If you’re still scanning NDAs at 7pm or stuck wrangling contract clauses instead of tackling bigger legal risks, this one’s for you.

AI-powered contract review tools are no longer just for the Big Four or tech giants. They're fast becoming a go-to for in-house teams who want to do more with less – and still sleep at night. But tech alone won’t fix the overload. The secret sauce? A solid, bespoke playbook that tells your AI what “good” looks like for your business.

Here’s how to build one.

1. Start with the contracts you hate the most

Keep it simple to start with. Focus on the agreements that are:

• High volume and low risk, like NDAs or supplier Ts&Cs.
• Heavily templated, so you can standardise faster.
• A known time suck, either because they’re fiddly or always end up on your desk.

2. Define what ‘acceptable’ looks like

This is where your legal judgment comes in. For each clause type (think liability caps, termination rights, indemnities), decide:

• What’s a red flag – and needs escalation or a proper legal review.
• What’s a business risk – and might be acceptable depending on value or commercial context.
• What’s fine to auto-approve – or push through with a quick sanity check.

Work with your commercial teams to reflect real-world risk appetite, not just legal theory.

3. Translate that into AI-friendly rules

Your playbook needs to work with your AI, not against it. That means:

• Using clear, structured rules (e.g. “Limitation of liability must include a monetary cap no less than £1m”).
• Mapping clauses to your preferred fallback positions.
• Flagging trigger words or phrases that indicate higher risk.

Good AI tools will let you build this logic into templates, workflows, and clause libraries.

4. Test it in the wild

Pilot your AI review playbook with a real contract workflow. Then:

• Track where the AI gets it wrong or needs a nudge.
• Tweak your rules to fix false positives/negatives.
• Get feedback from legal and commercial users.

Remember: the goal is progress, not perfection. You can always evolve your playbook over time.

5. Embed it into your wider processes

Once it works, make sure it sticks:

• Train commercial teams on what’s changed (and what they still need to flag).
• Set thresholds for when contracts can bypass legal.
• Use data from the AI tool to track what’s getting signed, and where you might need to intervene.

This isn’t just about saving time. It’s about freeing up your legal team to focus on what really matters – and building more resilience into the business as you grow.

6. Some practical tips and prompt types to help draft your AI-powered contract review playbook

• Clause standards and positions

Use prompts to define your organisation’s risk thresholds, fallback positions, and non-negotiables.

“Draft a playbook entry for limitation of liability clauses. Our standard is a cap of £1m, excluding fraud or wilful misconduct. What alternative positions should we consider acceptable or risky?”

“Summarise the key risks of uncapped indemnities and suggest fallback wording aligned with a medium-risk tolerance.”

“List common termination clause types in supplier contracts and recommend preferred vs acceptable language.”

Ask AI to help spot or structure what should be flagged for manual review.

“What language in a confidentiality clause might indicate an overly broad or perpetual obligation?”

“Generate a checklist of red-flag terms in SaaS contracts that should always trigger legal review.”

“Which indemnity clauses are likely to be one-sided or unusually risky for a customer?”

• Red flags and escalation triggers

• Commercial context and risk balancing

AI can help you tailor your playbook based on contract value, type or business use case.

“Help me write a review rule that allows higher liability caps if the contract value is above £500k.”

“What risk terms might be acceptable in a low-value short-term services agreement but not in a master agreement?”

“Generate sample playbook guidance for NDAs used in M&A vs routine supplier onboarding.”

• Language detection and clause categorisation

You can use AI to build rules that identify or sort clauses by type or content.

“Create a clause recognition rule that identifies limitation of liability clauses even if they don’t use the phrase ‘limitation of liability’.”

“Give me examples of indemnity clauses that shift all risk to the customer.”

“Tag these 10 sample clauses as ‘acceptable’, ‘needs review’ or ‘reject’ with reasons.”

• User guidance and escalation instructions

Draft instructions to guide commercial teams when they’re reviewing contracts without legal.

“Write a plain English note to explain why automatic renewal clauses should be reviewed carefully.”

“Suggest wording to include in our playbook that explains when commercial teams should escalate an IP clause.”

“Draft internal playbook guidance for business users reviewing DPAs.”

• Playbook structure and templates

Get AI to help you build the format of your playbook so it's easy to use and maintain.

“Suggest a template for structuring each clause type in a contract playbook, including fields for red flags, fallback positions, and rationale.”

“Create a sample layout for a contract review playbook focused on SaaS vendor agreements.”

“What sections should I include in a practical AI-assisted playbook for junior legal reviewers?”

• Bonus tip: Ask AI to compare your position to market norms

You can also feed in a clause from a third-party contract and ask:

“Is this indemnity clause market standard for a UK customer agreement?”

“Rewrite this clause to align with our playbook preference for mutual liability caps.”

“What are the risks of accepting this governing law clause as-is?”

The key here is smart contract triage at scale.

You don’t need a huge budget, a legal ops team, or a machine learning degree to make this work. Just a bit of upfront thinking and the right tool to back it up.