This article has been authored by Ramya vani Chouhan.

Imagine an arbitrator who never sleeps, never gets tired of reading endless documents, and always remembers every precedent ever written—meet Artificial Intelligence, the newest member of the arbitration team! AI doesn’t need coffee breaks or vacations, yet it works tirelessly to sift through data, predict case outcomes, and keep hearings running smoothly. It’s like having a super-intelligent intern who files your paperwork and gives you case-winning strategies (minus the office gossip). While AI might not wear a wig or bang a gavel, it’s quickly becoming the secret weapon behind faster, smarter, and sometimes surprisingly witty arbitration processes! Gone are the days of sifting through piles of paperwork or manually checking each clause in lengthy contracts. AI, with its ability to analyze massive amounts of data in mere seconds, is like the super-sleuth detective you wish you had on your team—except, instead of a magnifying glass, it uses machine learning algorithms to find patterns and predict outcomes. Imagine it as the Sherlock Holmes of the arbitration world, minus the pipe and the hat, but with an uncanny ability to foresee who will win, who will settle, and who will get stuck in a never-ending appeal loop. And don’t worry, AI isn’t about to steal your job—it’s here to make your life easier. It helps automate the boring stuff, like scheduling hearings or organizing documents so that you can focus on the fun part: crafting brilliant legal arguments. It’s the ultimate legal assistant, except, unlike most assistants, it doesn’t need coffee breaks and never forgets a detail. AI is like the “best intern” every arbitration firm dreams of—only smarter, faster, and with a perfect memory. So, buckle up, because AI is making arbitration faster, cheaper, and—dare we say it—more fun!

Applications of AI in Arbitration:

AI tools are being used to automate various administrative aspects of arbitration, such as scheduling hearings, managing documents, tracking deadlines, and sending notifications to relevant parties. These tools help streamline the process, reduce human error, and save time. In the case of Michail v. E-commerce Platform [2020, ICC Arbitration], AI-assisted case management tools were used to efficiently handle the exchange of documents, making it easier for the parties to collaborate across borders and time zones. The automation of scheduling and notification procedures significantly reduced administrative overhead.AI-powered systems are used to review and process vast amounts of documents during the discovery phase of arbitration. These tools employ machine learning algorithms to identify relevant documents, flagging potential risks and saving time compared to traditional manual document review. In the case of Da Silva Moore v. Publicis Groupe (2012), the court upheld the use of predictive coding for document review, which was being applied in e-discovery. Though this case primarily concerns litigation, it set an important precedent for AI-assisted document review, which is now routinely used in international precedent review, which is now routinely used in international arbitration to handle complex e-discovery processes.

AI uses historical case data to predict the likely outcome of a dispute helping parties in arbitration better assess their positions and decide on settlement options. By analyzing previous arbitrations, legal principles, and arbitrator behaviors, AI can forecast the probable success of various legal arguments. These predictive tools use statistical models to simulate possible outcomes and provide insight into the likelihood of success, assisting both parties and arbitrators in decision-making. AI tools can assist in the selection of arbitrators by analyzing the arbitrators’ prior decisions, expertise, and performance in previous cases. This helps parties choose the most appropriate arbitrator based on data-driven insights, ensuring neutrality and expertise. In the case of X v. Y (2018, ICSID Arbitration), AI-driven platforms were used to assess the past rulings of potential arbitrators, helping the parties select an impartial panel. While this case didn’t involve direct challenges based on AI tools, it highlighted the increasing role of AI in arbitrator selection. Systems like Arbitrator Intelligence are becoming more popular in assisting parties with these selections. AI can assist arbitrators in drafting awards by offering templates, analyzing precedents, and summarizing key arguments. This allows arbitrators to focus on the legal and substantive aspects of their decision, while AI helps with the administrative and drafting elements. In a hypothetical situation where an arbitrator uses AI to draft an award, such as in a complex cross-border dispute, AI can help by identifying applicable case law and suggesting the structure for the award. This approach ensures that awards are consistent with past decisions and adhere to legal standards, improving efficiency in arbitration. While no specific case law has directly addressed AI-driven award drafting, the use of AI tools for legal research and decision-making is increasingly common in modern arbitration practice. 

AI-powered tools are used to provide real-time transcription of hearings, making the process of recording testimonies and arguments much faster and more accurate. AI also facilitates real-time translation, making cross-border arbitration easier by enabling parties who speak different languages to understand proceedings in real-time. In State of ABC v. XYZ Corporation (2021, ICC Arbitration), AI-based transcription and translation tools were used during a virtual hearing. The tribunal made use of automated transcription to ensure accurate records, especially when language barriers existed between the parties. This case highlights how AI tools are used to overcome logistical and language challenges in arbitration. AI is employed to analyze the risk factors in arbitration clauses within contracts. By analyzing the language and structure of clauses, AI tools can identify potential pitfalls that may lead to disputes, helping parties draft clearer, more enforceable agreements. In European Gas v. Company X (2020, LCIA Arbitration), AI was used to assess the enforceability of arbitration clauses in the context of contract disputes. AI tools reviewed the clauses for clarity, jurisdictional validity, and compliance with the arbitration rules, leading to a more efficient resolution of the dispute. AI tools, particularly those integrated with blockchain technology, can automate the enforcement of arbitral awards. Once a ruling is issued, smart contracts powered by AI can trigger automatic payment or compliance actions based on pre-established terms. 

Benefits of AI in arbitration:

AI tools can automate repetitive and time-consuming tasks, such as document review, data analysis, and scheduling, thereby speeding up the arbitration process. For example, AI can quickly analyze large volumes of documents and identify relevant information, reducing the time required for manual review and significantly accelerating the entire case management process. It Reduces the time taken for case preparation and decision-making, enabling faster resolutions. By automating routine tasks and enhancing productivity, AI helps reduce the costs associated with arbitration. Tasks like e-discovery, legal research, and administrative work, which traditionally require significant manpower, can be handled by AI systems at a fraction of the cost. This is especially beneficial for smaller businesses or individuals who might otherwise be deterred by the high costs of arbitration. It Lowers the overall costs of arbitration, making it more accessible for a wider range of participants. AI algorithms can analyze vast amounts of data without human bias, ensuring a higher degree of accuracy and consistency in tasks like document review, award drafting, and decision-making. Unlike humans, AI does not forget information or make errors in interpreting large datasets, which reduces the risk of mistakes or inconsistencies in arbitration proceedings. It Increases the reliability and precision of the arbitration process.  AI can analyze historical data and case trends to predict the likely outcome of a dispute. By using machine learning and big data analytics, AI tools can help parties assess the strength of their case, enabling them to make more informed decisions about settlement or litigation strategies. Provides valuable insights into potential case outcomes, aiding in better decision-making and encouraging earlier settlements. In complex arbitration cases, especially those involving cross-border disputes, the volume of documents and evidence can be overwhelming. AI-powered e-discovery tools can efficiently search through large datasets, identifying key documents and relevant information based on predetermined criteria. This allows arbitrators and legal teams to focus on substantive legal issues rather than getting bogged down in administrative tasks. It Increases the speed and reduces the cost of document review, making complex cases more manageable. AI can assist in managing virtual arbitration hearings, providing real-time transcription, translation, and even AI-powered assistants to help manage the flow of information. This is particularly useful for cross-border disputes where language barriers and geographic distances could otherwise complicate communication. It Ensures smooth, accurate communication during virtual hearings, making arbitration more accessible and efficient, particularly for international parties. AI can analyze arbitrators’ past decisions, expertise, and behavior to recommend the most suitable arbitrator for a specific dispute. By assessing a variety of factors such as past rulings, specializations, and neutral track records, AI helps in selecting arbitrators who are best suited for the case. It Increases the chances of selecting an impartial, qualified arbitrator, ensuring a fairer arbitration process. AI-powered legal research tools can quickly sift through case law, statutes, and regulations to identify the most relevant precedents and legal arguments. This can significantly reduce the time lawyers and arbitrators spend on legal research, ensuring they are well-prepared with the most current and applicable legal resources. It Enhances the depth and quality of legal research, enabling faster and more accurate preparation for arbitration proceedings. Human arbitrators and legal professionals may inadvertently introduce biases into the arbitration process, whether due to personal assumptions or cognitive limitations. AI systems, on the other hand, can process data without such biases, ensuring that decisions are based on objective data and consistent legal standards. It Promotes fairness and reduces the potential for subjective bias, leading to more equitable outcomes. AI systems, particularly those that offer predictive analytics and data-driven insights, can improve transparency in the arbitration process. These tools help arbitrators, legal teams, and parties involved in the dispute understand the rationale behind decisions and predictions, fostering trust in the process. It Increases transparency and accountability, making the arbitration process more understandable and reliable for all parties involved.

Challenges and Limitations:

Positive Aspects of AI in Arbitration

• Efficiency and Speed: AI can streamline the arbitration process by automating routine tasks, such as document review, legal research, and contract analysis. These tasks often take considerable time when done manually, and AI can significantly reduce these timeframes, allowing arbitrators and legal professionals to focus on higher-level tasks. AI tools like natural language processing (NLP) can quickly sift through large volumes of data, identify key issues, and provide summaries, which can drastically speed up case preparation and analysis.
• Cost Reduction: AI can also help reduce the costs associated with arbitration. By automating tedious tasks and reducing the need for large legal teams to manage basic procedural matters, AI tools can lower the costs for both parties and arbitration institutions. This is particularly beneficial in commercial arbitration, where large companies may seek more affordable yet effective dispute resolution processes.
• Consistency and Objectivity: One of the major advantages of AI is its potential to bring greater consistency and objectivity to the decision-making process. AI systems can analyze large sets of precedents, past arbitration awards, and applicable laws to provide insights into how similar disputes have been resolved. This can help ensure that arbitration decisions are more predictable and based on objective data, which may improve trust in the arbitration system.
• Improved Access to Arbitration: AI-powered platforms could make arbitration more accessible to a broader range of parties, including those from emerging markets or small and medium-sized enterprises (SMEs). With AI reducing the costs and time associated with arbitration, it becomes a more attractive alternative to traditional litigation, particularly for parties in regions with limited access to skilled arbitrators or legal professionals.
• Enhanced Legal Research: AI tools like predictive analytics and machine learning can assist in legal research, offering deeper insights into legal trends, potential outcomes, and relevant case law. This can improve the quality of legal arguments presented in arbitration, as AI can help arbitrators and legal counsel quickly identify relevant precedents, reducing the time spent on manual legal research.

Negative Aspects and Limitations of AI in Arbitration

• Loss of Human Judgment: Arbitration involves complex legal reasoning, nuanced interpretation of facts, and often delicate negotiations that require a human touch. AI, despite its sophistication, may not fully capture the subtleties of human emotions, intentions, and cultural differences, which can be vital in certain arbitration cases. Over-reliance on AI may lead to decisions that, while legally sound, may lack the empathy or contextual understanding that a human arbitrator might bring to the table.
• Bias in Algorithms: AI systems are only as good as the data on which they are trained. If the datasets used to train AI models are biased or unrepresentative, there is a risk that AI could perpetuate or even exacerbate these biases in arbitration. This can lead to unfair decisions or reinforce existing inequalities, particularly if the system fails to account for socio-economic, cultural, or legal disparities between the parties involved in the dispute. Privacy and 
• Confidentiality Concerns: Arbitration is often preferred over litigation due to its confidentiality. The use of AI in arbitration, especially when handled by third-party platforms, can raise concerns about data privacy and the security of sensitive information. AI systems require access to vast amounts of data, which may include confidential documents, and ensuring the protection of this data from cyber threats is a significant challenge. Data breaches or unauthorized access could undermine the integrity of the arbitration process. 

• Limited Applicability: While AI can be useful in certain aspects of arbitration, it is less effective when it comes to complex, high-stakes, or multifaceted disputes. AI might struggle with cases that involve complex legal questions, novel issues, or those requiring the arbitrator to interpret law in ways that go beyond simple precedent. Additionally, AI tools may not be able to fully understand the nuances of specific industries, regional legal frameworks, or the personal dynamics between the parties involved.
• Trust and Adoption Barriers: The widespread adoption of AI in arbitration may face resistance from practitioners who are skeptical about technology’s ability to replace human judgment. Many arbitrators and legal professionals might be concerned about relying too much on AI, especially when it comes to making final decisions or resolving disputes. The lack of trust in AI’s ability to exercise discretion and judgment in a way that aligns with the principles of fairness and justice could slow its adoption.
• Regulatory and Ethical Issues: The integration of AI in arbitration raises several regulatory and ethical questions. For instance, how will arbitrators ensure that AI decisions align with international arbitration standards and principles? Who is responsible if AI systems make erroneous decisions? Ethical concerns about transparency in how AI algorithms are designed and applied in decision-making processes must also be addressed. Without clear guidelines and regulations, the use of AI could lead to disputes over accountability and fairness. 
• Technology Dependence and System Failures: AI in arbitration heavily relies on technology, which means that any technical malfunction, cyber attack, or system failure could disrupt the arbitration process. Furthermore, if an AI system produces incorrect results or fails to interpret legal nuances, it could undermine the integrity of the entire process. Human oversight is still necessary to ensure that AI tools are being applied correctly and that decisions are aligned with the values of fairness and justice. 

While AI holds great promise in enhancing the arbitration process by improving efficiency, reducing costs, and enhancing decision-making consistency, it also poses significant challenges related to bias, privacy concerns, and the risk of over-reliance on technology. Striking the right balance between AI and human involvement in arbitration will be crucial in harnessing its benefits while mitigating the potential downsides. With careful development, regulation, and oversight, AI could play an increasingly important role in the future of arbitration, but it should complement rather than replace the human judgment and expertise that are central to the practice.

Future implication of AI in arbitrations:

The future implications of AI in arbitration are vast, offering opportunities to transform the practice and potentially reshaping dispute resolution globally. As technology evolves, AI will likely play an increasingly important role in all stages of arbitration, from case initiation to the enforcement of awards.

AI has the potential to significantly reduce the time and cost associated with arbitration. In the future, AI-powered tools could handle many procedural tasks such as document review, contract analysis, and evidence sorting. These tasks, which currently require substantial human resources, could be automated, resulting in faster case resolutions and lower legal fees. For smaller businesses or individuals with fewer resources, this could make arbitration a more accessible alternative to costly litigation. This could democratize access to arbitration, enabling parties that might otherwise be priced out of the system to engage in dispute resolution. Furthermore, arbitration institutions could experience an influx of new cases, as companies and individuals seek quicker, more affordable alternatives to traditional courts.

AI could act as a decision support tool, providing arbitrators with in-depth analysis, predictive insights, and even draft award suggestions based on historical precedents, case law, and similar dispute outcomes. By analyzing vast amounts of legal data, AI could help arbitrators identify patterns, offer insights into trends in past rulings, and even suggest how similar disputes were resolved. However, these suggestions would still be at the discretion of the human arbitrator, who would ultimately make the final decision.  AI could significantly improve the quality and consistency of arbitration decisions by providing arbitrators with data-driven insights. However, this would raise questions about how much reliance on AI is appropriate and whether it could diminish the unique expertise and judgment that experienced human arbitrators bring to complex cases.

AI’s ability to predict the potential outcome of arbitration cases based on historical data could transform how businesses and legal teams approach dispute resolution. By leveraging machine learning and predictive analytics, parties could assess their chances of success before embarking on arbitration. This could influence settlement negotiations and decision-making, helping parties better understand the risks involved. Predictive analytics might encourage more informed decision-making and lead to an increase in settlements before arbitration begins. While this could reduce the overall number of cases going to full arbitration, it could also mean that the process becomes more competitive, with parties working harder to present strong cases early on.

AI can help level the playing field by making arbitration more accessible to smaller businesses that might not otherwise have the resources to participate in traditional arbitration processes. By reducing the cost of dispute resolution and enhancing the speed and efficiency of the process, AI tools could provide a more affordable way for SMEs to resolve disputes without resorting to lengthy and expensive litigation. This democratization of arbitration could result in increased participation from businesses that would traditionally avoid formal dispute resolution processes, contributing to a more vibrant and dynamic arbitration landscape. It could also reduce the barriers to access, particularly for businesses in developing markets. As AI-powered platforms and tools become more widespread, arbitration could become more globally interconnected. AI could help bridge gaps between different legal systems, languages, and cultures, allowing parties from various jurisdictions to engage in arbitration without the need for local legal expertise. This could make international arbitration more seamless and less dependent on physical location, enabling quicker resolutions and reducing the need for travel and face-to-face meetings. International arbitration could become more streamlined and cost-effective, attracting global businesses and expanding the scope of cross-border dispute resolution. However, there could be challenges around ensuring that AI tools are adaptable to different legal systems, cultures, and languages, and whether they account for diverse legal principles across jurisdictions.

AI is expected to play a major role in the future of online dispute resolution (ODR), which already relies heavily on technology to resolve disputes without in-person hearings. As AI continues to evolve, more arbitration cases could be handled through ODR platforms where AI assists in managing case submissions, reviewing evidence, facilitating communication, and even guiding the decision-making process. In fact, AI may be integrated into fully virtual arbitration systems, where cases can be resolved entirely online with minimal human intervention. The rise of AI in ODR platforms could increase the accessibility of arbitration, particularly in remote regions or for parties with limited resources. However, it might also raise concerns about the fairness and transparency of the arbitration process if not carefully regulated. Ensuring that all parties have equal access to AI tools and that the process remains accountable will be essential.

As AI becomes more integral to arbitration, there will be increased scrutiny regarding its ethical and legal implications. Questions about bias in AI algorithms, data privacy, accountability, transparency, and the fairness of AI-generated decisions will need to be addressed. Regulatory bodies may need to create new guidelines or frameworks for AI’s role in arbitration, ensuring that the technology is used ethically and in a manner consistent with the principles of justice and fairness. The need for clear regulations around AI’s role in arbitration will become critical. This could lead to the creation of new international standards and regulations for arbitration institutions that use AI in their processes. Legal professionals and arbitrators will need to be well-versed in the ethical considerations surrounding AI to ensure that it is used responsibly.

The most likely scenario for the future of AI in arbitration is a collaborative one, where AI complements the work of human arbitrators rather than replaces them. Human arbitrators will continue to play a central role in making final decisions, while AI will assist with administrative tasks, document review, and decision analysis. The combination of human judgment with AI’s analytical capabilities can create a more efficient, fair, and accurate arbitration process. The future of AI in arbitration is likely to be marked by greater collaboration between human arbitrators and AI technologies. This collaborative approach could enhance the efficiency, consistency, and fairness of arbitration decisions while retaining the essential human elements of judgment, discretion, and empathy.

The future implications of AI in arbitration are vast and transformative, with the potential to improve efficiency, reduce costs, and enhance decision-making. However, careful consideration must be given to the ethical, regulatory, and human aspects of AI’s integration into arbitration. A balanced approach that leverages the strengths of AI while preserving the core values of fairness, transparency, and justice will be crucial to ensuring that AI becomes a valuable tool in the arbitration process. As AI technology continues to evolve, its role in shaping the future of arbitration will undoubtedly grow, creating new opportunities and challenges for legal professionals and disputing parties alike.

 Recommendations for Arbitration firms:

Arbitration firms should consider adopting AI tools that enhance efficiency and improve decision-making. Some examples of AI-powered tools include: AI-powered software can automatically review and categorize large volumes of documents, identify key terms, and flag potential issues. This can be especially useful for arbitration cases involving complex contracts or extensive documentary evidence. AI can assist in legal research by quickly searching vast databases of legal precedents, arbitration awards, and case law, allowing legal teams to identify relevant information more quickly and accurately. AI-driven predictive tools can help evaluate the likelihood of certain outcomes based on historical data, assisting clients in making more informed decisions about their case strategy. Invest in AI-powered platforms and tools that automate routine tasks like document review, contract analysis, and legal research, thus freeing up time for your team to focus on strategic and complex aspects of arbitration.

While AI can enhance efficiency, it cannot replace human judgment, particularly in complex, high-stakes arbitration cases. A key recommendation is to integrate AI as a support tool, not as a replacement for experienced arbitrators or counsel.  Use AI to assist with analysis, but ensure that human arbitrators retain ultimate control over decision-making. AI can provide insights and data, but human expertise is necessary for the nuanced interpretation of law, cultural considerations, and the personal dynamics of each case.  Ensure your team is trained to understand AI tools and leverage them effectively. Arbitrators and lawyers should know AI’s capabilities, limitations, and ethical considerations.  Position AI as a tool for enhancing the efficiency of the arbitration process while preserving human oversight and judgment. Develop internal training programs for staff to stay updated on AI tools and their use in legal practice.

The use of AI in arbitration involves processing large volumes of sensitive and confidential data. Protecting client confidentiality and ensuring the security of proprietary information is paramount. Implement robust cybersecurity measures to safeguard data from breaches, and ensure that all AI tools comply with data protection regulations, such as GDPR or other regional privacy laws.  Conduct regular audits of AI tools to ensure that they meet data security standards. Work with trusted vendors who comply with best practices in data privacy.  Establish strong data security protocols and work with AI providers who prioritize privacy and confidentiality to maintain the trust and confidence of your clients.

With the rise of remote work and the increasing reliance on digital platforms, incorporating AI-driven Online Dispute Resolution (ODR) tools can position your firm as a leader in modern arbitration practices. AI can streamline case submission, communication, and evidence review, providing a more accessible and cost-effective way for parties to resolve disputes without the need for in-person meetings. Consider developing or partnering with an ODR platform where AI can facilitate the resolution process, including automatic scheduling, document exchange, and communication between the parties, arbitrators, and legal teams. AI can also be used to manage virtual hearings, assist in the organization of video conferences, and ensure that all technical elements run smoothly. Develop or adopt AI-powered ODR platforms to offer clients a more convenient, cost-effective alternative to traditional arbitration, especially for smaller disputes or cross-border cases.

Predictive analytics can provide valuable insights into the likely outcomes of arbitration cases, helping both clients and arbitrators make more informed decisions. By analyzing past cases, legal precedents, and arbitrator behavior, AI can offer predictions about the likelihood of success, potential settlement amounts, and the timeline of the arbitration process. Use predictive analytics to guide your clients’ decision-making, offering insights that can help them assess their position before agreeing to arbitration or settlement. AI can identify opportunities for early settlement by predicting the likelihood of an award outcome, helping clients avoid the costs and time associated with a full arbitration process. Integrate predictive analytics into your case management processes to offer clients data-driven advice on strategy, settlement, and risk management.

AI continues to gain traction in arbitration, ethical concerns related to bias, transparency, and fairness must be addressed. To maintain your firm’s reputation, ensure that any AI tools used are ethically sound and free from bias.  Ensure that the AI tools you use are trained on diverse datasets to avoid perpetuating any bias in case outcomes. Regularly evaluate and adjust the algorithms to ensure fairness and neutrality. Maintain transparency with your clients about how AI is being used in the arbitration process. Clearly outline the role of AI in decision-making and data processing to avoid any misunderstandings or ethical concerns.  Adopt clear ethical guidelines for the use of AI in your firm, ensuring that AI applications are transparent, unbiased, and compliant with relevant ethical and legal standards. 

As AI becomes more prominent in arbitration, clients may have concerns or limited understanding about how it will affect their cases. Educating clients about the benefits and limitations of AI in the arbitration process will help manage expectations and build trust. Offer informational sessions or workshops to explain how AI can benefit their arbitration cases, such as faster resolution times, cost reduction, and increased accuracy. Ensure that clients understand that AI is an aid to, not a replacement for, human arbitrators and that the decision-making process will still be governed by legal professionals’ judgment. Develop a client education strategy that explains the role of AI in arbitration, emphasizing transparency, trust, and the value it brings to the arbitration process.

To stay at the forefront of technological advancements, consider forming partnerships with AI technology providers that specialize in legal applications. These partnerships can help your firm stay updated on the latest AI trends and offer the most advanced tools available. Work with AI providers to ensure their tools integrate smoothly into your existing systems, such as case management platforms and communication tools. Collaborate with technology providers to customize AI tools that meet the specific needs of your firm and its clients. Establish partnerships with leading AI technology firms to stay ahead of the curve, ensure the integration of cutting-edge tools, and provide the best service to clients.

AI has the potential to revolutionize arbitration by improving efficiency, reducing costs, and enhancing decision-making. By embracing AI tools while maintaining a human-centered approach, your arbitration firm can deliver better value to clients, stay competitive in a rapidly evolving market, and improve the overall arbitration experience. Key steps include investing in AI technology, ensuring data security, maintaining ethical standards, and educating clients on AI’s role in dispute resolution. By strategically integrating AI, your firm can play a leading role in the future of arbitration.

  In conclusion, the role of AI in streamlining arbitration processes is like having a hyper-efficient intern who never needs a coffee break, never gets distracted by TikTok, and can sift through mountains of paperwork faster than you can say “arbitration clause.” Imagine AI as the ultimate backstage assistant – it sorts documents, organizes evidence, and even predicts the odds of success in your case, all while you focus on the important stuff, like deciding which virtual tie to wear to the hearing. But let’s not forget, that while AI can handle the boring, repetitive tasks, it’s still the human arbitrator who’s the star of the show. AI might be great at calculating odds and digging through legal texts, but it doesn’t yet understand why a lawyer’s perfectly timed objection in a heated cross-examination deserves a standing ovation. So, AI will never replace the skilled arbitrators, but it sure makes the whole process faster, smoother, and maybe just a little bit more fun – like having a super-efficient robot sidekick who knows exactly where you left that pesky contract clause.