain accountable; AI expands options, processes information, and prepares drafts; the process specifies where review or stopping is necessary. In this setting, critical thinking and quality control are operating conditions.
Three actions for the next 90 days
First, choose two or three high-frequency, moderate-risk workflows, such as customer-feedback synthesis, weekly reporting, or sales-request classification. Measure processing time, rework, and output quality before scaling. Second, state the rights of AI and users explicitly: what the AI may suggest, draft, or execute; which actions always need approval; and which data must not enter the tool.
Third, change management meetings so they track decision quality rather than only usage. A small operating dashboard can record AI-supported cases, the percentage of reviewed outputs accepted, recurring errors, time saved, and feedback from customers or employees. This tells the organization whether AI is improving a value flow or merely creating more activity.
Conclusion
Vietnam shows encouraging signals of AI-user readiness, but readiness becomes advantage only when businesses change how work is organized. The question is not to chase more tools. It is to turn each use case into a process with a purpose, data, authority, checks, and measures.
Vietnamese original: Nhân sự đã sẵn sàng, doanh nghiệp đã sẵn sàng tái thiết kế công việc?.
Author: Assoc. Prof. Nguyen Hai Ninh.
References
Microsoft. (2026, June 24). 2026 Work Trend Index: Vietnam’s workforce is ready for the AI era. https://news.microsoft.com/source/asia/2026/06/24/bao-cao-chi-so-xu-huong-cong-viec-nam-2026-luc-luong-lao-dong-viet-nam-da-san-sang-cho-ky-nguyen-ai-doanh-nghiep-can-chuyen-minh-de-but-pha/?lang=vi
Microsoft. (2026, May 5). 2026 Work Trend Index. https://www.microsoft.com/en-us/worklab/work-trend-index/agents-human-agency-and-the-opportunity-for-every-organization
