AI Enabled e-discovery: Expectations vs Reality


As we rewrite the rules of business in this decade, artificial intelligence (AI) assumes the limelight. Legal professionals believe that a lot of promise offered by AI in law is unproven with several use cases unable to deliver. However, one area easier to embrace, adapt, and prove is digital discovery (erstwhile called e-discovery). It involves AI, other analytics, automation of workflows to handle new sources of data, expanding volumes, and additional capacity for legal professionals.

AI-Enabled e-discovery

Given the volumes of data, both structured and unstructured that lawyers must otherwise manually analyze, AI can have a drastic impact on the actions, outcomes, costs, and efficiency of each case. According to American Bar Association, 62% of lawyers already use some variant of traditional e-discovery solution and this level of adoption is easier to AI-enable and automate unlike other legal use cases of AI.

AI Use Cases in e-discovery

AI can reduce the burden on legal professionals by identifying patterns, relevant phrases, sentences prone to omission when traditional methods. There are many low hanging opportunities with AI and e-discovery, let us discuss a few below:

Case Research

AI helps sift through high volumes of data to identify key documents and associated information adding speed and efficiency to legal teams. It can also benchmark whether a claim is legit, strong, or worthy. If terabytes of data can be analyzed to chart the strength of the case to focus on what matters, it is worth its weight in gold.

Structuring Data

Managing an e-discovery case is complex, there are numerous documents and facts with a lot of details e.g., contract scope, date of the breach, ramifications of violation, etc. Using clustering techniques, AI can group patterns of terms that appear together while creating multiple angles on each case issue. Unstructured data being clustered into logical packages for analysis simplifies the data and imparts speed that humans cannot achieve.

Technology-Assisted Review (TAR)

The most cumbersome and expensive part of e-discovery is the review stage. AI can expedite classification, tagging, relevance, etc. AI also learns from human actions for additional keywords or facts of interest and this additional data can train review models for higher ranges of accuracy which limits the time humans have to spend on these mundane tasks.

Digital Discovery Analytics

The discovery process is intense and deep, people can lose sight of their depth of analysis and go past the point of diminishing returns. These efforts add costs are rarely quantified, compared, or improved. KPIs correlation activities, costs, and financials are seldom analyzed for productivity, efficiency, or returns. AI can compile this information in real-time and roll it up into a visual layer eliminating recurring problems and enhance the cost efficiency of the entire function.

Data Redaction

Data redaction is one of the key concerns e.g., PII, consent, etc. Withholding data and privileged, anonymous, redacted, etc. is time-consuming and AI alleviates this issue. Shielding data from disclosure with high accuracy and greater levels of compliance is another important use case for AI in digital discovery.

Risks and Opportunities

There are several risks and opportunities with using AI in digital discovery, we can however categorize them into two broad themes with opacity being a perceived risk and cost being the opportunity.

Cost Reduction

AI enables substantial savings on the e-discovery spend by cutting human effort, avoiding rework, reducing errors, and imparting speed. Smaller and less complex cases can be optimized by automating research, reviews, redaction, etc. AI can also bring forth effort and spend patterns through real-time analytics enabling focus and operational decisions.

Explainability Risks

When data is limited, expensive to collect, or in unknown formats, it becomes prohibitive, and hence, traditional rule-based, automation workflows could prove better than AI. On top of this, cost, access, and limitations of data can affect accuracy. It takes massive amounts of data to train AI models. These constraints push legal departments back to legacy methods.

Finally, the opacity within algorithms creates a lack of explainability. Clarity by explainability is one of the most important factors in decision-making and a major area of discomfort for lawyers.

Future of AI-Enabled e-discovery

AI use cases are rapidly evolving and changing the way the e-discovery process is designed, operationalized, and delivered. The future brings multiple shifts to this space.

Native AI

Today, AI is layered on top of existing discovery processes i.e., it is not native to it. The integrations into ESI (Electronically Stored Systems) must be deeper. The extraction of information, categorization of data, clustering, and contextualization by case will be seamless in the future. Deep learning algorithms will simulate human thinking and the e-discovery software (not the AI module) will learn to learn driving exponential value.

Upstream Adoption

AI must be leveraged more upstream in the EDRM process, deeper integrations will enhance visibility into data sources before collection. In the future, AI will deploy sophisticated data mining algorithms to widen scope outside of ESI and correlate them with context, custodians, and anomalies. These algorithms will inform AI-based recommendation engines to identify additional custodians to be involved or placed on legal hold without compromising defensibility. Pushing AI more upstream will create significant downstream efficiencies by reducing data volumes sent out for review. Integrating AI upstream will also allow legal teams to set case strategies and tactics as legal teams transition from insight to foresight.

AI-Enabled Orchestration

AI will evolve from its tactical roles today i.e., collector, curator, advisor, etc. to an orchestrator. AI will strengthen its role as an advisor through recommendation engines proposing additional documents, custodians, etc. In the future, AI will be an orchestrator learning from past actions, correlating actions and decisions across multiple channels. For example, if the relevance rate of document review was X% then AI will help fine-tune the collection parameters, this learning can be leveraged to target the collection and map them to the right reviewers. AI, as an orchestrator will also help automate tasks, predict case, and sentence outcomes based on its own learning.

Concluding Thoughts

AI creates a range of opportunities for legal professionals to drive value in digital discovery. E-discovery is more suitable with acceptance higher than other areas of Legal Tech for using AI as it eliminates mundane human tasks like targeted collection, early data assessment, relevance, issue categorization, and quality control while preserving human bandwidth in areas of higher value.

About the Author

Nitin Kumar is a two-decade veteran in the Hi-Tech industry. His career has spanned many intrapreneurial and entrepreneurial roles in executive positions across Software, Services, and Management Consulting. His passion is propelling organizations to greater levels of success through strong relationships and differentiated products. He is considered a business builder, thought leader, and pioneer of many innovative approaches. During his operating and consulting roles, he has grown, transformed, and pivoted many software businesses and scaled businesses successfully. Nitin is currently the CEO of Ligl, a pioneer in digital discovery orchestration and is an author of many articles, and is often seen speaking at conferences on AI, IoT, SaaS, and Blockchain. He is a veteran CEO and has added value to 1,000 plus M&A transactions in his career.


By Nitin Kumar, CMC, CMAA

Keywords: AI, Digital Disruption, Legal and IP

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