There are a host of new tools and technologies that can help streamline and simplify each stage of eDiscovery. But how well are those potentially helpful tools being adopted and, how are legal professionals dealing with the growing complexity of data today?
To better understand the state of artificial intelligence (AI) and technology adoption in eDiscovery, ZyLAB—an IPRO company—and the Association of E-Discovery Specialists (ACEDS) recently surveyed eDiscovery practitioners about their use of technology. The 2021 State of AI in eDiscovery survey asked 184 experienced eDiscovery practitioners on the use and perception of AI and other technologies.
The eDiscovery hype cycle
What we found is that AI and other technologies aren’t simply adopted or rejected; rather, their popularity waxes and wanes along a fairly predictable path known as the eDiscovery hype cycle.
Data automation techniques such as deduplication and data processing are fully adopted, having reached the plateau of productivity. However, with data volumes continuing to expand, these approaches are no longer sufficient. We have the tools needed to efficiently manage ever-greater data volumes and surface the highly relevant, valuable data that eDiscovery depends on, but those tools—such as sentiment analysis and dark language detection—have yet to reach peak adoption.
Legal professionals shouldn’t have to collect data to then cull and search through it or determine its relevance. By deploying in-place search functions and in-place data analytics, organizations can avoid the costs—and the security risks—associated with unnecessary data collection, excessive storage, and review of unresponsive data. In addition, in-place analytics have the potential to unlock new value adds for legal professionals, by proactively reducing legal risks in data as it sits in place.
Perceptions of AI among eDiscovery practitioners
To improve the acceptance and adoption of these underused capabilities, we sought to understand the barriers to their implementation. What’s holding eDiscovery professionals back from adopting tools like dark language detection? Many of these capabilities are baked into broader eDiscovery platforms, indicating that cost and availability aren’t the only factors at play.
Other barriers delaying adoption could be related to ethical concerns. Attorneys ultimately bear responsibility for every aspect of a legal representation. That includes the choices that an AI system makes during eDiscovery. Attorneys may be uncomfortable with their lack of awareness or understanding of how an AI system reaches those decisions. Without a strong sense of the benefits of AI, attorneys might continue to choose older methodologies rather than trusting AI in eDiscovery.
93% of respondents agreed that AI can help to automate eDiscovery work. 87% believe that AI is useful for generating better insights from data. But over half of respondents stated that AI cannot match human competencies across every stage of eDiscovery, from data identification and collection to review and production.
Responses were more optimistic regarding the eventual capacity of AI to reach parity with human competencies for data processing (74%) and review (68%), but by and large, our respondents seemed to expect that AI would not outperform humans in most eDiscovery applications.
More specifically, just over half (53%) of respondents didn’t believe that AI could reduce risk by improving the quality of their work. This result may reflect the underlying notion that while AI is capable of automating aspects of data management and providing some insights into data patterns, it isn’t “smart” enough to make contextual decisions. In this regard, AI is seen
as an augmenter of human intelligence rather than a replacement for it.
The future of the legal profession and the value of AI for eDiscovery
Legal professionals are under unrelenting pressure to add more value for their clients. With increased competition in the legal market, adaptation and innovation are critical for continued success. Those who find ways to leverage the enormous volume of organizational data to gain actionable insights will be well positioned to outperform their competitors.
This white paper reports our results and recommends next steps for how eDiscovery leaders can advance the use of AI and other technologies in eDiscovery.
