Author: Onit

How Sales CLM with Contract AI Helps Business Development Automation

Posted on February 9, 2021 by - Artificial Intelligence, Business Process Management, Contract Lifecycle Management

It’s impossible to imagine any organization’s sales function without considering contracts. Sales operations professionals handle some of a company’s most value-generating activities: overseeing daily sales activity, meeting with major clients, drawing up sales reports, designing new and more effective sales strategies, and working to market and promote company products and services. 

None of those activities are possible if you can’t effectively manage your sales contracts and glean insights from them. Tools to manage contracts, including contract AI, are designed to ensure that sales operations and the VP of sales have a single point of truth for contracts. In addition, business development automation makes the sales Contract Lifecycle Management (CLM) process more efficient, and the entire sales function more effective. 

Contract AI Software in Sales

The sales function at any enterprise encompasses various duties, ranging from discrete contractual tasks to overarching strategy. On the contractual front, sales operations are responsible for many phases of contract lifecycle management, including: 

  • Creating requests for contracts 
  • Drafting contracts 
  • Monitoring the progress of those contracts 
  • Keeping contracts moving forward when things stall 
  • Approving contract terms 
  • Delivering contracts to customers for signature 

The sales function doesn’t end there. In addition to handling contracts daily, sales operations professionals find new opportunities to expand the organization’s client base and devise new and innovative ways to market products and services. It’s also responsible for setting specific critical enterprise goals and ensuring they’re met, including quarterly or annual sales and ongoing productivity goals. 

Underlying all sales functions, whether drafting a contract or setting the right sales goals for the entire company, is the expectation that the VP of sales and the sales operations team will continuously improve the sales team’s effectiveness and productivity. 

Technology is the key to ensuring continuous improvement. Sales CLM tools and contract AI empower sales operations professionals to better tackle both aspects of the job by making the contract process more efficient and effective from start to finish. In addition, AI–enabled sales management software helps create a central repository for the organization’s contract data. The central repository is a critical source of information for making informed decisions about sales goals and marketing strategies.

Finding the Right Sales CLM Solution and Contract AI Software

While every company and sales department is different, some common barriers prevent sales operations from performing effectively and efficiently. These include: 

  • Having little to no insight into where deals are, who’s responsible for them, and what the next steps are
  • Missing an easy way to keep deals moving forward
  • Not having mobile technology options to effectively handle work tasks in today’s on-the-go and remote working scenarios
  • Lacking self-service options that allow the various interested parties to create and manage that contract or request information directly.

A valuable sales CLM tool and contract AI software will remove barriers and help sales by: 

  • Closing deals faster with features such as self-service and contract AI that reviews, redlines and edits first-pass review within two minutes
  • Automating the contract request process to improve sales representative productivity 
  • Giving sales a real-time view of bottlenecks, where every contract is, and if the process is stalled 
  • Only showing the information you need when you need it, rather than burying you in a mountain of data irrelevant to what you’re doing at any given moment. Having immediate access to the correct data is critical to setting the right sales and productivity goals for your team and the entire organization. 

Speeding Up CLM for Sales – and Welcoming Revenue More Quickly

Sales operations professionals understand the role of contracts in doing their job right. However, they need the right CLM tools for sales to manage those contracts end-to-end. Onit’s CLM solution helps with business development automation for the entire contract management process, allowing for a more efficient sales function and better insight into sales data. Contact us today to learn more.

AI for Legal: Making Sense of the Hype

Posted on February 8, 2021 by - Enterprise Legal Management

John McCarthy, the computer scientist and “father of AI,” defined artificial intelligence as the science and engineering of making intelligent machines, especially intelligent computer programs.

When AI gets mentioned in the context of enterprise legal applications, it is usually referring to “machine learning.” In machine learning, systems learn from outcomes and decisions and improve with experience without being directly programmed to take certain actions or reach specific conclusions. These machines analyze data and discover patterns without significant human intervention, typically requiring only a training dataset.

Machine learning is often confused with rules-based automation, workflows based on pre-programmed “if this then that” algorithms. Legal buyers need to recognize the difference when looking to deploy AI within their departments. If the machine isn’t analyzing and learning from the data but is using pre-programmed, non-evolving rules to automate processes and outcomes, then it’s not AI.

Legal teams usually bring in machine learning to improve efficiency and productivity, as machines can perform tasks faster than humans, freeing legal counsel to do higher-value work. These applications include:

  • Legal Research: reviewing, tagging, and ranking documents relevant to a matter or eDiscovery, highlighting questionable ones that need human review.
  • ReviewAI: Identifying and flagging clauses for review, searching for missing clauses, and redlining in bulk and at speed.
  • Invoice Review: Coding, approving, rejecting, or flagging line items and invoices (where rules-based automation isn’t an option.)
  • Data Extraction: This can apply to invoices, contracts, documents, or any requirement where a mass of non-structured data must get organized and classified.
  • Litigation analytics: Analyzing trial data to predict outcomes of litigation.

GETTING THE MOST FROM MACHINE LEARNING

The above use cases and benefits can transform the legal profession. However, legal departments currently implementing an AI-powered legal solution may be disappointed by the true scope of these tools, especially if they are at the start of their digitalization journey. Buyers do not see the promised benefits and are beginning to question the hype.

The very nature of machine learning is that it needs data to deduce the patterns that help it to evolve and learn. This data doesn’t just need to be abundant in volume; it needs to be complete, accurate, fair, and free of bias. Improved accuracy vs. a human is a benefit often touted, but this is only the case if the data from which the machine is learning is accurate in the first place. Poor or insufficient data will mean the machine does not have enough data to learn from and will not fully deliver the anticipated outcomes and benefits.

Perhaps even more concerning, however, is that the machine will draw partial or incorrect conclusions from a deficient dataset and take the wrong action or reach the erroneous conclusion – thereby creating hidden risk. Ironically, AI can negatively impact productivity if a human must go back over the work, identify issues, and correct them. More severe, though, is if these incorrect conclusions result in damaging actions for the business, even litigation. The reliability of your machine learning needs to be a factor when accounting for legal risk, and legal teams need to understand their role in feeding machine learning tools with quality data and training to avoid these issues; as the saying goes, “you get out what you put in.”

If this sounds paranoid, some examples from other industries will help show why it is critical to be careful when deploying AI. In 2018, Amazon created a tool to review engineering CVs and flag the top ones for an interview. The intention was to automate a time-consuming process. To train the machine, they used the dataset of current Amazon engineering employees plus applications from the last ten years, which happened to be predominantly men. The machine “learned” that ‘more male’ candidates were the best for the role. Amazon soon ditched the tool. Poor data was also at the heart of IBM Watson’s failure to accurately diagnose and treat cancer patients. The data used to train the machine was hypothetical rather than real patient data and frequently gave poor advice. These examples demonstrate not only the importance of complete data for machine learning but the fact that it is hard to predict unexpected consequences before they happen.

The above examples demonstrate the importance of quality and unbiased data, even when the aims are straightforward. AI is not for complex legal work; it speeds up routine tasks, supports better decision-making, and sometimes takes actions based on those decisions. In fact, some of the best examples of AI deployment are where machine learning tools have been combined with rules-based systems to first identify and categorize data and then take defined steps based on that categorization.

MACHINE LEARNING AND E-BILLING

Spend management is a legal-specific application using rules-based automation and machine learning together. For example, Onit’s European legal spend management solution BusyLamp uses the following AI functionality for clients and/or law firms that prefer not to use LEDES files:

  • Data extraction: Pulling relevant information from PDF invoices, relieving smaller law firms from the burden of generating complex invoice files.
  • Invoice Reviews: Some law firms struggle to code invoices in a way that clients can understand. BusyLamp AI takes unstructured invoice data and auto-classifies every task to enable automated invoice review.
  • Legal Analytics: Unstructured invoice and matter data can be analyzed to enhance strategic decision-making.
  • Block Billing: English time narratives can be analyzed so that block billing, a practice that usually contravenes billing guidelines, can be identified.

IS AI RIGHT FOR YOUR LEGAL DEPARTMENT?

Using point solutions such as the e-billing example above allows legal departments to take advantage of machine learning benefits for gains in specific areas of legal operations. But machine learning is by no means critical to make efficiency and productivity gains; most BusyLamp clients start small and aim big by tackling the issues of collating knowledge, structuring, and cleansing their data sets, and then building automated workflows.

When you gather requirements for your next legal technology project, start by mapping out your current processes, roadblocks, and desired outcomes before looking at any specific technology tool. As you evaluate software vendors, you will discover various solutions and workflows to your problem, which may or may not involve AI.

Remember, you should never use AI for AI’s sake – it is rarely the silver bullet. Almost every legal technology tool uses rules-based (non-AI) automation to relieve the legal team of admin and mundane, repetitive tasks; this will be a fantastic starting point for most teams setting out on their digital journey.

There is no doubt that machine learning is playing a huge role in improving the productivity of the legal profession and will allow in-house teams to take a more pivotal, strategic role in their businesses. But as a profession familiar with risk mitigation, a degree of caution must be applied when looking to reach the machine learning “promised land.” Accurate, high quantities of data alongside a careful selection of technology tools will significantly reduce your exposure to these risks and help you make a success of your team’s digital transformation.

Because AI is so dependent on the data it receives, the real transformational tipping point will not be in using these solutions within the legal function alone but in the enterprise-wide application of machine learning tools. Imagine the insights and outcomes achieved by analyzing documents and data across an entire organization, not just the legal function. This is only achievable with integrated legal and enterprise tech tools and robust, extensive, consistent data.

The “power of AI” and its ability to change the legal profession are beyond question. However, it is essential to proceed with caution and lay the groundwork to ensure that your legal department sees the benefit of machine learning rather than learning that it has been sucked in by the AI hype machine.

Request a demo of BusyLamp eBilling.space today. 

KI IM RECHTSBEREICH: WAS STECKT HINTER DEM HYPE? 

Posted on February 8, 2021 by - Enterprise Legal Management

KÜNSTLICHE INTELLIGENZ (KI) UND MASCHINELLES LERNEN IN DER RECHTSBRANCHE 

Der Informatiker John McCarthy gilt als „Vater der KI“. Er definierte den Begriff als Wissenschaft und Ingenieurskunst bei der Herstellung intelligenter Maschinen, insbesondere intelligenter Computerprogramme. „Er war sehr unglücklich mit einem Großteil der heutigen KI, die zwar einige sehr nützliche Anwendungen bietet, sich aber allein auf maschinelles Lernen konzentriert.“ (Daphne Koller, Professorin, Stanford University) 

Spricht man bei juristischen Anwendungen von KI, handelt es sich in der Tat meist um maschinelles Lernen. Es beschreibt den Prozess, bei dem Systeme aus Ergebnissen und Entscheidungen lernen und sich durch diese Erfahrung verbessern. Dabei ist das System selbst zunächst nicht direkt darauf programmiert, bestimmte Aktionen durchzuführen oder eigenhändig Schlussfolgerungen zu ziehen. Diese Anwendungen analysieren vielmehr Daten, erkennen und lernen Muster ohne nennenswerten menschlichen Eingriff – dabei brauchen sie normalerweise nur einen ersten Trainingsdatensatz, um loszulegen. 

Maschinelles Lernen wird oft mit regelbasierter Automatisierung verwechselt, also mit Workflows, die auf vorprogrammierten „wenn, dann“-Algorithmen beruhen. Rechtsabteilungen sollten den Unterschied kennen, wenn sie KI einsetzen wollen. Wenn das Programm nicht in der Lage ist, selbständig Daten zu analysieren und aus ihnen zu lernen, dann handelt es sich folglich auch nicht um KI. 

Rechtteams implementieren maschinelles Lernen in der Regel, um ihre Effizienz und Produktivität zu verbessern. Bestimmte Anwendungen können administrative Aufgaben schneller erledigen als ein:e Mitarbeiter:in. Die freigewordenen Arbeitszeiten können Rechtsberater:innen dann für ihre eigentlichen Aufgaben nutzen. KI-Programme können u.a. folgende Aufgaben in der Rechtsabteilung übernehmen: 

  • Legal Research: Überprüfen, Markieren und Bewerten von für Matter oder eDiscovery relevanten Dokumenten und Hervorheben von fragwürdigen Unterlagen, die manuell gesichtet werden müssen. 
  • Vertragsprüfung: Identifizieren und Markieren von Klauseln zur Überprüfung; Suche nach fehlenden Klauseln sowie Redigieren von immensen Textmengen in hoher Geschwindigkeit. 
  • Rechnungsprüfung: Codieren, Genehmigen, Ablehnen oder Markieren von Line Items oder ganzen Rechnungen (wenn eine regelbasierte Automatisierung nicht möglich ist). 
  • Datenextraktion: Dies kann für sowohl für Rechnungen, Verträge, wie auch Dokumente gelten: überall dort, wo eine Masse von unstrukturierten Daten organisiert und klassifiziert werden muss. 
  • Prozessanalytik: Analyse von Prozessdaten, um den Ausgang von Rechtsstreitigkeiten zu prognostizieren.  

MASCHINELLES LERNEN MAXIMAL NUTZEN 

Die oben genannten Use Cases und Vorteile einer KI-Software könnten künftig die Rechtsbranche verändern. Rechtsabteilungen, die derzeit eine KI-gestützte Rechtslösung implementieren, sind jedoch anfangs möglicherweise enttäuscht – vor allem, wenn sie noch am Anfang ihrer Digitalisierungsreise stehen. Die Investoren sehen die versprochenen Vorteile nicht direkt und beginnen daher, den Hype in Frage zu stellen. 

Zunächst muss verstanden werden, dass maschinelles Lernen Daten benötigt, aus welchen das System Muster ableitet. Im zweiten Schritt lernt es dann daraus und entwickelt sich so eigenhändig weiter. Die Daten müssen dabei nicht nur im Überfluss vorhanden sein, sondern auch vollständig, genau, fair und frei von Verzerrungen. Ein oft genannter Vorteil von KI ist die höhere Genauigkeit der Funktion im Vergleich zu einem Menschen. Voraussetzung dafür ist aber, dass die Daten, aus denen das Programm lernt, sehr genau sind. Schlechte oder unzureichende Daten bedeuten, dass das Programm nur unzureichende Informationen hat. Folglich kann es nicht lernen und liefert somit auch nicht die erwarteten Ergebnisse und Vorteile. 

Im schlimmsten Fall kann das Programm aufgrund des unzureichenden Datensatzes unvollständige oder falsche Schlüsse ziehen und dadurch falsche Maßnahmen ergreifen oder zu einer falschen Schlussfolgerung gelangen – entsprechend birgt es auch ein gewisses Risiko. Daraus kann wiederum resultieren, dass die Produktivität durch KI schlussendlich negativ beeinflusst wird, wenn das Team die Arbeit noch einmal durchgehen, Probleme identifizieren und korrigieren muss. Noch schwerwiegender können Folgen aus falsch gezogenen Schlüssen wiegen, die zu schädlichen Handlungen für das Unternehmen oder sogar zu Rechtsstreitigkeiten führen. Die Zuverlässigkeit Ihrer KI-Software muss ein wichtiger Faktor bei der Bewertung rechtlicher Risiken sein. Rechtsteams müssen ihre Rolle bei der Versorgung von maschinellen Lerntools mit qualitativ hochwertigen Daten und Trainings unbedingt verstehen, um die genannten Herausforderungen zu meistern – wie das Sprichwort sagt: „Wir ernten, was wir säen“. 

Auch in anderen Branchen findet man schnell Beispiele, die zeigen, warum es so wichtig ist, beim Einsatz von KI gewisse Punkte zu beachten. 2018 hat Amazon ein Tool entwickelt, um Lebensläufe von Ingenieur:innen zu prüfen und die besten unter ihnen für ein Vorstellungsgespräch zu markieren. Die Absicht war, einen zeitaufwändigen Prozess zu automatisieren. Um die Anwendung zu trainieren, wurde der Datensatz der aktuellen Amazon-Engineering-Mitarbeiter:innen sowie die Bewerbungen der letzten 10 Jahre verwendet, bei denen es sich überwiegend um Männer handelte. Die Maschine „lernte“, dass „eher männliche“ Kandidaten am besten für die Rolle geeignet waren. Amazon stoppte die Nutzung des Tools wenig später. Schlechte Daten waren auch der Grund für den Fehlschlag von IBM Watson. Sie versuchten anhand einer Software Krebspatienten richtig zu diagnostizieren und zu behandeln. Die Daten, mit denen die Anwendung trainiert wurde, waren allerdings keine echten Patientendaten, deshalb gab das Tool häufig schlechte Ratschläge. Diese Beispiele zeigen nicht nur, wie wichtig vollständige Daten für maschinelles Lernen sind, sondern auch wie schwer es ist, unerwartete Konsequenzen schon vor dem eigentlichen Eintreffen zu prognostizieren. 

Die Wichtigkeit von qualitativ hochwertigen und unvoreingenommenen Daten wird schnell deutlich, selbst bei einfachen und überschaubaren Zielen. KI ist nicht dazu gedacht, komplexe juristische Arbeit zu erledigen; sie beschleunigt vielmehr Routineaufgaben, unterstützt die Entscheidungsfindung und ergreift in manchen Fällen eigenständig Maßnahmen, die auf diesen Entscheidungen basieren. Tatsächlich sind einige der besten Beispiele für den Einsatz von KI dort zu finden, wo Tools für maschinelles Lernen mit regelbasierten Systemen kombiniert wurden. Daten werden so zunächst identifiziert und kategorisiert, damit zuvor definierte Schritte dann auf der Grundlage dieser Kategorisierung unternommen werden können. 

MASCHINELLES LERNEN UND EBILLING 

Das Legal Spend Management ist eines der Beispiele für rechtsspezifische Anwendung, bei der regelbasierte Automatisierung und maschinelles Lernen gemeinsam genutzt werden. Onit’s Legal Spend Management-Lösung BusyLamp eBilling.Space verfügt beispielsweise über die folgenden KI-Funktionen für Mandanten und/oder Kanzleien, die keine LEDES-Dateien verwenden möchten: 

  • Datenextraktion: Relevante Informationen können aus PDF-Rechnungen gezogen werden, was kleinere Kanzleien von der Erstellung komplexer Rechnungsdateien entlastet. 
  • Rechnungsprüfung: Manche Kanzleien haben Schwierigkeiten, Rechnungen so zu kodieren, dass sie für Mandanten verständlich sind. Die KI von BusyLamp nimmt unstrukturierte Rechnungsdaten und klassifiziert automatisch jede Aufgabe, um eine automatisierte Rechnungsprüfung zu ermöglichen. 
  • Legal Analytik: Unstrukturierte Rechnungs- und Vorgangsdaten können analysiert werden, um die strategische Entscheidungsfindung zu verbessern. 
  • Block Billing: Englische Zeitangaben können analysiert werden, sodass Block Billing, eine Praxis, die in der Regel gegen die Billing Guidelines verstößt, identifiziert wird. 

IST KI DAS RICHTIGE FÜR IHRE RECHTSABTEILUNG? 

Der Einsatz von Punkt-Lösungen ermöglicht es Rechtsabteilungen, die Vorteile des maschinellen Lernens für sich zu nutzen. Aber maschinelles Lernen ist keineswegs entscheidend, um Effizienz- und Produktivitätsgewinne zu erzielen; die meisten unserer BusyLamp-Kunden fangen klein an und haben große Ziele. Sie beschäftigen sich zunächst mit dem Sammeln von Wissen, dem Strukturieren und Bereinigen ihrer Datensätze und dem Aufbau automatisierter Workflows.

Wenn Sie Ihr nächstes juristisches Technologieprojekt planen, beginnen Sie damit, Ihre aktuellen Prozesse, Problematiken und gewünschten Ergebnisse festzuhalten, bevor Sie sich eine bestimmte Softwarelösung ansehen. Wenn Sie Anbieter evaluieren, werden Sie verschiedene Lösungen und Workflows für Ihre Anforderungen entdecken, die KI beinhalten können oder nicht. 

Setzen Sie KI nie um der KI willen ein – denn nicht immer ist künstliche Intelligenz zielführend. Fast jede juristische Anwendung verwendet eine regelbasierte (nicht KI!) Automatisierung, um Ihr Team von administrativen und sich wiederholenden Aufgaben zu entlasten. Diese Automatisierung ist für die meisten Teams, die sich auf ihre digitale Reise begeben, ein fantastischer Ausgangspunkt. 

Es besteht kein Zweifel daran, dass maschinelles Lernen eine große Rolle bei der Verbesserung der Produktivität der Anwaltschaft spielt. Auch steht fest, dass es den Inhouse-Teams ermöglicht, eine zentrale, strategische Rolle in ihren Unternehmen zu übernehmen. Da KI jedoch so sehr von den Daten abhängt, mit denen sie gefüttert wird, wird der wirkliche transformative Wendepunkt erst durch eine unternehmensweite Nutzung von Machine-Learning-Tools erreicht. Die Erkenntnisse und Ergebnisse aus der Analyse von Dokumenten und Daten aus dem gesamten Unternehmen sind viel größer und wertvoller als solche, die allein aus der Rechtsabteilung stammen. Die Umsetzung ist aber nur mit integrierten juristischen und unternehmensweiten Tech-Tools sowie robusten, umfangreichen und konsistenten Daten zu erreichen. 

Die „Macht der KI“ und ihre Fähigkeit, die Rechtsbranche wirklich zu verändern, steht außer Frage. Es ist jedoch wichtig, mit Vorsicht vorzugehen und eine Grundlage zu schaffen. Nur so kann sichergestellt werden, dass Ihre Rechtsabteilung einen tatsächlichen Nutzen daraus ziehen kann und nicht einfach auf den KI-Hypetrain aufspringt. 

Aus dem englischen Original-Blog übersetzt. 

The Future of the Legal Profession, AI and Legal Work

Posted on February 4, 2021 by - Artificial Intelligence, Business Process Management

The legal profession faced down seemingly endless changes this past year, and many people are understandably wondering what’s in store for the future. In a recent webinar sponsored by Onit and titled The Future of the Legal Profession, leading economist Daniel Susskind tackled exactly that question, offering insights on what changes the industry should expect in the future, what role technology and AI will play and much more.

A Tale of Two Futures

Susskind envisions two possible futures for the legal profession, both rooted in technology: one that’s simply a more efficient version of the current profession, and another in which technology actively displaces professionals.

In the first, today’s professionals continue to incorporate more technology to streamline and optimize the traditional ways they’ve worked, changing practices that may have been in place for several decades. In the second, technology isn’t just streamlining and optimizing traditional work practices, but fully replacing professionals with increasingly capable systems and machines. In the short term, these two divergent futures will develop in parallel. However, in the long term, Susskind expects the second future to dominate due to its greater efficiency and more effective problem-solving abilities.

How Technology Affects Professions

Professions evolved in modern society because no one was capable of doing everything, and therefore specialists – lawyers, doctors, educators, etc. – were needed to solve common challenges that people couldn’t solve on their own. Each profession became a gatekeeper for a unique body of knowledge.

Technology has been changing all that in recent years. Today, institutions are using technology to solve problems that were traditionally only solved by specific professionals. For example, in the case of law, three times as many disputes are resolved each year on remediation platforms without traditional lawyers than are filed in the legal system. Other technologies are similarly replacing hundreds of thousands of hours of traditionally billable time by addressing discrete legal tasks.

How Technology and AI Are Changing

There’s no finish line when it comes to technology. Today, technology is seeing exponential growth in prevalence, power, and capability, performing tasks that were once the sole province of humans. More and more people own devices, and both those devices and their owners are becoming increasingly connected. Over time, technology will only continue to improve.

Artificial intelligence has seen some of the most significant evolution. While AI once focused on copying human thinking and reasoning, today’s AI tools perform judgments that humans once exclusively performed and do so based on much larger volumes of data than humans could ever tackle.  (To see an example of how AI can quickly review, redline and edit all types of contracts including NDAs, MSAs, SOWs, purchase agreements, lease agreements, employment agreements, construction and sub-contracting agreements, visit here. You can also schedule a demo of Onit’s Review AI by filling out this quick form.)

The Future of Legal Work

We won’t be seeing robot lawyers any time soon, but we will see changes. Rather than eliminating entire jobs, technology will likely displace humans from particular tasks and activities, while making others more valuable and more important for humans to perform. Technology is a story not of mass unemployment, but of mass redeployment, changing the tasks and activities lawyers will be expected to perform in carrying out their work.

The Pandemic Effect

While the pandemic may have spurred recessions in some areas, recessions often lead to an increase in automation. Automation, in turn, tends to replace the tasks of middling-skilled workers, rather than lower-skilled or higher-skilled workers.

The pandemic has also created a unique incentive to automate work, since machines don’t have to worry about challenges like contagion or isolation. Some automation experiments necessitated by the pandemic are likely to become permanent fixtures of the profession, as there’s been a significant shift in the belief that most work needs to be performed face-to-face.

How This All Impacts You

Susskind closed with three pieces of advice for lawyers going forward:

  1. Explore new roles, skills and capabilities that might not be traditional in the profession.
  2. Learn from the pandemic. Understand what’s worked well and what hasn’t and apply that going forward.
  3. Imagine the future of the profession like a clean slate, figuring out how to solve problems in new and fundamentally different ways.

To learn more about Daniel Susskind, visit here.

To see how Onit’s AI solutions – including Precedent, ReviewAI and ExtractAI – schedule a demonstration here.

Coming Soon: InvoiceAI: AI for Legal Invoice Review

Posted on February 2, 2021 by - Company News and Events

Today, Onit kicked off its next phase of AI innovation at Legalweek(year) with the announcement of InvoiceAI, an AI-enabled legal invoice review offering for enterprise legal management. The offering, which will launch in May for both Onit and SimpleLegal, uses AI to create greater efficiencies in invoice review and allows general counsel and in-house counsel to focus on what they do best for their companies.

The invoice processing AI speaks to Onit’s founding principle: Help lawyers practice law more effectively. InvoiceAI eliminates tasks that aren’t related to practicing law – in this case, removing legal invoice review friction by relying on AI.

Onit leadership served as pioneers for legal e-billing, championing the Legal Electronic Data Exchange Standard (LEDES), the Uniform Task-Based Management System (UTBMS) and more. Now, that experience is taking legal invoice review to the next level with AI.

If you’re interested in learning more about Onit’s AI for legal invoice review, please speak with your account manager or email [email protected].

AI Innovation from Onit

When it launches in May, InvoiceAI will join three other AI offerings from Onit:

  • Precedent, Onit’s AI-powered business intelligence platform that automates and improves both legal and business processes for corporate legal departments, law firms, contract professionals, and procurement teams
  • ReviewAI, contract AI for pre-signature contract review that quickly and accurately reviews, redlines and edits all types of contracts in minutes.
  • ExtractAI, contract AI for post-signature contract management that extracts usable data from executed, legacy and third-party paper contracts.

You can schedule a demonstration of these three solutions by visiting this page.

The Sentinel Effect: 4 Ways Transparency Drives Better Business Outcomes

Posted on February 2, 2021 by - Legal Operations

Ever grabbed an extra cookie from the cookie jar as a kid because you knew no one was watching? Or maybe you skipped out on your last set of reps during a workout because, hey, who’s going to know? 

Sure, we’ve all been guilty of cutting corners when we know there are no consequences. But did you ever attempt to take that cookie with a parent in the kitchen? We’re guessing you probably wouldn’t risk losing precious screen time over one cookie. 

That reaction – not going for the cookie when your parent was right there – is commonly known as the “Sentinel Effect”. The tendency for one’s performance to improve when they know they are being monitored can be observed in all walks of life, both at home and on the job.

The simple act of monitoring – even without applying penalties – is proven to improve behavior. It’s a great passive management technique: show someone you’re monitoring them, and regardless of whether you are actually watching or not, performance will improve. 

This is especially true when managing outside counsel. Historically outside counsel has operated without the oversight – or even visibility – of their clients. That lack of transparency fostered a situation where not only were guidelines consistently ignored, but sloppy business practices were allowed to fester. 

But how do you transparently monitor outside counsel to create the Sentinel Effect? 

The answer is simple — data. All the answers you need are right there in your billing invoices; you just have to know how to use it to create not only a feeling of increased accountability but also to stoke that competitive drive within every lawyer. 

Armed with data, in-house teams have the power to:

1. Enhance Law Firm Relationships

Law firms are retained to act in the best legal interest of the clients, but when it comes to billing, firms are still businesses and can be self-serving. The relationship becomes one-sided as clients are often left as idle price-takers while firms receive hefty paydays. 

Having regular meetings with law firms fueled by a variety of quantitative and qualitative metrics, such as billing rates, discounts, and more, will lead to more effective, robust conversations about what’s working well or where adjustments might be needed. By letting the data do the talking, the conversation is much easier – and clients get better results from their firms. 

Simple practices such as incorporating firm report cards and quarterly business reviews to regularly discuss KPIs serve as a reminder to your law firms that you’re closely monitoring the value the relationship delivers. Nobody likes a “gotcha” moment, so these regular check-ins can help quickly correct firms’ course of action, leading them to become more disciplined in their billing practices and act as a strategic business partner.

2. Eliminate Inefficiencies

From overstaffing to partners handling associate-level tasks, and everything in between, efficiency isn’t always top of mind to law firms. In fact, there’s a strong possibility that the firm that billed the most hours in your panel may actually be the most inefficient.

Inflated invoices are often the byproduct of inefficiency. But transparency on key metrics, like average partner hours, matter duration, or cost can quickly highlight where inefficiencies rack up the cost of your matters.

Examining and discussing these key metrics with your firms regularly shows that you’re analyzing their efficiency and comparing them to other – potentially more efficient – firms. Your firms don’t want to lose your business, so expect a material boost in productivity and the elimination of inefficient practices. Once you show you’re serious about evaluating performance – and value efficiency – managing your outside counsel effectively will be much easier. By using data to set transparent expectations, you provide a clear roadmap to a stronger relationship. 

3. Influence Strategic Decisions

To effectively manage spend you need to think strategically — and to truly think strategically, you need data. Corporate legal departments are increasingly demanding transparency around everything from rates to work allocation to the diversity of timekeepers and more. But visibility isn’t the only thing that data provides.  

Armed with apples-to-apples comparisons and data-backed insights, corporate legal departments can take a more informed, strategic approach towards the major decisions that impact their department and the business. 

For example, as COVID-19 rapidly accelerated the need to optimize spend and cut costs, many corporate legal departments began to reevaluate their outside counsel spend. Many departments needed to quickly review their rates and obtain rate freezes and discounts from their firms. In turn, this forced competitive firms to follow suit. 

4. Drive Down Costs & Increase Savings

Pricing is arguably the most critical part of any business – product or service. By its very nature, pricing is complicated. “Pricing tricks” are a fact of life – that’s true if you’re pricing sneakers or legal services. 

Now, it’s no secret that law firms use “pricing tricks” – some more above-board than others – on both rates as well as in the manifestation of those rates; their invoices. From block billing to unapproved rate increases to footing the bill for printing, there’s no doubt you’ve fallen victim to unnecessary costs. But the good news is that you have the power to change this narrative. 

By leveraging data to track the performance of your panel firms, you can easily identify the common law firm antics driving up your spend and address them quickly. Using data to show a pattern will, almost miraculously, make certain antics disappear. You may have to negotiate or address other patterns by changing your billing guidelines. Nonetheless, data is your launchpad for not just identifying those antics, but for actioning solutions to them (clawbacks and markdowns, anyone?). 

It’s pretty clear that transparency in business – legal or otherwise – has the power to influence performance and drive better outcomes.

But, you don’t have to take our word for it! One of Bodhala’s clients, a major insurance carrier, was having issues with their panel firms consistently exceeding budget on corporate matters. Prior to starting a very large matter merger, the organization’s in-house team worked with Bodhala to create report cards to share with the top firms in their panel. After receiving a detailed analysis of their performance from the insurance carrier, each firm immediately brought down their budget projections significantly.

Without data, you’ll be hard-pressed to hold your law firms accountable for the quality of service you expect. Data sets the foundation for a successful, equitable partnership in which you receive the value you pay for. 

So, will you continue to turn your back on the cookie jar or will you leverage data to drive better business outcomes?

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Get in touch with our team of legal billing and data experts to find out how Bodhala can transform your legal department.

Harness the Power of AI in Operations Management for Corporate Legal Departments

Posted on January 28, 2021 by - Company News and Events, Contract Lifecycle Management

By now, businesses across all sectors recognize the benefits of legal AI in operations management – especially for processes such as contract management. Along with other technologies, AI is helping to reduce the financial pressure on operations teams and corporate legal departments who need to find ways to be more efficient. Particularly in the past year with the pandemic demands, there have been significant investments to decrease workloads for employees across businesses by streamlining things like workflow and approval processes. For example, this study found that contract AI in legal departments can increase efficiency by more than 50%.

In the first installment of our three-part blog and podcast series published earlier this month, we touched on AI’s ABCs. Now, we take a more in-depth look at some fantastic ways AI for operations is powering corporate legal departments. (You can find the podcast of this by scrolling down.)

Pushing Past the Buzz: Is It Really AI?

There’s no disputing that AI is a hot commodity now and a buzzword you hear often. AI in operations management and for legal teams is no exception. While you think your organization may be using it, you may be surprised. In reality, it can be challenging to identify, as AI in legal operations in day-to-day practice doesn’t always look like the images of AI we might have in our heads.

There are five ways to determine if you have an AI-driven system in place.

  1. Use of an interactive system – A fundamental cornerstone of AI is the ability to interact with your system more conversationally through the concept of a virtual agent.
  2. A wizard powered by learning to guide users – AI-enabled wizards lead users to the right workflows and tools, such as contract templates. This is based on learning from previous contract requests to offer more interactive guidance for your staff.
  3. Identification – Semantic analysis by AI can find patterns in related words relevant to an issue and then applies appropriate tags. This AI enabled semantic analysis is frequently used to identify issues in contracts, for example.
  4. Advanced analytics – AI builds off the identification process and allows you to utilize the identified terms very quickly by providing actionable recommendations for tasks.
  5. Robotic Process Automation (RPA) – RPA can be used for approval of changes, not only in workflow but to help your system streamline the approval process by learning from decisions made in prior cases. Essentially, you’re changing the workflow based on past learning and providing recommendations to approvers based on previous actions.

Corporate legal departments vary widely in their current technology levels, so you may not see all of these hallmarks in your organization. Nonetheless, if you can do any (or all) of the things listed above, you’re currently using AI. The next question is how to ensure you’re fully taking advantage of it.

The Benefits of AI in Legal Operations

AI has significant impacts on lawyer productivity. Onit recently conducted a study of legal AI contract review software to see how it affected in-house lawyers’ productivity. The results showed that new users were immediately 34% more efficient and 51.5% more productive. Team leaders could reallocate 15% of their time from contract work and team management to higher-value activities if they use AI in operations.

Consider those results in the context of a typical midsize company that has 28 lawyers and reviews 4,850 contracts annually. With 51.5% more productivity, that same team of 28 lawyers could process 2,498 additional contracts each year. That’s the equivalent of adding nine lawyers to the team. The additional capacity could also reduce costs and free up lawyers to perform higher-value functions to support the business.

The benefits of legal AI don’t stop with productivity. Legal departments must have access to data, and AI for operations allows departments to combine data from all corporate data sources. AI can also flag suspect transactions or questionable third-party relationships and quickly assess their risk level. Having a value chain of data with an intelligence layer around it is essential. Being able to connect that intelligence layer to your legal operations is crucial.

Listen to the Podcast Now: Contract AI in Legal Operations

For a more in-depth discussion of AI enabled contract management and its importance for legal operations, you can listen to the entire podcast interview below.

In our third and final installment of this blog series coming next week, we’ll dive into some of the most useful forms of AI being used in business today.

 

Looking to Control Legal Spend? Don’t Forget These Enterprise Legal Management Features

Posted on January 27, 2021 by - Business Process Management, Enterprise Legal Management

When it comes to legal spend, corporate legal departments share a unanimous thought: It’s time to control costs. A recent survey from Gartner showed that the proportion for legal spend for outside counsel has decreased from 50% to 44% since 2018 – a trend predicted to evolve further as the effects of the pandemic influence companies’ priorities.

Before you can right-size legal spend, you have to understand how you’re spending. That’s where an enterprise legal management solution (ELM) comes in. Comprised of legal spend management and matter management, modern ELM solutions no longer land in the “nice to have” category. GCs and legal operations professionals are quickly outgrowing (and getting more and more frustrated with) dated ELM systems with non-intuitive interfaces and limited functionality that cannot meet their business needs.

Six Must-Have Enterprise Legal Management Features You Need Now

ELM solutions give users the tools to analyze legal spend, manage matters, minimize company risk and drive process efficiency, giving legal operations managers the ability to reduce legal spend with surgical precision.

The leading ELM solutions offer several crucial features (outlined here) that can help control costs and increase efficiency – an overall win on multiple fronts. However, here are six additional legal spend management and matter management features to consider when evaluating ELM options.

  1. Flexible Workflow

Corporate legal departments need flexible enough workflows to match business requirements. The workflows also need to be simple enough to manage or change without IT personnel reliance. Different work types, such as matters related to employment, litigation, or mergers, can have their unique workflows. Likewise, workflows change based on participants or collaboration with other departments such as sales, procurement and marketing.

  1. Timekeeper Management

Corporate legal departments need the ability to keep track of authorized timekeepers and rates and do it in one solution. This informs not only legal spend but other essential initiatives such as diversity. (For an example of using technology to drive diversity and inclusion, visit Hack the House and select “Team Diversity” for a demo. They created and deployed an app in less than three weeks.)

  1. Billing Guidelines

When a corporate legal department’s outside counsel spend reaches beyond $100 million, the number, size and amounts of legal bills go beyond manual processing capabilities. Submitted invoices may contain charges that do not comply with billing guidelines. ELM will enforce billing guidelines, automatically flagging or denying payment for suspect charges and supporting legal spend managment.

  1. Reporting and Analytics

When it comes to having insight into reporting, a leading ELM solution will provide you with a dashboard view that makes it easy to analyze invoices, evaluate performance and see trends in matter portfolios, which are vital for understanding and controlling legal spend.

  1. Advanced Security

Each day, news breaks of security breaches. Corporate legal and the law firms they work with are now prime candidates for hacking attempts. Industry-standard security and bank-level encryption ensure billing and matter data remains confidential. A three-pronged approach is optimal: custom-hardened Unix kernels, managed virtual private cloud and continuous firewall monitoring.

  1. Outlook Integration

For years, there has been one cry that continually arises when it comes to technology and communications: Email is dead! However, it remains a highly used tool for knowledge workers such as lawyers and operations professionals. Synchronizing matter information between Outlook and associated matters is a quick way to ensure your ELM information remains up-to-date and eliminates duplicative, manual work.

For more ELM and legal spend inspiration, take a look at the following resources:

 

Onit Achieves Important Milestones in 2020 While Positioned for Growth in 2021

Posted on January 26, 2021 by - Company News and Events

Despite COVID-related complications presented to businesses worldwide, Onit had a successful year. Milestones achieved this year included the launch of an artificial intelligence-powered business intelligence platform and AI contract review, two acquisitions in 30 days, increasing employee headcount by 22% and a market-leading NPS score.

According to Onit CEO and Co-Founder Eric M. Elfman, the credit for these accomplishments goes to the company’s ability to unite behind common goals.

“While Onit faced pandemic-related challenges similar to many other businesses, we succeeded by focusing on what matters most: our customers, innovation and employees,” said Eric M. Elfman, CEO and co-founder of Onit. “I credit the dedication and efforts of our employees for making us even stronger than ever before. We are well-positioned for continued growth in 2021, which has been our trajectory since the founding of the company.”

Acquisitions

In November, Onit acquired McCarthyFinch, reinforcing its innovation strategy by delivering powerful AI-based workflow and business process automation solutions – Precedent and ReviewAI. McCarthyFinch is now the Onit AI Center of Excellence, with a mission to further AI innovation for Onit and SimpleLegal products.

Onit’s second acquisition in 2020, AXDRAFT, expands its contract lifecycle management offerings with document automation technology. AXDRAFT, an independent subsidiary of Onit, offers technology that drafts contracts and other legal documents in less than five minutes.

Additional Milestones in 2020

In 2020, Onit also:

  • Processed more than $5.6 billion in law firm invoices in more than 140 countries
  • Established company operations on five continents
  • Added 93 new customers and more than 330 expansions for existing customers
  • Implemented 90 customers
  • Launched OnitCX, a program dedicated to customer success
  • Won more than 15 awards for fast growth and customer success, including being named on the Inc. 5000 for the fifth consecutive year and the Deloitte Fast 500 for the third straight year and winning the ACC Value Champion award.

In 2021, the company will continue to build on its success by capitalizing on the lessons learned during this pandemic and new technologies, including artificial intelligence and document automation to increase growth.

To learn more, read our 2020 milestone press release.

Join Onit at Legalweek(year) for a Discussion on Contract AI  

Onit is excited to continue its momentum in February with a presentation and virtual exhibition at Legalweek(year) 2021.

Experts from Onit and Adobe will present “The Potential Impact of AI on Managing Contracts” during the conference. The CLE-eligible session, scheduled for Tuesday, February 2, at 3:45 p.m. EST, features a discussion on how AI and automation can address contract management challenges. Speakers include:

  • Stasha Jain, Vice President of Legal and Compliance for Onit
  • Nick Whitehouse, General Manager of the Onit AI Center of Excellence
  • Jean Yang, Vice President of the Onit AI Center of Excellence
  • Letitia Hsu, CIPP/US, Associate Legal Counsel, Adobe Inc.

To register for Legalweek(year) and attend the session, visit here.

Onit will also host a series of demonstrations of its AI and automation technologies during Legalweek. From February 2-4, attendees can view demonstrations of the Onit’s Precedent AI platform, Enterprise Legal Management, Contract Lifecycle Management and Legal Service Request. Email [email protected] for more information on the schedule or to set up an appointment.

We look forward to seeing you at Legalweek(year)!