Cash Poor to Cash Rich. How Legal AI Solves Cash Flow Problems of Middle-Market Companies

Introduction

When businesses encounter cash flow challenges, they typically resort to seeking external financing or cutting operational expenses, especially in non-revenue-generating departments.

For a department such as legal, AI technology provides an opportunity to embrace a broader role in driving business and financial outcomes that positively impact cash flow management and contract compliance.

This article explores a practical, high-ROI use case for purpose-built, legal AI tools. These tools leverage machine learning, natural language processing, and generative AI systems to monitor executed contracts for payment terms, cycles, and penalties, in order to recommend strategic adjustments that solve cash flow problems and improve contract compliance rates.

The Problem

For middle-market manufacturers and supply chain firms, cash flow is king. These organizations often operate on thin margins, long production cycles, significant upfront costs (inventory, labor), and often, slow payment from large customers – for instance, paying upfront for raw materials or components, then waiting months for finished goods to be delivered and paid for.

Two essential metrics for in measuring cash flow management are Days Sales Outstanding (DSO) and Days Payable Outstanding (DPO). DSO measures the average number of days it takes a company to collect payment after a sale has been made, and high DSO values suggest delays in collecting receivables. DPO measures the average number of days a company takes to pay its suppliers after receiving an invoice.

In manufacturing, the average DSO is around 50–58 days in recent years, meaning companies are unable to collect payment for nearly 2 months after a sale is made[1]. On the other hand, median DPO sits at 43-49 days[2]. These figures demonstrate that median, middle-market manufacturing and supply chain firms are paying suppliers one week earlier than they are collecting from customers, at minimum. Alternatively, these figures highlight an extremely tight deadline to pay suppliers after collection dates.

These figures raise problems with both working capital management and compliance with contractual obligations. For instance, if a company’s standard supplier payment terms are Net 30, but it takes on average 43–49 days to pay suppliers (DPO), this means most invoices are paid late.

With these statistics representing the average middle-market firm, there is a high-impact opportunity to solve this challenge. With new AI technology, businesses can approach problem-solving in novel ways – unlike external financing or budget cutting in non-revenue generating departments as they typically do.

With legal departments approving and monitoring the valuable business intelligence contained within company contracts, generative AI technology stands to empower lawyers with a unique opportunity to: (1) monitor and renegotiate payment terms and policies in novel ways, (2) strategically lower DSO and raise DPO, and (3) mitigate the problems of cash flow management and contract compliance.

Demystifying The Technology

An AI-native contract monitoring module, powered by machine learning and natural language processing (NLP), can automatically extract key data that impacts cash flow management from contracts – such as payment due dates, net payment terms (e.g. Net 30, Net 60), late payment penalties, early payment discount clauses, and renewal dates.

How does it work? The AI is usually trained on vast, publicly-available legal datasets, and possibly company-specific contracts, enabling it to recognize important contract language (like “Net 60 days from invoice date” or “1.5% per month interest on late payments”), as well as when important clauses are missing. Through bulk upload or integrations into CLM/DMS systems already in place, the AI system would ingest the text of executed contracts – typically PDFs or Word documents – and uses NLP to locate and extract important text. Once the deadlines, obligations, and terms are ingested, the data can be analyzed and visualized to measure KPIs.

Generative AI can be leveraged to interpret and deliver the data through a conversational format to users. In other words, generative AI technology presents an “understanding” of the financial levers embedded in contract terms. The technology can identify patterns or anomalies (for example, a subset of supplier contracts with unusually short payment cycles that strain cash flow, or customer contracts lacking late-fee clauses) and then generate insights and recommendations in plain language for the legal, contracting, and finance teams to take action upon. Going further, generative AI can be leveraged to review and draft negotiation guidance and contract language to impact working capital.

Some examples of this in practice include:

1. By analyzing all contract payment terms in aggregate, AI gives a clear picture of how cash moves through the business. Executives can see, for example, the weighted average payment cycle of receivables and payables. If the data shows that on average the company pays suppliers in 30 days but collects from customers in 60 days, that 30-day gap must be financed – either by dipping into cash reserves or borrowing.
2. On the receivables side, AI can help identify ways to get paid faster by recognizing what leverage exists: Do contracts include late payment penalties? Are there early payment discounts for customers? What’s the historical payment behavior of each client? Using this information, the AI might highlight that Customer A routinely pays 15 days late, but the contract includes no late fee clause. The insights provided by the technology present a renegotiation opportunity to either enforce timely payment or compensate for delays. In the alternative, Customer B has a clause for a 2% discount if invoices are paid within 10 days, which the customer never does. In this case, the legal department could propose a reminder to the Customer of this promotion to incentivize earlier payment and improve cash inflow timing or instantly insert a redlined edit to the contract language when a renewal period arrives.
3. A company’s agreements contain payment schedules with several key dates. The AI tool could automatically extract key deadlines and dates, integrate into relevant employee calendars, and send smart reminders – ensuring that payment deadlines are not missed by the organization, or its customers. The overall effects are reductions in DSO and revenue cycle leakage. Alternatively, in many supply agreements, late payments incur fees or interest rates. Failing to meet certain contractual obligations can trigger expensive penalties on an organization. AI tools that monitors contract obligations and deadlines will send alerts well before a penalty is incurred.

This technology offers a shift from reactive contract administration to the proactive optimization of vital business intelligence already stored in executed company contracts. With these insights, lawyers and contracting teams can work to strategically renegotiate, or enforce, contract terms that will positively impact working capital challenges to the company.

Real-World Impact & Case Studies

If an organization is struggling with cash flow management, assessing real world instances of the technology improving other organizations can support you in experimenting and assessing the value of an AI contract monitoring tool.

1. Pentair, a global manufacturing company known for industrial pumps and water solutions, had been facing challenges with a complex and fragmented procurement system. By implementing an AI-driven contract and spend management solutions, Pentair was able to analyze and standardize payment terms across its business units. AI found contractual terms that were negatively impacting working capital in supplier agreements, such as short payments cycles. Pentair realized a $15 million improvement in working capital by optimizing payment terms[3].
2. Another instance involves a global chemicals manufacturer with close to $3 billion of spend in a specific category. This organization’s contracts contained 200+ different payment terms, ranging from NET0 to NET150, with some based on specific dates in a month. Leveraging an AI module focused on payment term optimization, it was revealed that:
   1. 8%-9% of the total in spend lacked any payment terms stated on the PO.
   2. Another 3%-5% of the total spend had a payment term offering early payment discounts of up to 5%. Had the client had made early payments, the organization could have resulted in $3 million worth of savings. Further, if all long-duration payment terms were negotiated to include a 2% early payment discount, the total potential discount would reach approximately $80 million.[4]
3. Keller Williams Realty, a large real estate enterprise, has high contract volume. The procurement team had a process of manually keeping track of renewals, which resulted in several deadlines being missed. Then, Keller Williams adopted an AI-powered contract intelligence platform to centralize and analyze their contracts, which led to the discovery of two sizable service contracts that had auto-renewed, unnoticed, until data had been centralized and visualized in a dashboard. The legal department was able to help procurement identify these costly agreements, which were no longer needed, thanks to the monitoring capability, and the team was able to effectively terminate the agreements[5].

Conclusion

Legal AI technology enables organizations, such as middle-market manufacturing and supply chain firms, to leverage their legal teams for solving working capital issues. Purpose-built tools offer multiple levels of ROI, from improved cash flow to the strategic alignment between legal departments and the financial goals of the organization.

To properly implement and capture this same value, I encourage you to educate and explore the available solutions on the market. If you’re a GC or legal operations head who hasn’t yet looked deeply into legal AI tools, now is the time to do so. Invite a few solution providers for demos, and partner with them to develop a proof-of-concept with a subset of contracts.

It’s important to remain vendor-neutral and focus on business needs. There are multiple reputable players in the market that offer AI-driven contract management and analysis – startups specializing in legal AI, or established CLM software companies integrating AI features.

[1] The Growth Corporates Working Capital Index – Visa

[2] Working Capital Management, How Much Cash Is Your Business Tying Up? – National Center For Middle Market

[3] Pentair with Sievo

[4] Payment Term Optimization

[5] Keller Williams & Evisort