AI Accounting: The Journey from Dashboard to Automation
On August 22, 2026,
100×100 AI Startup × 100 Potential Customers took place at SIHUB – Ho Chi Minh City Innovation Startup Hub, bringing together Founders, Business Owners, C-level executives, Managers, startups, and businesses developing AI solutions.
What stood out about the program was not simply the number of AI solutions presented, but the question behind each one:
What business problem is AI actually solving?
For accounting, this question becomes even more relevant. An AI model may be able to process data quickly, but for AI to truly become part of accounting workflows, businesses need more than a language model. They need standardized data, well-defined processes, and professional knowledge translated into a system.
This was also the perspective shared at the event through the story of
AI Accounting: the journey does not start with AI. It starts with data, dashboards, and automated accounting.
1. Top 100 AI Startups: When AI Meets Real Business Problems
100×100 AI STARTUP × 100 POTENTIAL CUSTOMERS was not organized as a traditional technology conference.
The program brought AI startups closer to real business needs through presentations, product experiences, and direct conversations between startups, Founders, C-level executives, Managers, and potential customers.
This approach reflects an ambition to build a national AI infrastructure, with local data serving as a core competitive advantage.
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McKinsey survey also shows that AI adoption has become widespread across businesses: 88% of respondents said their organizations were regularly using AI in at least one business function. However, nearly two-thirds of organizations had not yet begun scaling AI across the enterprise, while only around one-third reported having started to scale their AI programs.
This highlights the significant gap between “using AI” and “integrating AI into actual business operations.” AI may already be present in individual tasks, but turning it into part of a core workflow requires businesses to redesign processes, standardize data, and build the right infrastructure for AI to operate reliably at scale.
2. Different Perspectives, One Common Challenge
With 13 speakers from different fields, the program offered perspectives on building AI startups, applying AI in marketing and operations, AI-driven companies, Agentic AI, practical GenAI applications, and investment strategies for startups.
Despite coming from different industries, the sessions ultimately revolved around one question:
How can businesses turn AI from emerging technology into a solution that creates real value?
The answer does not lie solely in the power of the model. It lies in the ability to define the right problem, standardize the underlying data, and build processes that allow AI to participate in the work reliably.
For accounting, this is almost a prerequisite. An AI tool may produce an answer that sounds perfectly reasonable, but accounting cannot operate on the basis of something that merely “sounds reasonable.” Financial data needs a clear basis, processes must be consistent, and results must be explainable.
3. AI Accounting in Practice: “We Teach AI to Do Our Profession”
One of the notable sessions at the event was delivered by Genbook, under the topic:
“We Teach AI to Do Our Profession - The Story of Accountants.” This framing offers a different perspective on AI Accounting. Instead of starting with technology, the story starts with the accounting profession itself: where the data comes from, how it is processed, which tasks can be automated, and which still require professional judgment.
3.1. Some Industries Simply Make Accounting Harder
Accounting is not equally complex across every industry. For a traditional domestic business, a transaction may often correspond relatively directly to revenue and cash flow. But in e-commerce accounting, particularly for cross-border e-commerce, a single order can involve multiple layers of financial data.
An order on a marketplace can generate gross revenue, platform fees, payment processing fees, PPC costs allocated to a product, potential returns in a subsequent period, foreign exchange differences, and a settlement cycle in which funds are transferred to a wallet days later. One order, therefore, does not necessarily correspond to a single accounting entry.
Multiply that by thousands of orders every month across platforms such as Amazon, Shopify, or Etsy, multiple markets, and multiple currencies, and financial accounting shifts from recording transactions to reconciling data. Manual reconciliation is also where e-commerce businesses can spend significant time and face a high risk of discrepancies. Data from marketplaces, payment wallets, advertising systems, and accounting software needs to be connected. A single missing fee or transaction recorded in the wrong period can cause the period-end figures to no longer reconcile.
This is precisely why automated accounting has become an important direction for e-commerce businesses. When a process is sufficiently complex, repetitive, and resource-intensive, continuing to handle it manually is no longer the most efficient option.
3.2. Dashboard → Automation → AI: Three Layers That Cannot Be Reversed
AI Accounting does not start with AI. The journey toward an AI Accounting system can be viewed through three layers, each creating a different type of value:
The first layer is consolidating fragmented data from multiple marketplaces, wallets, and currencies into one system. The value of a dashboard is not simply better data visualization. It gives businesses a centralized source of data instead of requiring them to manually consolidate information from multiple reports and spreadsheets. This becomes the foundation for accounting automation.
Once data has been consolidated, repetitive tasks can be automated. Instead of accountants repeating the same sequence of tasks every month, the system can execute these activities according to predefined workflows. This is the essence of automated accounting: not eliminating accountants, but removing unnecessary manual work from the accounting process.
AI can only deliver meaningful value once the two foundational layers are sufficiently robust. At this stage, AI can help identify unusual transactions, detect discrepancies between marketplace data and accounting records, suggest potential treatments, or flag transactions that require review. The value of AI lies in helping accountants identify issues earlier, rather than simply dealing with the consequences after the accounting period has closed.
AI Accounting built on unstandardized data can simply make errors happen faster. A tool cannot automatically fix a system that records data incorrectly. Instead, it can replicate those errors at a larger scale, making them harder to trace and resolve.
3.3. Why AI Accounting Must Be Taught by Accounting Professionals
A general-purpose AI model can certainly produce an accounting entry that sounds highly reasonable. But “sounds reasonable” is not the standard for accounting. The standard is whether the treatment aligns with accounting standards, tax regulations, and can be explained when questioned by auditors or investors.
This is the gap between
AI that can process information and
AI that understands how the profession works.
Accountants know how a marketplace fee should be reflected in the Profit and Loss Statement (P&L). They understand when revenue should be recognized, how returns should be handled, and how to reconcile figures across different systems. This is professional knowledge. To build
AI Accounting, that knowledge needs to be translated into rules, logic, and workflows that a system can execute.
Therefore, AI Accounting is not about replacing accountants with machines. It is about accountants codifying professional rules into a system, allowing technology to handle repetitive work while people retain responsibility for judgment and accountability.
As the idea was framed at the event: accountants are simply doing what they have always done -
practicing their profession with the tools of their time.
4. AI Experience: From Technology to Practical Solutions
The AI Experience area gave attendees an opportunity to explore and discuss AI products from various startups and businesses.
Rather than simply hearing about AI, participants could interact with concrete solutions for marketing, operations, customer service, automation, data, and growth.
For e-commerce businesses, the questions at the booths were highly specific: "Can marketplace data be pulled automatically?", "How should platform fees and advertising costs be allocated by SKU?", "Can the final figures be exported into financial reports that are suitable for audit purposes?"
These questions show that the market is moving beyond the stage of simply being curious about AI and into a stage where businesses are demanding measurable outcomes. For accounting, this is also the line between an AI-powered demo and an automated accounting system that can actually operate through the month-end closing process.
5. Genbook: The Foundation for Automated Accounting and AI Accounting
For cross-border e-commerce businesses, the problem is rarely a lack of data.
The problem is that data is scattered across multiple marketplaces, wallets, and currencies, then manually consolidated through spreadsheets. As a result, financial data arrives late, and business decisions are always one step behind reality.
Genbook consolidates sales data from e-commerce platforms into a unified system and standardizes the data in line with accounting standards, rather than simply displaying figures on a dashboard. This allows businesses to generate financial statements and general ledgers suitable for audit purposes, while tracking profitability by SKU after accounting for platform fees, PPC costs, and returns. More importantly, this standardized data provides the foundation for the next layers of automation and AI Accounting to operate reliably.
Try Genbook free for one month to bring your e-commerce data into a standardized accounting system.
