{
  "id": 16592534,
  "title": "Why human judgement still matters in AI-powered finance",
  "url": "https://urgent.news/2026/10/26/why-human-judgement-still-matters-in-ai-powered-finance",
  "topic": "ai",
  "section": "AI",
  "published": "2026-10-26T10:57:22.000Z",
  "source": {
    "name": "TechRadar",
    "slug": "techradar",
    "url": "https://www.techradar.com/pro/why-human-judgement-still-matters-in-ai-powered-finance"
  },
  "original_language": "en",
  "account": "As artificial intelligence continues to gain traction in the finance world, it is becoming increasingly clear that human judgment remains crucial. A recent Deloitte study revealed that nearly 93% of Chief Financial Officers (CFOs) now utilize AI across key operations, marking a significant shift from the experimental phase just a few years ago. However, those who are successfully integrating AI into their finance functions are being strategic in their approach, focusing on the tasks that truly benefit from this technology rather than automating for the sake of it.\n\nFinance departments are filled with tasks that appear automatable but actually rely on contextual understanding that data alone cannot capture. For instance, a sudden spike in supplier costs could be mistaken for a contract issue until it is understood that it reflects a strategic shift discussed during a leadership meeting. Similarly, a financial variance might seem like a warning sign at first but could be explained by a planned operational change.\n\nWhile AI excels at identifying anomalies, it lacks the ability to intuit the complex organizational nuances behind them. This gap between the data and the business's understanding is where trust can erode. Finance teams must not only be confident that AI can spot an issue but also when they can trust its interpretation and when human judgment needs to intervene.\n\nThis evolving relationship between AI and human judgment is not about removing the human element from the process but rather shifting where it is applied. Instead of finance professionals spending time checking every transaction or addressing every variance, AI can handle the initial analysis, leaving humans to set parameters, decide what requires attention, and step in when something doesn't align. This already evident in how finance leaders are utilizing AI, with nearly 70% relying on AI-driven insights in conjunction with their own experience-based judgment, rather than solely on AI.\n\nAs more responsibilities are handed over to AI, the question shifts from whether the technology can complete a task to whether finance teams are comfortable relying on the results. This is particularly important when dealing with sensitive data and decisions where even minor errors can have significant financial or regulatory consequences. Trust needs to be ingrained in the system itself, rather than being introduced through policies or training later on.\n\nOne of the biggest challenges to implementing AI in finance is not the technology itself, but the quality of the underlying data. Finance teams have long struggled to consolidate data from various sources such as accounting systems, expense platforms, procurement tools, and spreadsheets. If these systems contain inconsistencies like duplicate suppliers, inconsistent coding, or outdated information, AI will not be able to rectify the problem; it will merely amplify the consequences. Prioritizing clean, consistent, and accessible financial data is essential before moving forward with more advanced AI projects.\n\nData quality issues can hinder AI's effectiveness, making even sophisticated systems unreliable. The most impactful AI applications in finance occur before deployment, focusing on connecting systems, cleaning data, and establishing a unified view of financial information. Once this foundation is established, AI can do much more than just automate administrative tasks. It can help identify anomalies, understand spending patterns, and provide finance teams with better information when making critical decisions.\n\nInstead of viewing AI as a means to create a finance function that does less, organizations should aim to develop one that dedicates less time to administrative work and more to strategic decision-making. As AI takes over repetitive tasks, finance teams can shift their focus towards analyzing the business, identifying risks and opportunities, and challenging assumptions. With AI handling routine work, finance teams can enjoy continuous analysis and decision-making, rather than waiting for month-end to understand what has occurred.\n\nUltimately, the true potential of AI in finance lies in its ability to enhance rather than replace the expertise of finance professionals. It is not about automating everything, but about enabling teams to make better decisions with less administrative burden. The key for CFOs is to determine where AI can operate independently, where it should provide recommendations, and where human judgment remains essential. The finance teams that strike the right balance between AI adoption and human judgment will transform AI from a mere experiment into a powerful tool for achieving genuine gains.",
  "summary": "AI is accelerating finance, but real progress depends on human judgement guiding every decision.",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}