Walk into any boardroom today and you will hear the same tension: How fast can we move on AI, and how do we manage the risk while we do it. Legal teams are central to that discussion. Many in-house teams have tried generative tools for drafting, research, or automation. In most organisations, adoption has outpaced governance. Policies trail practice.
This is why General Counsels (GCs) are being asked to do two things at once: enable innovation and set the guardrails. It is also why AI governance and Legal Ops transformation have moved from operational topics to board level priorities. Organisations that take a structured approach will move faster.
1) The big picture: AI is reshaping legal work
Legal workloads have not eased, but the way work is delivered has changed. Drafts arrive faster, reviews are triaged by automated workflows, and routine questions can be handled in minutes. Without clear rules, teams risk data exposure, reliance on unverified outputs and inconsistent vendor safeguards. Boards expect GCs to present a combined view of opportunity, controls, and measurable value.
2) Why this matters for GCs?
Regulatory and liability risk: AI creates exposure across confidentiality, accuracy, explainability, bias, and lifecycle oversight. If a tool mishandles data or performance drifts without monitoring, accountability rests with Legal. GCs need a governance framework which can be clearly communicated in a board pack as well as being usable day to day.
Strategic expectations from the board: Boards want to see how Legal will reduce cycle times, improve contract hygiene, prove savings, and give better risk visibility. Legal Ops is the engine and AI is the accelerator. Together, they translate strategy into outcomes.
Talent, culture, and adoption: Teams want clarity on when AI can be used, what to verify, which tasks are suitable, and what must stay in secure environments. The GC sets the tone: When expectations are clear and training supports them, adoption is confident.
3) The reality: adoption outpacing readiness
Across organisations, three patterns recur:
- Tools before policies: Pilots start before risk assessments, usage tiers, and audit trails exist.
- Governance gaps: Standards for explainability, bias testing, lifecycle reviews, and human in the loop controls vary widely.
- Leadership expectations: GCs are the convenors across Legal, IT, Risk, and Security, building one coherent approach.
This is often where DWF is engaged, turning scattered efforts into a governed, value focused program.
4) What GCs should do now
a) Stand up AI governance that people can use
- Establish simple and outcomes-based principles such as transparency, security by design and human oversight.
- Create usage tiers separating low risk drafting aids from higher risk work that affects rights, obligations or regulatory outcomes.
- Build vendor diligence, data boundaries and approval paths from the outset.
How DWF helps: we co-create frameworks, playbooks, risk registers, and lightweight approvals that teams adopt easily.
b) Connect AI to Legal Ops and to business value
- Fix the underlying process before adding automation.
- Prioritise use cases with clear value such as intake triage, playbook guided negotiation and clause extraction for repapering.
- Measure time saved, cycle time reduction, quality improvements, and avoided risk.
How DWF helps: we run maturity assessments, design roadmaps, pilot high value use cases, and build dashboards fit for a board review.
c) Upskill the team and make change stick
- Provide role specific guidance for lawyers, Legal Ops and business teams.
- Train on verification habits, confidentiality hygiene, and escalation thresholds.
- Refresh controls as capabilities evolve.
How DWF helps: we deliver practical training, embed “guardrails in workflows”, and align Legal, IT, and Risk to support consistent adoption.
5) Embed legal AI into your existing workflows to get the best results
Most legal teams already use platforms such as CLMs, matter management tools, and contract automation systems or are in the process of selecting/ implementing to manage their business. Effective legal AI builds on the structured data in these platforms or integrates alongside them. An AI enabled CLM can use clause patterns and metadata to generate insights powered by large language models. One of our recent flagship projects highlights the strength and success of this approach:
A quick vignette
DWF turned a fragmented contracting process into a transparent, intelligent, and fully governed experience. By redesigning how contracts move through the business, we delivered a workflow that feels effortless where every stage is predictable, every template is consistently applied, and Legal finally gets the visibility it deserves.
Through smart automation, clause level insights and a curated clause library, DWF implemented a contracting engine that runs with precision. We converted legacy documents into actionable data, highlighted negotiation hotspots, reduced rework, and ensured teams always had the right content at their fingertips. The result? Faster turnaround, cleaner collaboration, and a contracting function that operates with confidence.
DWF was needed not for a new tool, but for the expertise to orchestrate how the tool should work for legal and business teams in the long run. By pairing legal operations discipline with practical AI enablement, we built a contracting ecosystem where processes are transparent, decisions are informed, and teams finally have the clarity to focus on high value work.
If you are ready to turn momentum into a governed, valued-led programme, we are ready to help.
