At Concord, we have the advantage of direct visibility into how teams engage with our AI Copilot. By analyzing thousands of queries, we can see what people are asking and how adoption patterns emerge.

This post shares two critical insights. First, the top queries reveal that teams are leaning on Copilot for the business-critical clauses that matter most in contracts. Second, usage follows a long-tail pattern where many people test the tool lightly, but a smaller group of power users return frequently and drive the majority of value.

The top queries inside Copilot

Since launch, Copilot has processed 7,491 queries across 392 organizations and 723 unique users, spanning more than 3,100 active documents. The average user submitted 10.36 queries, with most responses arriving in under five seconds.

When we analyze the actual queries, the results are striking. The top categories are not general questions or exploratory prompts. They are tightly focused on the clauses that carry risk, revenue, and compliance implications.

The most common query categories include:

• Payment structures and obligations (527 queries)

• Risk analysis (431 queries)

• Revenue identification (244 queries)

• Party responsibilities (218 queries)

• Period of performance (160 queries)

• Change of control clauses (139 queries)

• Insurance requirements (101 queries)

• Data security provisions (95 queries)

These results show that Copilot is being used for serious, outcome-driven work. Users are not asking it to draft poems or generate boilerplate text. They are asking it to summarize renewal clauses, validate payment terms, identify compliance risks, and extract key responsibilities.

Graph 1: Horizontal bar chart of top 10 queries

Distribution of query categories

Looking at the overall distribution, a small number of query types account for the majority of usage. Payments, risk, revenue, and responsibilities dominate the landscape, while other categories like insurance and data security appear less frequently but remain significant.

Graph 2: Pie chart of distribution of top six query categories

This concentration is telling. When AI adoption sticks, it is because people return to the tool for a handful of high-value, repeatable use cases. Copilot’s adoption is driven by exactly these types of queries.

Why these questions matter

Contracts are dense documents, but most business risk and value is concentrated in a few clauses. Payment terms determine revenue recognition and cash flow. Renewal and termination rights affect forecasting and risk. Compliance provisions such as GDPR or HIPAA can determine whether an agreement is enforceable in regulated industries.

By clustering around these questions, Copilot demonstrates that AI in contract management is not a novelty. It is being used to speed up the review of the clauses that drive the biggest financial and compliance outcomes. This is why adoption is coming not only from lawyers but also from finance, procurement, and operations teams who need to extract these answers directly.

The long tail of adoption

The second insight from our data is that adoption follows a long-tail pattern. While thousands of queries have been submitted overall, usage is unevenly distributed across users.

• About half of users submitted only 1–2 queries.

• The top quartile of users submitted 5 or more queries.

• The single most active user submitted 907 queries.

Graph 3: Histogram of queries per user (log scale)

This pattern is typical of technology adoption. Many people test a new feature lightly, but a smaller group sees immediate value and begins using it heavily. Those power users often become internal champions who drive broader adoption in their organizations.

The cumulative distribution curve illustrates this clearly. A relatively small number of users account for the majority of total queries.

Graph 4: Cumulative distribution of queries per user

What this means for AI adoption

These two insights together tell us something important about AI adoption in real-world workflows.

First, adoption is not about breadth alone. It is not enough to count how many people have tried a tool once. What matters is whether people return to it for repeatable, high-value tasks. The distribution of queries shows that most users experiment, but the ones who find value keep coming back.

Second, adoption is anchored in the questions that matter most to the business. Payments, renewals, compliance, and responsibilities are the clauses that create real friction in contract work. When AI makes those tasks easier, adoption follows naturally.

Recommendations for teams rolling out AI

Based on these patterns, there are several practical lessons for organizations introducing AI into their workflows.

• Focus training on the top use cases. Build onboarding materials around the handful of queries that matter most. For contracts, this means payment terms, renewals, compliance, and risk.

• Measure repeat usage, not just signups. Track how often people come back after their first query. This is the best indicator of real adoption.

• Identify and support power users. The small group submitting dozens or hundreds of queries are your champions. Give them tools and recognition to spread adoption.

• Use adoption data to refine enablement. Query categories can show you where teams lean on AI most, and where they may need additional training or guidance.

Conclusion: Adoption is in the questions and the patterns

The story of Copilot usage is not about hype or speculation. It is about the real questions teams are asking and the patterns that emerge when adoption takes hold.

The data shows that teams use Copilot for the most critical parts of their contracts. Payments, renewals, risk, and compliance dominate the query list. And the long-tail distribution of queries proves that while many users experiment, a smaller group of heavy users quickly emerges and drives real value.

For leaders wondering how to separate AI usage from AI adoption, the lesson is clear. Look at the questions people are asking. Look at whether they are returning to the tool again and again. Adoption is not defined by novelty. It is defined by habits, and by the alignment of AI to the outcomes that matter most.