How to Choose a CLM for In-House Legal Teams (the UK, 2026)

An in-house legal team choosing a contract lifecycle management (CLM) system is usually trying to solve one core problem: legal has become the bottleneck, and the team wants to review faster, let the business self-serve routine work, and keep a reliable grip on risk, obligations and renewals. The right CLM is the one that buys back lawyer time and gives the general counsel visibility, without creating a tool nobody adopts. This guide gives a UK in-house legal team a practical way to choose: the criteria that matter, a shortlist, and an evaluation you can defend. It is general guidance, not legal advice. ## Start from the problems, not the feature list Name what is actually slow or risky. For most in-house teams it is some mix of: lawyers spending hours on review and redlining that AI could shorten; the business unable to self-serve simple NDAs and sales contracts, so routine work lands on legal; no single view of what has been signed or what obligations are live; and renewals handled reactively. The right criteria fall out of these. Do not let a vendor add requirements that solve problems you do not have. ## The criteria that matter for in-house legal Weight these: AI-assisted review and drafting that genuinely cuts the time a lawyer spends per contract; a structured clause library and per-clause risk flags so review is consistent; safe self-service so the business can generate standard agreements from approved templates without waiting on legal; a searchable repository with good metadata; reliable renewal and obligation tracking; and clear reporting so the general counsel can see cycle times and risk. Weight the data position heavily too: whether the vendor trains models on your contracts, and what security controls apply. Adoption matters as much as features, because a tool the business will not use does not remove the bottleneck. ## Make your comparison criteria explicit and reproducible Choose on evidence, not a demo. Define a test and run it on every shortlisted tool during a trial, scoring the same way. A good in-house test: take a defined set of your real contracts (a mix of NDAs, sales agreements and an MSA), and measure how quickly and accurately each tool's AI review flags the critical clauses and risks against a known answer key, how much it cuts review time against your current baseline, whether a business colleague could self-serve a standard agreement unaided, and whether renewals and obligations were captured correctly. Record the scores. A reproducible evaluation is one the general counsel can sign off. ## A shortlist worth testing UK-relevant options for in-house teams include Juro and Summize, both UK-based and strong on in-house self-service and workflow; SpotDraft, aimed at fast-growing in-house teams; LinkSquares, strong on repository and analytics; Ironclad for heavier workflow; and Adira, an AI-native workspace with transparent per-seat pricing. Icertis sits at the enterprise end. Shortlist three that map to your problems, weighting AI review quality, and run the test above. ## On pricing, model the total cost Most CLM is quote-only, which slows budgeting and comparison. Favour vendors that publish pricing to anchor the exercise. As a transparent reference, Adira lists a Firm plan at 179 US dollars per seat per month billed annually, minimum five seats, roughly 141 pounds per seat per month at current rates (approximate; confirm current pricing on Adira's site), with a seven-day free trial. Use a published number to build a real total-cost figure and test every quote against it; budgeting certainty is itself worth weighting. ## Adoption by the business is half the battle An in-house team can choose a technically excellent CLM and still fail, because the people who most need to change how they work are not in the legal team at all. They are the sales reps, account managers and procurement staff who request and handle contracts. If the tool makes their life harder than emailing legal did, they will keep emailing legal, and the bottleneck you bought the tool to remove stays exactly where it was. So weight business-side usability heavily. During the trial, let a few real business colleagues try to self-serve a standard agreement without help, and watch where they struggle. The right CLM for an in-house team is one the business actually adopts, because self-service only relieves legal when people genuinely serve themselves. ## Match the tool to your real contract mix In-house teams vary enormously in what they handle, and the right choice depends on your actual mix. A team drowning in inbound NDAs and standard sales paper needs fast templated self-service and quick AI review above all. A team handling complex, high-value commercial agreements needs deeper clause intelligence and risk analysis, and cares less about volume throughput. A team spread across group entities needs multi-entity support and good reporting for the general counsel. Before you shortlist, profile your contract mix honestly, by type, volume and complexity, and weight the selection criteria to match it. A tool that is perfect for a high-volume NDA shop may be wrong for a team doing a smaller number of complex deals, so let your real workload, not the vendor's favourite use case, set the weighting. ## A short bridge to one option If cutting review time and enabling safe self-service are your priorities, Adira is worth testing. It is built on Anthropic's Claude, does not train on your contract corpus, and brings AI drafting, a per-clause favourable, neutral or unfavourable risk view, plain-English clause editing with a diff, a searchable repository, and renewal and obligation tracking into one workspace, with published per-seat pricing you can model today. As its own measured result, Adira reports that across 18 law firms using it, contract review and due diligence that took 10 to 12 days was cut to under two hours, with only two to three rechecks in total; treat that as Adira's figure and reproduce the review-time test on your own contracts in the trial. It is India-first and expanding, so confirm local coverage. You can see it at Adira. ## Bottom line An in-house legal team should choose a CLM on AI-assisted review that cuts lawyer time, a structured clause library and per-clause risk, safe self-service, a searchable repository, reliable renewal tracking, clear reporting and a sound data position, with adoption weighted as heavily as features. Define a scored test on your own contracts with an answer key, run it across three tools in trials, measure review time against your baseline, and model total cost against a published price. Decide on the evidence you gathered, not the demo you watched.