By Martin Shillo, Founder & CEO of PRACTICL AI
AI contract review isn’t science fiction anymore. It’s here, it’s real, and for many deal teams it’s already shaving days off due-diligence timelines. What used to take junior associates a week now takes a well-trained model a few minutes. Accuracy is up, costs are down, and the word “pilot” is finally giving way to “standard workflow.”
But even as adoption accelerates, there’s an awkward truth: most firms and in-house teams are still tripping over the same invisible obstacle. The problem isn’t that the tools can’t review contracts – it’s that no one has time to tell them what to look for.
The Setup Bottleneck
Every AI review engine, no matter how advanced, needs to know your rules of engagement: which clauses matter, which deviations are risky, which exceptions you’ll tolerate, and how you define “acceptable.” For a typical vendor-contract review, that can mean 120 or more checkpoints — indemnities, liability caps, data-protection terms, governing law, payment schedules, renewals, the works — plus the added fun of amendments and SOWs.
Before the AI can deliver, someone has to feed it that playbook. And building that playbook is still a human-heavy exercise — the “setup phase” that so many legal teams quietly dread. It’s tedious, time-consuming, and often the reason an otherwise great AI system sits unused after the demo.
You could call it the “what-to-ask” problem. But the machine is ready. Humans simply haven’t told it what to ask.
The Good News: The Machines Work
That’s the irony. The tech itself works better than ever. Accuracy rates north of 85 percent are common in structured clause extraction and review cycles that once took 10 hours can be done in two. Human-in-the-loop models, where AI handles the grunt work and a lawyer does the final pass, are now delivering reliable results in M&A, compliance, and vendor reviews.
A 2025 State of Contracting survey found that AI adoption in legal review was up 75 percent year-on-year. More than half of the respondents said the turnaround time improved significantly and nearly a third reported measurable cost savings. So clearly, the generative-AI wave didn’t just create buzz; it produced working tools.
The Missing Step
So if the tools are good and the lawyers are willing, why isn’t everyone using them? Because defining what to review still takes too long.
That setup process — translating the firm’s internal know-how into machine-readable playbooks — is where many projects die. You can buy speed, but you can’t buy context. And context is what most legal-AI tools still can’t self-generate.
Enter PRACTICL AI
Instead of starting from a blank screen, we analyze your existing material — sample reports, SOPs, even annotated contracts — and automatically build the review logic. We figure out what to ask.
In other words, our AI doesn’t just automate the review. It also automates the setup of the review.
PRACTICL AI turns that messy pre-flight checklist into a structured “playbook”, a digital rulebook that the AI can actually execute. You can edit, refine, or add human-oversight layers as needed. But the heavy lifting — the translation of institutional knowledge into structured prompts — happens for you.
The result is contract reviews that start faster, align better with firm policy, and integrate cleanly into diligence or compliance workflows.
Why This Matters
Time kills deals. Whether you’re assessing a target company’s vendor stack in an M&A, running a compliance audit, or triaging risk in an insolvency, every extra day of manual review slows momentum and burns margin.
Structured review automation attacks that lag directly and collapses the weeks spent configuring an AI tool into hours. That’s real ROI: faster cycle times, fewer errors, more consistency, and a higher-confidence audit trail.
It’s no wonder legal-ops leaders have noticed. The new competitive advantage isn’t just having AI review capability – it’s how quickly you can tailor it to a client, a deal, or a jurisdiction. The faster you can teach the machine your rules, the faster you capture value.
The Market Direction
Legal tech is in a phase shift. First-generation tools automated tasks: redlining, metadata extraction, clause comparison. Second-generation tools (the “gen-AI era”) automate reasoning: summarising, flagging, proposing edits.
The third wave — where PRACTICL AI positions itself — automates structure: turning messy human processes into machine-executable playbooks that can be shared, reused, and improved.
That’s a subtle but important evolution. The future isn’t one giant AI replacing lawyers. It’s many small, specialised AIs executing well-defined tasks inside a controlled framework, overseen by humans. That’s how you get reliability, regulatory defensibility, and trust — three words every GC still loses sleep over.
Human in the Loop, Always
Let’s be clear: no one sane is arguing that lawyers should be cut out of the loop. The real innovation is making that loop tighter — less about manually hunting clauses, more about interpreting results, refining rules, and exercising judgment.
AI doesn’t replace expertise; it amplifies it. But only if that expertise is structured in a way the machine can learn from. PRACTICL AI’s pitch is that it can turn that unstructured know-how into a digital asset — quickly.
The ROI Story
Here’s the math every managing partner understands:
- Review cycles drop from weeks to days.
- Cost per contract plunges.
- Throughput per lawyer rises.
- Institutional knowledge, once trapped in Excel sheets or partner heads, becomes scalable.
That’s not theoretical. Early adopters of structured-review automation are already reporting double-digit efficiency gains. And in a market where clients demand fixed fees and transparency, those margins matter.
The Bottom Line
AI review is no longer the wild west. The tools are mature and the guardrails are there. The “human + machine” model works. What’s been missing is the bridge between your expertise and the AI’s engine — the structured layer that tells the system exactly what to look for and how to judge it.
PRACTICL AI wants to be that bridge: a platform that builds playbooks from your own SOPs and makes configuring AI reviews as easy as running them.
The takeaway? If you’re serious about scaling diligence, compliance, or vendor-contract review, the question isn’t whether you’ll use AI. It’s whether you’ll waste months setting it up.
The firms that win the next wave won’t simply be faster at reviewing contracts. They’ll be faster at teaching machines what matters. And in law, as in tech, that’s where the real leverage lives.
