Market View
Navigating the AI transformation journey: How global service providers are reimagining value creation
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Stephanie Hamon
Head of Legal Operations Consulting at Norton Rose Fulbright
About Stephanie Hamon
Stéphanie Hamon is the Head of Legal Operations Consulting at Norton Rose Fulbright, where she has served since August 2019. She is also the Co-Founder of The Bionic Lawyer Project. With extensive experience in legal operations, Stéphanie helps General Counsels and in-house teams evolve beyond risk management to become true business partners by applying business principles to legal service delivery. Her consultancy work focuses on optimising operations and transforming service delivery approaches for legal functions across more than 200 in-house teams annually.
How do you see the service provider landscape evolving as AI capabilities mature?
There's currently a significant gap between what people are saying about AI and what they're actually doing. AI has been around the legal industry for some time, but the emergence of generative AI is likely to have a more substantial impact.
Our surveys with in-house teams indicate they expect to see impact from generative AI from their law firms within 6-12 months, while they anticipate changes to their own workload within 2-3 years. However, I'm not convinced many law firms will be able to transform their service offerings that quickly. The evolution will happen, but likely over a longer timeframe, with a gradual shift towards more strategic work and less administrative tasks.
Two critical side effects we need to consider are: how do we train junior lawyers when the repetitive tasks traditionally used for training are automated, and what impact will this have on the hourly rate pricing model that's so embedded in the industry?
People are falling into the trap again of going for the shiny new tools, jumping to the solution, forgetting that they first need to identify what problem they're trying to solve, how big that problem is, and whether it warrants a Gen AI solution.
How are you currently identifying where AI can add the most value in service delivery?
We're working with numerous in-house teams through workshops to surface valuable use cases. Currently, we're seeing two main areas of impact.
The first is business productivity – functions that aren't specific to legal work. Whether using ChatGPT or Microsoft Copilot to summarise email chains, identify key messages and players, generate meeting notes, or create first drafts – these applications significantly speed up work processes across any department.
The second area, when looking at legal-specific applications, requires a methodical approach. Many are rushing to adopt new tools without first identifying the actual problem they're trying to solve. You need to start with the problem, assess its scale, and then determine if generative AI is the appropriate solution.
People are falling into the trap again of going for the shiny new tools, jumping to the solution, forgetting that they first need to identify what problem they're trying to solve, how big that problem is, and whether it warrants a Gen AI solution.
What foundational improvements in service delivery are needed before implementing AI?
Before implementing any AI solution, you need to address three critical areas: people, processes, and data. Particularly important is understanding the processes you're trying to improve – there's no point fixing something that isn't broken or improving a process that's fundamentally unsuitable.
The most significant challenge lies in data management. The power of generative AI depends on the data it can access, and for a knowledge-based industry, it's remarkable how little organisation exists around data storage and tagging. Many in-house teams don't even have a document management system, with documents saved on hard drives or in paper form. No matter how powerful AI becomes, it cannot help with documents sitting in cardboard boxes.
What you want is for people to play with it, experimenting with it, to really then understand what the capabilities are and then choose the right use cases going forward.
How do you decide between quick AI wins and longer-term scalable solutions?
This isn't just an AI question but a technology question more broadly. Every legal team needs a technology roadmap with a three-to-five-year plan. In-house teams typically have limited budgets and resources for technology investments, so every decision must deliver ROI while supporting long-term strategic goals.
A good technology roadmap combines quick wins and longer-term projects. Quick wins maintain momentum and demonstrate capability, getting people excited about the potential. But some initiatives require longer implementation timeframes and more substantial investment. You can't rush these projects, so it's important to balance both approaches while ensuring alignment with your organisation's broader strategy and the legal department's objectives. What you want is for people to play with it, experimenting with it, to really then understand what the capabilities are and then choose the right use cases going forward.
What specific roles or capabilities have you needed to add to support AI implementation?
You need people who understand the technology and its applications in the legal context, but these specialists are rare. The challenge we're facing is finding individuals who possess both legal expertise and technical knowledge.
I witnessed a demonstration where a technical expert had developed an impressive 3,000-word prompt for contract review that delivered results in seconds rather than hours. Unfortunately, the prompt engineer lacked legal background and omitted crucial clauses like limitation of liability. When presented to lawyers, they immediately distrusted the entire tool because of these critical omissions.
The intersection of legal expertise and technical knowledge is essential for successful AI implementation. We need to develop what I call "cognitive skills" – research abilities, understanding how to effectively engage with AI, and comprehending how these systems work to optimise their performance. Domain expertise remains far more important than technical skills, as the legal professionals need to learn how to properly communicate with and direct AI tools. That's where the roles are going to evolve – at that intersection, that Venn diagram of legal expertise and tech expertise.
That's where the roles are going to evolve – at that intersection, that Venn diagram of legal expertise and tech expertise.
About Norton Rose Fulbright
Norton Rose Fulbright is a global law firm with roots dating back to 1794. As one of the world's largest legal practices, it maintains a significant international presence across major financial and commercial centres. The firm offers comprehensive legal services including corporate, banking, dispute resolution, intellectual property, and regulatory law to major corporations, financial institutions, and governments worldwide. Recognised for its expertise in financial institutions, energy, infrastructure, technology, and healthcare sectors, Norton Rose Fulbright consistently ranks among the most prestigious law firms globally.
About Enate
Enate is the leading SaaS solution for business services. Enate orchestrates work from start to finish, giving clients the visibility and control needed to deliver better services. From email management and data analysis to intelligent document processing, Enate also offers a host of touch-button AI features designed to slash the time spent on manual work. Trusted by global service teams, Enate ensures smooth, consistent operations that help clients perform at their best.