Responsible AI in practice: managing risk across global operations – LexisNexis
Responsible AI is no longer an innovation question for large law firms. It is a reputation risk management issue that directly affects client confidence, regulatory exposure and operational resilience across global networks.
AI adoption across the legal sector has accelerated rapidly, particularly within larger firms that have the scale and data to deploy advanced tools. At the same time, regulators and professional bodies are making clear that enthusiasm for AI must be matched by discipline. For firms operating across jurisdictions, responsible AI is now inseparable from managing law firm reputational risk and sustaining trust in complex client relationships.
Why responsible AI is now a reputational issue
Reputation risk management has traditionally focused on conduct, conflicts and crisis response. AI changes the risk profile. Errors generated at scale, biased outputs or misuse of data can affect multiple clients simultaneously and travel quickly across borders. This compresses reaction time and magnifies impact.
The Law Society of England and Wales has consistently emphasised that AI use in legal services must be ethical, transparent and risk-aware. Its policy work on web-scraped data, copyright and AI management frameworks highlights that failures in governance are not merely technical issues. They go to professional credibility and public trust. For global firms, this reinforces the need to explain the reputation risk management process internally and to clients, particularly where AI tools influence advice, drafting or research.
LexisNexis research on measuring the success of AI across the law shows that firms often focus on productivity gains while underestimating reputational exposure. Responsible AI requires leadership to view technology decisions through the same lens as client confidentiality or conflicts management.
Governance before deployment
One of the clearest lessons from regulators is that responsible AI cannot be retrofitted. The Bar Council’s guidance on generative AI stresses that professional judgement, verification and accountability remain with the lawyer, regardless of the tool used. This principle has direct implications for law firm crisis governance strategies.
Before deployment, firms need a clear governance framework that defines acceptable use, approval thresholds and escalation routes. This includes understanding where AI systems are trained, how data is processed and which jurisdictions’ laws apply. For firms operating internationally, strategic compliance in law firms increasingly depends on harmonising AI policies while allowing for local regulatory nuance.
Governance also supports strategic agility in law firms. When rules, disclosure expectations or client demands change, firms with documented controls can adapt faster without pausing innovation. This is one of the less obvious benefits of reputation risk management: it enables confidence-led growth rather than defensive restraint.
Human oversight and operational transparency
Operational transparency law firms provide around AI use is becoming a differentiator. The Bar Council and the Law Society both emphasise the need for human verification of AI outputs, particularly given risks such as hallucinations, bias and misplaced confidence in machine-generated answers.
For large firms, this is not simply about telling lawyers to double-check work. It requires process design. Clear workflows that show where AI is used, how outputs are reviewed and how decisions are documented reduce ambiguity and protect both the firm and the client. They also support scenario analysis for legal leadership by making it easier to model where failures might occur and how they would be contained.
Transparency extends to clients. Increasingly, sophisticated clients want to understand how technology supports their matters. A clear explanation of AI use, its benefits and its limitations helps to build trust and demonstrates maturity in managing law firm reputational risk.
Training as a risk control
The Law Society has repeatedly called for improved AI literacy across the profession. This is not about turning lawyers into technologists. It is about ensuring they understand the strengths, limitations and risks of the tools they rely on.
From a reputation risk management perspective, training is a preventative control. Lawyers who understand bias risks, data provenance and verification obligations are less likely to create errors that escalate into complaints or regulatory scrutiny. At scale, this becomes a material risk mitigant.
LexisNexis insights consistently show that firms which invest in structured training and shared guidance are better placed to embed responsible behaviours. Tools such as Lexis+ Legal Research provide fast and comprehensive access to the latest legislation, case law and expert commentary, helping lawyers sense-check AI-assisted outputs against authoritative sources. This supports accuracy while reinforcing professional judgement.
Responsible AI as a client trust signal
Responsible AI is often framed as a defensive necessity. For leading firms, it can also be a trust signal. Clients managing their own regulatory and reputational exposure are increasingly sensitive to how advisers use technology. Clear policies, transparent communication and demonstrable controls contribute to a stronger client experience and law firms relationship.
The benefits of reputation risk management extend beyond risk avoidance. Firms that can evidence disciplined AI governance are better positioned to win complex mandates, particularly in regulated sectors or cross-border matters where scrutiny is intense. Responsible AI becomes part of the firm’s value proposition rather than a background compliance issue.
Lexis+ AI is designed with this reality in mind, offering fast and accurate generative legal AI that lawyers can actually trust. By embedding AI within established research and content environments, firms can support innovation without compromising professional standards or client confidence.
From policy to practice
Ultimately, responsible AI in practice is about execution. Policies matter, but they only protect reputation when they shape daily behaviour across offices and teams. For global firms, this requires consistent leadership messaging, aligned incentives and ongoing review.
Reputation risk management is not static. As AI tools evolve, so too will expectations from regulators, courts and clients. Firms that treat responsible AI as a core operational discipline rather than a one-off project will be better equipped to manage uncertainty and sustain trust across borders.
In a market where technology decisions increasingly define professional credibility, responsible AI is no longer optional. It is a strategic capability that underpins resilience, differentiation and long-term growth.
