Over the past year, the role of artificial intelligence in corporate transactions in the UAE has shifted meaningfully. Where earlier discussions focused on proof-of-concept efficiencies, primarily in document review and summarisation, the current phase is characterised by deeper integration into transactional workflows and increasing relevance to legal judgement and deal execution.
This article focuses on how AI is being deployed in practice across transactions, what the next phase of adoption may look like, and the emerging risks associated with AI.
From Acceleration to Augmentation: The Current State of Play
AI is no longer confined to discrete tasks within transactions. It is now embedded across the deal lifecycle, particularly in:
- Due diligence at scale: While this functionality is still in its early stages and is not without flaws, AI systems can review entire data rooms, rather than sampled documents, identifying change-of-control clauses, and other contractual obligations relevant in M&A transactions in significantly less time than traditionally required.
- Contract analysis and deviation detection: Tools can compare agreements against playbooks/standard forms of agreements and flag non-standard provisions, enabling faster identification of issues and more consistent outputs.
- Drafting and iterative negotiation support: AI-generated first drafts and mark-ups are increasingly used as a starting point for negotiation, particularly for ancillary documents and standardised provisions. Certain platforms are also capable of doing so based on the law firm’s precedents, and can be adjusted when acting for a buyer or a seller.
- Transaction management and knowledge retrieval: AI is now used to extract insights from prior deals, internal precedents, and know-how systems.
The key shift is qualitative rather than purely quantitative: AI is not only speeding up work, but shaping how lawyers approach it. For routine legal tasks, lawyers are slowly moving from completing the work itself to supervising, validating and refining AI outputs. This allows lawyers to focus on higher-value analysis, strategic advice and client interaction.
General-Purpose AI Agents: Practical Use Cases
General-purpose AI agents are software systems powered by advanced AI models that can perform a wide range of tasks while operating with a degree of independence, functioning more like a digital assistant than a static tool. Built on technologies developed by organizations such as Anthropic, these agents can take high-level instructions, break them down into smaller steps, and execute them with minimal ongoing input, reviewing information, making decisions within defined parameters, and producing complete outputs. In this sense, they resemble a “robot assistant” for knowledge work: capable of handling multi-step tasks autonomously, iterating on their own outputs, and managing workflows end-to-end rather than simply responding to one-off prompts.
While much attention has focused on bespoke legal platforms, general-purpose AI agents are beginning to present some compelling use cases in transactional work. Rather than acting purely as a reactive assistant, AI can be configured to run structured workflows across a transaction, such as systematically reviewing an entire data room against a predefined diligence checklist, tagging documents, extracting key clauses, and populating a live issues list without continuous human prompting. For example, once provided with parameters (materiality thresholds, risk categories, and output format), general-purpose AI agents can autonomously iterate through hundreds of contracts, identify provisions of interest (e.g., change-of-control, exclusivity, or other clauses relevant to M&A transactions), and organise findings into a coherent, prioritised report. This reduces the need for manual first-level review and allows lawyers to focus on validation and judgment rather than document triage.
General-purpose AI agents can also operate as persistent “transaction managers” that sit alongside the legal team and continuously process new information as it becomes available. For instance, they can ingest updated data room uploads, and incoming correspondence, and automatically reconcile these against prior findings, flagging newly introduced risks, changes in commercial terms, or inconsistencies with earlier disclosures. AI can also suggest actions in response to such new information; for example, if an email is received from a client asking a question based on a share purchase agreement, an agent can retrieve such information and suggest a response for the lawyer’s consideration.
When integrated into broader workflows, AI agents can effectively run parallel workstreams in the background, surfacing only the most relevant outputs for human review and thereby allowing legal teams to focus their attention on negotiation strategy, client advice, and high-value decision-making. Of course, much of this discussion is theoretical, as this technology has yet to be fully deployed and is in the stage of rapid development.
Emerging Risks
The use of AI in corporate practice also introduces risks, particularly around confidentiality and over-reliance by lawyers. Many AI systems require the input of sensitive client information, raising concerns about compliance by law firms and lawyers with their obligations under UAE (including ADGM and DIFC) data protection laws and their professional obligations, including under the UAE Federal Law Regulating the Legal Profession and the Legal Consultation Profession. At the same time, there is a growing risk that lawyers may over-rely on AI-generated outputs, treating them as authoritative without sufficient verification. This can lead to errors, overlooked nuances, or misapplied legal principles, particularly in complex transactional contexts where judgment and context are critical. Accordingly, while AI can enhance efficiency, it must be used as a support tool rather than a substitute for professional expertise, with appropriate safeguards (in particular with respect to confidentiality and data protection), review processes, and internal policies in place.
Conclusion
AI is no longer an emerging concept in UAE corporate transactions - it is an operational reality. The current phase is defined by integration, testing and increasing reliance, coupled with a growing recognition of the associated risks.
For transactional lawyers practicing in the UAE (or any other) M&A market, the challenge is not whether to adopt AI, but how to do so effectively, leveraging its capabilities while maintaining the rigour, judgement and accountability that underpin legal practice.
As AI continues to evolve, those who can integrate it thoughtfully into their workflows will be best positioned to deliver faster, more informed and ultimately more valuable outcomes for their clients. At Hadef & Partners, we are actively deploying AI capabilities to achieve all of this for our clients, including on corporate transactions.
This article is intended for general informational purposes only and does not constitute legal advice. Readers should seek independent legal counsel in relation to their specific circumstances.