Demystifying ai for finance: is prompt engineering just clear communication?

Summary:

• If you can provide clear instructions to a junior professional, you can already prompt an ai effectively.

• Your expertise in writing standard operating procedures (sops) and month-end checklists makes you a natural at structuring ai inputs.

• Vague requests lead to generic answers, while precise financial constraints yield audit-ready results that satisfy internal controls.

• Use the role, task, and constraint framework to immediately improve the quality and reliability of ai outputs.

• Mastering this skill amplifies your existing excel and erp expertise rather than replacing the human judgment required in modern finance.

If you have ever drafted a standard operating procedure (sop), reviewed a complex variance report, or trained a junior analyst in your team, you already possess the foundational skills required. You do not need to master programming languages like python to unlock the value of generative ai. Instead, you simply need to apply the same rigorous logic you use every day during the month-end close or the preparation of annual accounts. This article will demonstrate how to bridge the gap and transform ai into a highly efficient assistant that complements your existing workflow.

Why prompt engineering sounds more complex than it is

There is a common misconception within Dutch boardrooms and shared service centers that effective use of generative ai requires a deep technical background. This is not the case. Tools like chatgpt or microsoft copilot do not require you to speak in code. They respond to natural language, though they interpret that language quite literally.

In the Dutch financial sector, where precision and "in-control" statements are paramount, this literal interpretation is actually an advantage. Finance professionals are already trained to think like engineers. You operate in a world of debits and credits where everything must balance. You work with rj (raad voor de jaarverslaggeving) or ifrs standards where definitions are exact. You build sophisticated financial models where a single incorrect reference can compromise the entire dataset. These traits (logic, specificity, and process orientation) are the exact requirements for high-quality prompt engineering.

The overlap between sops and prompts

Consider the structure of a standard operating procedure for a bank reconciliation. You would never simply instruct a colleague to "check the bank." Instead, you would provide a structured sequence: "download the statement from ABN Amro or ING, match transactions against the erp ledger for the current period, and flag any discrepancies exceeding a specific threshold."

That sop is, in essence, a perfect prompt. You are already performing the intellectual labor. You simply need to direct that logic toward a new digital interface. We are currently witnessing a significant shift in the market where ai is moving from a mysterious technology to an everyday finance tool. It is following a similar trajectory to excel, which eventually replaced the physical ledger book.

Industry insights suggest that the ability to communicate with ai is becoming a top-tier professional skill. This is not because the skill is inherently difficult to learn, but because few professionals realize they already have the foundation. Writing software code requires learning new syntax, but writing prompts requires applying your existing procedural thinking to a digital chat interface. By treating ai as a logic engine, you can blend your professional knowledge with the speed of modern automation.

The "garbage in, garbage out" principle in finance

In financial modeling, if your inputs are flawed, your forecast becomes redundant. The same golden rule applies to ai interactions. This principle is the primary reason why some finance teams fail to see immediate value in their initial ai experiments.

Vague prompts almost always fail. If you ask an ai to "analyze this data," you are creating a situation prone to error. The ai does not know if you are a cfo looking for strategic risks or an auditor looking for compliance failures under the avg (gdpr). Without guidance, the model will provide a generic summary that lacks professional depth.

To resolve this, you should adopt a "junior analyst" mental model. Treat the ai like a bright but inexperienced intern. You would never hand a raw dataset to an intern without explaining what the data represents, what specific anomalies you are looking for, and how the final result should be presented. You must be explicit about outcomes, assumptions, and formatting requirements to ensure the output is useful for decision-making.

The context sandwich: a framework for professionals

To make this actionable, we can use a framework specifically designed for finance and accounting professionals: the context sandwich. This ensures that your instructions are never incomplete.

1. The role (the top layer): tell the ai exactly who it is representing. For example: "act as a senior fp&a manager for a large netherlands-based logistics firm."
2. The task (the core): provide the specific instruction. For example: "review these variance comments and identify the primary drivers of cost overruns compared to the budget."
3. The constraint (the bottom layer): set the boundaries and format. For example: "ensure the summary is suitable for a board report, use dutch gaap terminology, and format the output as a concise table."

This framework is particularly effective because it forces the ai to adopt a professional tone. If you do not specify the context, the ai might use international terminology that does not align with local standards, such as using "checking account" when you require "rekening-courant." Constraints also reduce the risk of "hallucinations," which are instances where the ai invents data, by narrowing the scope of its search.

Turning procedures into ai advantages

You can begin integrating these techniques into your daily routine by mapping existing procedures to prompts. For reconciliations, instead of manually matching descriptions, you can use a prompt to identify entries in two lists that do not match based on invoice numbers. For anomaly detection, you can instruct the ai to act as an internal auditor and flag duplicate payments or transactions made on Dutch public holidays.

Research indicates that generative ai can automate a significant portion of business activities. In finance, the most immediate win is the time saved on the first draft of any document or analysis. If ai can reach eighty percent accuracy in a matter of seconds, your professional expertise is then reserved for the final twenty percent of high-value validation and strategic interpretation.

The finance professionals who will excel in the coming decade are not those who focus solely on manual calculations. Success will belong to those who can orchestrate ai to perform these tasks with precision. Prompt engineering does not replace your knowledge of Dutch gaap (rj) or your judgment regarding provisions. It simply accelerates the underlying processes.

Imagine a workflow where ai flags outliers in a ledger and you provide the final validation. The human professional remains "in the loop," verifying the accuracy while the manual labor is eliminated. This represents the evolution of a modern finance career.

Conclusion

Prompt engineering is not a technical hurdle: it is a communication skill that you likely already possess. It is structured thinking applied to a new medium. By providing context and clarity, you can unlock the potential of ai without writing a single line of code.

Ready to advance your career in the evolving financial landscape? Treat AI as a new team member: brief it thoroughly, review its work with a critical eye, and refine your instructions over time.