The adoption challenge is real
Last week, I wrote about the buzz at the CLOC Global Institute, where legal operations professionals weren’t debating if AI would transform legal work, but when and how deeply it would be embedded in their operations.
The energy around AI in the legal space is undeniable. But there’s a gap between excitement and implementation that’s becoming increasingly apparent—one that Geoffrey Moore recently highlighted in his analysis of agentic AI’s path to adoption.
AI must target the most painful bottlenecks
In his recent LinkedIn article, Geoffrey Moore pointed out that agentic AI (which aims to take humans completely out of the loop) hasn’t yet achieved mainstream adoption. He argues that to gain traction this year, agentic AI vendors must “target the most impactful value-trapping bottlenecks in the industries where they are causing the most pain.”
Moore identifies these bottlenecks as processes that “suck up an enormous amount of human resources and time to deliver what are atomically low-value transactions” while holding “an end-to-end value delivery process hostage.”
At Pramata, we’re seeing the same pattern playing out in contract management. Legal teams are eager to leverage AI, but they need solutions that address specific, painful bottlenecks without requiring them to rebuild their entire contracting infrastructure.
Bottlenecks in contract management
Contract management is rife with exactly the type of bottlenecks Moore describes:
- Contract reviews that require skilled attorneys to scan dozens of pages looking for non-standard terms
- Obligation management where critical dates and requirements get buried in amendments and appendices
- Cross-functional collaboration that breaks down when Sales, Finance, and Procurement can’t access the contract data they need
- Legacy clean-up where thousands of historical contracts need consistent organization and extraction
These bottlenecks hold entire business processes hostage while consuming valuable legal resources that could be better deployed on higher-value strategic work.
For a real-world example of how a legal team used Contract AI to break free from its most painful bottlenecks to turn a multi-day process into as little as 30 seconds, read symplr’s case study here.
Practical solutions breaking through the noise
So what does a practical Contract AI solution that can bridge the adoption gap look like? We’re finding success by focusing on tools that target specific bottlenecks with immediate, measurable value.
Take one recent customer example: A company’s quality teams frequently needed to update their quality agreements and determine whether corresponding commercial agreements required amendments as well. Their legal team was spending countless hours manually comparing documents against standard templates.
Working with their team, we implemented a service terms checklist that could automatically analyze contract language against their standards. The system quickly identifies standard versus non-standard language across key provisions like payment terms, liability, confidentiality, and termination rights.
The results? Reviews that used to take 2-3 hours now take 20 minutes. Their legal team can instantly see whether terms are standard (highlighted in green) or non-standard (flagged in red), with the specific clause text available for reference.
This wasn’t a complete transformation of their end-to-end contracting process; it was a targeted solution to a painful bottleneck that was consuming valuable legal resources. And that’s precisely how Contract AI begins gaining meaningful adoption.
Why AI still needs humans-in-the-loop
Another key insight from Moore’s analysis is that successful early adoption of agentic AI requires “guardrails already built in.” He points out that deployment must leverage “enterprise applications already proven to be reliable.”
This aligns perfectly with what we’re seeing in Contract AI. The solutions gaining traction aren’t standalone AI experiments. They’re AI capabilities built on top of proven, reliable contract management foundations.
At Pramata, our approach integrates human expertise at critical junctures in the AI pipeline. When contract data is first ingested, our system automatically flags potential issues like duplicates or inconsistencies. Then, human experts review these exceptions, making judgment calls that would be risky to fully automate.
This hybrid approach delivers the best of both worlds: AI handles the vast majority of the heavy lifting, while humans provide oversight for the edge cases and complex decisions that require judgment.
Building on established enterprise foundations
Moore emphasizes that successful agentic AI must be “deployed atop enterprise applications already proven to be reliable.” This is precisely why Pramata’s integrations with established systems like Salesforce are so critical to adoption success.
By connecting Contract AI capabilities directly into the tools teams already use every day, we’re eliminating friction in the adoption process. Sales teams don’t need to learn a new system to access contract data—they can see critical information right in their Salesforce environment. Finance teams can pull payment terms and renewal dates directly into their systems of record.
These integrations mean that Contract AI isn’t creating yet another information silo. Instead, it’s enhancing existing workflows by feeding clean, AI-processed contract data into established, structured systems that organizations rely on. This approach dramatically accelerates adoption because users experience the benefits of AI without disrupting their familiar processes.
From demonstrations to deployment
Moore observes that many enterprises today are pursuing AI initiatives where “the demos work great, but because they are not leveraging decades of real-world testing, they inevitably hit serious deployment roadblocks when they go to scale.”
We’ve seen this pattern repeatedly in Contract AI. Impressive demos that analyze a dozen carefully curated NDAs fall apart when faced with thousands of multi-entity, amended, OCR-scanned contracts from the real world.
Successful Contract AI adoption requires solutions that are built for the messiness of real contract environments, not just clean demo data. It needs to handle complex hierarchies of agreements, tangled amendment chains, and all the variations in format and language that exist in real contract portfolios.
The path forward
As Moore concludes in his article, it’s time to “stop talking and start doing.” The processes addressed by Contract AI are known bottlenecks in core business operations that affect efficiency across entire organizations.
The path to successful Contract AI adoption isn’t through flashy demos or theoretical capabilities. It’s through targeted solutions that:
- Address specific, painful bottlenecks with immediate ROI
- Build on reliable foundations rather than starting from scratch
- Integrate human expertise at critical decision points
- Are designed for real-world complexity, not just demo scenarios
- Connect seamlessly with existing enterprise systems like Salesforce, where teams already work
We’re seeing success patterns across financial services, healthcare, technology, and manufacturing—each with their own unique bottlenecks being targeted by tailored Contract AI solutions.
The adoption gap for Contract AI is real, but it’s being bridged one bottleneck at a time. Organizations that focus on these practical approaches find themselves not just talking about AI’s potential, but actually realizing its value.
As Moore might say, it’s time to just do it. Your contracts—and your overworked legal team—will thank you.
