Why Data Matters to Legal

Throughout the enterprise and legal landscape, the thing that continues to hamper applications of AI the most is data, or, more specifically, a lack of useful, relevant data. In contracts, there remains a significant data gap. When available, information is generally subjective and anecdotal, making it hard to understand a contract portfolio’s financial health.

So, why does making legal more data-driven matter? In short, data supports better decision making.

We live in a data-driven world. More and more industries rely on data to make day-to-day and strategic decisions. The good news is that the legal sector — particularly contract management — is finally catching up with access to powerful AI technologies that can extract and classify data from unstructured documents like legal agreements and integrate data from financial systems.

Sales teams already use many measures to quantify success, such as Total Contract Value (TCV), Annual Contract Value (ACV), and Annual Recurring Revenue (ARR). Similarly, private equity firms use a sophisticated set of metrics to assess investment in startups. While not perfect, we can also apply these measures to compare contract performance (in terms of contract values, costs, and risk exposure) within an organization and across industries.

There are, however, no similar measures for legal operations. A simple metric for legal operations could focus on costs. This, though, obscures the critical fact that operations are a part of the value chain. Just as the defense can score a goal, legal operations can also add value. Here are some areas where we could begin to add value through the extraction and classification of data.

Calculating the Average Contract Margin

The metric calculates contract cost as a percentage of value. As a normalized value — like the sales measures — it can compare contract performance within an organization, with peers, and across the entire industry. One such universal standard is the contract net margin, namely the contract’s cost throughout its lifecycle compared to the transaction’s value. Managers can determine overall costs at the portfolio level by dividing the total number of contacts by the allocated resource costs (where resource costs comprise personnel time, system resources, and external costs).

Average Contract Margin = Average Contract Costs / Average Contract Value

We can start with survey data to estimate average contract margin, such as the recent World Commerce and Contracting analysis, shown in the table below. The costs are the results of the survey. The values and resulting margins are estimates.

Looking at the chart, above it’s plain that the majority of the costs are in management and operations. Legal expenses represent a relatively small percentage. Additionally, it indicates that cost margins typically decrease with increasing contract values.

Calculate Individual Contract Variance

The second metric calculates how the cost of each contract compares to the standard. For this metric, the first question to ask is: What drives cost variance in individual contracts compared to average? In simple terms, there are two main factors:

• Length and complexity
• Deviation from standards

Quantifying the variances here should have a positive or negative impact on contract margin.

There is a direct correlation between contract length and complexity and contract cost. Concise, readable contracts:

• Reduce the time and cost of drafting, reviewing, and managing contracts.
• Accelerate cash-flow by significantly shortening pre-contract negotiation time.
• Reduce disputes because they offer clear, understandable terms

Estimated Contract Cost = Time x Number of Reviews x Number of Reviewers/ Administrators x Average Payscale

The time required to read, review, and administer agreements significantly increases as the language used becomes more complex. This can be measured — and the cost savings of simplification — using the Flesch-Kincaid readability tests.

There is also a correlation between deviation from standards and increased costs. For example, it is well understood that the cost of processing third-party contracts exceeds the cost of processing company paper. It is, however, much more challenging to place a precise value on variance.

Adjusted Deviation Margin = Average Contract Costs plus or minus ⅀ Sum of (cost or value of each deviation x probability)

Each contract deviation is assigned a cost or a value multiplied by the probability of occurrence. This analysis applies to both company paper (whereby the deviation is compared to company standards) or even third-paper paper (where the deviations are compared to market standards). A prior post‒Quantifying Contract Risk-Reward‒provides an example of the calculation.

Some deviation can be anticipated due to the requirements of each transaction. However, in an ideal world, companies will limit variation to circumstances merited by increased value. In other words, for lower value, routine transactions, companies should stick to their standards, acknowledging that more complex, higher-value transactions may require some flexibility. With data, an organization can examine in great detail if it is making sound judgments. In the example below anomalies can be seen in the blue-colored bars.

For example, the chart displays the clause outline in a contract on the vertical axis and the contracts on the horizontal axis. In each intersecting cell, a conformity-risk score is plotted in a color code from green (for most conforming, least risky) to yellow (for moderate conforming and risky), to red (for least conforming and most risky). The chart is then sorted by value. A clear pattern of quadrants emerges, helping the company to determine if it is more likely to deviate from its standards for higher-value contracts or whether unnecessary risk occurs in lower-value agreements.

Why is this all so important?

You can’t manage (or optimize) what you can’t (or don’t) measure. It’s an oft-quoted saying but still rings true. Today, AI tools can analyze contracts, extract key metrics, and provide deep insights into a contract’s economic performance. With these tools, individuals and organizations can identify over-performing agreements and learn from the best practices they contain, and equally identify underperforming agreements and reap significant returns from rectifying the points of deviation. Understanding the challenges we face around which data to focus on and extract also shines a light on the considerable opportunities for AI in the legal arena. Legal disruption is a net positive and digital maturity isn’t some far-off goal but, rather, something imminently achievable and it’s through the data that we’ll get there fastest.