Technology can help businesses recruit faster, assess performance more frequently, and deliver training at scale. But technology alone does not improve human resources (HR) management. If criteria are unclear, data is unreliable, and accountability is undefined, digitalization will amplify existing weaknesses rather than fix them.
Digital HR management should therefore not begin with choosing software or deploying AI. It should begin by asking: What standards should guide people management, what data should be used, and who is accountable for technology-supported decisions?
Common challenge
The rapid advancement of AI is placing growing pressure on businesses to accelerate digital transformation, particularly in workforce management.
The International Labour Organization’s (ILO) Generative AI and Jobs in Vietnam brief, released on April 16, 2026, estimates that 20.8 per cent of all jobs, or approximately 11.5 million workers, are in occupations where at least some tasks could be affected by generative AI.
Vietnamese businesses are already accelerating the adoption of digital technologies and AI across HR functions, from timekeeping and payroll to recruitment, onboarding, personnel records, training, performance management, employee engagement, workforce planning, and employee support.
What is particularly notable, however, is that digital HR management in Vietnam is progressing unevenly. Many large corporations have integrated recruitment, performance management, learning, and HR data into unified platforms. By contrast, around 1 million small and medium-sized enterprises (SMEs), representing nearly 98 per cent of all enterprises, remain at very different stages of digitalization. Beyond them are more than 5.2 million household businesses. With an average workforce of just 1.7 employees, HR management in this segment is rarely a dedicated business function.
As a result, each group faces different priorities. Large corporations must tackle issues such as data integration, access control, privacy protection, algorithmic bias, and the responsible use of AI. SMEs often need to start by standardizing job descriptions, performance criteria, personnel records, and HR processes. For household businesses, the first step may simply be digitizing employee records, attendance, payroll, work assignments, and basic skills training.
No single model can therefore be applied equally to a corporation with tens of thousands of employees, one with a few hundred staff, or a family-run business employing only a handful of people. Technology choices, investment levels, implementation scope, and rollout speed must all reflect an organization’s size, needs, and management capacity.
Despite these different starting points, all businesses face the same fundamental challenge: how can technology help organizations make better decisions about people, rather than simply execute flawed processes more quickly? That question leads to the first principle of digital HR management: before accelerating, organizations must first determine where they want to go.
Digitizing standards
Digital HR management should not begin with purchasing software and reshaping processes to fit the system. The sequence should be the reverse: establish standards 1 standardize data 1 automate processes 1 monitor and continuously improve.
Organizations must first define what constitutes sound hiring, strong performance, effective training, and the responsible use of HR data. These principles should then be translated into clear criteria, standardized data, and evidence requirements before any process is automated. Even after implementation, outcomes must be monitored, reviewed, explained, and continuously refined.
The risks of skipping these foundations are evident across HR functions. In recruitment, systems may prioritize keyword matches over genuinely qualified candidates if concepts such as “high potential” or “cultural fit” are poorly defined. In performance management, technology can track results and flag anomalies, but it cannot determine what constitutes good performance or explain why performance falls short. If metrics are used primarily to assign blame, employees are likely to optimize for scores rather than value. Similarly, digital learning platforms can deliver training at scale, but course completion and certificates measure participation, not capability or improved job performance.
Across all three functions, technology can process information faster, but managers remain responsible for defining standards, interpreting results, considering context, and making final decisions.
The International Labour Organization’s (ILO) Generative AI and Jobs in Vietnam brief, released on April 16, 2026, estimates that 20.8 per cent of all jobs, or approximately 11.5 million workers, are in occupations where at least some tasks could be affected by generative AI.
The broader lesson is clear: digitizing processes is only the visible layer of transformation. The quality of a digital HR system depends on the standards, criteria, and accountability built into it. The next question, then, is what those standards should include.
The Six Cs
A responsible digital HR system should be built around six principles: Clear, Clean, Consistent, Confidential, Contestable, and Coaching. Together, they form an integrated governance framework.
Clear: Criteria must be clearly defined
Every digital HR system begins by defining what is being evaluated and why. Employees should understand what data is collected, how it will be used, and the criteria on which it is assessed. Concepts such as “cultural fit,” “high potential,” and “strong performance” must be translated into specific, verifiable requirements backed by objective evidence. Even the most accurate system will struggle to earn trust if people do not understand why a decision was made. Clear standards are also essential for determining what data should be collected and ensuring that only relevant information is used.
Clean: Data must be reliable
Data must be accurate, complete, relevant, and representative. Algorithms are not inherently objective: if historical data is biased, unrepresentative, or collected for the wrong purpose, technology can reinforce and amplify existing inequalities. Organizations should regularly verify data quality and compare outcomes across different employee groups to identify potential bias, rather than relying solely on technical accuracy. Reliable data alone, however, is not enough. The same data and evaluation criteria must be applied consistently throughout the organization.
Consistent: Standards must be applied consistently
Comparable cases should be assessed according to the same principles. Employees in equivalent roles or with similar contributions should not receive different evaluations because of their department, gender, age, or employment arrangement. Technology promotes fairness only when criteria are clear, data is reliable, and standards are applied consistently. Otherwise, it can reinforce unequal treatment under the appearance of objective decision-making.
Confidential: Data must be protected
Recruitment records, payroll, health, biometric, location, and employment data can directly affect employees’ rights, career opportunities, and personal dignity. Organizations must clearly define why this data is collected, how it will be used, who can access it, how long it be retained, and who is accountable for any misuse or breaches. Tools such as surveillance cameras, location tracking, and activity-monitoring software should be used only for legitimate purposes, with monitoring proportionate to the risks involved and employees fully informed. Protecting data is essential, but employees must also understand how their information is used and have the opportunity to challenge outcomes that affect their rights or interests.
Contestable: Decisions must be open to challenge and review
For decisions with significant consequences - such as rejecting applicants, reassignments, disciplinary action, bonus reductions, or termination - algorithms should serve only as decision-support tools. Those affected should understand the basis for the decision, be able to correct inaccurate data, respond, and request a review by an authorized decision-maker. Decision-makers must consider the full context, explain their rationale, and remain accountable for the final outcome. The ability to challenge and review decisions is essential for accountability. But digital HR management is about more than preventing poor decisions, it should also support employee development, strengthen management, and improve organizational performance.
Coaching: Data should support human development
HR data should do more than rank, monitor, or assign blame. It should help managers identify capability gaps, improve work organization, provide meaningful feedback, and support employee development. A low performance score should prompt questions, not conclusions. Does the issue stem from individual skills, insufficient resources, poor work allocation, or management quality? This requires HR professionals to go beyond operating software. They must be able to analyze data, identify bias, and distinguish correlation from causation.
The Six Cs are therefore not a collection of isolated principles but an integrated governance framework. If any one of these links is missing, the quality, credibility, and legitimacy of the entire system may be compromised. Together, they represent a common destination for all organizations, regardless of their size, resources, or stage of digital transformation.
(*) Dr. Pham Manh Hung & Dr. Tran Thi Nhung are from the Institute of Business Administration at the VNU University of Economics and Business.
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