Presentation Review: “Artificial Intelligence - Changing the Practice and Marketing of Legal Services”

Presented at the 2017 LMA Annual Conference, by Mark Greene and Elonide Semmes

Summary by Brian C. Dare, Chief Business Development Officer, Strasburger, for the May 2017 LMA Southwest #LMA17 Conference Recap.

Session description:

Businesses worldwide will be transformed over the next few years by enormous advances in machine learning. Very large investments are being made by some of the world’s largest tech companies, including several in the legal space. In this session, you’ll explore how the provision of legal services soon will bear little resemblance to the bill-by-the hour profession of the last century. As the legal services evolve, so must your marketing. This panel discussion will include three TED Talks reviewing this revolution and a panel discussion of the implications for legal marketing.


Topics included:

  • How and when AI is likely to transform the business of your law firm
  • How to become part of this strategic discussion within your firm
  • The most likely impacts of this transformation on legal marketing
  • How you and your team can stay on top of this change and prepare for it


Session summary:

  • Artificial Intelligence (AI) is a very broad area and is attracting a significant amount of attention in the legal world. It's real, relevant and creating a revolution in how companies operate.
  • AI is much more about computing power than consciousness, although the pace of developing self-learning applications is rapidly changing.
  • Many companies are developing AI-based tools to disaggregate legal services – rather than paying a single firm for managing the entire matter, they are developing solutions that focus on specific, routine tasks to reduce the amount of dependency on lawyers. Tasks involving documents (management, review, comparisons), research/knowledge management/competitive intelligence (case law, market information, sharing information across network), business automation (analyzing relationships in a CRM system, integrating systems to minimize data entry), and predictive analytics (projecting outcomes, risk profiles, budget estimates, etc. based on data review) are transitioning away from humans to software tools.
  • Companies are using AI to pull apart processes and disaggregate services handled. Goal is to be better, faster, cheaper.
  • Must haves for law firms before investing in AI (or while investing in AI)
    1. Clean up your data. Data is critical and needs to be quality. Centralize systems require data be clean and consistent.
    2. Aim for the clouds. The technology is going to be cloud-based so plan for this.
    3. Integration is critical. Pick platforms that can integrate and “talk to each other” to get the most value.
  • Lots of players in this growing area. Here are three things to consider before investing:
    1. Be wary of vendors that are focused on technology but are not familiar with legal industry
    2. Don’t underestimate cost and time involved and don’t overestimate internal support for change
    3. Don’t panic – but get moving on a plan to use AI because competitors are
  • Two business models for law firms are emerging, and sometimes within the same firm:
    1. Traditional – focus on the people and culture
      • Relationships, processes, knowledge
      • High value, bespoke work
      • AI used to help people learn the laws better by creating ownership in building the expert systems or machine learning
      • Will provide legal training as a service to clients – using AI to draft and develop documents for review
    2. Internet/software startup – focus on the service delivery process and profitability
      • High upfront costs to develop and refine AI tools, but profitability will increase as “units” are sold
      • Must use different sales model to promote; pricing for AI-supported activities is tough to estimate
  • Routine and commodity operations and services are priorities – must dig very deep into the specifics of the law to break down processes and disaggregate functions – use the outside perspective (non-lawyers or service provider) AND don’t just rely on lawyer’s views
  • Six ways for the marketing function to create more value in support of technology adoption, to help find the next client, understand buyer behavior, and create more meaningful interactions and dialogues. AI tools supporting these strategies       give firms the ability to take historical, proprietary, 3rd party or social media data and create meaningful insights.
    1. Uncover best prospects
      • Social media
      • Online directories
      • Secretary of state listings
      • Court dockets
      • SEC filings
      • Firm data
    2. Monitoring market behavior --
      • Demos of who has bought services before
      • Who has viewed technology related to the problem
      • Opened job positions based on the issue
      • Used LinkedIn to discuss the issue
      • Attended conferences on the subject
      • Who has replied to email outreach on issue
    3. Learn from social semantics
      • Deep learning – not just what they were saying but analyzing the emotions behind the content
      • Taking an idea/problem/issue and study what’s being said about it –then create a “narrative graph” to determine drivers for messaging
    4. Hyper-personalization
      • Generate a general email based on recipients email profile
      • Collect data at scale to craft specific messages
      • Change content based on what’s discovered
    5. Real-time programmatic advertising
      • Driven by profile, preferences and previous choices
      • Publishers developing technologies to provide stronger targeting for users
        • C-suite, decision-maker from company of a certain size and geography
        • Will promote competitive bidding from law firms trying to reach certain audiences
    6. Content generation
      • Analysis of structured data to improve relevance to audience
      • Generate article based on issues and then lawyer reviews
      • Use AI to then self-learn on article topics to then generate new content based on information collected through multiple channels
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