Building an AI-first legal department will require enterprises to look beyond simply deploying new tools and instead rethink processes, skills, governance and the way value is measured, legal leaders said at the ETLegalWorld AI-Powered Legal Transformation Summit 2026.
During the panel discussion, “Building the AI-First Legal Department: Strategy, Roadmap & ROI,” at ETLegalWorld’s AI Powered Legal Transformation Summit 2026, panelists argued that successful adoption will depend on identifying the right legal use cases, preparing employees to work alongside AI, maintaining human accountability and linking every investment to measurable business outcomes.
Moderated by Khushboo Singh, Growth & Legal Engineering, Legora, the discussion brought together Dilip Manepalli, Vice President, Analytics & Innovation, Vodafone; Namrata Arora, General Counsel & Chief Ethics Officer, Blupine Energy; Karthik Kannappan S., Global General Counsel and Chief Compliance Officer, Indegene; Subhadip Sarkar, Vice President – Legal and Corporate Administration, Cognizant; Mukesh Kumar, Regional Compliance Manager for South Asia, Southeast Asia, and India, Hitachi Ltd.; and Sahana Chandrika, Director Legal, Syngene International.
The broader consensus was that becoming AI-first is less a technology implementation exercise and more an operating-model transformation.
Build the use-case factory before scaling AI
For Dilip Manepalli of Vodafone, an AI-native legal department is one where human lawyers and AI agents can work together, with repetitive processes increasingly automated while high-consequence decisions remain under human control. He outlined an operating model built around several pillars, beginning with what he described as a “use case factory”.Legal teams, he said, should break down functions such as contracts, intellectual property, litigation and dispute management into individual processes and sub-processes, map how they operate today, and then reimagine where AI, automation and analytics can create value.
Those use cases should subsequently be prioritised based on their potential impact, including productivity and cost efficiency. “AI is no longer a technology question, but it’s a workforce transformation,” Manepalli said.
Alongside use-case development, he emphasised employee upskilling, responsible AI governance and data quality. AI systems, he argued, will only be as useful as the underlying data available to them, making diversified, unbiased and well-governed data an essential foundation for adoption.
Privacy and ethics cannot be an afterthought
For organisations working in highly regulated industries, the AI roadmap must incorporate compliance requirements from the beginning.Sahana Chandrika, Director Legal, Syngene International, said this is particularly critical for companies operating in sectors such as life sciences, where businesses handle confidential customer information and intellectual property while serving clients across multiple jurisdictions.
Even if AI-specific regulation in India remains at an evolving stage, organisations with global customer bases cannot afford to wait for domestic regulation before introducing safeguards, she said.
Privacy, ethics and legal compliance therefore need to be integrated into AI systems at the design stage. “It should be by design, and it definitely cannot be an afterthought,” Chandrika said.
The issue goes beyond regulatory compliance, she added. The way companies deploy AI also affects customer assurance, organisational reputation and the trust enterprises build with clients.
One recurring theme across the panel was that no single function can independently own enterprise AI adoption.
Chandrika said Syngene’s legal and privacy teams work alongside a wider AI governance group involving cross-functional stakeholders. Manepalli similarly described AI as a shared responsibility between legal, AI and IT teams, with technology functions enabling innovation and infrastructure while legal retains broader responsibility for its own business priorities.
Namrata Arora of Blupine Energy also placed primary responsibility with legal, arguing that the function is best positioned to identify the risks associated with deploying AI into areas such as governance, compliance and contract lifecycle management.
Karthik Kannappan S. of Indegene highlighted a similar shared structure involving legal, IT and risk functions, while stressing the importance of having clearly identifiable accountability.
At Hitachi Ltd., Mukesh Kumar described a multi-layer approach consisting of users, approvers and enablers. Legal may identify an AI use case, but governance committees assess the proposal before IT enables its implementation.
For Subhadip Sarkar of Cognizant, the model is necessarily cross-functional because the organisation both consumes AI internally and implements technology for customers. Legal, IT and security therefore work alongside a dedicated responsible AI function.
AI is standardising contract drafting
At Blupine Energy, Arora said AI adoption has already altered how the legal team approaches contract drafting.
Earlier, lawyers might draw from different contract databases or individual templates and then customise documents based on a particular requirement. Introducing AI-supported tools has brought greater consistency and standardisation.
Rather than approaching colleagues for previous versions or searching multiple sources, lawyers can access standardised contracts through the platform, populate the required information and produce a working document significantly faster.
The technology may be available, Arora said, but organisations still need employees to become comfortable with a different way of working.
As enterprises invest more heavily in AI, proving return on investment is becoming an unavoidable part of legal technology strategy. Manepalli argued that the starting question should not be whether an organisation is “technology first” or “business first”. The answer, he said, is unambiguously business first.
With potentially hundreds of AI use cases competing for resources, organisations need to assess them based on business value and ease of implementation. He described a value-realisation framework that follows AI initiatives from initial prioritisation through proof-of-value, minimum viable product and eventual scale.
Practical AI use cases will determine the business case
Kannappan illustrated the point with examples of AI being applied to privacy and incident-management processes within organisations serving the pharmaceutical sector.
Instead of relying on static forms for privacy impact assessments, he described systems that can interact conversationally with project managers, gather information about the type of data being processed, identify sensitive categories and jurisdictions, and route the information to relevant privacy teams.
He also outlined the use of AI-driven crisis playbooks for potential data incidents. Such a system could gather information about an incident, identify applicable regulatory requirements and guide employees on actions required over the following hours based on reporting obligations.
For Singh, the discussion demonstrated that strategy, roadmap and ROI cannot be treated as separate stages of the AI journey. Change management, adoption barriers and measurement need to be considered from the outset if organisations want to move technology beyond pilots and into meaningful legal operations.
Ultimately, the AI-first legal department emerging from the discussion is not one that removes lawyers from the process. It is one where routine work is increasingly automated, legal data is better structured, governance is shared across functions and lawyers spend more of their time on judgment, risk and strategic decision-making.
Today, the AI-Powered Legal Transformation Summit 2026 by ETLegalWorld has brought together general counsel, in-house legal leaders, and technology innovators, in Bengaluru, to chart the next phase of legal transformation in an AI-driven world.
