Businesses that know how to follow the right AI application roadmap – start small, scale smart, measure continuously – will be the pioneers in the AI era in Vietnam.
This article by Bizzi will serve as an “AI journey starter guide” for Vietnamese businesses:
- Guide to deploying AI in businesses as well as AI application roadmap
- How to apply artificial intelligence
Context: Vietnamese businesses are facing an “AI turning point”
According to a McKinsey survey, 63% global businesses have applied at least one AI technology to their operations – from process automation, customer care to advanced data analysis. This shows that the AI application roadmap is no longer a future trend, but has become a current competitive advantage in management and decision making.
In Vietnam, AI implementation guidance in businesses is considered the "next step of digital transformation", inheriting the stage when businesses have invested in ERP, CRM or data platforms.
However, most businesses are still struggling and facing difficulties. difficulties in implementing AI due to the following reasons:
- Lack of standardized data → cannot train or apply AI models effectively.
- Lack of specialized personnel → internal team does not have knowledge of data and machine learning models.
- Lack of a clear AI roadmap → businesses do not know which problem to start with and what scale is appropriate.
The current stage is the decisive moment:
- Businesses that proactively adopt AI early will create advantages in productivity, costs and speed of decision making.
- Any business that hesitates or implements in the wrong direction will fall behind, no matter how much technology investment it makes.
7-Step Process to Guide AI Implementation in Business
How to apply artificial intelligence is not just a technology problem, but a comprehensive transformation strategy related to data, people and processes. Below is a 7-step process to help Vietnamese businesses start their AI journey systematically and effectively.
Step 1: Assess readiness (AI Readiness)
Before investing in technology, businesses need to examine their current capabilities in four aspects: data, infrastructure, human resources, and budget.
The question to ask:
- Is the data clean, centralized, and uniformly managed?
- The business already has a system ERP or EPM to connect and standardize data yet?
- Are internal staff able to read, understand and use AI analysis results, and have a good grasp of how to apply artificial intelligence?
For example, a manufacturing company has sales data scattered across three different software. When the AI model learns on inconsistent data, the raw material demand prediction results are incorrect → production planning is affected.
Step 2: Define the problem and AI goals
Instead of “chasing the AI trend”, start with a real problem that can be measured effectively.
Some specific examples:
- Forecast inventory needs to optimize imports and exports.
- Automate invoice checking and accounting reconciliation to reduce manual errors.
- Optimize marketing campaign performance with customer behavior prediction models.
Each problem needs to be associated with a clear KPI and ROI (e.g., reduce 20% in costs, increase 30% in productivity).
Step 3: Data Preparation
AI is only effective when the “fuel” is clean data. In fact, up to 80% implementation time is often spent on this step. Enterprises need to build a tight Data Governance process:
- Verify data validity and origin.
- Eliminate duplicate or misformatted data.
- Clear access permissions for security and legal compliance.
Real Case: It took a financial company nearly 6 months to clean 5 years of accounting data before the AI model could be trained accurately.
Step 4: Choose the right AI technology and model
Depending on the goals, businesses can choose between:
- Traditional AI (Machine Learning): Suitable for prediction, classification, behavior analysis.
- Generative AI (LLM-based): Suitable for content creation, semantic analysis, report synthesis.
Popular platforms: Google Cloud AI, Azure AI, OpenAI API, IBM Watson, or Bizzi AI – a solution combining RPA and finance for Vietnamese businesses.
Additionally, it is necessary to decide how to apply artificial intelligence: AI-as-a-Service (cloud) to save costs, or on-premise (in-house) if high security is required.
Step 5: Testing (Pilot Project)
Don't take a comprehensive AI roadmap from the start. Choose 1-2 small use cases to test, verify the model (Proof of Concept).
Track metrics:
- Data processing time.
- Model accuracy.
- Performance and cost savings.
After 3-6 monthsIf the pilot project proves effective, it can be expanded to the entire enterprise.
Step 6: Measure and optimize performance (KPI & ROI)
How to apply AI artificial intelligence is only valuable when make a real impact for business operations. Some commonly used indicators to evaluate performance:
- Reduce data processing time from 50% and above.
- Reduce operating costs 20-30%.AI is gradually becoming the “digital right arm” of every department in the enterprise. From finance, marketing to production and customer care – every department can take advantage of AI to automate, optimize processes and improve decision-making capacity.
- Increase productivity of finance or FP&A teams 30%.
Compare before and after implementing AI methods to quantify the impact, thereby optimizing the model and operating process.
Step 7: Expand and integrate the entire system
Once the AI application roadmap achieves stable results, businesses can expand the application and deeply integrate into the existing ecosystem:
- Connecting AI with ERP, CRM, EPM to form a unified data stream.
- Internal training to foster an “AI-driven” culture – decision-making based on data and technology.
- Setting up the mechanism monitoring, security, and algorithmic risk management, ensuring AI operates transparently and responsibly.
How to apply artificial intelligence in business departments
AI is gradually becoming the “digital right arm” of every department in the enterprise. From finance, marketing to production and customer care – every department can take advantage of applying AI to automate, optimize processes and improve decision-making capacity.
Finance – Accounting: Standardize data & accelerate analysis
How to apply artificial intelligence:
- Automate document & invoice processing (Invoice OCR, RPA): AI identifies, compares and records accounting data in just seconds.
- Fraud Detection: AI learns from unusual transaction patterns to provide early warning of discrepancies or fraud risks.
- Financial Planning & Forecasting (AI Forecasting): Machine learning models predict cash flow, costs, and revenue under multiple scenarios.
Benefits:
- Reduce 70-90% manual data entry time.
- Increase financial reporting accuracy and make decisions faster.
- Reduce the risk of error and fraud with automated monitoring.
For example, many Vietnamese businesses have taken advantage of Bizzi's invoice processing feature to automatically read and reconcile electronic invoices – saving hundreds of hours of processing time each month.
Marketing & Sales: Personalizing the Customer Experience
How to apply artificial intelligence:
- Customer Segmentation & Behavior Analysis: AI segments customers based on purchasing behavior, engagement, and spending frequency.
- Optimize ad campaigns: AI predicts the channels, timings, and content that drive the highest conversions.
- Chatbot & Generative AI: Create advertising content, marketing emails, or automatically respond to customers using natural language.
Benefits:
- Increase conversion rates with personalized campaigns.
- Reduce marketing costs through smart budget allocation.
- Create seamless and fast customer experiences.
For example: A cosmetics brand uses ChatGPT API to automatically reply to inbox, helping to respond to customers within 5 seconds - increasing the closing rate by 40%.
Manufacturing & Logistics: Forecast, Optimize and Reduce Waste
How to apply artificial intelligence::
- Demand forecasting & inventory management: AI analyzes sales trends, predicts the amount of raw materials needed to be imported.
- Predictive Maintenance: AI detects early signs of equipment failure to prevent downtime.
- Optimize transportation & supply chain: AI calculates optimal routes, shipping schedules, inventory levels.
Benefits:
- Reduce 15-25% in inventory and operating costs.
- Increase production forecast accuracy and reduce downtime.
- Improve supply chain efficiency.
For example: FMCG business uses AI to forecast sales demand by region → reduces 20% of inventory and increases 10% of sales.
Human Resources (HR): Recruiting and Developing Talent Smarter
How to apply artificial intelligence::
- Automatic profile screening: AI reads CVs, assesses skills, and ranks candidates according to suitability.
- Performance Evaluation & Training Recommendations: Analyze KPIs and work behavior to suggest personalized courses.
- Attrition Prediction: AI detects early risk of leaving the job to have appropriate retention policies.
Benefits:
- Shorten 50% recruitment time.
- Improve the quality of input human resources.
- Building a “data-driven HR” culture helps retain and develop sustainable employees.
Customer Care (CSKH): Improve experience - Reduce costs
How to apply artificial intelligence::
- Chatbot and virtual assistant 24/7: Answer questions, guide customers, handle basic requests instantly.
- Sentiment Analysis: AI recognizes emotions in customer feedback to adjust service strategies.
- Automatically record and classify feedback: The system automatically creates tickets and assigns them to the correct processing department.
Benefits:
- Reduce 40-60% of manual workload of customer service staff.
- Increase customer satisfaction (CSAT) and retention rate.
- Enables businesses to respond instantly and consistently across multiple channels.
5 Common Mistakes When Deploying AI in Businesses
AI offers great potential, but many businesses “stumble” during implementation due to lack of proper strategy and management. Below are 5 most common mistakes causing many AI projects to fail or not be as effective as expected.
Following trends without a clear strategy
Many managers start to guide the implementation of AI in the enterprise just because “everyone is doing it”, not based on real needs. Without an overall AI vision and strategy, leading to a situation where:
- Each department works in one direction, no data connection.
- The project was “frozen” after the testing phase because no concrete results were seen.
The consequences are lWaste of resources, investment costs that do not bring business value.
Missing or unclean data
AI is only smart when the data is good enough. However, many Vietnamese businesses today still store data in fragmented, non-standardized formats or with errors in time and source.
For example, a CRM system stores customer data missing the zip code, while an ERP stores it in a different format – skewing the predictive purchasing behavior model.
Pilot project without specific KPIs
Many businesses deploy AI experiments (PoC) but no defined KPI measurement: What is "success" and what is "effective"?
As a result, after a few months, the project failed to prove its value → it was difficult to convince the management to expand.
For example: Make a chatbot without measuring CSAT (satisfaction level), response time, or conversion rate from interactions.
Too much dependence on third parties, no internal training
Businesses often hire AI providers for the entire package, but do not invest in training internal staff to operate, understand and adjust the model.
When the project ends, the internal team cannot maintain or expand the AI application → “AI dies young” after handover.
No security and monitoring mechanism for AI models
AI uses large amounts of internal data – from customer information to financial information – but many businesses lack access controls or model oversight. Additionally, AI can produce misleading or biased results if not monitored regularly.
Bizzi – Solution for accounting and finance departments in the journey of digital transformation and AI application
Bizzi is a digitalization and automation platform for financial and accounting processes for Vietnamese businesses, with a focus on supporting the finance department to transition from manual operations to “data-driven” and “AI-enabled”.
Bizzi's platform integrates modules such as: e-invoice processing, expense & budget management, debt management, corporate card integration, payment automation and real-time data analytics.
Bizzi also applies technology such as RPA (Robotic Process Automation), AI (especially invoice processing, anomaly detection) and integrates with existing ERP systems.
Why finance departments need Bizzi when implementing AI
When you read the description AI implementation journey As for “starting from data, process, people”, Bizzi meets data platform + process for finance department.nInstead of starting from “buying big AI software” right away, Bizzi allows to start from automating invoices, cost management & budgeting – the prerequisite step for AI.
For example, Bizzi Bot automatically identifies and processes invoices; budget management dashboard displays real-time; unusual spending alert feature – all help standardize data, make processes transparent, and create “fuel” for AI models.
Supports integration with existing ERP and accounting systems, helping the finance department not have to change the entire system immediately but still upgrade the digital transformation orientation.
Outstanding features supporting digital transformation & AI
- Invoice Processing Automation (IPA): Scan, extract data, compare with PO/warehouse, transfer data to accounting software or ERP. Bizzi recorded a reduction of up to ~80% in processing time and cost of only ~10% compared to manual method.
- Budget & Cost Management: Set up budgets by project, department or cost center; track actual/planned spending; alert when spending exceeds limits.
- Automatic control of debt & cash flow: Track debt aging (DSO), reconcile debt, automatic debt reminders, help improve operating cash flow.
- Corporate credit card integration & payments: Helps control card spending, integrate spending data into the Bizzi platform, helping accountants focus on analysis rather than data entry.
- Dashboard & real-time analytics: Visually displayed financial indicators help CFOs and finance teams make decisions faster, avoiding having to wait for weekly/monthly reports.
- Automatic anomaly detection and risk warning: Bizzi applies technology to warn early about invoice discrepancies and cost overruns, helping the finance department be more proactive.
How Bizzi Supports the AI Roadmap for Finance
- Start-up step (Data & Process Readiness):
- Use Bizzi to centralize & standardize data from invoices, expenses, budgets, and accounts receivable.
- Set up processes to automate repetitive tasks (data entry, checking, reconciliation) – enabling AI models later.
- Testing Step (Small Pilot Use-Case):
- Pick 1–2 scenarios like “automate incoming invoice processing” or “manage project cost budgets” to measure performance with Bizzi.
- Set KPIs (reduce processing time, reduce errors, increase accuracy) before scaling.
- Advanced AI Integration & Scaling Steps:
- Once the data is clean and the process is good, businesses can combine Bizzi with AI models to analyze cost forecasting, detect fraud, and analyze cash flow trends.
- Bizzi acts as a “data source & platform” for AI models, helping AI operate more efficiently.
- Measurement & Strategic Decision Making:
- Use Bizzi's dashboards and analytics to track AI application performance (e.g., reduce % time, reduce costs, speed up decision making).
- Adjust processes, resources, and strategies based on real data.
- Nurturing a data culture & AI-mindset:
- Finance departments need to change from “data entry” to “data-driven analysis and decision making”. Bizzi helps save manual time, allowing finance staff to focus on analysis.
- Train your team to use Bizzi and understand how to read dashboards, understand analytics – and then deploy complex AI models.
Clear benefits for the accounting-finance department
- By drastically reducing manual workload, accounting staff can shift from data entry to analysis and strategic consulting. For example: “Bizzi frees the Accounting – Finance department from 50-80% of manual work time” for a distribution/retail business in Vietnam.
- Increase accuracy and reduce the risk of error and fraud by automating invoice and expense checks.
- Faster decision-making times and more transparent data help CFOs/executives grasp financial health in seconds instead of waiting for manual compilation.
- Optimize cash flow and costs: Better manage debt, costs, and budgets, helping businesses compete in the digital age.
Notes when implementing Bizzi
Although Bizzi has supported automation and AI applications, businesses Don't expect "one time buy, one time run" but need a clear roadmap – start small, measure, then expand. DInput data must be clean, standardized, and well-governed before any AI model can be applied. Bizzi helps with this, but businesses still need to proactively build data governance. Finance staff also need to be trained to apply new tools, understand analytics, and transition from manual to digital operations.
It is necessary to set clear KPIs for Bizzi + AI implementation (e.g. reduce % invoice processing time, increase accuracy, reduce costs, increase decision speed) – avoid falling into “try and stop” because there is no measurement index. Pay attention to data security, compliance with e-invoice laws, expense management – Bizzi has supported related functions but businesses still need internal policies.
Conclude
Guide to implementing AI in business is not only a tool to optimize performance, but also a strategic leverage helping businesses make data-driven decisions, improve customer experiences, and maintain competitive advantage.
Which business knows? Integrating AI across departments, instead of just discrete applications, will be the people who actually step in Intelligent Enterprise.
However, AI cannot create value if businesses There is no standardized data, digital processes and internal culture ready for changeNo matter how advanced an AI implementation roadmap is, it will fail if the data “inputs” are fragmented, or if the team does not understand how to use the analysis results to make decisions.
Therefore, the roadmap for effective AI application must start from 3 pillars:
- Data: clean, centralized and tightly managed.
- Strategy: clearly define the problem, KPI and ROI of each stage.
- People & Culture: A team ready to apply technology and data-driven decision-making.
In particular, Bizzi plays a strategic role in helping Vietnamese businesses, especially the finance and accounting department, build a solid data foundation and automate core financial processes. Once data and processes have been digitized with Bizzi, businesses can easily integrate AI models to analyze, forecast and make more accurate financial decisions, thereby optimizing operational efficiency and creating sustainable competitive advantages in the AI era.
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