Why This AI Finance Story Matters to You
For many Malaysian SME owners, finance work still means collecting invoices, checking transactions, updating spreadsheets, chasing approvals and preparing reports before a decision can be made. By the time the information is ready, the business situation may already have changed.
A recent article from OpenAI describes how its finance team is redesigning work around artificial intelligence, with ambitions including a “zero-day close” and continuously updated forecasting. The important lesson is not that every SME needs to copy a large technology company. It is that you can redesign finance around the decisions you need to make, instead of simply adding another tool to an old process.
For you, this could mean spotting a slow-moving product earlier, identifying an overdue customer follow-up, understanding why a branch is missing its target or testing whether a new hiring plan is manageable before committing to it. The practical opportunity is to move from reports that describe yesterday to workflows that help you act while there is still time to change the outcome.
What Happened
Sarah Friar, OpenAI’s Chief Financial Officer, explains that the company began with a small finance team supporting rapid growth. The team found that recurring work still involved manually finding information, explaining changes and assembling inputs for decisions. OpenAI then set two ambitions: a reconciled, traceable view of its financial position available immediately, and forecasting that updates continuously as new information appears. Source: OpenAI
The article makes clear that these ambitions are still being developed. The wider shift is already visible: static spreadsheets and presentation documents are being replaced by live tools connected to business context and source data. AI can prepare explanations, identify exceptions and bring evidence together, while finance professionals remain responsible for validation, judgment and final approval. Source: OpenAI
OpenAI highlights five lessons for finance leaders. The first is to give people access to AI while creating a clear reason to use it. The second is to redesign the complete workflow around the decision, rather than automating only one task. The third is to help finance professionals become builders who can create dashboards and tools for their own work. Source: OpenAI
The article also stresses accountability and dependable measurement. AI should not be treated as a replacement for review. It should work inside a controlled process where people know which information was used, which assumptions were made and who owns the final decision. Source: OpenAI
Why This Matters for Malaysian SMEs
Your finance process may involve several disconnected sources: a cloud accounting platform, bank records, e-commerce marketplaces, point-of-sale systems, payroll records, messaging apps and spreadsheets maintained by different people. When these sources are not connected, your team spends time searching and reconciling instead of investigating what the information means.
An AI-enabled workflow can help organise this process. For example, a distributor in Shah Alam could ask an internal assistant to prepare a weekly view of sales orders, outstanding collections, stock commitments and unusual transaction changes. A restaurant group in Penang could use a controlled workflow to compare outlet performance, flag unexplained changes and prepare questions for the manager. A professional services firm in Johor could connect project status, staff availability and approved work so that leadership can see which commitments require attention.
The goal is not to let an AI system approve transactions by itself. The goal is to make the first review faster and more consistent. You can require the system to show the original record behind each alert, identify the person who reviewed it and separate suggestions from approved actions. This creates a practical balance: automation handles repetitive preparation, while you and your team retain judgment.
The same approach can improve planning. Instead of preparing one static forecast at the start of a quarter, you can create scenarios around real business questions. What happens if a major customer delays an order? What happens if a supplier changes its delivery schedule? What happens if a campaign produces more enquiries than your team can handle? What happens if a key employee is unavailable during a busy period?
These questions are useful because they connect information to decisions. An AI assistant can gather relevant records, summarise the evidence and show which assumptions affect the result. You still decide whether the information is reliable and what action is appropriate.
“Begin with a consequential decision and work backward. Map the data, tools, approvals, and handoffs required to support it.” — Sarah Friar, OpenAI Source
A Practical Starting Point for Your Business
Do not begin by asking, “Where can we use AI?” Begin by asking, “Which decision is repeatedly delayed because information is difficult to assemble?” Choose one process that matters to the business and occurs frequently enough for improvement to be visible.
| Business challenge | Useful AI workflow | Human control |
|---|---|---|
| Unexplained changes in sales | Compare current records with approved targets and summarise unusual movements | Manager checks source records and confirms the explanation |
| Slow approval handoffs | Route requests to the correct person and prepare a concise approval brief | Authorised person approves or rejects the request |
| Unclear customer follow-up | Identify open conversations and draft next-step reminders | Staff review the message before sending |
| Manual reporting | Generate a recurring management view from approved data sources | Owner reviews definitions, exceptions and final interpretation |
Next, document the workflow from beginning to end. Note where the data comes from, who changes it, which approvals are required and what evidence must be retained. This step matters because automating a broken process can make mistakes happen faster.
Then create a small pilot using non-sensitive or carefully controlled information. Give the team a specific output to produce, such as an exception list, a management summary or a scenario comparison. Ask users to record inaccurate results, missing context and unnecessary steps. Improve the workflow before expanding it to other departments.
The Bigger Picture
The deeper message from OpenAI’s experience is that AI adoption is not mainly about purchasing a chatbot. It is about changing how work is designed. When the person closest to the problem can help shape the solution, the resulting tool is more likely to reflect the real workflow. OpenAI reports that finance professionals are increasingly using AI for work beyond traditional finance, including engineering-related tasks. Source: OpenAI
This is especially relevant for SMEs because your teams often wear several roles. The person managing accounts may also handle purchasing. The operations manager may prepare sales reports. The owner may be the final approver for nearly every important decision. A well-designed AI workflow can reduce repeated preparation and make expertise easier to apply across the business.
However, trust must be designed into the process. Keep approved source documents, define who can access sensitive information, require human approval for important actions and review the system’s output regularly. Your AI assistant should explain where an answer came from rather than producing an unsupported conclusion.
The best first step is therefore modest but deliberate: choose one recurring decision, connect the right information, define the approval rules and measure whether your team can reach a better-informed decision sooner. That is the practical path towards an AI-native finance function for a Malaysian SME—not replacing responsibility, but giving you clearer visibility while your choices can still shape what happens next.
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