Once legal marketing teams move past the question of whether AI is relevant, the next concern is usually risk. We don’t mean technically risky, we mean professionally risk. Such examples of risk, might include:
- The risk of overstating experience on a barrister profile.
- The risk of implying outcomes in a case study.
- The risk of publishing something that feels subtly off in tone and prompts uncomfortable internal questions.
In chambers and law firms, those risks carry more weight than speed or efficiency. Reputation is cumulative and hard-won. One poorly phrased sentence can create disproportionate friction.
This is where many teams hesitate. They can see the potential efficiency gains, but they are wary of introducing something that feels uncontrolled, difficult to govern, or hard to explain to senior colleagues.
The good news is that AI use in legal marketing does not need to be all-or-nothing. The safest teams are not avoiding AI entirely. They are using it within clear, defensible boundaries that reflect how legal organisations already manage risk.
This article sets out a practical way to do that.
Why safe use matters more than fast use
AI tools are easy to access and simple to experiment with. That accessibility is part of the challenge.
Without guidance, usage spreads informally:
- A marketing executive uses it to draft a blog post
- A fee earner experiments with profile wording
- Someone pastes in notes from a recent matter to see what it produces
Individually, these actions seem minor. Collectively, they introduce inconsistency, risk, and internal uncertainty.
In legal marketing, the consequences are rarely dramatic. They are gradual:
- Content becomes harder to approve
- Partners or members begin to question phrasing more closely
- Tone drifts from established brand positioning
- Confidence in the marketing function weakens
A simple framework can prevent that slow erosion of trust and actually increase the wider organisation’s confidence in the marketing team.
A practical framework: green, amber, red
One of the most effective ways to manage AI risk is to categorise use cases by how the output will be used.
A green, amber, red framework works particularly well in legal environments because it mirrors how risk is already assessed in other contexts. It shifts the conversation from ‘should we use AI?’ to ‘where is it appropriate?’
Green use cases: operational support
Green use cases are where AI supports language, structure, or efficiency, and where errors are easy to identify and correct.
This is usually the safest place to begin.
Examples include:
- Drafting outlines for blog posts or practice area pages
- Rewriting existing, approved copy for clarity and concision
- Repurposing signed-off content into social posts or email summaries
- Standardising tone and length across barrister or partner profiles
- Summarising previously approved articles or seminars
In these scenarios, AI is not generating new claims. It is reshaping material that has already been approved or assisting with structure.
The principle is straightforward. If the underlying substance has been validated, AI can help present it more efficiently.
For busy legal marketing teams managing website updates, directory content, and profile consistency, this alone can be significant.
Amber use cases: judgement required
Amber use cases are not inherently unsafe, but they require deliberate oversight. They typically involve judgment, implication, or interpretation. These are areas where nuance matters.
Examples include:
- Drafting practice area pages from high-level bullet points
- Writing thought leadership commentary on recent legal developments
- Producing website copy describing expertise or approach
- Editing senior barrister or partner bios
- Summarising seminar content that includes legal analysis
In these situations, AI can produce a credible first draft. The risk is rarely blatant inaccuracy. It is subtle implication.
Phrases such as:
- Extensive experience in
- Regularly instructed in
- Handling complex litigation from start to finish
may read smoothly but prompt internal questions:
- Is that frequency demonstrable?
- Does that wording reflect chambers positioning?
- Would we be comfortable defending this phrasing in a directory submission?
Amber use cases work best when:
- The brief to AI is tightly defined
- The constraints are explicit, including no outcome claims, no exaggeration, and no assumptions
- A knowledgeable reviewer edits with legal and reputational context in mind
AI output in this category should always be treated as a draft, never as finished copy.
Red use cases: disproportionate risk
Red use cases are those where AI introduces more risk than value. These typically involve confidentiality, legal interpretation, or unverified claims.
Examples include:
- Entering confidential or client-specific information into a public AI tool
- Asking AI to interpret legal issues or apply the law
- Drafting case studies that reference outcomes or results without verification
- Creating comparative claims about strength, ranking, or success rates
- Publishing AI-generated content without human review
Even when the output appears polished, these uses are difficult to justify within chambers governance or firm compliance structures.
A useful test is simple. If the role of AI would be uncomfortable to explain to a head of chambers, managing partner, or risk committee, it likely falls into this category.
What makes AI output risky in legal marketing
Understanding the common issues and pressure points makes review more effective.
Overstatement through fluency
AI is designed to sound confident. In legal marketing, confidence without evidence is problematic. Marketing language that feels routine in other sectors can carry regulatory or reputational implications in law.
A phrase as simple as “market leading” may trigger internal resistance. Not because it is dramatic, but because it requires justification. Careful legal marketing relies on credibility, not amplification.
Loss of qualification
Legal services are nuanced. AI often compresses that nuance into simplified statements.
For example:
“Advising at various stages of proceedings”
may become
“Managing cases from start to finish”
The latter sounds stronger but may not accurately reflect the role performed. Review should always focus on reinstating precision rather than simply softening tone.
Generic voice
Without guidance, AI defaults to a broad corporate marketing style. For chambers in particular, this can feel misaligned. Understatement, clarity, and technical credibility tend to carry more weight than promotional language.
Consistency with your existing website tone and directory submissions should be a review priority.
Reducing risk before it appears
The most effective risk management happens before drafting begins.
Be specific in the brief
Vague prompts create vague and potentially risky outputs. A strong brief should include:
- The audience, such as instructing solicitors, general counsel, or lay clients
- The context, for example, a UK chambers or law firm website
- The tone, which should be measured and professional rather than promotional
- Clear constraints, including no outcome claims, no assumptions, and no exaggeration
For example:
- “Rewrite this to sound stronger” introduces ambiguity.
- “Rewrite this to improve clarity and flow without changing meaning or introducing new claims” is far safer.
Separate drafting from approval
AI belongs firmly in the drafting stage. Final approval should remain with someone who understands the following:
- Regulatory context
- Brand positioning
- Internal sensitivities
- Directory alignment
This does not require a complex process. A short internal checklist is often sufficient.
Agree internal boundaries
Early clarity reduces later anxiety. Even a simple internal framework covering who can do what can prevent inconsistent practice across teams. This framework should cover:
- Who may use AI tools
- What types of content are appropriate
- What must never be entered into AI systems
- Where review is mandatory
In both law firms and chambers, this also reassures senior stakeholders that governance has not been sidelined.
What an effective review looks like
When reviewing AI-assisted content, polish is not the focus. Defensibility is. Useful questions include:
- Is every statement accurate and supportable?
- Does anything imply frequency, scale, or outcome beyond what can be evidenced?
- Does this align with directory submissions and existing website wording?
- Would this wording withstand internal scrutiny?
In most cases, small refinements are sufficient. The issue is rarely structural. It is typically about implication.
Why cautious adoption builds credibility
Teams that introduce AI deliberately often find that internal confidence increases over time. Stakeholders see that:
- Standards are maintained
- Tone remains consistent
- Governance has not weakened
AI then becomes operational infrastructure rather than a perceived shortcut.
In legal marketing, credibility compounds. The way technology is introduced reflects the professionalism of the function itself.
The bottom line
AI does not change the standards that apply to legal marketing. It exposes how rigorously those standards are upheld.
Used within clear, agreed boundaries, AI can improve efficiency and consistency across websites, profiles, and thought leadership content. Used carelessly, it amplifies overstatement, weakens nuance, and creates avoidable internal friction.
A green, amber, red framework keeps the focus where it belongs. On judgment, precision, and long-term credibility.
