Sometimes, a company is so accustomed to a process that its participants don’t realize how manual it actually is. This is commonly the case for contract management and contract review.

Many corporations rely on vastly manual processes to handle contracts, such as cutting and pasting into templates, emailing, searching for documents and saving to multiple drives. However, a manual approach for contract management can come with significant risks such as inadequate delivery to customers, failure to enforce negotiated supplier terms, time lost from disorganization and errors and additional work due to inefficient processes.

One area of particular concern is contract review. When combined with highly manual or ineffective processes, it has the potential to hinder the execution of powerful agreements that lead to increased revenue, enhanced partnerships and valuable purchases. In short, a nickel – albeit a necessary nickel for legal review – is holding up a dollar.

Contract Review and Artificial Intelligence (AI)

Legal teams have long been asked to do more with fewer resources and a shrinking budget – all while taking on more work. This is not a scalable process without technology. Artificial intelligence and advancements in machine learning, natural language processing and deep learning are evolving the legal profession as we know it.

While legal professionals’ expertise and judgment will always be the core of legal processes, AI can provide pre-work much in the same way that a paralegal or junior lawyer might mark up a document or run a checklist before a partner’s final review. As a result, corporate legal departments can use AI to decrease the time it takes to review contracts, increase productivity, reduce risk and save time.

Here are five ways AI can accelerate the pre-signature contract review process.

  1. Self-Service Contract Review

With AI, legal professionals can slash the time for a first-pass review from days or hours to mere minutes. A business user can request a standard contract or submit a third-party contract for initial review via email or a web portal. AI learns corporate standards from transaction histories and feedback and then reviews and redlines contracts and returns them in Microsoft Word – often within two minutes.

  1. High-Volume/Low-Edit Contract Review

Some contracts, like nondisclosure agreements, require near real-time turnaround and often do not depart from standard terms. They’re high-volume and low-edit documents – prime candidates for AI review. Instead of an attorney handling contracts like this, AI can review the contract and suggest revisions to bring it to corporate standards if necessary. From there, the NDA or similar contract can be tendered directly to the other party or undergo one last round of internal review if deemed necessary. Lawyers can spend time on projects that bring more value to the company.

  1. Complex Contract Drafting and Negotiation

Master service agreements, statements of works and other complex sales or purchase agreements can also benefit from AI. It leverages the full company playbook and clause library to guide the contract drafter and reviewer along the negotiation at agreement pass.

  1. Third-Party Contract Risk Review

AI assesses the risk of contracts during the pre-signature phase by reviewing third-party paper against corporate standards and checklists. It then summarizes the risks, flags key issues using contract review templates and unique company clauses and suggests proper edits.

  1. Playbook Management

Combined with a user-friendly AI platform, AI-driven contract review allows legal teams to manage, collaborate and use AI to apply corporate playbooks and precedents automatically. Legal professionals can then use the real-time data and insight provided by the platform to improve playbook standards and understand enforcement across the business.

Conclusion

Businesses want as many agreements on their contract terms and paper as possible. When a contract is on “other party paper,” it is difficult to adhere to a company’s playbook and enforce guidelines. Ultimately, it slows down contract execution. However, by relying on AI, corporate legal departments are aptly equipped to pave a rapid path to contract closure and signature and accelerate business while increasing contract compliance.

To learn more about AI and contract management, read a recent study that details how AI and contract review increases corporate legal productivity by more than 50%.

/by

Artificial intelligence is one of the hottest buzzwords in legal technology today, but many people still don’t fully understand what it is and how it can impact their day-to-day legal work.

According to  Brookings Institution, artificial intelligence generally refers to “machines that respond to stimulation consistent with traditional responses from humans, given the human capacity for contemplation, judgment, and intention.” In other words, artificial intelligence is technology capable of making decisions that generally require a human level of expertise. It helps people anticipate problems or deal with issues as they come up. (For example, here’s how artificial intelligence greatly improves contract review.)

Recently, we sat down with Onit’s Vice President of Product Management, technology expert and patent holder Eric Robertson to cover the ins and outs of artificial intelligence in more detail. In this first installment of our new blog series, we’ll discuss what it is and its three main hallmarks.

Podcast alert: Hear Eric discuss artificial intelligence in more detail by listening to the podcast below.

What Is Artificial Intelligence?

At the core of artificial intelligence and machine learning are algorithms, or sequences of instructions that solve specific problems. In machine learning, the learning algorithms create the rules for the software, instead of computer programmers inputting them, as is the case with more traditional forms of technology. Artificial intelligence can learn from new data without additional step-by-step instructions.

This independence is crucial to our ability to use computers for new, more complex tasks that exceed the manual programming limitations – things like photo recognition apps for the visually impaired or translating pictures into speech. Even things we now take for granted, like Alexa and Siri, are prime examples of artificial intelligence technology that once seemed impossible. We already encounter in our day-to-day lives in numerous ways and that influence will continue to grow.

The excitement about this quickly evolving technology is understandable, mainly due to its impacts on data availability, computing power and innovation. The billions of devices connected to the internet generate large amounts of data and lower the cost of mass data storage. Machine learning can use all this data to train learning algorithms and accelerate the development of new rules for performing increasingly complex tasks. Furthermore, we can now process enormous amounts of data around machine learning. All of this is driving innovation, which has recently become a rallying cry among savvy legal departments worldwide. 

Once you understand the basics of artificial intelligence, it’s also helpful to be familiar with the different types of learning that make it up.

The first is supervised learning, where a learning algorithm is given labeled data in order to generate a desired output. For example, if the software is given a picture of dogs labeled “dogs,” the algorithm will identify rules to classify pictures of dogs in the future.

The second is unsupervised learning, where the data input is unlabeled and the algorithm is asked to identify patterns on its own. A typical instance of unsupervised learning is when the algorithm behind an eCommerce site identifies similar items often bought by a consumer.

Finally, there’s the scenario where the algorithm interacts with a dynamic environment that provides both positive feedback (rewards) and negative feedback. An example of this would be a self-driving car where, if the driver stays within the lane, the software will receive points in order to reinforce that learning and reminders to stay in that lane.

The Hallmarks of AI

Even after understanding the basic elements and learning models of artificial intelligence, the question often arises as to what the real essence of artificial intelligence is. The Brookings Institution boils the answer down to three main qualities:

  1. Intentionality – Artificial intelligence algorithms are designed to make decisions. They’re not passive machines capable only of mechanical or predetermined responses. Rather, they’re designed by humans with intentionality to reach conclusions based on instant analysis.
  2. Intelligence – Artificial intelligence often is undertaken in conjunction with machine learning and data analytics, and the resulting combination enables intelligent decision-making. Machine learning takes data and looks for underlying trends. If it spots something relevant for a practical problem, software designers can take that knowledge and employ data analytics to understand specific issues.
  3. Adaptability – Artificial intelligence has the ability to learn and adapt as it compiles information and makes decisions. Effective artificial intelligence must adjust as circumstances or conditions shift. This could involve changes in financial situations, road conditions, environmental considerations, military circumstances, and more. Artificial intelligence needs to integrate these changes into its algorithms and decide on how to adapt to the new circumstances.

For a more in-depth discussion of artificial intelligence, you can listen to the entire podcast interview with Eric here.

 

/by

Each day, the accomplishments of artificial intelligence multiply. AI recently solved Schrödinger’s equation in quantum chemistry. It regularly diagnoses medical conditions, pilots jets and fetches answers for our everyday queries. And now, it might dance better than you do.

The ever-improving abilities of AI are having marked positive impacts on a wide variety of industries and professions – especially corporate legal departments and the in-house counsel and legal operations professionals that run them. So, what can corporate legal professionals expect from AI in 2021?

Ari Kaplan, attorney, legal industry analyst, author, technologist and host of the Reinventing Professionals podcast, recently interviewed Nick Whitehouse, General Manager of the Onit AI Center of Excellence. Nick, who is the 2019 IDC DX Leader of the Year and Talent’s 2018 Most Disruptive Leader Award (as judged by Sir Richard Branson and Steve Wozniak), shared the AI trends that general counsel and legal operations professionals should keep an eye on for 2021, including:

  • Accelerated adoption – The pandemic has greatly affected the use of AI, spurring businesses and their corporate legal departments to recategorize it from curiosity to necessity. For example, 2020 saw many companies having to quickly reassess large numbers of contracts (such as leases). Legal AI allowed in-house teams to quickly assess their contracts and take action, helping their businesses survive and thrive.
  • Banishing the black box – Legal departments have historically been perceived as black boxes – work goes in and decisions come out slowly with little transparency. AI reduces the time spent on individual transactions, increasing transparency by enabling consistent use of playbooks and the ability for the business to self-serve.
  • Focus on solving in-house challenges  – The technology has shifted from a project-based law firm focus toward products that are centered on solving in-house problems like contract lifecycle management and AI contract review. With 71% of lawyers saying they are mired in manual tasks, these AI products can drive a massive amount of value for corporate legal.
  • AI in the near future – In addition to the shift from law firm focused AI services to more in-house based services, corporate legal can expect a greater blending of AI into contract lifecycle management and third-party review as well as AI-assisted document automation and billing management.

Visit the Reinventing Professionals website to listen to the podcast. You can also find it (and subscribe) on Apple podcasts.

/by

Contract review and drafting can take up to 70% of an in-house legal department’s time. The process is often painfully tedious and repetitive – especially if it is paper-based or spread across multiple systems like emails and private drives. Without a more effective digital enablement, the process to review and draft contracts is slow and inconsistent, requires enormous attention to detail and continues to be prone to costly errors. These challenges directly impact a company’s ability to reach favorable contract outcomes and achieve business objectives.

With ever-increasing pressure on legal teams to do more with less, enhancing contract efficiency through automation and the latest technologies represent a significant opportunity to improve business performance.

Artificial intelligence has the power to deliver significant productivity gains and allow lawyers to utilize their skills, experience and talent on higher-value business objectives. Onit undertook a study of its AI for the pre-signature contract phase, ReviewAI, to determine just how much it can help and found commendable results (you can read more about them here.)

Key takeaways from the study include:

  • Testers found that ReviewAI accelerated contract reviews and approvals by up to 70% and increased user productivity by more than 50%.
  • New users were immediately 34% more efficient with their time and 51.5% more productive. The average midsize company employs 28 lawyers who review 4,850 contracts annually. Unlocking more capacity – up to 51.5% – means those same lawyers can now process 2,498 more contracts annually. It’s like adding nine lawyers to your team.
  • The team leader, a senior lawyer, was able to reallocate 15% of his time from contract work and team management to higher-value activities.
  • The efficiency and productivity gains from using ReviewAI increased over time, allowing corporate legal departments to optimize team performance, reallocate resources to engage the business better and reduce the amount of contract work handled by external counsel.

To learn more about artificial intelligence and contract review and drafting, read about the study’s results.

/by

Onit ReviewAI – Contract AI Review that Increases Velocity & Reduces Risk

Onit’s ReviewAI software uses artificial intelligence (AI) to quickly and accurately review, redline, and edit all types of contracts in minutes. Non-legal business users can now automatically receive a reviewed, redlined, and approved contract via email or self-service portal in less than two minutes. For more hands-on functionality, the ReviewAI Word Add-in designed for lawyers and contract professionals automatically drafts, reviews, redlines, and edits contracts against corporate standards. Precedent learns as you work, and comes with a wide range of pre-trained skillsets so you can quickly configure the AI for use on a wide range of use cases, including NDAs, MSAs, SOWs, purchase agreements, lease agreements, employment agreements, construction and sub-contracting agreements and many more. When paired with a contract lifecycle management system like Onit’s, organizations obtain an AI-driven workflow that automates the entire contract lifecycle from creation to execution.

With ReviewAI, corporate legal professionals can:

– Approve contracts 60-70% faster 
– Review and redline a contract in 2 minutes or less
– Increase user productivity by up to 51.5%
Learn more here 0r schedule a demonstration here.
/by

TAR Solutions for a New Decade

These days, it seems impossible to talk about eDiscovery or document review without mention of Technology Assisted Review (TAR). In its broadest use as a technical term, TAR can refer to virtually any manner of technical assistance – from password cracking to threading to duplicate and near-duplicate detection. In its narrower use, TAR refers to techniques that involve the use of technology to predict (or to replicate) the decision a human expert would make about the classification or category of a document. In this narrower sense, TAR often comes with a version number – TAR 1.0, TAR 2.0, and more recently, TAR 3.0. While some are inclined to advocate for the superiority of a single approach, each version has its merits and place, and understanding the underlying process and technology is crucial to selecting the right approach for a specific discovery need.

We recently authored a white paper to offer a discussion of the variables to consider when choosing the right TAR workflow for a specific matter, as well as the main principles behind different TAR solutions. By doing so, we make the claim that true preparedness lies in understanding the range of core technology within the TAR landscape, and further knowing how and where to access the right combination of people, process, and technology to meet any discovery need.  If you or your team have had mixed results with TAR, or want some guidance on deciding your approach with TAR in your next matter, you may find this paper helpful.

TAR Solutions for a New Decade

/by Brian Meegan

Expanding data volumes are having a significant impact on ediscovery, but what are the specific challenges being faced? Lighthouse’s Nick Schreiner outlines six challenges when working with large data sets and offers up insights into how to address these challenges with data re-use, AI, and big data analytics in a recent blog: https://lnkd.in/dYjcY6W

/by Nick Schreiner

eDiscovery itself is a big data challenge, but recent advances in AI and machine learning can help mitigate risks by breaking down the silos of individual cases and leveraging prior case data. Lighthouse’s Karl Sobylak discusses the benefits of bringing technology to bear to understand large data sets at scale in a recent blog: http://ow.ly/QbzZ50COKK8

/by Karl Sobylak

As data volumes continue to grow so does the need for AI and machine learning. In fact, adopting AI can be a catalyst for revitalizing your organization’s ediscovery model. Lighthouse’s Rob Hellewell makes the case for AI including cost reduction, lower risk, and improved win rates in a recent blog: http://ow.ly/MoNs50CMwws

/by Rob Hellewell

Artificial intelligence, advanced analytics, and machine learning are no longer new to the ediscovery field. While the legal industry admittedly trends towards caution in its embrace of new technology, the ever-growing surge of data is forcing most legal professionals to accept that basic machine learning and AI are becoming necessary ediscovery tools.

However, the constant evolution and improvement of legal tech bestow an excellent opportunity to the forward-thinking ediscovery legal professional who seeks to triumph over the growing inefficiencies and ballooning costs of older technology and workflow models. In this article, we provide you with arguments on how leveraging the most advanced AI and analytics solutions can give your organization or law firm a competitive and financial advantage, while also reducing risk.

/by Rob Hellewell