The Role of AI and Data Analytics: Supporting, Not Replacing, Human Judgment
In recent years, artificial intelligence (AI) and advanced data analytics have emerged as powerful tools in finance and auditing. These technologies can process vast datasets, spot patterns, and perform routine tasks at lightning speed. This raises the question: can AI make accounting judgments? The consensus is that AI and analytics are excellent in a supportive role, but they cannot replace the nuanced professional judgment of human accountants and auditors – at least not for the foreseeable future. Instead, the ideal scenario is a synergy where AI handles data-heavy analysis and humans provide context, interpretation, and ethical oversight. Let’s delve into how AI and analytics are being used and why human judgment remains irreplaceable.
AI’s Strengths – Speed, Scale, and Pattern Recognition: AI algorithms, particularly machine learning models, excel at finding anomalies or trends in large datasets that might elude a human. For example, an AI can examine every journal entry in a ledger (millions of transactions) and flag those that deviate from patterns (say, odd times, round amounts, unusual account combinations) for an auditor to review. Traditionally, an auditor might sample a few hundred entries; AI can check them all, effectively moving audit toward full-population testing. Similarly, AI can be fed all lease contracts of a company and extract key terms like lease term, payment, renewal options – doing in minutes what might take humans weeks. This ensures completeness of information for lease accounting judgments. Data analytics tools can also crunch historical data to help with estimates: for example, analyzing years of collections to better inform a credit loss model, or scanning global sales data to see how often performance obligations are delivered late (impacting revenue timing). By freeing professionals from tedious data gathering and calculation, AI allows them to focus on higher-level analysis and decision-making. In an audit context, AI can reduce the time spent ticking and tying, allowing auditors to devote more energy to assessing judgments and risks. In accounting departments, AI can automate reconciliations and identify unusual variances, alerting the team to areas that need judgment.
AI’s Limitations – Context, Common Sense, and Adaptability: Despite these strengths, AI lacks the deep understanding of business context, economics, and intent that humans have. AI works on patterns learned from historical data; if a situation is truly novel, AI has no basis to handle it. For instance, during the COVID-19 pandemic, AI models for credit losses might have mispredicted outcomes because there was no precedent – human judgment had to step in to overlay adjustments【19†(context)】. AI also doesn’t inherently understand ethical or qualitative factors. It might find that a certain accounting treatment maximizes some metric because in past data it correlated with stock price increases, but it doesn’t grasp concepts like fairness or substance over form. As one article put it, “AI cannot make judgements that require human nuance, ethical considerations, or understanding of business context.”. For example, an AI might comb through contracts and say “by the letter, this is not explicitly a lease,” but a human might see that the supplier’s substitution right is illusory, and thus determine it is a lease in substance. AI would miss such subtleties. Gary Kabureck humorously noted that if detailed rules could cover everything, “a computer with a decision tree would do” all accounting – but since rules can’t cover everything, judgment is needed. AI is like that decision tree computer: extremely useful for following set rules or finding correlations, but it doesn’t truly “understand” the why behind transactions.
Hybrid Approach – “Tool, Not a Takeover”: Business leaders and accounting tech experts emphasize that AI should be viewed as a tool to augment human capabilities, not a replacement. Artie Minson, a tech CEO with accounting background, said he sees AI as something that makes work more efficient, but ultimately “the human accountant’s role is to interpret the insights, apply business context, and make strategic decisions.”. This “trust but verify” approach means: let AI do the heavy lifting, but humans must question and validate its output. An example is using AI to draft financial policies or even initial financial statement notes (some companies are exploring this). AI can pull the relevant standard text and some industry benchmarks, but a human needs to tailor it to the company’s specifics and ensure it’s appropriate. In auditing, AI might directly confirm 100% of transactions that follow expected pattern, leaving humans to investigate the flagged 2%. This indeed changes the auditor’s role – more analysis and higher-level thinking, less rote work. It requires a hybrid skill set: auditors and accountants need to know how to work with AI and interpret its results, combining “AI literacy with critical thinking, professional skepticism, and ethical judgment”.
Preventing Over-Reliance: One risk is that users over-rely on AI outputs without applying judgment. To counter this, professionals are trained to ask, “Does this result make sense?” For instance, if an AI in an ERP flags no anomalies, the auditor should still consider, “Did we configure the AI correctly? Could it be missing something unusual that isn’t in its pattern library?” Another instructive example is from the Trullion piece referencing Enron: “If you put [Enron’s structured transactions] through AI, a machine might say ‘they checked all the boxes in the rules.’ But when you stand back as a human, it doesn’t make business sense.”. This underscores that AI, especially if based on rules or historical patterns, might have approved Enron’s accounting because it was technically rule-compliant. Only human judgment recognized the absurdity and ethical problem. So humans are needed to catch situations where technical compliance diverges from substance or fairness – something AI wouldn’t inherently catch since it lacks moral and conceptual reasoning.
Benefits of Analytics in Judgment: Data analytics can enhance judgment in more subtle ways too. Visualizations and dashboards can help accountants see trends and outliers, informing their estimates. For example, an auditor might use a visualization of revenue by region and notice one region’s growth is anomalously high – prompting a conversation (judgment) about whether there’s a cut-off issue or an aggressive practice there. Before advanced analytics, that might go unnoticed in aggregated accounts. Similarly, management might use scenario analysis tools (which simulate outcomes under different assumptions) to better understand the sensitivity of their judgments. If an impairment test’s outcome swings wildly with a slight discount rate change, analytics can illustrate that, leading management to maybe take a more cautious approach or ensure robust disclosure. In essence, analytics broadens the information base for judgment decisions, which generally improves judgment quality as long as one doesn’t drown in data.
AI in Internal Controls and Audit: Another area is using AI to continuously monitor transactions (“continuous auditing” or “continuous controls monitoring”). This can catch unusual events in near real-time, which management can then investigate using human judgment. For example, an AI might detect that in the last month of the quarter, an unusual spike of sales went to a distributor with extended payment terms. It can’t conclude if that’s a channel-stuffing issue or a legitimate large order – but it flags it. Management’s judgment (and perhaps internal audit’s work) is then applied to ascertain if revenue was recognized appropriately. In the absence of AI, such patterns might be found only in hindsight or not at all. So AI acts as a judgment trigger: pointing professionals to areas that need their attention.
Training for the Digital Age: Part of developing judgment now includes training on how to effectively use these tools. Firms like Deloitte, EY, etc., have been investing in training their staff in data analytics and AI interpretation skills. This means not just how to run the tools, but how to question their output. Younger accountants are often digitally savvy, but they need to also build the professional skepticism to not take analytic outputs at face value. Conversely, experienced accountants are learning to trust analytics over gut in areas where data is clearly superior (e.g., not hunching an allowance number without looking at the data trends that tools provide). The best outcomes mix the strengths: human insight and AI efficiency.
Ethical and Control Considerations: With AI, new risks emerge as well: if AI algorithms are opaque (“black boxes”), how do we audit their decisions? Regulators have begun to consider if, say, an AI model materially impacting financial reporting needs to be validated or its assumptions disclosed. Another consideration is bias in AI – if the training data had biases, AI could perpetuate them. For instance, an AI credit model might inadvertently be biased against a group if historical data were biased; a human needs to catch that and correct it, aligning with ethical judgment (ensuring compliance with laws like fair lending, etc.). So ironically, AI introduces new areas where human judgment is needed – judging the AI itself. In audit, standards are evolving on how to treat information from AI as audit evidence and how to validate it. The human auditor must judge if the AI’s method is reliable and if the results are plausible.
Realistic Outlook: As of 2025, AI is not at the stage of making strategic accounting decisions. It doesn’t negotiate with auditors, present to audit committees, or make calls on grey areas like “Is this more like equity or debt in substance?” Those tasks are firmly with humans. What AI can do is shorten the path to get information and perform calculations, so humans can spend more time on those higher-order questions. The revolution is in efficiency and scope, not in autonomy of judgment. Perhaps in the distant future, AI might evolve to reason more abstractly, but even then, many would argue ethical and professional judgment involves values and accepting responsibility – things that we wouldn’t delegate to a machine. Investors and courts will hold management and auditors accountable, not an AI.
To encapsulate, one CFO said about AI, “It falls short in complex financial scenarios. You still need the human + machine element because if you only rely on AI, it might say all rules are followed, but a human can see the business sense isn’t there.”. This captures well why AI cannot fully supplant human judgment in accounting. Instead, by embracing AI as a “powerful ally”, accountants can achieve better coverage, more timely insights, and focus their judgment where it’s most needed. The future likely holds a collaborative paradigm: CPAs working alongside data scientists, audit teams with AI specialists, and accounting systems embedded with AI that the finance team oversees. This will shift the skill set (less manual reconciliation, more analysis of analytic outputs) but the core need for human judgment – to interpret context, ensure fairness, and make decisions when rules end – will remain indispensable.