Why You Need a Contract Lawyer for AI and Tech Agreements in 2026
22 Sep
Last updated on August 10th, 2026 at 04:38 pm
TL;DR: A contract lawyer who has never worked on an AI or technology deal can still draft a competent, enforceable agreement, but competent is not the same as adequate here. AI and tech contracts raise a specific set of questions, who owns AI training data and outputs, who is liable when an algorithm makes a harmful decision, how a fast-moving regulatory environment should be built into a contract today, that ordinary commercial contract law was never designed to answer, and a generalist lawyer without direct experience in this area will often miss exactly the clauses that matter most. This guide covers what makes these agreements genuinely different, what a generalist typically misses, and what to actually look for when choosing a lawyer for this specific work.
Quick overview: This page is about choosing the right expertise, not the specific documents you need. For the actual list of contracts an AI startup should have in place, our guide on the essential contracts every AI startup must have covers that directly, and for the specific clauses to check in a vendor deal, our guide on AI vendor contracts does the same. This page focuses on why the lawyer drafting or reviewing those documents needs a different kind of expertise than a general commercial contract requires.
Why AI and tech agreements are genuinely different
Traditional software contracts were relatively contained: license some code, integrate an API, define uptime, done. AI and modern tech agreements introduce categories of risk that simply did not exist in that older model.
Data ownership and training rights. Every AI system needs data to train on, and the questions that follow are not ones ordinary licensing law answers cleanly: who owns the data used for training, what happens to it after training is complete, can it be reused for other purposes, and what happens if personal data was mixed into the training set. These questions barely mattered for traditional software; for an AI company, they are close to existential.
Liability for algorithmic decisions. When an AI system makes a consequential decision, a loan denial, a content moderation call, a medical triage suggestion, who is responsible if it gets it wrong is a genuinely unsettled question that courts and regulators are still working through. Our guide on who is actually liable when AI causes harm covers the legal theories currently used to answer this, and it is exactly this uncertainty that makes liability and indemnity clauses in an AI contract far more consequential than the equivalent clause in an ordinary services agreement.
A regulatory environment that is still being built. The EU AI Act is now in force and being phased in through 2026 with risk-based obligations for providers and deployers of AI systems. Data protection regimes, including India’s DPDP Act, layer further obligations on top wherever an AI system processes personal data. A contract drafted without this regulatory backdrop in mind risks being non-compliant on the day it is signed, not just eventually.
Uncertain intellectual property in AI-generated output. When an AI system generates code, content, or a design, who owns it, and was the underlying training data itself used lawfully, are questions that are actively being litigated right now, not settled areas of law a generalist can simply apply from precedent. Our guides on AI training and copyright and, for the mechanics of executing agreements involving AI-adjacent automation, smart contracts and digital agreements go deeper on these specific uncertainties.
What a generalist contract lawyer typically misses
This is not a criticism of general commercial lawyers; it is a mismatch of experience to problem. A lawyer without direct AI and tech experience is likely to draft data provisions that read like a standard data processing agreement without addressing what happens to data specifically used for model training, a gap that a court or regulator increasingly treats as materially different. They are likely to write a liability clause calibrated for ordinary software failure rather than the harder, less settled question of algorithmic decision-making. They may miss that a contract silent on AI training use of shared data can, by default in many jurisdictions, leave the party providing that data with far more exposure than either side intended. And they are less likely to have current, working knowledge of a regulatory landscape that is genuinely moving month to month, which means yesterday’s compliant clause can be today’s gap.
None of this means a generalist lawyer produces a bad contract. It means the contract is calibrated for the wrong set of risks, ones a technology or software deal would have, not the additional layer AI specifically introduces.
What to actually look for in an AI and tech contract lawyer
Direct experience with AI companies specifically, not only general software or technology clients. The issues genuinely differ, and experience with traditional SaaS licensing does not automatically transfer to AI training and model licensing questions.
Working technical understanding, not the ability to code, but a real grasp of how the systems they are drafting for actually function: what data a system needs, how it is trained, and broadly how it makes decisions. A lawyer who cannot engage with these basics at a working level will struggle to spot the risks specific to your actual product.
Active, current regulatory knowledge. Ask directly how they track developments like the EU AI Act’s phased obligations or evolving data protection requirements, and how recently they have actually applied that knowledge to a client’s contract, not simply read about it.
Commercial judgment, not just risk avoidance. The best contract lawyers in this space do not simply pile on defensive language; they structure deals that support what you are actually trying to build, understanding startup constraints and where flexibility genuinely matters versus where it does not.
Clear communication. AI and tech agreements are already dense; a lawyer who cannot explain the risk in a clause in plain language makes it harder for you to make good decisions about your own contracts, not easier.
Making it work on a startup budget
Legal input on every document from day one is not always realistic, and it does not need to be. Prioritise the highest-risk agreements first, usually your core customer contracts, key data and model licensing arrangements, and significant partnership deals, and build outward from there. A working relationship with a lawyer who reviews documents as you need them is typically far cheaper than trying to fix a problem after it has already surfaced. Investing in a solid, properly built template for your most common agreement, reviewed once by someone who actually understands your product, then reused with confidence, is usually more cost-effective than ad hoc drafting every time. Our guide on what should be included in every business contract covers the underlying drafting discipline that still applies beneath all of the AI-specific questions above.
Where to start
If you are building or scaling an AI or technology company and have not had your core contracts reviewed by someone with genuine experience in this space, that review is the highest-value next step, not a full rebuild of every document you have. Start with whichever agreement carries the most exposure right now, usually a data or model licensing arrangement, a significant customer contract, or a key vendor deal, and work outward. Our guide on the essential contracts every AI startup must have is a useful map of what that fuller document set looks like, and our technology lawyers for tech startups, SaaS, and AI platforms can review or build the specific agreements your business actually needs.
Frequently asked questions
Why can’t a general contract lawyer handle AI agreements?
A general contract lawyer can produce a technically valid agreement, but AI contracts raise specific questions, data and training rights, liability for algorithmic decisions, and a fast-moving regulatory environment, that ordinary commercial contract experience does not directly prepare someone to address. The resulting contract is often calibrated for the risks of a standard technology deal rather than the additional risks AI specifically introduces.
What makes an AI contract different from a regular software contract?
Traditional software contracts mainly address licensing, integration, and uptime. AI contracts add questions about who owns data used for training and what happens to it afterward, who is liable when an AI system’s decision causes harm, how AI-specific regulation like the EU AI Act applies, and who owns content or code an AI system generates, none of which have settled, well-established answers the way traditional software licensing questions do.
What should I ask a lawyer before hiring them for an AI contract?
Ask whether they have worked directly with AI companies, not only general software or technology clients, whether they can explain at a working level how the type of AI system you use actually functions, how they stay current on regulatory developments like the EU AI Act and data protection law, and for an example of how they have structured a data or liability clause specifically for an AI use case. Their answers reveal whether their expertise is current and specific, not general.
How much should an early-stage AI startup spend on legal review?
Rather than a fixed budget, prioritise by risk: get your highest-exposure agreements, typically core customer contracts, data or model licensing arrangements, and significant partnership deals, reviewed first, and build outward. A well-built, reviewed template for your most common agreement is usually more cost-effective over time than repeated ad hoc drafting, and catching a gap before signing is consistently cheaper than fixing one after a dispute.
Does AI-specific regulation actually affect my contracts right now?
Yes, if your business processes personal data, operates in or serves the EU, or deploys AI systems that make consequential decisions about people. The EU AI Act is being phased in with obligations for providers and deployers of AI systems, and data protection regimes such as the DPDP Act in India impose their own requirements wherever personal data is involved. A contract that does not account for this can be non-compliant from the day it is signed, not just as regulation evolves further.
Authored and reviewed by Prakhar Rai, Advocate, founder of My Legal Pal. Prakhar is enrolled with the Bar Council of India and has over ten years of experience advising technology and AI companies on contracts, liability, and regulatory compliance across India and cross-border. He is an alumnus of the National Law School of India University, Bangalore, where he completed his Master of Business Laws, and of La Martiniere. Connect on LinkedIn.
This article is general information, not legal advice. AI and technology law is developing rapidly and varies by jurisdiction. For advice on your own agreements, speak to a qualified lawyer with direct experience in this area.
Prakhar Rai is the founder of My Legal Pal and a licensed attorney. He started the practice after watching businesses that operate across borders get legal advice in fragments: a clause here, a reaction to a problem there, with no one looking at the whole picture or thinking a few steps ahead.
With more than a decade in business and corporate advisory, he came to a simple view. As companies started running on cross-border deals, digital platforms and overlapping regulation, they needed legal strategy built around how they actually work, not just documents drafted after the fact. My Legal Pal is built on that idea: foresight and clarity first, paperwork second.
He studied at La Martiniere College, holds an LL.B, and earned a Master of Business Laws from the National Law School of India University, Bangalore, specialising in corporate, banking, intellectual property, finance and securities law. That mix of academic grounding and hands-on advisory work shapes how he and the team approach every matter: commercially, not just technically.
