In this fifth article in our series on AI’s impact on accounting and finance, we look at a critical challenge: how to implement AI responsibly. The emergence of “AI slop” – unreliable, unverified AI-generated content – serves as a stark reminder that innovation without accountability comes with significant risk.
For finance and accounting professionals, where accuracy and reliability are non-negotiable, responsible AI use isn’t optional.
AI Is Not A CFO
Perhaps the most useful way to understand AI’s current limitations is to think of it as an ambitious junior analyst: eager to make recommendations, produce insights, and prove its worth, but (almost always) lacking the experience needed to work unsupervised.
The foundational principle is “trust but verify”. Regardless of how impressive initial results may be or how sophisticated a program claims to be, skipping oversight can allow errors to creep in, errors that compound over time. Unwinding those compounded errors can be very expensive, time consuming, and severely damage a company’s reputation with customers and investors.
The old programming axiom “garbage in, garbage out” is even more applicable to AI in accounting. That’s because AI is not just churning out data, it’s interpreting it too. Data integrity requires vigilance on two fronts: confidentiality of data and cleanliness of data.
Garbage in can take multiple forms. Data hygiene issues – ensuring your dataset contains the right information – represent one challenge. But equally important is context and framework. And just like mentoring that junior analyst, you must set AI tools up for success by providing proper context and clear parameters. This requires active management and designated accountability. Someone must own data quality, and that responsibility must be clearly defined.
The Security Reality
Garbage out is further complicated by another uncomfortable truth: despite vendor promises, no cloud, AI program, or software is entirely safe for sensitive financial data. AI has particular issues – it sometimes behaves like humans with secrets, seeks to please, and can sometimes be tricked into divulging confidential information. This isn’t theoretical. Major, multi-billion dollar companies have made the wrong kind of headlines with AI-related data breaches.
A multi-billion dollar corporation can survive such catastrophes. But for smaller companies a data breach can be fatal. This reality must inform every AI adoption decision.
Human Oversight That Actually Works
With AI capabilities expanding at an exponential rate, there is a temptation to just assume a program is performing exactly as expected. But until accountability for data is passed on to another party, your own oversight obligations never end.
Practical oversight doesn’t necessarily mean manually checking every data point. Instead, apply core accounting fundamentals: sample selection testing, anomaly detection, root cause analysis of failures, and pattern recognition for systematic errors. These established methodologies work just as well for AI outputs as they do for human output.
Accounting Standards Must Evolve to Govern AI
Looking forward, the accounting profession faces policy and regulatory headwinds. AICPA and GAAP standards will need to evolve to address AI implementation in financial reporting and analysis. Audits will become more technical, requiring verification of the financial statements and the artificial intelligence that produced them.
This isn’t a distant future scenario – it’s already beginning. The firms that establish strong governance frameworks today will be the ones shaping industry standards tomorrow. Responsible AI adoption means building guardrails now, maintaining rigorous oversight, and never sacrificing fundamental accounting principles for the sake of cost cutting. The technology may be new, but the responsibility remains timeless.
AI amplifies everything – including mistakes – so responsible adoption is a strategic necessity, not a cautious choice. AI is an advantage but must be approached with care. Strong governance today will establish durable accounting and finance procedures, and help shape new industry standards.
