The Metrics That Actually Prove AI Is Working
Every legal AI vendor promises transformation. Faster onboarding. Fewer errors. More time for fee earners. Almost none of them tell you how to check. That’s not an accident. Vague claims are easy to make and hard to disprove. But a firm weighing up legal AI shouldn’t have to take anyone’s word for it. There are concrete numbers you can track before adoption and after, and the difference tells you whether the tool is actually working. Billable hours recovered Start with the admin load on your fee earners. Client intake forms, duplicate data entry, chasing documents. Log how many hours per week go into this before any AI tool is introduced. After adoption, measure the same task load again. The gap between the two numbers is the real return, expressed in hours your fee earners get back for billable work rather than paperwork. Enquiry-to-client conversion rate Most firms lose prospective clients simply because the response is too slow. Track what percentage of enquiries currently convert into signed clients, and how long that journey takes from first contact. Faster, more consistent responses should move that conversion number in one direction. If it doesn’t, that’s a signal worth investigating rather than ignoring. Onboarding turnaround time New client onboarding is where duplicate data entry and manual checks tend to pile up. Measure the average number of days from signed engagement to fully onboarded client, before any change is made. Then track it again post-adoption. A shorter turnaround means clients start work sooner and fee earners spend less time on setup rather than casework. Compliance flag accuracy Risk and compliance checks are only useful if they catch the right things. Track how many flags are raised, how many are genuine issues, and how many are false positives that waste review time. A well-tuned system should raise fewer false positives over time while still catching what matters. That balance is a clearer measure of quality than a simple count of flags raised. Proof, not promises None of these numbers require a leap of faith. They’re the same figures your practice already generates, just captured properly before and after a change is made. If a vendor can’t help you track them, that’s worth noting too. Kyanite’s Karli is built around this kind of reporting from day one, across enquiries, onboarding, and risk. If you’d like to see what that looks like for your firm, we’re happy to walk through it. Book a Demo The Metrics That Actually Prove AI Is Working The Metrics That Actually Prove AI Is Working • July 17, 2026 Every legal AI vendor promises transformation. Faster onboarding. Fewer errors. More time for fee earners. Almost none of them tell you how to check. That’s not an accident. 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It starts the moment they make first contact, and the experience they have in those early days … How Law Firms Are Automating Client Onboarding End-to-End, Without Losing Control How Law Firms Are Automating Client Onboarding End-to-End, Without Losing Control • May 1, 2026 There is a legitimate tension at the heart of legal AI adoption. Law firms know they need to modernise. The administrative cost of onboarding a single client manually runs to … AML Compliance in Law Firms: Why Inconsistent Processes Are the Real Risk AML Compliance in Law Firms: Why Inconsistent Processes Are the Real Risk • April 16, 2026 The SRA’s 2024–25 AML report makes uncomfortable reading. 426 potential breaches reported. 151 enforcement actions issued. 32.4% of inspected firms found to be non-compliant. Almost double the breach figures from …
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