Following the class for personnel of the Party Committee and Vietnam Fatherland Front, Lead-UP Academy of Practical Management, in collaboration with MobiFone Đắk Lắk – a branch of MobiFone Telecommunications Corporation, delivered the training course “Applying AI in Public Administration” for officials, civil servants, and public employees working within the People’s Council and People’s Committee of Quảng Phú Commune, Đắk Lắk Province. This class also concluded the training programme held on 29–30 September 2026, which aimed to translate technological knowledge into improved workplace effectiveness at the local level.

Applying AI to enhance productivity and service quality

The work of commune-level People’s Councils and People’s Committees involves multiple concurrent requirements: reviewing documents, synthesising information, preparing reports, monitoring tasks, coordinating the processing of administrative applications, and providing information to residents. These activities are closely interconnected; data quality and progress at one stage can affect subsequent stages.

For work supporting the People’s Council, the requirements include compiling comprehensive documentation, systematically organising comments and recommendations, and providing substantiated information for deliberation and oversight. For the People’s Committee, productivity improvement involves completing work on schedule, preparing accurate reports, maintaining clear task tracking, and providing residents with accessible guidance.

In this context, repetitive operations such as reviewing numerous documents, converting notes into draft meeting minutes, consolidating data tables, or preparing different versions of notices can consume considerable working time. AI offers opportunities to support these steps, enabling officials to focus more on substantive review, addressing specific situations, and coordinating the resolution of work-related matters.

However, productivity must be considered alongside quality. A rapidly generated draft that lacks supporting evidence or contains inaccurate figures may increase the time required for revision. The programme therefore emphasised the skills needed to use AI within a structured workflow: appropriate inputs, clear instructions, verifiable outputs, and defined responsibilities at each stage.

Building understanding through opening activities and developing skills through practice

The course was delivered by Mr Phạm Tô Hoài – Founder of Lead-UP Academy of Practical Management, consultant and training specialist. Drawing on his experience in delivering AI application training in communes across Đà Nẵng City, the instructor linked the tools to specific responsibilities undertaken by commune-level officials.

The “Warm-up – Overcoming Obstacles” activity opened the class with questions exploring foundational AI concepts. Through interactive participation, learners progressively clarified the capabilities of the tools, the limitations requiring attention, and the user’s role in maintaining control over information.

Building on this foundation, the programme’s five thematic areas were delivered in sequence, combining explanation, live demonstrations, practical exercises, and question-and-answer sessions.

Giving AI clear instructions to improve output quality

In the section on understanding AI in the context of public administration, participants were guided in identifying suitable task categories, selecting input data, and determining the stages requiring human review and approval. Information security, sensitive information, and source verification were integrated into scenarios involving the use of AI tools.

The section on techniques for using AI effectively focused on the CRAFT+ prompt structure. Rather than issuing a general request such as “write a report,” participants were guided to specify the reporting period, data sources, intended audience, priority content, output structure, and procedures for handling missing information.

A three-round refinement process helped participants understand a workflow incorporating review: generating an initial draft, asking AI to identify shortcomings, and then refining the output under the user’s supervision. Guidance on configuring AI assistants extended this approach to recurring task categories, such as compiling weekly reports, preparing meetings, summarising documents, and reviewing drafts.

Connecting AI with documents, data, and advisory work

In the section on document, records, and data processing, practical scenarios involved drafting notices, invitations, meeting agendas, reports, and minutes. Participants were guided to extract tasks, responsible focal points, and deadlines from documents; convert notes authorised for use into structured drafts; and flag missing information rather than allowing the tool to supply it independently.

For administrative application data, the training emphasised accurately defining the scope, reporting period, and calculation methods before aggregation. Users need to check figures against their original sources, distinguish factual information from interpretation, and clarify the issues requiring attention. This approach aims to support reporting and monitoring on the basis of information that can be cross-checked.

In the section on information synthesis and advisory support, participants were introduced to using NotebookLM to work with document collections, extract requirements, compare responsibilities, and develop task-tracking tables. Drawing on selected sources, AI can assist in preparing a summary or a draft one-page advisory brief outlining the issue, supporting basis, proposals, and matters requiring consultation.

For work supporting the People’s Council, this approach can assist in organising documents, synthesising comments and recommendations, and preparing information for oversight activities. For the People’s Committee, the content was linked to monitoring progress, consolidating implementation challenges, and preparing options for carrying out assigned tasks. All conclusions and proposals must be checked against the original documents, the scope of assigned responsibilities, and the relevant authority to act.

Communicating clearly to improve public access to information

The section on AI in communication and digital content creation focused on outputs relevant to local activities: guidance on online public services, notices, Zalo posts, broadcast scripts, and short videos.

Participants were guided to identify their intended audiences, select appropriate messages, and translate specialised content into accessible language. For information intended to guide residents, details concerning required documents, timing, links, and contact points must be verified before publication. This also offers a means of improving service efficiency: clear information helps residents understand requirements accurately while reducing the need for officials to repeat explanations arising from incomplete or difficult-to-understand content.

Live demonstrations and practical support in the classroom

In each section, the instructor demonstrated the process from input data to draft outputs. Participants completed guided exercises, discussed their results, and adjusted prompts when outputs did not meet the requirements. Support addressed both tool operation and output evaluation: whether the content was complete, whether figures matched the sources, which interpretations required verification, and which elements required consultation with an authorised decision-maker. Question-and-answer sessions helped participants connect their learning with the task categories associated with their current roles.

The guiding principle throughout the programme was that AI supports information processing, while officials review, refine, and remain accountable for the resulting work products. This is a prerequisite for technology adoption to contribute to sustainable productivity improvement.

Concluding the programme and continuing workplace practice

The review and closing session consolidated the core knowledge and skills while providing direction for application after the course. Participants were encouraged to begin with a recurring task that has a clearly defined output, use appropriate data, and record areas for adjustment after each practice session.

Following the programme, Mr Phạm Tô Hoài shared with Lead-UP Academy’s Media Team: “After the class, each participant can select one task to practise regularly. Monitor the processing time, the quality of the results, and the errors requiring correction. Only when work is completed more quickly and with greater clarity, while appropriate controls remain in place, do we have a basis for assessing the value that AI delivers.”

The Zalo learning group is being maintained to share guidance, receive questions, and support practice after the programme. In this way, learning remains connected to application within participants’ agencies and organisations. Lead-UP Academy sincerely thanks MobiFone Đắk Lắk for its collaboration, the leadership of Quảng Phú Commune for its support, and all participants for their engagement in the two training classes.

Partnering to strengthen workforce capabilities and productivity

Building on the programme in Quảng Phú Commune, Lead-UP Academy continues to pursue a work-oriented approach to training: identifying specific needs, practising with relevant scenarios, and supporting learners in translating knowledge into applied skills.

Lead-UP Academy welcomes collaboration with public agencies, organisations, businesses, and technology partners to deliver programmes that develop digital capabilities, strengthen AI application skills, and enhance work productivity. Programmes can be tailored to specific job categories, workflows, and organisational objectives, linking learning to work products that can be reviewed, evaluated, and progressively improved.

If your organisation is seeking solutions to help its team use AI more effectively, connect with Lead-UP Academy to jointly develop a suitable programme - from identifying tasks requiring improvement to practical training and support for post-training application.

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