AI in Legal Practice: What the Next Generation of Lawyers Must Learn About Contract Law and Technology

Why the Legal Profession Is at an Inflection Point for AI Adoption

The conversation around AI in legal practice has shifted from speculation to implementation. Jen Leonard's book Unprecedented, discussed at a recent Practising Law Institute event, joins a growing body of thinking that treats the transformation of legal work not as a distant possibility but as a present-tense management challenge. For legal teams evaluating AI contract review tools or considering broader contract lifecycle management platforms, the central question is no longer whether to adopt but how to adopt responsibly and effectively.

Law firms and in-house departments are contending with the same tension: AI tools can accelerate work dramatically, yet the profession's gatekeeping structures, its liability frameworks, its ethical rules, were not designed with machine-generated output in mind. Understanding that gap is the starting point for any honest assessment of legal technology adoption.

What AI Contract Tools Actually Do Inside a CLM Workflow

Contract lifecycle management covers the full arc of an agreement, from initial request and drafting through negotiation, execution, storage, and renewal. AI now touches every stage of that arc, but not uniformly. The highest-value applications at present cluster around three areas: first-draft generation trained on a company's own precedent library; clause-level risk identification during review; and post-signature obligation extraction for compliance monitoring.

AI contract drafting software of the kind Adira provides does something subtler than generic large-language-model tools: it drafts in the organisation's own voice, applies jurisdiction-specific legal knowledge, and reads contracts from the client's perspective rather than producing a neutral document neither side owns. That distinction matters because a contract is a business instrument, not just a legal artefact. Generic AI output requires heavy rework; purpose-built CLM AI reduces that rework significantly.

For in-house legal teams, the workflow benefit compounds over time. Each reviewed and approved contract refines the system's understanding of acceptable positions, creating an institutional memory that survives lawyer turnover.

The Skills Gap the Legal Profession Is Only Beginning to Reckon With

One of the more uncomfortable arguments emerging from legal educators and commentators is that law schools have been slow to build genuine AI literacy into their curricula. Lawyers graduating today may understand that AI tools exist without understanding how to evaluate their output, prompt them effectively, or identify the failure modes that create professional liability.

This is not a criticism unique to legal education. Medicine and accountancy face the same challenge. But the consequences in law are acute because a lawyer cannot disclaim responsibility for a contract clause or a legal opinion simply because an AI system generated it. The duty of competence, articulated in professional conduct rules across most jurisdictions, now implicitly includes understanding the tools being used.

For firms and legal operations teams, this creates a near-term training obligation. Knowing how AI contract management software interprets a limitation-of-liability clause, and where its confidence may be misplaced, is a professional skill, not a technical luxury.

Honest Challenges for Legal Teams Adopting AI Today

Adoption is uneven, and the reasons are instructive. Larger firms and well-resourced in-house teams have moved fastest, partly because they can afford dedicated legal technology roles and partly because their transaction volumes justify the investment. Smaller teams often find themselves evaluating tools without the internal expertise to benchmark them properly.

Data governance is a persistent friction point. AI contract review tools require access to contract data to function well, and that data frequently contains commercially sensitive information. Choosing a platform whose data-handling commitments meet the organisation's security and confidentiality standards is a prerequisite, not an afterthought.

Change management is the underrated challenge. The lawyers who will use these tools every day have often built careers on skills that AI now partially replicates. Framing AI adoption as augmentation rather than replacement is not merely a communications tactic; it is the accurate description of how high-performing legal teams are actually deploying these tools. The lawyer's judgment, commercial awareness, and client relationship remain the differentiating factor. AI handles the volume and the first pass.

Where Contract Lifecycle Management AI Is Heading

The near-term trajectory points toward tighter integration between CLM platforms and the enterprise systems legal teams already use: ERP, procurement, and CRM tools. Contracts do not live in isolation; they govern relationships that span finance, operations, and sales. AI that can surface contract obligations inside the workflow where those obligations become relevant, rather than requiring a lawyer to go and find them, represents a meaningful operational improvement.

Longer term, the more significant development may be AI that can model risk across a contract portfolio rather than a single document. Concentration of particular liability positions, expiring indemnities across a supplier base, or renewal cliffs across a customer book: these are the kinds of portfolio-level insights that legal teams have historically lacked the bandwidth to generate. AI contract management tools with strong analytics layers are beginning to make this possible.

The lawyers and legal operations professionals who engage seriously with these capabilities now, learning their limits as well as their strengths, will be better positioned than those who wait for the tools to become self-explanatory. They will not become self-explanatory. They will become more powerful, which is a different thing entirely.