We’re excited to announce that Onit has won a prestigious Business Intelligence Group (BIG) 2022 Innovation Award for our AI-enabled eBilling validation and processing technology, InvoiceAI. The awards program recognizes organizations, products and people bringing new ideas to life in innovative ways.
InvoiceAI debuted to customers in May of 2021 and then launched publicly four months later in September. It works with Onit’s enterprise legal management solution and traditional billing rules to conduct a first-pass review of outside counsel and vendor ebills submitted to corporate legal departments. Since AI and machine learning power it, it understands context, learns and can identify additional potentially noncompliant charges on top of what billing rules have already found.
This is the next evolution of eBilling review. Traditional billing rules rely on set parameters to identify invoices containing noncompliant line items, such as a specific word or series of words. Suppose a law firm or legal vendor uses billing descriptions that are similar but differ from what is outlined in the billing rules. In that case, an invoice may be automatically approved for payment.
How InvoiceAI Addresses eBilling Review Challenges
The review of a high number of invoices and their collective dollar total have challenged large corporate legal departments for decades. They’re often pressed to accomplish more in less time with fewer resources and lack the bandwidth to chase corrections or parse a large number of warnings. And they’re faced with invoice issues that include vague or insufficient details, block billing, improper coding of invoices and work being done by the wrong staff class. All of these issues are difficult for traditional billing rules to uncover and sort.
InvoiceAI finds issues such as administrative tasks and improper billing for time during travel and then integrates these findings with ELM and existing rules engines for a more intelligent eBilling review that continuously learns. As a result:
- Attorneys can quickly review invoices
- Legal operations can identify noncompliant billing guidelines and harvest early savings
- General counsel can gain greater visibility into law firm performance and legal spend metrics.
As one customer commented:
“Now, our invoice review team doesn’t have to feel like the bad guy. The machine catches these issues (and does it better) so that we can focus on the relationships and let the system drive the needed action.”
Historical and Real-Time eBilling Review – Results
Before its public launch, InvoiceAI combed through the eBilling from 2020 for a set of Fortune 500 customers. It identified an average of six figures of savings in travel-related billed time and expenses submitted to the customers during the onset of COVID-19 when travel was severely restricted. These were invoices previously sent through traditional billing rules, meaning these savings surpassed what had already been found.
InvoiceAI yields positive results for real-time invoice review as well. When it is used with ELM and billing rules, some Onit customers have found up to 20% in savings.
Continuing AI Excellence
InvoiceAI is one of the latest AI innovations Onit has introduced since the founding of its AI Center of Excellence in 2020. Since then, we have released four other AI offerings in addition to InvoiceAI:
- Precedent, an AI platform that automates and improves both legal and business processes for corporate legal departments, law firms, contract professionals and procurement teams.
- ReviewAI, which quickly drafts, reviews, redlines and edits all types of contracts.
- ExtractAI, AI which extracts and obtains usable data from executed, legacy and third-party paper contracts.
- Automate NDA, an easy-to-implement, best practice AI solution that helps you manage and automate the end-to-end non-disclosure agreement process.
How to Learn More About InvoiceAI
Interested in learning more about InvoiceAI and how it helps eBilling? Start here or request a demonstration. You can also learn more about AI and corporate legal with these blog posts:
