AI contract intelligence: what companies miss (and how to avoid it) 

Artificial intelligence (AI) is reshaping how organizations manage contracts, but realizing its value requires more than turning on new technology. We sat down with Jason Smith, Global Director of CLM Product Launch and Legaltech Evangelist at Conga, to talk candidly about where AI contract intelligence delivers real impact, where companies get tripped up, and what leaders should look out for when adopting or optimizing AI in contract lifecycle management (CLM).

Contracts: the largest unstructured data challenge

Contracts hold critical information about obligations, risk, revenue, and compliance, but they are uniquely difficult to manage at scale. As Jason put it:

“Contracts are probably the biggest source of unstructured data in a corporation.”

Written by different people, at different times, and for different purposes, contracts create a fragmented data landscape. This is why keyword search alone often falls short. AI contract intelligence must go beyond locating clauses to understand patterns and relationships across an entire contract population.

Jason explained that many organizations underestimate how complex this data problem really is, which leads to unrealistic expectations about what AI can deliver out of the box.

AI contract intelligence is not a shortcut

Despite growing hype, AI is not a plug-and-play solution. Jason was direct about the risks of rushing adoption without clear intent. Organizations need clarity on what they are trying to achieve, whether that is reducing risk, improving visibility into obligations, or supporting negotiations. Without a defined goal, AI can look impressive without meaningfully improving decisions.

One of the most common misconceptions is assuming AI can fix messy data automatically. Jason cautioned against dumping legacy contracts into a new system and hoping technology will sort it out:

“That’s like squeezing the toothpaste out of the tube. You can’t put the toothpaste back in.”

When data is incomplete or poorly structured, AI will surface insights that appear confident but are fundamentally unreliable.

Why data preparation matters more than expected

Cleaning and organizing contract data is foundational work. Jason stressed that there are no real shortcuts here:

“The hard part… there are no shortcuts in this. I think there’s a lot of people selling shortcuts right now.”

Taking time upfront to understand what data exists, what is missing, and how contracts should be structured enables AI to deliver useful insights instead of amplifying existing problems. Teams that skip this step often encounter frustration later when results fail to meet expectations.

Where AI contract intelligence delivers real value

When implemented thoughtfully, AI contract intelligence can significantly improve how organizations operate. Jason pointed to consistent impact in areas such as regulatory analysis, obligations management, and large-scale contract review. These are tasks that would otherwise take weeks or months to complete manually.

However, speed alone is not the goal. Jason warned that faster results without proper structure and oversight can introduce new risks:

“If you don’t build those kind of context clues into your prompts and into your system, you’re going to get speed for speed’s sake, not necessarily quality.”

AI delivers the most value when insights are connected to ownership and real workflows, so teams know how to act on what they see.

What to look for in AI contract intelligence solutions

Focus on outcomes that matter

Before evaluating AI based on features or demos, Jason encouraged leaders to start with outcomes. Regulatory analysis, obligations tracking, and large-scale contract review are areas where AI consistently reduces manual effort and supports better decisions. Solutions that prioritize these use cases, tend to deliver clearer returns than general purpose AI tools.

Support adoption by meeting teams where they already work

AI contract intelligence only delivers value if people actually use it. Jason emphasized that adoption is often a change management challenge rather than a technology one. Legal teams work in Word. Sales teams work in Salesforce. When intelligence lives outside the tools people already rely on, insights often arrive too late or go unused.

When evaluating solutions, look for a CLM that brings AI contract intelligence into the systems users are already in. That way, they can act on insights without changing how they work. This focus on working within familiar tools is central to how Conga approaches AI contract intelligence adoption.

Improve communication across teams

Jason also stressed that contracts are not owned by legal alone. They act as a shared reference point for sales, finance, procurement, and operations. When each group works from a different view of the contract, misalignment is common.

AI contract intelligence delivers greater value when it improves communication between teams by creating shared visibility into terms, obligations, and risk. This reduces back and forth, limits misunderstandings, and helps teams align faster.

Be wary of shortcuts in data preparation

Jason’s warning about shortcuts applies equally when evaluating vendors. Solutions that promise instant results without addressing data quality and structure often create more issues than they resolve. The right approach supports thoughtful preparation and sets realistic expectations rather than masking foundational gaps.

How Conga supports a responsible approach to AI contract intelligence

Conga supports a responsible approach to AI contract intelligence, helping teams prepare their data and apply insights into day-to-day work.

Higher adoption with less disruption 

By bringing contract intelligence into tools teams already use, such as Word and Salesforce, organizations reduce friction and increase the likelihood that AI insights are actually used.

Clearer communication across teams 

Shared visibility into contract terms, obligations, and risk helps legal, sales, finance, procurement, and operations work from the same information and align more quickly.

More reliable insights from better data preparation 

A structured approach to contract data reduces noise and improves trust in AI outputs, limiting rework and frustration later.

Fewer disconnects across the contract lifecycle

Connecting contract intelligence with quoting and document processes helps reduce inconsistencies between what is sold, what is agreed, and what is executed.

Decisions supported at the moment of work 

Embedding intelligence into workflows allows teams to act on insights as decisions are being made, rather than after issues surface.

See how Conga helps teams move from AI promise to practical contract intelligence.