Implementing agentic AI in legal workflows promises major changes for in-house legal counsel. When managed correctly, it can speed up processes, automate routine tasks and free up time for more complex matters. But how can you make sure you’re using the right tools for the right tasks, and what are the pitfalls you should be aware of?
LegalEdge’s Farzana Gul shares her thoughts on the growing need to implement agentic AI into legal workflows and how agentic work can add real value, the common mistakes businesses make, and the unexpected hidden benefits of agentic AI.
Getting started with AI agents
Before implementing AI agents in legal workflows, Farzana recommends mapping out all workflows to clarify what legal work is currently happening, where the bottlenecks are, and how processes can improve through more efficient work. This foundational work provides the manual scaffolding needed to identify and implement the tools that will provide the most value.
The goal of implementing AI agents is to automate routine, low-risk tasks, which allows legal counsel to prioritise more complex matters that require clear human oversight.
Practical examples of how an AI agent can work
📥 Agents can collect contract or legal requests through tools such as Google Forms, classify them, and route them to the right destination.
💬 A Slack chatbot could answer routine questions by reading an internal knowledge base, reducing the need for lawyers to respond to repeated queries.
📑 Agents can compare proposed contractual changes against a legal playbook.
✅ A rule-based checklist supported by an AI agent could assess whether a software transaction triggers export-control requirements. The agent can identify whether the matter meets predefined criteria.
Human judgement will always remain essential
AI agents can offer significant value by making standardised manual processes much faster. But human judgement and expertise will always be essential within the legal sector, because LLMs cannot apply judgement in the same way as qualified professionals. If agents are implemented in workflows, clear mechanisms and processes must ensure legal professionals check the output.
Farzana believes key areas of legal work such as negotiating, governing law, IP structuring, data protection, confidentiality, and regulatory exposure are high risk and, as such, will need qualified human judgement. She points out that AI agents “cannot be a substitute for an in-house legal team or fractional legal counsel” because of the legal and commercial consequences.
Accountability and governance need to be part of the planning process
Technology has long changed how legal professionals can do their work, with Farzana pointing out that “we’ve already moved away from physically signing paper contracts to actively using digital tools like DocuSign, where all contracts are held in the cloud.” The ongoing evolution of Legal Tech means that governance is now more important than ever. If an AI agent is used, it can only work within the confines of how it’s been trained and implemented because it doesn’t have the pragmatism of human knowledge.
An agent cannot replace experience, knowledge, or expertise, and responsible legal teams will still need to check AI-generated contract reviews, clauses, and references as part of strict quality assurance processes. That’s why businesses need to put clear governance guardrails in place for any AI agent. This might include disclaimers, automated checklists to verify details against legal playbooks, and stringent approval processes.
However, even with all of those safeguards in place, errors can still emerge, which is why human oversight remains important.
Farzana also reiterates that accountability cannot be outsourced to technology. If a legal team is using AI to support contract reviews or drafting documents, the responsibility for the final output lies with the legal provider, not the technology. If an AI agent makes a mistake, it’s the responsibility of the lawyer to check the work and make sure that the advice is correct.
She adds, “Inevitably, things will fall through the net, and that’s why human input is still crucial because agentic technology is not yet at the point where you can leave it alone.”
The third-party AI risks that can damage trust in legal processes
There are many legal-specific platforms available which will offer more advanced features and safeguards designed specifically for legal workflows. Different tools will have different strengths, and it’s important to use the right one for specific tasks.
Legal teams may be under increasing pressure to do more work and scale up output, but third-party tools can hallucinate and make mistakes, so humans must fact-check and verify the output. Using third-party tools can also create potential issues around client confidentiality, data handling, and rogue behaviours. These risks must be identified and mitigated with continual reassessments as technology changes.
Farzana says new problems could emerge if clients input advice or contracts from legal providers into their own AI tools, which may falsely flag things as outdated or incorrect. This could potentially create additional work for legal teams, who then need to explain why those concerns are incorrect. She says, “The AI tools we use are legal-specific and are trained on the right data. If a client is using a tool such as ChatGPT or Claude to recheck that information, it’s not built to have that same level of legal knowledge. This is something we are seeing happen, and also clients using tools to prepare a first draft of an agreement, which quite often needs to be rewritten.”
Common mistakes made by businesses implementing AI agents
When asked about common implementation mistakes, Farzana says poor tool selection and failure to map workflows accurately are the biggest problems that businesses need to address.
- Using incorrect AI tools to review contracts or provide advice will create problems if they are not legal-specific. They’re unlikely to generate output that fits the purpose.
- Too many businesses reach out for tools without being clear on what processes they need to automate. Before implementing any AI agent, teams need documented workflows, clear policies, knowledge databases, and escalation routes. Those processes may need to be reviewed and re-optimised several times before teams can choose the right tool.
- Businesses need to recognise what AI is suitable for, and when human input is needed. AI agents are good at triaging and can create suitable draft materials, but they can’t make judgement calls or understand risks. Teams may lose faith in the technology if they believe agentic AI can do everything.
The unexpected hidden benefit of agentic AI in legal workflows
An unexpected benefit of agentic AI in legal workflows is its potential to change internal attitudes toward legal teams. Often seen as a stumbling block, in-house legal teams can use AI agents to streamline workflows so other departments can access appropriate legal support faster when needed, which will naturally change internal attitudes.
AI agents can process routine, pre-approved requests through clear escalation routes when additional support is needed. Sales teams can therefore send contracts faster, while amendments that remain within agreed parameters can be approved without legal review. This agile approach lets legal teams focus on complex matters that require human oversight, while other departments handle routine, low-risk tasks without unnecessary delays.
Proving ROI on agentic AI is more than just time-saving metrics
Investing in agentic AI requires a significant time and financial investment. Businesses should ensure their AI agent delivers value by measuring metrics such as turnaround time, enquiry volume, escalation rates, recruitment costs, wellbeing statistics, and bottom-line impact.
- Operational efficiencies can accelerate routine activities by automating repetitive requests and expediting specialised tasks. For example, time-consuming manual contract reviews can be expedited, potentially reducing billable-hour expenses. Faster turnaround times for standard contracts can also support more rapid business growth.
- If agents handle routine tasks, lawyers can focus on more complex work such as negotiations or higher-value legal work. This increases teams’ capacity and can directly improve financial metrics.
- Automating low-risk work could reduce the need to hire additional internal personnel, resulting in further cost savings. Farzana points out that this isn’t about replacing qualified legal staff; if CFOs need to save money, they could consider alternative provisions such as fractional support, part-time staff or paralegals.
- There are also soft metrics that can be monitored, such as offering a better work-life balance for legal teams, which could in turn improve staff retention and reduce recruitment costs.
If you’d like help implementing agentic AI tools into your workflow or need support mapping existing workflows to identify clear bottlenecks that automation could improve, please get in touch.
