Research and Articles

• Artificial intelligence (AI) is rapidly transforming the M&A lifecycle by automating deal sourcing, streamlining due diligence, and enhancing post-acquisition integration, enabling faster, data-driven, and more strategic transactions.

• While AI increases efficiency, its effectiveness depends on high-quality data, regulatory compliance, and robust human oversight to mitigate risks such as bias, privacy breaches, and unreliable outputs.

• Role of lawyers is shifting from manual processing to strategic advisory, as AI handles routine tasks, prompting lawyers towards tangible impact and risk mitigation.

A. Introduction

Artificial intelligence (“AI”) is the latest stage in the ever-developing field of technology which is changing the way we process information. From limited and controlled value propositions, AI has evolved into a global phenomenon; reshaping how decisions are made across industries, businesses and practices. In a surprisingly short span of time, AI has become ubiquitous, signalling a structural shift in how data is processed, insights are generated, and strategies are formulated. Today, AI is used for a vast range of activities, including, generating analytical/ practical insights, classifying information, predicting outcomes, and generating solutions tailored to specific enterprises or data subjects or situations.

One of the fields where AI is increasingly becoming more visible is mergers and acquisitions (“M&A”). M&A involves merging/ combining, purchasing (either completely or partly), or selling companies for varied reasons, including, growth, efficiencies or entrance into new markets or as an exit for profits or wriggling out of potential losses. As M&A transactions and the law and processes surrounding it becomes progressively complex and lengthy, the adoption of AI-driven tools to streamline processes and reduce labour-intensive tasks has emerged as a natural progression. AI is being deployed to identity opportunities, detect potential risks, shorten transaction timelines, and ease the process involved, each of which marks a shift in global M&A strategies. AI leverages sophisticated algorithms to accelerate deal sourcing, enhance the accuracy of due diligence, and streamline post-merger integration, thereby supporting more informed and strategically sound decision-making.

This article examines the role of AI across the M&A lifecycle, explores its legal and business applications, and assesses the associated risks, while outlining emerging trends and the evolving role of legal advisors.

B. AI and the M&A Lifecycle

The lifecycle of an M&A transaction can be broadly divided into three key stages: (i) pre-acquisition, involving deal sourcing, screening, valuation determination, identification of key financial factors, sharing of information, legal structuring and evaluation or regulatory requirements¹; (ii) acquisition, involving, due diligence, documentation, negotiations and conditions to closing, regulatory filings; and (iii) post-acquisition, involving, integration and synergies.

This part will provide a brief stage wise analysis of how AI is shaping deal lifecycles. Beginning with pre-acquisition stage of deal sourcing and target screening, AI enables faster and more accurate identification of high-potential opportunities by analysing both structured and unstructured data to shortlist targets aligned with strategic objectives. The focus then shifts to the acquisition stage which includes due diligence and deal execution, where AI is useful in reviewing documents, flagging critical clauses, assisting in drafting of documentation, and identifying hidden liabilities. The final stage of integration and synergies addresses post-acquisition processes, highlighting how AI supports system integration, cultural alignment, and real-time assessment of financial and operational synergies, making the integration process more structured and predictable.

(1) Pre- Acquisition: Deal Sourcing & Screening

An M&A transaction is not a single step but a series of multiple steps that take shape with parties and their teams of professionals engaged in identifying and evaluating potential investment targets over a course of time, generally ranging between 3 to 6 months or more if the transaction involves substantial complexity. While financial statements remain central to this assessment, the decision-making process extends well beyond numerical analysis. Considerations such as alignment with the acquirer’s long-term strategy and values, the degree of control sought, the target’s integration history, and the conduct of existing investors, all influence the selection of a suitable target, the reputation of the target in market, the market presence in the media and the public view impacts the long term growth of a target, especially in a country like India where brand reputation hold immense value. Traditionally, assessing these variables has involved a time-intensive and largely manual exercise of monitoring and reviewing information and analysing data, often limited by the sheer volume and fragmentation of available inputs.

With the introduction of AI tools, early stages of M&A lifecycle can be made significantly more efficient. Financial data and risk analysis can be processed rapidly, while vast amounts of publicly available information can be filtered, correlated, and synthesised to generate more actionable insights and alignment with long term plans. Historical transactions, for example, can be analysed across multiple companies to assess patterns relating to integration complexity and management adaptability and the governance structure of the company, which may also give an edge to the investor its initial offer.

By analysing both structured and unstructured data, including news reports, regulatory filings, patents, industry research, customer and employee reviews, AI provides a more comprehensive, data-driven view of the competitive landscape and analytics of the target entities for investment. This level of automation allows deal teams to shift their focus away from repetitive data gathering and risk monitoring toward higher-value assessments of strategic alignment and value creation, thereby improving both the speed and quality of early-stage deal evaluation.

Building on this, AI also plays a critical role in post-signing integration planning and, by extension, in more accurate price discovery. By leveraging predictive analytics, AI can simulate integration scenarios across operational, cultural, and technological dimensions, enabling investors to assess the feasibility, timeline, opportunities and risk involved and cost of integration with far greater precision. This allows the deal teams to move beyond broad assumptions and instead quantify potential integration risks, and execution challenges in advance. Where AI-driven insights indicate a higher probability of seamless integration, there is greater possibility of higher returns on investment. This enhanced visibility often translates into greater pricing confidence, thereby potentially allowing investors to pay a premium where the perceived execution risk is lower and value creation is more certain. In competitive bidding situations, this effect is even more prominent, bidders equipped with AI-enabled integration assessments can make more informed, conviction-backed bids, reducing the speculative element traditionally associated with M&A pricing and thereby contributing to more efficient and rational price discovery.

(2) Acquisition: Due Diligence & Deal Execution

The core of any M&A transaction is shaped by documentation, risk assessment and allocation, identification of potential roadblocks, and negotiations. Each of these steps is directed towards preserving the integrity of the deal, ensuring that the transacting parties have a clear delineation of roles and responsibilities and awareness as to the terms on which they are engaging. In respect of purchasers, an important lookout is a clear and informed view of the target and minimising the risk of post-acquisition surprises that may prove costly or difficult to unwind. As transactions become more complex and data-heavy, this phase of the M&A lifecycle has grown increasingly demanding, placing significant pressure on deal teams to balance speed with accuracy. While having watertight indemnity clauses provides future risk protection, however, detection and resolution of risk at an early stage is always a preferred option for purchasers.

Due diligence sits at the heart of the process of an M&A transaction. The growing volume of documents and the expanding scope of legal, regulatory, operational, and commercial review have made traditional diligence exercises both time- and resource-intensive. AI is increasingly being deployed to address these challenges, particularly within legal teams, where in-house and firm-specific AI tools are used to analyse large document sets while managing confidentiality and data protection concerns. These tools can respond to targeted queries within lengthy agreements, cross-reference documents to surface inconsistencies, identify missing licences or approvals, and verify disclosures against publicly available information. In the Indian context, for instance, data from the Ministry of Corporate Affairs (“MCA”) portal, when combined with court judgments, regulatory filings, and media reports, can be analysed by AI to quickly identify compliance gaps, related-party transactions, or litigation risks that might otherwise take weeks to uncover.

Having said that, certain operational roadblocks continue to impede the automation of the diligence process. For instance, although AI tools can process large volumes of data and flag potential risks and gaps across multiple public sources, there are credential based restrictions and tools to block data scrapers from infringing on materials hosted on various sites. Another challenge is adequate maintenance of relevant data and records – a practice which has varied levels of implementation. Similarly, tools that claim to predict judicial outcomes based on case facts, precedents, and the adjudicating bench remain largely unreliable, given the inherently subjective nature of judicial decision-making, which cannot be accurately predicted.

As diligence findings feed into deal negotiations and may also impact the valuation of the transaction, AI also begins to influence how commercial terms are shaped. By analysing deal databases, market behaviour, red flag risks, and sector-specific patterns, AI-driven models can support valuation exercises and risk-adjusted pricing decisions. Where heightened regulatory or litigation risks are identified, negotiation strategies can be recalibrated to include stronger representations and warranties backed by indemnities. AI also plays a significant role in cross-border transactions by enabling faster translation of documents, analysing multi-jurisdictional regulations, and identifying jurisdiction-specific risks, while also helping streamline coordination across different legal and regulatory frameworks. This enables clients to engage with and communicate through a single diligence or transaction team that has a holistic understanding of the entire transaction, rather than adopting a fragmented, piecemeal approach.

Looking ahead, more advanced applications of AI are expected to reshape how negotiations are conducted, particularly through the use of agentic AI² models and digital twins³. These systems function by observing and learning from historical negotiation data, drafting styles, communication patterns, and decision-making behaviour of the parties involved. Over time, an AI model can develop a digital representation of a negotiator or an organisation, capable of simulating how that party is likely to respond to specific proposals, risk allocations, or commercial trade-offs. For example, in a transaction where a buyer has historically taken a conservative approach to regulatory risk, the digital twin can simulate likely counter-positions on indemnities or closing conditions when new risks are introduced. While such tools are still in an early stage of adoption, they signal a future in which negotiations become more predictable, strategically informed, and scalable, enabling professionals to participate in multiple transactions simultaneously without diluting the quality of outcomes. In an increasing trend, AI is likely to impact the due diligence process in the future, reducing human effort of reviewing large set of documents and limiting it to analysing the critical clauses and risks that are identified by the AI and providing advisory support to include it in the larger picture of the deal making.

(3) Post-Acquisition: Integration & Synergies

The integration and planning stage is often decisive in determining whether an acquisition delivers long-term value. Beyond combining operations, this phase requires careful alignment of systems, people, and culture, alongside timely realisation of both cost and revenue synergies. AI supports this process by helping teams identify synergies with greater accuracy, design structured transition plans, and monitor performance against defined objectives, ensuring that the combined entity remains aligned with its strategic goals.

Post-acquisition, AI plays a key role in integrating IT, finance, legal, and HR functions by analysing system architectures, data standards, and operational interdependencies. It assists in determining which systems should be consolidated or retired, streamlines financial reporting and compliance, and flags integration risks across technology and legal workflows. By automating these complex and time-intensive tasks, AI reduces operational risk and allows management to focus on higher-value strategic priorities. In future, it is likely that the secretarial work such as form filing and submissions to the governmental authorities may become automated at the target or purchaser level itself.

AI can also help address cultural and people-related challenges, which are often a source of integration failure. By analysing unstructured data such as employee feedback and engagement patterns, AI can surface cultural misalignments early and support smoother HR and payroll integration. However, the application can be limited based on the challenges highlighted in Section C of this paper. Post-closing, it tracks whether anticipated synergies, such as cost savings or cross-selling opportunities, are being realised, while enabling scenario-based simulations to test integration strategies and support informed decision-making.

C. AI in Legal and Business Dimensions

Understanding the role of AI in M&A requires a balanced appreciation of both its business potential and its legal implications. While AI can significantly enhance efficiency, insight, and value creation across the transaction lifecycle, its deployment also raises questions around data integrity, confidentiality, regulatory compliance, and accountability. Evaluating AI solely through a commercial lens risks overlooking legal constraints that may shape outcomes, just as a purely legal assessment may miss its strategic impact on deal execution and integration. A holistic approach, therefore, is essential to fully assess how AI can be effectively and responsibly embedded into the M&A process.

(1) Legal Applications

AI’s impact on M&A is most visible in contract drafting and negotiation, where established legal technology platforms have already become part of everyday deal work. AI tools are increasingly being used by law firms to review large volumes of transaction documents, extract key clauses based on precedent and facilitate drafting. In drafting, generative AI systems are increasingly used to prepare first drafts of ancillary agreements or relevant clauses by drawing from a firm’s historical deal database. During negotiations, these tools can benchmark proposed clauses against market standards, flag non-standard indemnity caps or termination rights, and suggest fallback language that has historically been accepted in similar transactions, allowing lawyers to negotiate faster without compromising risk positions.

As mentioned above, a more forward-looking application is the use of AI “digital twins” for scenario testing, which is beginning to emerge in complex transactions. For example, contract analytics platforms have demonstrated the ability to model how a portfolio of commercial contracts might behave following a change of control, simulating outcomes such as consent requirements, termination risks, or pricing renegotiations.

(2) Identified Issues

AI delivers significant time and cost efficiencies across the M&A lifecycle, but its adoption introduces several legal, regulatory, and operational risks that require careful oversight. While AI enhances data processing, document review, and risk identification, its output remains dependent on data quality, regulatory compliance, and human supervision. If not properly managed, these risks can create material legal, commercial, and confidentiality concerns in transactions, which may lead to significant costs. Key risks include:

1. Data Quality Risk (Garbage in, garbage out) – The reliability of AI outputs depends heavily on the accuracy, completeness, and neutrality of the input data. Poor-quality or biased datasets can lead to flawed legal analysis and decision-making.

2. Privacy and Confidentiality Risks – AI tools often process sensitive personal, financial, and transactional data, raising concerns regarding data protection, cybersecurity, and compliance with cross-border data transfer and data privacy regulations. Given the increased scrutiny of regulators on use of personal data, this may result in imposition of significant penalties and loss of reputation, given the sensitivity of data involved.

3. Hallucinations and Reliability Issues – AI systems may generate inaccurate or fabricated legal authorities, regulatory interpretations, or factual conclusions, making human validation essential. This would be particularly significant when dealing with matters involving human resources, as that deals with sensitive data and affects livelihoods.

4. Regulatory and Compliance Risks – Use of AI in M&A must align with evolving regulatory frameworks, such as the EU AI Act⁴ and emerging Indian regulatory guidance⁵, which impose obligations relating to data governance, transparency, and accountability.

5. Lack of Human Judgment and Commercial Intuition – AI cannot fully replicate human judgment, contextual understanding, or qualitative assessments, such as evaluating management capability, negotiation dynamics, or strategic business considerations.

Together, these risks highlight the importance of integrating AI as a support tool rather than a substitute for professional legal and commercial expertise.

(3) Business Opportunities and Application

Due diligence and screening in M&A have extended beyond financial and legal analysis to include a deep assessment of human resources and organizational culture. AI not only strengthens traditional diligence by analysing structured and unstructured data but also helps surface cultural alignment issues and operational gaps at an early stage. By mapping data across IT, finance, CRM, HR, and payroll systems, AI enables acquirers to assess the degree of integration required and identifies potential friction points well before closing. In parallel, AI-driven cultural alignment and employee sentiment analysis, drawing on internal communications, engagement metrics, and publicly available employee feedback, helps predict workforce integration challenges and informs strategies around harmonizing benefits, policies, and organizational practices prior to the transaction.

From a value creation perspective, acquisitions rely on the timely realization of both cost and revenue synergies. AI supports this by enabling predictive modelling of operational synergies, helping deal teams estimate cost savings from eliminating overlaps in infrastructure and personnel, as well as revenue upside from cross-selling and market expansion. AI can translate these insights into structured integration roadmaps and execution plans, ensuring preparedness from day one. Post the closing of a transaction, AI-driven dashboards provide real-time integration monitoring across functions, tracking progress against synergy targets, flagging deviations, and benchmarking performance against comparable transactions. Combined with continuous market and competitor monitoring and, in financial contexts, enhanced fraud detection and compliance oversight, AI helps transform post-acquisition integration into a measured, data-driven process rather than a reactive exercise.

(4) Business Risks

The growing use of AI in M&A and legal practice underscores the need for strong human oversight, judgment, and accountability. While AI can enhance efficiency and decision-making, over-reliance on automated outputs, particularly in high-stakes legal and enforcement contexts, can undermine sound judgment and erode trust in leadership if those outputs are flawed. This makes it essential to retain a clear human-in-the-loop framework, including the ability to question and override AI recommendations. The risk is especially pronounced with agentic AI models, where semi-autonomous decision-making heightens the consequences of error and reinforces the need for consistent oversight.

These governance concerns are further amplified in people-centric areas such as employee and cultural analysis, where overlooked nuances or undetected AI bias can lead to integration challenges, higher attrition, and reduced operational efficiency. Regulatory developments, such as the EU AI Act’s emphasis on human oversight in high-risk AI use cases, signal that these principles will increasingly become mandatory.⁶ At the same time, the rapid adoption of AI within legal teams has outpaced the development of governance frameworks, creating regulatory, ethical, and malpractice risks. Addressing this gap requires organizations to balance AI capabilities with human judgment, cultural sensitivity, and robust internal controls to ensure responsible and sustainable AI deployment.

As AI becomes more deeply embedded in the M&A lifecycle, effective governance emerges as a critical safeguard. Ensuring meaningful human oversight and clear accountability is essential to prevent over-reliance on automated systems and to preserve trust in decision-making. Equally important are the ethical considerations that accompany AI-driven insights, particularly where outcomes affect employees, stakeholders, and market fairness. A robust governance framework, grounded in transparency, human judgment, and ethical responsibility, will be central to enabling organizations to harness AI’s advantages while mitigating the risks inherent in its use.

D. Future Outlook and Role of Law firms

(1) Recent Trends

The integration of AI has started to fundamentally reshape the roles of law firms and business offices, marking a sharp departure from the pace of technological adoption seen even a few years ago. What was once characterised by cautious experimentation and isolated pilot projects has evolved into widespread, embedded use across legal and commercial functions. Today, AI is no longer viewed as a support tool limited to efficiency gains, but as a strategic capability influencing how advice is delivered, risks are assessed, and decisions are made. This acceleration in adoption has redefined expectations from legal and business teams alike, requiring them to adapt their skill sets, workflows, and governance models to keep pace with both rapid technological advancement and evolving client demands.

As indicated in an analysis of a poll conducted by Bain & Company on the views of 300 M&A practitioners about generative AI, while current adoption of AI in dealmaking remains limited, its transformative potential is widely anticipated across the industry. As per the statistical analysis of the survey, 16 % practitioners were using generative AI in their M&A practice as of 2024. However, a significant 80 % of respondents expect to integrate generative AI into their workflows within the next three years, highlighting a strong belief in its future relevance. Early users report tangible benefits: 78 % cite productivity gains from reduced manual effort, 54 % note accelerated timelines, and 42 % see lower costs and sharper focus on high-value tasks. Moreover, 85 % of adopters say that the technology has met or exceeded their expectations, despite challenges such as data accuracy, privacy concerns, hallucinations and the need for human oversight.⁷

(2) Anticipating Changes: Three Horizons of AI Evolution in M&A

Horizon 1 – The Efficiency Phase (Short-Term)

The legal profession’s relationship with AI has changed noticeably in a very short time. What began as a tool for basic drafting and content creation has evolved into something far more embedded in day-to-day legal work. Law firms and in-house teams are now using AI to streamline core functions such as contract review, due diligence, and document management, signalling a clear shift from experimentation to reliance over AI. AI is no longer just about saving time, it is increasingly shaping how legal services are delivered and how lawyers approach complex work in an efficient time.

This shift is already visible in practice. Lawyers can now ask AI tools to compare clauses, explain their implications, or suggest alternative language tailored to a specific context, making the technology feel less like software and more like a dependable colleague. These systems are designed to work with trusted legal sources, helping reduce the risk of errors while improving speed and consistency. In M&A transactions, AI-driven due diligence is becoming a practical necessity for handling large volumes of data, even though its effectiveness still depends on how much reliable information is available and subject at all times to human oversight. Overall, AI is steadily becoming a natural part of the legal workflow, changing not just how fast lawyers work, but how they think about their role.

Horizon 2: Strategic Force Multiplier (Medium-Term)

In the medium term, AI’s role in deal-making shifts from quietly optimising routine tasks to actively shaping strategy. Instead of just speeding up what lawyers already do, AI starts acting as a decision-support partner, helping teams negotiate smarter, price risk more accurately, and plan integrations more confidently. The real change is not efficiency alone, but the way judgment itself is informed. AI begins to sit alongside lawyers at the negotiating table, feeding them insights drawn from precedents, market data, and past outcomes, while allowing professionals more time to focus on the calls and discussions that actually require human instinct and experience.

This change is already visible in negotiations and risk assessment. AI tools can analyse draft contracts in real time, compare proposed terms against playbooks and historical deals, flag dispute risks, and suggest tactical responses as negotiations unfold. In parallel, predictive analytics are turning legal risk into numbers rather than gut feelings, moving from vague statements like “this litigation is risky” to quantified probabilities, likely damages, and settlement ranges. For M&A teams, this means legal risk can be priced directly into valuation, escrows, insurance premiums and earn-outs, replacing broad indemnities with far more precise, data-backed deal structures.

Beyond signing, AI is also changing how integrations are planned. As mentioned earlier, digital twins are also increasingly used during negotiations to simulate scenarios, assess outcomes, and support more informed decision-making by the parties. Digital-twin models and simulation tools allow acquirers to test regulatory compliance, IT compatibility, and even cultural fit before the deal closes. By analysing data across HR systems, technology stacks, and communication patterns, AI can highlight integration “hotspots” and stress-test scenarios, essentially running a pre-mortem on the transaction. This enables legal teams to draft sharper, more tailored representations and warranties and helps businesses anticipate where deals might stumble, long before those problems surface in the real world. Agentic AI, which refers to AI systems capable of independently performing multi-step tasks with minimal human intervention, is emerging as a strategic force multiplier in the medium term. In complex transactions, it can autonomously coordinate workflows, track regulatory requirements across jurisdictions, and assist in real-time decision support. By enhancing efficiency and reducing manual intervention, agentic AI enables professionals to focus on higher-value strategic and advisory functions.

Horizon 3: Autonomous Future (Long-Term)

Looking further ahead, AI points to a much deeper shift, one where the basic unit of legal work moves from individual documents to interconnected systems. Instead of lawyers reacting to issues after they arise, the focus turns to simulation and design. Straight-through processing and digital-twin mergers signal a future where deals are tested, stress-checked, and refined in virtual environments before they ever touch the real world. Legal work becomes less about fixing problems and more about preventing or restructuring or cushioning them before they could ever arise.

The most transformative idea, though, is the digital-twin merger. By creating virtual replicas of both the buyer and the target, companies can simulate a merger before committing to it, testing IT integration, regulatory compliance, supply chains, and even cultural fit. These models can highlight where synergies are likely to materialize and where friction or value leakage may occur, allowing deal teams to adjust pricing, warranties, and integration plans upfront. In this horizon, lawyers act less like document processors and more like conductors of intelligent systems, guiding autonomous workflows while focusing their time on strategy, trust, and the human relationships that still sit at the heart of every deal.

(3) Role of Lawyers & Law firms

The rapid adoption of AI is fundamentally reengineering the M&A legal landscape, offering a pivotal moment to dismantle the status quo in favour of a more modernized lifecycle. As AI absorbs the "bread-and-butter" mechanical tasks, such as high-volume due diligence and routine drafting, lawyers are being liberated to act as true strategic architects. This shift moves the practitioner away from manual processing and toward high-value advisory, navigating the nuanced legal terrains that require human judgment. Consequently, the industry is pivoting from traditional time-based billing toward value-based models; in this new era, clients will no longer pay for the hours a lawyer spends, but for the tangible strategic impact and risk mitigation they deliver.

E. Annexure: Role of Nishith Desai Associates in the Integration of AI into the M&A Ecosystem

We at Nishith Desai Associates (“NDA”) have positioned ourselves at the forefront of integrating AI into the M&A ecosystem, recognising both its transformative potential and associated risks. The firm has developed and adopted proprietary tools, such as, NaiDA⁸, its in-house AI platform, specifically designed to mitigate common concerns associated with third-party AI systems, including black-box decision-making, data privacy vulnerabilities, and confidentiality risks. In addition, NDA has maintained comprehensive archives of communications, transaction documents, and institutional knowledge generated by team members. When combined with NaiDA’s analytical capabilities, this repository enables the firm to generate insights and opinions that align with the established advisory style, legal reasoning, and best practices historically adopted by NDA professionals. NDA has embraced transparency in AI usage while ensuring rigorous human oversight to maintain the highest standards of legal accuracy, efficiency, and reduced transaction timelines.

Beyond operational efficiencies, NDA has played a strategic role in guiding clients through the evolving intersection of AI and M&A. The firm has actively anticipated emerging risks and advised on necessary guardrails relating to privacy, regulatory compliance, and fairness in AI deployment in form of detailed research papers⁹. Through its proactive approach, NDA is positioning itself as a thought leader operating at the convergence of law, business, and technology, serving as a pole-bearer for the new era of legal technology-driven advisory services.

Sonakshi Babel, Divyansh Bhardwaj, Muqeet Drabu and Vaibhav Parikh
You can direct your queries or comments to the authors.

¹Please note that the term “acquisition” is used colloquially to refer to the wide range of transactions that form part of the field of M&A.

²Agentic AI refers to autonomous systems that go beyond generating content to proactively set goals, plan, and execute complex, multi-step tasks with minimal human supervision. These intelligent agents use AI reasoning, memory, and external tools to interact with systems, adapt to changing conditions, and self-correct, aiming to mimic human-like decision-making for workflow automation. 

³A digital twin is a dynamic, virtual replica of a physical object, system, or process that uses real-time data from sensors to simulate, monitor, and optimize performance. By bridging the physical and digital worlds, these models allow for predictive maintenance, enhanced decision-making, and simulation of scenarios, reducing costs and accelerating innovation across industries like manufacturing, healthcare, and urban planning. 

⁴Available here: https://www.europarl.europa.eu/topics/en/article/20230601STO93804/eu-ai-act-first-regulation-on-artificial-intelligence.

⁵Available here: https://static.pib.gov.in/WriteReadData/specificdocs/documents/2025/nov/doc2025115685601.pdf.

⁶Available here: https://artificialintelligenceact.eu/article/14/.

⁷Available here: https://www.bain.com/insights/generative-ai-m-and-a-report-2024/

⁸Available here: https://www.nishithdesai.com/fileadmin/user_upload/pdfs/NDA%20In%20The%20Media/News%20Articles/timesofindia-meet-naida-the-ai-bot-for-lawyers.pdf

⁹Available here: https://www.nishithdesai.com/fileadmin/user_upload/pdfs/Research_Papers/Generative-AI-and-Disruption.pdf.