The term “Artificial Intelligence” was first defined by John McCarthy in 1956 at the Dartmouth Conference, widely regarded as the birthplace of AI as a formal field of study. Since then, AI has evolved from simple systems to a complex machine learning and deep learning models capable of processing vast amounts of data and provide solution to sophisticated questions. In recent years, AI has made its way into specialized professional domains, with the legal and accounts sector emerging as one of the most transformative areas of application.
A typical day of lawyer or a company secretary goes into drafting a petition, finding case laws or regulation, rewriting proposition, preparing compliance reports, providing endless client solutions and other redundant desk work. This not only creates pressure but consumes a lot of time and efforts. As per the statistic published on National Judicial Data Grid, there are more than 50 million cases pending in the Indian court among this the district court encompasses a much higher proportion of pendency of cases.
However, to tackle this growing backlog, Artificial Intelligence is now entering into professional industry. One cannot deny how frequently young lawyers and professionals are using AI tools like Chat GPT, Google Gemini, Claude etc, though still looked down upon by their seniors but these tools do make tasks a lot easier. A research that takes hours to find and rewriting of words requiring all sort of permutation and combinations are done within minutes, these tools can review hundred pages of documents and create a petition, notices, or orders almost in a instant just by putting the right prompts, thereby proving death of billable hours.
Recently The Guardian reported that “An artificial intelligence law firm has won a case in an English court, in what is believed to be the first time a trial has been won using an AI lawyer” highlighting the changing landscape in the legal world. But the main reasoning that arise is how reliable it is and to what extent we can use it AI for complete our task efficiently.
AI and Professional Productivity
According to Thomson Reuters’ Future of Professionals Report, published in 2025, legal professionals surveyed expect AI to free up nearly 240 hours per year, up from 200 hours in 2024, based on the current pace of AI adoption. This translates into real financial impact as well, the report found that this shift unlocks an average annual value of $19,000 per professional, contributing to a combined annual impact of $32 billion for the legal and tax and accounting sectors in the US.
The transition in productivity gain is driven by AI’s growing role in routine legal tasks, including document review, legal research, and contract analysis. The benefits are significant, with the potential to reshape and redefine the way professionals deliver value and service to clients.
CURRENT SCENARIO
AI in Legal Research
Legal research stands out as one of the areas most visibly transformed by AI. What once took lawyers hours or days of manual digging through case law, statutes, and precedents can now be done in a fraction of second, thanks to AI powered research tools that scan and interpret legal texts far faster than any human could.
Harvey AI represents a different strand of this shift the legal sector’s push toward AI. It is built on OpenAI’s underlying GPT models and further trained specifically on legal material, Harvey was designed to support large firms with tasks like drafting documents, reviewing contracts, and conducting research. It runs on Microsoft Azure which gives it the infrastructure and security standards that enterprise clients demand, where its design and pricing keep it oriented mainly toward big law firms rather than smaller practices.
AI in Accounting and Taxation
The impact of AI isn’t confined to research and drafting alone it’s also changing how accounting and taxation work gets done. CA firms, for instance, are increasingly relying on automation to pull data directly from sources like AIS, Form 26AS, and internal accounting records, while machine learning models scan for differences before they turn into compliance notices.
Tasks like GST appeals, invoice matching, TDS compliance, and ITC risk detection are now handled in real time, sharply cutting down the manual labor that large-scale filing used to demand. As a result, many CA firms are shifting towards advanced approach as they are seeing fewer errors, quicker turnarounds, stronger audit preparedness, and room to take on more clients without needing to expand staff at the same rate.
India fits rightly into this global pattern. Deloitte runs its GenAI powered Omnia platform and the DARTbot assistant across its audit operations worldwide, which is used to highlight financial risk and ease auditing process. Interest in AI among accounting professionals is high, showing how big four are going towards automated tools.
Risks and Grey Areas of AI Adoption
However, these changing circumstances, created a need to look at the grey areas that these technology offer to us.
Accuracy, Responsibility and Human Oversight
AI tools raise real questions about accuracy and who is responsible when something goes wrong. Since AI can produce incorrect or misleading legal interpretations or analytical data, professionals must always verify its output before relying on it. Lawyers and CA are generally comfortable using AI for routine tasks like research and drafting, but more cautious about letting it handles confidential client information.
When AI errors do occur, it is often unclear whether the fault lies with the software developer or the firm using it.
Many professional bodies, including the American Bar Association, now treat “technological competence” as part of a professionals basic duty. One challenge is that AI often works like a “black box” even experts can struggle to explain how it reached a particular conclusion, which makes it harder to build trust.
The supreme court recently has set aside orders passed by the National Company Law Tribunal (NCLT) and the National Company Law Appellate Tribunal (NCLAT) after finding that both forums had relied on non-existent, AI-generated “hallucinated” judicial precedents while deciding an insolvency dispute, making it clear that human rationality cannot be compared and legal professionals has to use clarity while using ai for scrolling precedent
AI Bias and Explainability
Ai system also have the tendency to create bias since these systems learn from existing data, they can end up repeating and reinforcing biases already present in that data, leading to unfair or unequal outcomes. Keeping AI trustworthy means training it on diverse, representative data and making sure its decision-making stays as transparent and explainable as possible, which is exactly why oversight and checks remain necessary.
Data Privacy and Client Confidentiality
AI tools typically need access to sensitive data that includes legal contracts, NDA’s, account or tax details of the companies or clients, which raises serious concerns about privacy and client confidentiality. Professionals have a duty to protect client information and avoid conflicts of interest, so any AI use has to be handled with care.
Where AI plays a role in legal decisions, clients should be told about it and asked for informed consent. Regulations like the DPDP Act and EUs GDPR add another layer of complexity, since AI’s need for large volumes of data can sit in tension with principles like data minimization, even though GDPR and DPDP was not written with AI specifically in mind. But the main question arise shall the professional abandon its use or use it in such way that minimize its error and make their task easier.
The Use of AI in the World
AI’s role in tax and legal work isn’t confined to any single country. It’s a genuinely global shift governments, courts, and tax authorities on every continent are testing their own version of the same idea: that AI can absorb the data-heavy, pattern-matching work that used to consume hours of manual review.
AI in Justice Systems
Some of the earliest uses of AI in justice systems were risk-assessment tools. In the US, COMPAS has long helped correctional systems score the likelihood of reoffending, while the UK’s Durham Constabulary built HART to estimate a suspect’s risk of harm. Both have also faced scrutiny over how consistently they treat different groups, which is a reminder that even well-established tools still need human oversight.
China has gone further still, weaving AI into the daily running of its courts through a nationwide “smart court” system that helps judges draft documents, surface relevant precedent, and flag rulings that look out of step with similar past decisions.
United States
In United States, predictive analytics and document review are now mainstream. Litigation analytics platforms like Lex Machina mine past rulings to help shape strategy, while newer AI legal assistants including Thomson Reuters’ CoCounsel now handle research, drafting, and contract review in a fraction of the time it used to take.
Europe
In Europe, The EU has taken a more deliberate, rights-first path. GDPR already limits how legal data can be processed, and the EU AI Act in force since 2024 and rolling out in stages through 2027 reinforces that by requiring human oversight wherever AI plays a role in “high-risk” decisions.
China
In China’s courts are the clearest example of state-led adoption of “smart court” includes non-human judges, powered by artificial intelligence (AI) and allows participants to register their cases online and resolve their matters via a digital court hearing. at real scale, are piloting AI for everyday legal and administrative decisions.
Australia
In Australia, Legal-tech startups are building AI for contract review and compliance, universities are adding AI and legal-tech content to law degrees, and the Law Council of Australia has published its own ethics framework to keep human judgment in the loop.
AI in Tax Administration
Tax administrations are moving just as fast as legal ai changes. In Poland, The STIR system pulls daily transaction data from banks and credit unions, letting the National Revenue Administration spot carousel-fraud rings in near real time work that used to take up to two months.
In Malta, the UK, Canada, the Netherlands, and Ireland, the Tax authorities in each of these countries use AI to compare publicly visible wealth and spending against declared income, drawing on public registers and, in limited cases, bank data, to surface undeclared assets.
Xenon, a web-monitoring tool that originated in the Netherlands, is now used by tax authorities in six European countries to investigate undeclared online income.
Changes In Indian System
AI Adoption in the Indian Judiciary
The Indian judiciary has been unusually direct about wanting AI, but strictly on its own terms. The Supreme Court’s AI Committee has already rolled out indigenous tools such as SUPACE for analysing case records, translating judgments into 19 languages, and TERES for real-time court transcription.
The Judiciary treats artificial intelligence as a key lever for addressing India’s staggering judicial backlog of over pending cases, while making clear AI is meant to support judicial reasoning, not replace it.
Supreme Court’s 2026 Draft Regulations for AI in Courts
Recently, The Supreme Court’s 2026 draft Regulations for Use of Artificial Intelligence in Courts mark a pivotal moment not because they introduce AI to the judiciary, but because they acknowledge its inevitable presence in Indian Judicial System. By permitting AI for administrative tasks like transcription, translation, and case management while restricting its use from core judicial functions like sentencing and adjudication thereby maintaining the supremacy of human discretion, the draft establishes a solid foundation.
But professional using these services as clearly indicated in Rules can require assistance from AI advisory tools. The draft also draws hard red lines that courts cannot use AI to predict future criminal behaviour, assess risk, determine bail eligibility, or judge witness credibility, and any AI system too opaque to explain its own reasoning is barred from anything touching personal liberty.
The Supreme Court deserves a lot of credit for this draft. By supporting a “presumption in favor of responsible AI adoption,” the leadership shows it’s serious about tackling the huge backlog of pending cases. This is a forward-thinking approach it treats technology as a helpful partner for the courts, not something to fear. That said, to make sure these rules actually work in practice and don’t create new problems we would like to share a few practical suggestion.
Kerala High Court Policy on AI
Kerala High Court’s “Policy Regarding Use of Artificial Intelligence Tools in District Judiciary,” issued 19 July 2025, was the first formal AI-use policy from any Indian court, explicitly barring AI from producing findings or judgments and warning about the confidentiality risks of generative tools, considering growing concerns over using fabricated or hallucinated case laws.
Conclusion
At the end of the day, AI is no longer something lawyers, CAs, or judges can afford to ignore or look down upon. It’s already saving people hundreds of hours a year, cutting down grunt work like research, drafting, and compliance checks, and letting professionals focus on the things that actually need a human brain judgment, strategy, and client trust.
Whether it’s a young lawyer using ChatGPT to draft a notice in minutes, a CA firm catching GST mismatches automatically, or courts in India using tools like SUPACE to deal with a massive case backlog, the benefits are hard to argue with. A wave of AI has come and its impossible to ignore it so we should rather evolve and adapt it with diligence.
