Legal sector accelerates AI adoption by focusing on data strategy and talent inclusion

Legal firms are increasingly embedding generative AI and proprietary data into their operations, emphasising data architecture, measurable outcomes, and inclusive talent strategies to transform AI from experimentation to a sustained competitive advantage.

If there are any doubters about AI’s impact on business, IBM’s recent chief data officer survey would soon put them right. According to the original report, which drew responses from 1,700 senior data and analytics leaders across 19 industries, enabling AI to deliver value is now a primary strategic priority: 81% of CDOs prioritise investments to accelerate AI capabilities and 78% see leveraging proprietary data as a top objective to differentiate their organisations. The study also warns that skills gaps remain acute, with nearly half of respondents identifying advanced data skills as a significant challenge. ^[1][2]

Those headline findings map neatly onto trends already visible in the legal sector, where firms that were once labelled technology laggards have moved with alacrity to embed generative AI and data-driven tools into client work and operations. RSGI’s review of entries to the Financial Times’ Innovative Lawyers programme showed that nearly 60% of participating law firms this year highlighted digital or AI tools as central to their innovations, underscoring a sector-wide shift from experimentation to deployment. ^[1]

A recurring theme in both IBM’s study and the FT entries is the value of proprietary data as a competitive moat , the “secret sauce” that competitors lack, as the IBM report puts it. Law firms are turning that sauce into practical systems: Latham & Watkins’ 8-K Trigger Analysis Tool, for example, combines client and firm-held data to speed responses on SEC reporting obligations, freeing partners for higher-value work and contributing to the firm’s recognition at the FT awards. Similarly, Allen & Overy Shearman’s ContractMatrix drafting software and a loan-documentation analysis “module” developed with legal AI start-up Harvey illustrate how firms are marrying proprietary datasets with generative AI to create client-facing products. ^[1]

IBM’s admonition to “Don’t just collect data. Deploy it on a mission” is reflected in firms that link data strategy to measurable business outcomes. Morgan Lewis’ Custodian Connect, which standardises and centralises data gathered during custodian interviews, has reduced time and cost in discovery while giving clients clearer visibility into processes , an example of how better information architecture directly improves service delivery and ROI on AI investments. Industry data shows that organisations reporting the highest returns from data and AI are more likely to be able to explain how their data contributes to corporate goals, reinforcing the point that strategy and measurement matter as much as the technology itself. ^[1][2]

Democratising data access is the third pillar stressed by IBM , “Make every role a data role” , and several law firms have translated that principle into people-focused programmes. Orrick’s pairing of legal secretaries and executive assistants with “practice analysts” skilled in data management and AI literacy has driven improvements across billing, research and workflow, and won recognition for its people strategy. Elsewhere in the market, firms from Nishimura & Asahi to NautaDutilh and Browne Jacobson have been acknowledged at regional FT innovation awards for rethinking people structures and assignment models to harness diverse skills and broaden participation in digital transformation. Baker McKenzie’s inclusion among the Financial Times’ top 20 most innovative global firms of the past two decades and Gibson Dunn’s multi-category shortlisting for its three‑pronged AI strategy , governance, education and partnership , further illustrate how leading firms combine talent, structure and governance to de-risk and scale AI. ^{[1][4][7][5][3][6]}

The upshot for the legal sector is that generative AI has not provided a shortcut around the hard work of data and knowledge architecture; if anything, it has made that work indispensable. Savvy firms are therefore investing in collecting, cleaning and structuring proprietary data so it becomes reusable knowledge: “Information architecture has never been as important, because better IA means smarter AI,” as practitioners now argue. The firms that pair that architecture with clear objectives, measurable outcomes and inclusive people strategies are the ones turning AI from an experiment into a durable business advantage. ^[1][2]

📌 Reference Map:

##Reference Map:

• ^[1] (legaltechnology.com) – Paragraph 1, Paragraph 2, Paragraph 3, Paragraph 4, Paragraph 5, Paragraph 6
• ^[2] (IBM Newsroom) – Paragraph 1, Paragraph 4, Paragraph 6
• ^[3] (Baker McKenzie newsroom) – Paragraph 5
• ^[4] (Nishimura & Asahi news) – Paragraph 5
• ^[5] (Browne Jacobson news) – Paragraph 5
• ^[6] (Gibson Dunn news) – Paragraph 5
• ^[7] (NautaDutilh insights) – Paragraph 5

Source: Noah Wire Services