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HomeAIAI + KM, The Hour, Human Capital – Artificial Lawyer

AI + KM, The Hour, Human Capital – Artificial Lawyer

Screenshot 2025 08 13 at 09.49.21

Once again the team at Draftwise have kindly reported on some of the key sessions at ILTACon. Here are a collection of the things they witnessed on Day Two, which includes a huge roundtable on KM and AI; a debate on AI’s impact on the billable hour; and finally a session by Laurel about human capital and time.

Session title: KM Roundtable: Embracing the New Wave of Knowledge Management

Speakers:

  • Kim Stein, Director of Legal Solutions, Upland Software
  • Michael Owen Hill, Director, Product Marketing, NetDocuments
  • David Hobbie, Director, Knowledge & Innovation, Goodwin
  • Ted Theodoropoulous, Chief Executive Officer, Infodash
  • Michael Korn, Director, Knowledge & Innovation, Paul Hastings LLP
  • Nicole Brown, Director of Knowledge Management, DLA Piper
  • Elisabeth Cappunyns, Director of Knowledge Management, DLA Piper
  • Kate Simpson, Senior Director, Knowledge & Innovation, Fireman, an Epiq Company
  • Julie Wilson, Legal Resources & Innovation Lawyer, Gowling WLG
  • Alex Smith, Senior Product Director, iManage
  • Sara Miro, Director of Knowledge Solutions, Sullivan & Cromwell LLP
  • Abhijat Saraswat, Chief Revenue Officer, Lupi
  • Catherine Monte, Chief Knowledge & Innovation Officer, Fox Rothchild LLP
  • Rob Saccone, Chief Technology Officer, Lega Inc

Key topics: Drafting tools, DMS search (first discussion attended), legal AI platforms (second discussion attended), knowledge banks, change management.

Highlights:

  • General skepticism about AI and tools – feels early days and many feel unsure who will have longevity and hesitant to onboard and spend time to set up when you don’t know who will still be here in a year or three
  • Those who are using AI do see real value – not always just saving time, but redirecting time to what is more valuable (as there are only so many hours available) – want to spend on strategic understanding of the document, not editing and proofreading or starting from scratch
  • Even getting lawyers part of the way there (for redlines, search, etc.) is helpful – “lawyers prefer a red pen to a blank page.”
  • Many law firms are not keeping all their documents in the DMS and that is causing challenges of where to find things, not being able to search across, and security concerns (“stashing stuff everywhere”) – also concerns about versioning and knowing what the final version is (even the lawyers don’t always easily know)

Platform Uncertainty

  • Everyone is currently testing multiple AI tools, but no one knows which ones will survive long term.
  • KM teams are experiencing “POC fatigue” as they try to evaluate numerous tools while still managing their regular workload.

Where AI Helps

  • Junior associates are using AI to draft disclosure schedules from diligence results instead of starting from scratch.
  • Senior lawyers are spending more time on strategic analysis because they are no longer writing initial drafts themselves.
  • Lawyers work more effectively when editing an existing draft rather than starting with a blank page.
  • AI-assisted drafting still requires experienced attorneys who can spot when the output is wrong.

Document Storage Mess

  • Firm knowledge is scattered across OneDrive, SharePoint, Teams, and personal folders, making it difficult to locate and use.
  • Although firm knowledge is often described as the “lifeblood” of the organization, it is frequently so unorganized that it cannot be searched effectively.
  • Many firms do not add documents to their document management systems until matters are closed, which can take years
  • Poor version control makes it difficult even for the original lawyers on a matter to identify the final version of a document.

Security Reality Check

  • “Security by obscurity” no longer works, as AI tools like Copilot can surface documents that were previously hidden by poor organization rather than true security measures.
  • Many firms are discovering that their access-control issues are larger than they previously realized.

Integration Is Critical

  • Workflows cannot scale without integration across systems and tools.
  • Lawyers are unlikely to adopt tools that require juggling multiple browser tabs or learning several new interfaces.
  • A fragmented user experience will kill adoption faster than poor AI results will.

Evolving KM Roles

  • KM professionals are shifting from maintaining knowledge bases to orchestrating AI workflows and ensuring data quality.
  • KM teams may increasingly work directly with clients to help them implement their own AI solutions.
  • This evolution of responsibilities could require hiring more KM staff rather than reducing headcount.

Best Practices

  • Some firms are producing internal podcasts with expert litigators, offering knowledge that AI cannot replicate.
  • A people-first approach works better than a technology-first approach, which means building champion networks, addressing the true pain points of skeptics, and fixing processes before adding AI tools.

Overall Consensus

  • Strong processes and a healthy organizational culture are the foundation for successful AI adoption, and without them, even the best tools become expensive distractions.

Screenshot 2025 08 13 at 09.47.39

Session title: Bill(AI)ble Hours: The Debate Continues

Speakers:

  • David Cohen, Chief Executive Officer, AtJustice – Formerly: Practice Group Leader, Reed Smith LLP
  • Conan Hines, Director of Practice Innovation, Fried Frank Harris Shriver & Jacobson LLP
  • Hunter McMahon, Director of Data Analytics, iDiscovery Solutions, Inc.
  • Catherine McPherson, Founder & Consultant, This Might Help Consulting, LLC
  • Julio Sanchez, Sr. Pricing Manager, Perkins Coie

Key topics: Billable hours versus flat fee, financing the legal industry

Notes: style was a debate style and had the audience participate with thumbs up, thumbs down for ideas and to vote.

Highlights:

  • AFA (alternative fee arrangement)
    • Pro: result drives the price, predictability of cost to the client, shifts the focus to value based, frees lawyers to focus on the strategic
    • Against: Safe – price on hours actual spent or risky – pricing on guess of hours. Will not always be right, so someone will get the short end of that stick (some times client and sometimes the firm) and harder to rescope or manage disparities in scope
    • Result: more of the audience was on the pro side than the con side
  • What do clients want with firms using AI? Is it lower costs?
    • Pro: Cost is the KPI legal departments care about.
    • Against: 80% of the time – higher bid is selected (want to be better, higher quality). More effective use of spend.
    • Audience question: who is using AI? And who likes it? Almost all yeses in the audience to both.
    • Result: audience more on the side of it being more than just costs
  • Do AFAs benefit junior lawyers or sideline them?
    • Pro (benefit): Allows opportunity to give to junior associates because keeps us from fear of write offs and training junior lawyer is an investment
    • Against (sidelines): going to cost more to train associates because incentives to bring junior lawyers in to first drafts, in the room because you can do it yourself for less
    • Result: audience more on the side of AFAs being beneficial for junior lawyers
  • Are traditional law firm revenue structures a significant blocker to innovation?
    • Blocks innovation: Single biggest part of partner compensation is based on client-relationships. Need to show clients that you are delivering more value, and AI will be a tool to bringing in more clients. Want to encourage.
    • Does not block innovation: Clients pay for top-minded lawyers who are innovating (complex transactions, etc.), not your KM team.
    • Results: More of the audience thought it does block innovation.
  • Will AI cause allied professionals to evolve into a bigger part of the law firm revenue model?
    • Yes: More proportion of revenue from allied professionals because AI is having a bigger impact
    • No: Greater impact, but not going to become a line item that they weren’t already
    • Results: Audience more on the side of they will have a bigger role
  • Will AI make the billable hour largely obsolete over the next 5 years?
    • Yes: Change is accelerating quickly
    • No: Needs of clients continue to evolve
    • Result: Overall panel was on the no side – just don’t see it moving that quickly, and the same for the audience.

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Talking about AFAs – do they actually work?

Pros (Catherine, Hunter)

  • Catherine said AFAs and other value-based pricing shift the focus to quality and matter management instead of tracking hours.
  • They can give clients more predictable costs and let lawyers spend more time on strategy.
  • Hunter added that AFAs can help junior lawyers get more experience during a case.
  • Both agreed training juniors under AFAs can pay off long-term

Cons (David)

  • David said AFAs often waste time in the scoping stage, are hard to price right, and make scope changes tricky.
  • He thinks better budgeting, ongoing tracking, and updating budgets is a better approach than AFAs.

Audience: More people supported AFAs than opposed them.


What clients are asking about AI

Pros (Conan)

  • Conan said clients straight-up ask how AI will lower their fees, since cost control is a major KPI for legal departments.
  • Clients want firms to use AI to work more efficiently, with lower costs as a side benefit

Cons (general panel)

  • Others pointed out that in most cases, clients choose the higher bid if it promises better quality and more effective use of their budget.
  • The takeaway was that clients are looking for better service, not just a cheaper bill

Audience: Most agreed AI’s value is about more than just cost.


AFAs and opportunities for junior lawyers

Pros (Hunter, Catherine)

  • Hunter said AFAs take away the fear of write-offs, which makes it easier to involve junior lawyers
  • Catherine noted that training juniors is a smart long-term investment.

Cons (general con side)

  • The other side said that under AFAs, it can be more cost-effective for partners to just do the work themselves.

Audience: Most thought AFAs benefit junior lawyers.


Do law firm revenue structures slow down innovation?

Pros (panel)

  • Some argued that because partner pay is tied to client relationships, firms can be hesitant to try new things.
  • Showing clients more value is key, and AI could help win more work.

Cons (panel)

  • Others said clients pay for the expertise of senior lawyers doing complex work, so the revenue model itself isn’t the main blocker.

Audience: Most felt revenue models do get in the way of innovation.


Will AI make allied professionals more important for revenue?

Pros

  • Supporters said AI’s growth will naturally increase the role allied professionals play in generating revenue.

Cons

  • Opponents said the impact will grow, but it’s not going to create an entirely new revenue stream that didn’t exist before.

Audience: Most agreed allied professionals will have a bigger role.


Is the billable hour going away soon?

Pros

  • Some argued that change is moving quickly and the billable hour could phase out sooner than people expect.

Cons

  • Others said the billable hour isn’t going anywhere in the next five years because client needs are still evolving.

Panel & Audience: Majority agreed it will still be around for the next five years.

And finally

Session title: Master Class: Mapping Time to Outcomes: How Firms Must Focus Human Capital and Leverage AI Agents (Laurel)

Speakers:

  • Eric Zaarour, Co-Founder of Laurel

Key Takeaways:

  • Law firms are focusing on adoption metrics when they should be measuring the key metric that actually matters: time spent
  • Enterprises are planning $1T in AI spend over the next 5 years, yet the 3rd highest use case for Copilot is people asking what it does
  • One firm saw results from 100 hours per month down to 20 hours after Copilot implementation – but this was only visible through before/after measurement
  • Most firms are operating blind on their human capital supply chain and need to map time inputs to outcomes through proper data collection
  • Research and suggestions can be automated effectively, while strategy and persuasion still cannot be automated
  • The industry is shifting toward an agentic-first model for handling routine work
  • Better measurement and predictability directly improve realization rates
  • The core issue: you can’t optimize what you don’t measure – adoption metrics alone don’t reveal actual time savings or value creation

Thanks again to the team at Draftwise!


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