The Time for AI-Powered Information Self-Service Has Arrived

Organizations often have playbooks that spell out how to structure contracts with customers, vendors and other business entities. These playbooks outline necessary contract clauses that must be included and any parameters that need to be kept to, helping ensure that there is consistency across agreements.

Rather than constantly pinging the compliance department for advice on how best to structure a new client agreement, internal stakeholders can instead pose the question to generative AI using straightforward natural language. They can even paste sections of the contract into the generative AI interface and ask it, “Have I missed anything important in this particular section?”

Elsewhere within the organization, picture the IT team rolling out a brand-new document management system or practice management system. From there, the IT team could let a generative AI-powered chatbot provide product support and answer common questions that new lawyers might have about using the system, giving the IT team more bandwidth to focus on trickier questions or edge scenarios. 

MAKE SURE THE FOUNDATION IS SOLID

Before they can arrive at this self-service paradise, however, legal management professionals will need to prioritize data quality and architecture to ensure their AI-powered self-service solutions have access to accurate, up-to-date and reliable information.

Put another way, AI-powered self-service doesn’t work very well if the organization doesn’t first pay attention to their underlying information architecture (IA).

Picture our lawyers above asking a tech support chatbot a question about how to use their new practice management system and getting a wildly inaccurate answer. Likewise, imagine the associate seeking assurance that the contract they’re putting together ticks all the right compliance boxes and getting a thumbs up from the generative AI — despite the fact that the contract is missing some crucial clauses or language.

To avoid these scenarios, organizations need to shore up their IA to get the most out of AI.

NO BLOBS, PLEASE

Getting high-quality, trustworthy answers requires grounding the AI in good quality data. As a first step, organizations should identify where the trusted data sets within the organization exist. If they already have a document management system, that’s a good starting point, as many valuable knowledge assets, best practices and other resources will already be in that repository. Additionally, there might be key business functions that are managed via spreadsheets that should be identified and marked as unique.

There will likely be other knowledge resources to point the generative AI at so that it delivers the best self-service answers. For the IT professional, for instance, that might mean ensuring that the tool is pulling answers from an official support portal, user manual or other official documentation. 

The key here is to make sure you’re not just turning AI on against an amorphous blob of data and hoping that it can find the right answer. For best results, there should also be a specific person or role who’s in charge of determining what “good content” looks like for any particular legal administration workflow. Managing that content on an ongoing basis — and revisiting it annually or biannually to ensure it’s still delivering the right results to stakeholders — will be key to unlocking its self-service potential while minimizing any missteps.

READY TO REVOLUTIONIZE

As generative AI continues to evolve, its potential to revolutionize self-service across multiple areas of legal administration is becoming clearer. By grounding these AI systems in high-quality, trusted data, legal organizations can reduce repetitive inquiries, and ensure accurate, reliable responses. In the end, that’s a boon not just for those asking the questions but for those who historically have had to answer them — and it creates a more efficient, more knowledgeable organization.