AI Research
How generative AI can make accountants more productive

What you’ll learn:
- AI amplifies accounting experience: In a study of accountants, the most experienced used AI strategically to boost performance gains.
- Productivity and reporting quality both improve with AI: Working with AI-enabled software helped accountants support more clients and finalize monthly statements sooner.
- Expertise still matters: Human judgement is needed to evaluate AI’s work is and avoid errors.
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A new study confirms what some accountants may have already known: Generative artificial intelligence software is making them more productive and, in many cases, improving the quality of their reporting.
MIT Sloan assistant professor along with Stanford University’s Jung Ho Choi, partnered with a company that makes AI-based accounting software so they could analyze hundreds of thousands of transaction entries from 79 small and midsize firms. They also surveyed 277 accountants, about 10% of whom were using AI in their daily routines; another 10% had not experimented with AI at all.
A majority of the accountants surveyed felt that AI provided efficiency benefits and has the “potential to reduce repetitive work and improve analysis,” Xie and Choi write. And a majority felt that it would increase their job satisfaction, as it pertains to productivity, work-life balance, and their careers. While the research occurred early in the study of generative AI and accounting, its findings suggest practical implications for accountants, managers, and accounting firms.
Clear productivity and quality gains
The software in the study automates routine work and can help accountants make decisions. For example, it can classify transactions, summarize contracts, and detect anomalies in bookkeeping. Accountants using the software saw greater productivity on average, including:
- An increase in weekly client support.
- A “reallocation of approximately 8.5% of accountant time from routine data entry toward high-value tasks such as a business communication and quality assurance,” the researchers write.
Those same accountants also saw improved financial reporting quality, seen as:
- A 12% increase in general ledger granularity (a measure of reporting detail).
- A 7.5 day reduction in monthly close time.
“Essentially, firms embracing AI are able to finalize their monthly financial statements almost within two weeks after month-end, whereas others take over a week longer,” Xie and Choi write.
The researchers found that many accountants were using the tool to handle routine work, freeing up time for analysis and work with clients. They also found that “more experienced accountants tend to leverage the AI system more strategically and reap larger performance gains from it.” It’s possible that the more experienced accountants are better at interpreting the confidence scores the AI software applies to its own recommendations and thus are more likely than their less-experienced peers to intervene when the scores are low, they write.
Concerns about AI accuracy
Many accountants surveyed said they had concerns about using the AI software, including 62% who were worried about errors and accuracy in AI-generated reporting. Accountants were also concerned about data security issues and job displacement. One area of concern for the researchers: “When AI suggests diverging categories for uncertain transactions, accountants tend to still follow AI’s suggestions,” introducing the possibility of errors attributable to AI.
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What does this mean for accounting firms?
For managers: When integrating AI-assisted accounting systems, recognize that the technology works best when it augments your existing experts. Accounting isn’t just following a set of rules, Choi said. As powerful as AI is, it isn’t always able to consider all of the context around information. For example, when AI confidence scores are low, judgment from experienced accountants is needed.
For accountants: Consider using AI to automate much of the grind and boost job satisfaction. More experienced accountants may see even greater gains.
“There’s that really famous meme, ‘What I want AI to do is do my laundry so that I can write poetry, not write poetry so I can do laundry,’” Xie said. “In accounting, there’s laundry and there’s poetry.” Make the laundry — inputting and processing data, for example — more efficient and you free up time and space for the poetry, she said. That means more time for client interactions, financial forecasting, higher-level thinking, and the like.
“I think intrinsically, as human beings, we want to do creative judgment-based work,” Xie said.
For the profession: Getting AI and accountants to work together well will require AI literacy training, and clear oversight standards are needed to scale the net gains of AI. Xie and Choi’s research examines AI in accounting as it is employed now, and it poses questions for future work: How should organizations prepare for a generation of accountants who have never done accounting work without AI? What tasks or roles will still exist? And how should managers think about organizational structure for the accounting function?
Read the research
The full research paper, “Human and AI in Accounting: Early Evidence from the Field,” discusses AI adoption patterns, task reallocation, and how experienced accountants use AI confidence scores.
This article is based on work by Chloe Xie, an assistant professor of accounting at MIT Sloan. Her research focuses on capital market imperfections — such as limits to arbitrage, deviations from von Neumann-Morgenstern preferences, and criminal behavior — and how these frictions shape the information environment. Her research also considers how these frictions affect disclosure decisions, asset pricing, investor decision-making, and non-financial market outcomes.
AI Research
Measuring Machine Intelligence Using Turing Test 2.0

In 1950, British mathematician Alan Turing (1912–1954) proposed a simple way to test artificial intelligence. His idea, known as the Turing Test, was to see if a computer could carry on a text-based conversation so well that a human judge could not reliably tell it apart from another human. If the computer could “fool” the judge, Turing argued, it should be considered intelligent.
For decades, Turing’s test shaped public understanding of AI. Yet as technology has advanced, many researchers have asked whether imitating human conversation really proves intelligence — or whether it only shows that machines can mimic certain human behaviors. Large language models like ChatGPT can already hold convincing conversations. But does that mean they understand what they are saying?
In a Mind Matters podcast interview, Dr. Georgios Mappouras tells host Robert J. Marks that the answer is no. In a recent paper, The General Intelligence Threshold, Mappouras introduces what he calls Turing Test 2.0. This updated approach sets a higher bar for intelligence than simply chatting like a human. It asks whether machines can go beyond imitation to produce new knowledge.
From information to knowledge
At the heart of Mappouras’s proposal is a distinction between two kinds of information, non-functional vs. functional:
- Non-functional information is raw data or observations that don’t lead to new insights by themselves. One example would be noticing that an apple falls from a tree.
- Functional information is knowledge that can be applied to achieve something new. When Isaac Newton connected the falling apple to the force of gravity, he transformed ordinary observation into scientific law.
True intelligence, Mappouras argues, is the ability to transform non-functional information into functional knowledge. This creative leap is what allows humans to build skyscrapers, develop medicine, and travel to the moon. A machine that merely rearranges words or retrieves facts cannot be said to have reached the same level.
The General Intelligence Threshold
Mappouras calls this standard the General Intelligence Threshold. His threshold sets a simple challenge: given existing knowledge and raw information, can the system generate new insights that were not directly programmed into it?
This threshold does not require constant displays of brilliance. Even one undeniable breakthrough — a “flash of genius” — would be enough to demonstrate that a machine possesses general intelligence. Just as a person may excel in math but not physics, a machine would only need to show creativity once to prove its potential.
Creativity and open problems
One way to apply the new test is through unsolved problems in mathematics. Throughout history, breakthroughs such as Andrew Wiles’s proof of Fermat’s Last Theorem or Grigori Perelman’s solution to the Poincaré Conjecture marked milestones of human creativity. If AI could solve open problems like the Riemann Hypothesis or the Collatz Conjecture — problems that no one has ever solved before — it would be strong evidence that the system had crossed the threshold into true intelligence.
Large language models already solve equations and perform advanced calculations, but solving a centuries-old unsolved problem would show something far deeper: the ability to create knowledge that has never existed before.
Beyond symbol manipulation
Mappouras also draws on philosopher John Searle’s famous “Chinese Room” thought experiment. In the scenario, a person who does not understand Chinese sits in a room with a rulebook for manipulating Chinese characters. By following instructions, the person produces outputs that convince outsiders he understands the language, even though he does not.
This scenario, Searle argued, shows that a computer might appear intelligent without real understanding. Mappouras agrees but goes further. For him, real intelligence is proven not just by producing outputs, but by acting on new knowledge. If the instructions in the Chinese Room included a way to escape, the person could only succeed if he truly understood what the words meant. In the same way, AI must demonstrate it can act meaningfully on information, not just shuffle symbols.
Can AI pass the new test?
So far, Mappouras does not think modern AI has passed the General Intelligence Threshold. Systems like ChatGPT may look impressive, but their apparent creativity usually comes from patterns in the massive data sets on which they were trained. They have not shown the ability to produce new, independent knowledge disconnected from prior inputs.
That said, Mappouras emphasizes that success would not require constant novelty. One true act of creativity — an undeniable demonstration of new knowledge — would be enough. Until that happens, he remains cautious about claims that today’s AI is truly intelligent.
A shift in the debate
The debate over artificial intelligence is shifting. The original Turing Test asked whether machines could fool us into thinking they were human. Turing Test 2.0 asks a harder question: can they discover something new?
Mappouras believes this is the real measure of intelligence. Intelligence is not imitation — it is innovation. Whether machines will ever cross that line remains uncertain. But if they do, the world will not just be talking with computers. We will be learning from them.
Final thoughts: Today’s systems, tomorrow’s threshold
Models like ChatGPT and Grok are remarkable at conversation, summarization, and problem-solving within known domains, but their strengths still reflect pattern learning from vast training data. By Mappouras’s standard, they will cross the General Intelligence Threshold only when they produce a verifiable breakthrough — an insight not traceable to prior text or human scaffolding, such as an original solution to a major open problem. Until then, they remain powerful imitators and accelerators of human work — impressive, useful, and transformative, but not yet creators of genuinely new knowledge.
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AI Research
UTM Celebrates Malaysia’s Youngest AI Researcher Recognised at IEEE AI-SI 2025 – UTM NewsHub

KUALA LUMPUR, 28 August 2025 – Universiti Teknologi Malaysia (UTM) proudly hosted the Institute of Electrical and Electronics Engineers (IEEE) International Conference on Artificial Intelligence for Sustainable Innovation (AI-SI) 2025, themed “Empowering Innovation for a Sustainable Future.” The conference gathered global experts, academics, and industry leaders to explore how Artificial Intelligence (AI) can address sustainability challenges. Among its highlights was the remarkable achievement of 17-year-old Malaysian researcher, Charanarravindaa Suriess, who was celebrated as the youngest presenter and awarded Best Presenter for his groundbreaking paper on adversarial robustness in neural networks. His recognition reflected not only individual brilliance but also Malaysia’s growing strength in the global AI research landscape.
Charanarravindaa’s presentation, titled “Two-Phase Evolutionary Framework for Adversarial Robustness in Neural Networks,” introduced an innovative framework designed to improve AI systems’ ability to defend against adversarial attacks. His contribution addressed one of the most pressing challenges in AI, ensuring resilience and trustworthiness of machine learning models in real-world applications. Born in Johor Bahru, his journey into science and computing began early; by primary school, he was already troubleshooting computers and experimenting with small websites. At just 15 years old, he graduated early, motivated by a passion for deeper challenges. Participation in international hackathons, including DeepLearning Week at Nanyang Technological University (NTU) Singapore, strengthened his resolve and provided the encouragement that led to his first academic paper, now internationally recognised at IEEE AI-SI 2025.
Beyond academia, Charanarravindaa has also demonstrated entrepreneurial spirit by founding Cortexa, a startup dedicated to advancing AI robustness, architectures, and applied AI for scientific discovery. His long-term vision is to integrate artificial intelligence with quantum computing and theoretical physics to expand the boundaries of knowledge. This ambition is a testament to the potential of Malaysia’s youth in contributing to frontier technologies. His recognition at IEEE AI-SI 2025 reflects IEEE’s mission of advancing technology for humanity, where innovation is seen as a universal endeavour not limited by age. By honouring a young researcher, IEEE underscored its commitment to empowering future generations of scientists and innovators to shape technology for global good.

During the conference, the Faculty of Artificial Intelligence (FAI), UTM, represented by Associate Professor Dr. Noor Azurati Ahmad, extended an invitation to Charanarravindaa to explore possible research collaborations. This initiative aligns with FAI’s vision to be a leader in AI education, research, and innovation, with a particular focus on trustworthy, robust, and sustainable AI. Early discussions centred on aligning his research interests with UTM’s expertise in advanced architectures and digital sustainability. Such collaboration exemplifies how institutions and young talent can come together to accelerate innovation, while also strengthening Malaysia’s position as an emerging hub for AI research and talent cultivation.
At the national level, this achievement resonates strongly with the Malaysia National Artificial Intelligence Roadmap (2021–2025), which identifies talent development as a central pillar in building an AI-ready nation. Prime Minister Datuk Seri Anwar Ibrahim has repeatedly highlighted the urgency of nurturing local talent to enhance competitiveness and leadership in the global digital economy. Charanarravindaa’s success demonstrates tangible progress in this direction, showcasing how Malaysia can produce young innovators capable of contributing to both national aspirations and international scientific advancement. Through platforms such as IEEE AI-SI 2025, UTM reaffirms its role as a catalyst for excellence in AI research and talent development, embodying its mission to prepare the next generation of scholars and innovators who will drive sustainable futures.
AI Research
Databricks at a crossroads: Can its AI strategy prevail without Naveen Rao?

“Databricks is in a tricky spot with Naveen Rao stepping back. He was not just a figurehead, but deeply involved in shaping their AI vision, particularly after MosaicML,” said Robert Kramer, principal analyst at Moor Insights & Strategy.
“Rao’s absence may slow the pace of new innovation slightly, at least until leadership stabilizes. Internal teams can keep projects on track, but vision-driven leaps, like identifying the ‘next MosaicML’, may be harder without someone like Rao at the helm,” Kramer added.
Rao became a part of Databricks in 2023 after the data lakehouse provider acquired MosaicML, a company Rao co-founded, for $1.3 billion. During his tenure, Rao was instrumental in leading research for many Databricks products, including Dolly, DBRX, and Agent Bricks.
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