In our recent training programs on AI applications, we observed a common reality: departments are overloaded with administrative tasks, reporting, procedures, and decision-making in an increasingly volatile environment. Leadership wants innovation, employees want less pressure, but the recurring question is: Where can AI be applied concretely in management and operations, and how can it avoid becoming another burden?

The answer is: start with core activities that are tightly connected to daily operations in each department—from logistics, finance, HR, to customer service. And we have seen very tangible results across many Vietnamese enterprises.

Applying AI in Operations Management – Beyond Just Reporting

Looking at the bigger picture, operations management covers a wide range of activities: planning, control, resource optimization, ensuring smooth processes, and cross-departmental coordination. AI can “touch” almost all of these areas:

• Sales & Marketing: AI forecasts revenue, analyzes customer behavior, and suggests sales strategies.
• Operations/Logistics: AI optimizes delivery routes, automatically allocates containers, and forecasts transportation demand.
• Finance & Investment: AI analyzes portfolios, flags risks, and supports investment decisions based on big data.
• Human Resources: AI chatbots for recruitment, resume screening, automated payroll/bonus reports, and attrition forecasting.
• Customer Service: 24/7 chatbots, customer sentiment analysis, and AI-generated suggestions for complaint resolution.

Case 1: Logistics Company – From Manual Operations to AI Support

In a recent project with a large logistics company, we began with a familiar problem: operational reporting took far too long. Every day, the team had to compile data from multiple sources—warehouses, trucks, containers, and orders—taking 4–5 hours just to produce a report for management.

We helped them deploy AI to automate operational reporting and forecast transportation demand. The system generated dashboards in minutes, flagged routes at risk of delays, and alerted managers when warehouses were nearing capacity.

Results:

• Reporting time reduced by 80%.
• Operations staff gained time to address real issues instead of “drowning in numbers.”
• Management made faster decisions, reallocating trucks and manpower in real time.

One manager shared: “Before, I worried every day waiting for reports. Now, I just open my phone and see real-time data.”

Case 2: Investment Management Company – AI in Investment Decisions

At a leading investment management company in Vietnam, the challenge was different: massive but fragmented data. Analysts spent weeks consolidating financial reports, market news, and index forecasts.

We implemented AI to automatically analyze investment portfolios, detect risks, and suggest capital allocation scenarios. Instead of scrolling through hundreds of reports, analysts could now review AI-generated dashboards in minutes: which stocks were behaving abnormally, which sectors showed risk signals, and which portfolios needed rebalancing.

Results:

• Analysis time dropped from days to just hours.
• Analysts no longer “drowned” in data, focusing instead on strategy discussions.
• Executives made decisions with more confidence, backed by clear AI-driven scenarios.

One executive said: “AI doesn’t replace analysts, but it helps us see the bigger picture more clearly and faster.”

How to Prevent Employees from Feeling Overwhelmed

From both projects, we learned: AI only works when introduced through small, practical steps aligned with daily tasks.

• Start with one specific application: an operations report, an investment dashboard, or an internal chatbot.
• On-the-job training: let employees practice directly with real company data.
• Integrate into familiar platforms: Excel, Teams, or existing ERP systems—avoid forcing employees into sudden change.
• Scale through small wins: share success stories from one department to inspire adoption across the company.

Conclusion

Applying AI in management and operations is not a distant future. It already exists in daily reports, meetings, and decisions within Vietnamese enterprises.

The key is not “what AI can do,” but where businesses choose to start and how they apply it to deliver tangible results. Sometimes, just an automated report, an internal chatbot, or an AI-driven investment dashboard is enough to spark a transformation journey.

Wishing you success,

Lead-UP Academy | Learn to Act – Act to Lead