AI Is Not the Best First Step for Every Business Decision

For many Malaysian SMEs, artificial intelligence appears most useful when it can read documents, understand customer messages and make decisions automatically. You may already be considering AI for invoice checking, customer support, loan-document review, supplier screening, recruitment or internal knowledge searches.

However, a recent VentureBeat article highlights an important design lesson: you should not send every uncertain case directly to a large language model (LLM). Instead, your system should first resolve straightforward cases using rules and structured data, then use document retrieval for cases requiring context, and only involve the LLM when genuine ambiguity remains. The approach described in the article reduced LLM inference usage by roughly six times in one system while improving consistency on routine decisions. Source: VentureBeat

This matters to you because an AI system is not automatically reliable simply because it produces fluent answers. A business decision should be explainable to your manager, auditor, customer, supplier or regulator. You should be able to answer a basic question: “Why did the system reach this decision?”

What Happened

The VentureBeat article examines retrieval-augmented generation, commonly called RAG. A RAG system searches a collection of documents or previous decisions, places the relevant material into the model’s context, and asks the LLM to produce an answer or classification. This is useful when your business information is spread across standard operating procedures, customer records, contracts, product catalogues and past cases.

The problem begins when every case is routed to the LLM. Routine matters that could be resolved through a simple lookup or structured comparison are unnecessarily treated as language problems. That can create inconsistent results, make decisions harder to reconstruct later and increase the amount of information your system must process. The article notes that sending all cases through an LLM creates challenges in auditability, operational scale and consistency on straightforward cases. Source: VentureBeat

The proposed solution is a cascade architecture with three stages. First, deterministic rules handle exact matches, clear criteria and structured field comparisons. Second, retrieval searches for evidence relevant to cases that remain unresolved. Third, only the remaining genuinely ambiguous cases are sent to the LLM. In the example described by the author, routing only the genuinely ambiguous ten to fifteen per cent of cases to the LLM produced an approximately six-times reduction in inference expenditure compared with sending all cases to the model. Source: VentureBeat

The most valuable AI decision is sometimes deciding that the AI model should not be involved.

Why This Matters for Malaysian SMEs

You do not need a large enterprise environment to benefit from this idea. Consider a Malaysian wholesaler checking purchase orders. If the supplier code, product code, quantity and approved price field match the order record, a rule-based workflow can approve the document or send it to the next step. There is no reason to ask an LLM to interpret a case that your existing business system can verify directly.

The same principle applies to service businesses. A cleaning company may receive requests through WhatsApp, email and an online form. A booking that matches an existing customer, service area, operating schedule and appointment slot can be processed using structured rules. Only messages containing unusual requirements, conflicting dates or unclear locations need document retrieval and possible AI interpretation.

For retailers and distributors, AI can help classify customer enquiries, but simple cases should be separated from complex ones. A message asking about delivery status can be matched against an order number and tracking record. A complaint involving damaged goods, repeated delivery issues or unclear warranty coverage may require retrieval of the relevant policy and previous cases before an LLM drafts a response.

This approach also supports better accountability. If a customer asks why a refund request was rejected, you can show that the order was outside the stated return period or that the required evidence was missing. If the decision came entirely from an LLM, your team may struggle to identify which document, phrase or assumption influenced the result.

Practical Examples for Your Business

Business task	Rule-based first step	When retrieval or AI is useful
Invoice checking	Match supplier, invoice number, tax fields and purchase order details	Investigate conflicting descriptions or unusual supporting documents
Customer support	Match order number, delivery status and standard response category	Handle complaints involving several policies or unclear facts
Staff leave requests	Check leave balance, dates and approval rules	Interpret unusual requests or conflicting internal policies
Supplier onboarding	Check required fields and document presence	Review ambiguous company documents or compliance concerns
Sales enquiries	Match product, stock status and service area	Recommend alternatives when requirements are incomplete

The table illustrates a useful boundary: use automation for facts your system already knows, and use AI for interpretation where the available information is incomplete or ambiguous.

Designing a Safer AI Escalation Process

When a case reaches the LLM, you should not ask a vague question such as “Approve or reject this request?” Give the model the relevant evidence, define the decision categories and explain what should happen when the evidence is incomplete.

For many business processes, the risk of a false approval is not the same as the risk of a false rejection. For example, incorrectly approving a suspicious supplier document may create a larger operational problem than sending a legitimate document for human review. Your workflow should reflect that difference. The VentureBeat article recommends treating uncertainty as a reason to escalate rather than automatically clear a case, and collecting a confidence score alongside the classification. Source: VentureBeat

For your team, this can be implemented as a simple escalation policy:

• Clear rule match: process automatically.
• Missing or conflicting information: retrieve the relevant policy or previous case.
• Unresolved ambiguity: ask the LLM to classify and explain the evidence used.
• Low confidence or high-risk decision: send the case to a named staff member.
• Human correction: record the final decision and add it to the approved knowledge base.

This design keeps human attention focused on cases that genuinely require judgment. It also helps you avoid a common mistake: assuming that the most advanced model should handle every task from beginning to end.

The Bigger Picture

The bigger lesson is that AI adoption is not only about selecting a powerful model. It is about designing a sensible decision system around that model. Your business may have years of useful information in spreadsheets, accounting software, CRM records, shared folders and email conversations. Before adding an LLM, identify which decisions can already be handled through reliable fields, approved lists and documented procedures.

You should also evaluate retrieval separately from final answers. If the system retrieves the wrong customer policy, contract clause or previous case, the LLM may still produce a confident response based on bad evidence. The source article recommends measuring retrieval quality independently and deliberately testing the cases that reach the LLM stage, rather than allowing routine cases to dominate evaluation results. Source: VentureBeat

For a Malaysian SME, a sensible starting point is one narrow workflow. Choose a process with clear inputs, repeated decisions and a measurable review burden. Map its rules, identify exceptions, create an escalation path and keep a record of human corrections. Once the process is stable, you can expand to another department.

AI becomes more useful when you give it fewer, better-defined jobs. Start by deciding what should never reach the model. Then make the model responsible only for the decisions that truly need interpretation.

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