Five themes reshaping the legal function by 2030 according to GC research

General counsel research from 2024 to 2026 by KPMG, FTI Technology, LegalOn, Litera and the Association of Corporate Counsel points to five structural themes reshaping the legal function by 2030. First, managing AI and data risk becomes a core task of the general counsel. Second, governance shifts from stand-alone tools to entire workflows with built-in verification. Third, a data and evidence-focused working model emerges in which logging, auditability and hallucination control become part of legal quality.

Fourth, AI literacy and multidisciplinary collaboration become an organisational obligation. Fifth, legal teams orchestrate an ecosystem of vendors, AI platforms and IT to retain control over sensitive data. These themes are not speculation, but an extrapolation of priorities and shortcomings that these studies already identify.

Which studies point to these five themes?

The trigger is a series of recent studies that, from different angles, point to the same development. In KPMG's Global General Counsel Survey, general counsel name understanding and implementing AI as a leading operational priority for the coming years, alongside transformation themes such as transparency, automation, scalability and collaboration.

FTI Technology states in the Seventh Annual General Counsel Report that emerging technologies and data growth, including AI, now form the highest-ranked legal risk, while general counsel are most concerned about hallucinations and inaccurate results. LegalOn, together with In-House Connect, documents in The 2026 State of AI for In-House Legal how AI is shifting from experiment to operational deployment. Litera describes in the report Legal Departments at the Leading Edge that legal departments are strong on risk instinct but lag behind on governance infrastructure. The Artificial Intelligence Toolkit for In-house Lawyers from the Association of Corporate Counsel sets out AI governance, ethics, contract clauses and a new section on agentic AI as tasks of the legal function.

What are the five themes concretely?

In our assessment, these sources together show five interconnected themes. We summarise them below as an editorial synthesis; the underlying facts come from the studies named.

• AI-focused risk stewardship. The general counsel does not only advise after the fact, but helps design, verify and explain AI-supported decisions. FTI's finding that technology and data are the top risk supports this.
• Governance at the workflow level. Verification and control become embedded in contract review, compliance and research, not only in stand-alone tool agreements. This aligns with the shift from experiment to impact that LegalOn describes.
• Data and evidence-focused working model. Logging, auditability and hallucination control become part of legal quality. The hallucination concern from the GC Report 2026 and Litera's governance gap point to this.
• AI-literate, multidisciplinary teams. AI literacy, ethical obligations and change management become formal tasks, as the ACC toolkit sets out.
• Ecosystem-focused delivery. Legal teams orchestrate vendors, AI platforms and IT to retain end-to-end control over sensitive data.

For ordering what AI may do and what it can do, the governance framework that separates the autonomy of AI agents is a useful starting point.

Why does governance infrastructure lag behind AI adoption?

Litera signals a concrete gap: legal departments rely on risk instinct, but the underlying data, workflows, metrics and oversight mechanisms lag behind the pace of AI adoption. LegalOn points in the same direction: AI delivers impact, but must be embedded in daily workflows with clear accountability and risk controls.

In our analysis, this means that policy on paper does not suffice. If a legal team cannot demonstrate which AI systems were used, which controls surrounded them and what evidence trail exists, a gap arises between the advised care and the reconstructable practice. That touches on broader questions about the shift from logging obligation to reconstruction obligation and about layered hallucination detection instead of one stand-alone tool.

What does this mean practically for legal teams?

The studies describe a long transition, not a rupture. Those who want to translate the five themes into their own practice can consider the following steps. This is editorial advice, not an obligation from the sources.

1. Map where AI is already being used in legal workflows and which controls surround it.
2. Record per workflow what evidence trail exists: which models, which sources, which corrections and which deviations.
3. Arrange AI clauses, audit rights and accountability in vendor contracts, in line with the ACC toolkit.
4. Invest in AI literacy and multidisciplinary collaboration as an organisational task.
5. Treat governance of agentic AI separately, with explicit limits on what systems may do independently.

For teams deploying AI for research, a defensible workflow with citation verification is a concrete starting point. The broader framework on AI in professional practice places these themes in context.

In this landscape, a privacy-focused verification layer such as Vera is useful insofar as it makes visible, per workflow, which AI systems are in play, which verification steps, corrections and disagreements there were and what evidence trail is available. Vera can route a task through selected, independent AI models and show those steps for inspection. That supports control, but it does not guarantee that outputs are correct and it does not remove the risk of hallucinations. The professional final judgement remains with the lawyer. A verification console is thus a part of a broader governance architecture, not the architecture itself.

Sources: The article draws on general counsel research from KPMG, FTI Technology, LegalOn, Litera and the Association of Corporate Counsel.

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