You signed them all. Now find out what they say.

Accuracy is not one number and anyone quoting you a single percentage for your estate is guessing. It varies by document quality — a clean digital original and a poorly scanned fax are different problems — and by field type, since dates and party names extract far more reliably than a nuanced liability position. It also varies by how consistent the language is, because an estate built from your own templates behaves differently from one of counterparty paper. The only meaningful answer comes from measuring against a sample your own team has verified, per field, which is why that step comes before processing the estate at scale.

Yes, and the design question is where rather than whether. Extraction tells you what a contract appears to say; it does not tell you what it means in a dispute, how a particular jurisdiction would read it, or whether two individually acceptable clauses interact badly. The practical approach is to route by confidence and by consequence: low-confidence values go to review, and high-consequence fields such as liability and indemnity go to review regardless of confidence. Used that way the AI is a very fast first pass that finds what needs a lawyer's attention, which is a substantial saving without being a replacement.

They solve different problems and are often bought separately. CLM governs agreements going forward — clause library, approvals, negotiation, renewals. Contract Intelligence analyses the estate you already have. Plenty of organizations start with intelligence because the urgent question is about existing exposure rather than future process, and Conga's Discovery AI is available to users of its contract lifecycle products. Where both are in play, running them together is worth sequencing deliberately: the playbook that drives risk scoring and the clause library that governs new agreements should be the same source rather than two lists that drift. See Conga CLM implementation.

They work, but not equally well, which is why we triage the estate before processing rather than treating it as uniform. Scanned documents depend on the quality of the scan — a clean 300 dpi scan of a printed agreement usually extracts well, while a photocopy of a fax with handwritten amendments is genuinely hard and will produce a much higher review rate. The practical consequence is that expectations and review effort should be set per tier of document quality rather than as an estate-wide average, and that the worst tier is often best handled by prioritising only the agreements that are still live.

With the live, high-value subset rather than the whole estate. Agreements that have expired and will not renew rarely justify the processing and review effort, and starting narrow gets useful answers — upcoming renewals, obligations nobody is tracking — into the business in weeks rather than quarters. The assessment step usually finds the estate is smaller than expected once duplicates, superseded versions and expired agreements are removed. It also normally finds amendments that were never linked to their originals, which matters because the operative terms are the combination rather than the original alone.

By investing in the parts that do not depend on the model. Your extraction scope, your negotiation playbook, your gold-standard sample and your review workflow are all assets that survive a product changing underneath them — and they are what make it possible to evaluate a new capability quickly when one arrives, because you already have the ground truth to measure it against. This is a genuinely fast-moving area of Conga's portfolio, so we verify capability against current documentation at implementation time rather than working from what was true at the last project.