{"id":2948,"date":"2024-09-20T15:16:25","date_gmt":"2024-09-20T19:16:25","guid":{"rendered":"https:\/\/www.edegan.com\/articles\/?p=2948"},"modified":"2024-09-21T15:42:09","modified_gmt":"2024-09-21T19:42:09","slug":"economic-consultants-ai-capabilities-for-litigation","status":"publish","type":"post","link":"https:\/\/www.edegan.com\/articles\/economic-consultants-ai-capabilities-for-litigation\/","title":{"rendered":"Exploring Economic Consultants’ AI Capabilities for Litigation\u00a0"},"content":{"rendered":"\n

AI’s Value for Litigation Experts<\/span><\/h2>\n\n\n\n

This article compares 12 US economic consulting<\/a> firms’ Artificial Intelligence<\/a> (“AI”) capabilities, particularly those used to support litigation engagements. It uses the umbrella term AI to include Machine Learning<\/a> (“ML”), computational economics, and other modern computer science methods.1<\/a><\/sup><\/p>\n\n\n\n

In litigation engagements, leveraging AI can create valuable additional insights and reduce analysis costs, increasing the chances of winning a case and making cases more profitable. So, it is unsurprising that around half of US economic consulting firms have begun hiring in-house engineers and scientists and adding new management to embrace modern computer science methods to support their antitrust<\/a>, bankruptcy<\/a>, IP<\/a>, M&A<\/a>, securities<\/a>, regulatory, and other practices.<\/p>\n\n\n\n

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The integration of AI in litigation, disputes and investigations has undoubtedly altered legal practices, offering unprecedented efficiencies and findings.<\/p>\nJon Chan, Senior Managing Director, FTI Consultants<\/cite><\/blockquote>\n\n\n\n

The State of Economic Consultants’ AI Capabilities<\/span><\/h2>\n\n\n\n

Economic consultants’ AI capabilities for litigation currently appear nascent and mixed. None of the firms I reviewed had anything like McKinsey’s Quantum Black<\/a>, BCG’s X<\/a>, Bain’s Vector Digital<\/a>, or even FTI’s Digital<\/a>, which are closer to the Big Four Accounting Firm<\/a>‘s AI groups and joint ventures.2<\/a><\/sup><\/p>\n\n\n\n

Nevertheless, economic consulting firms do seem to be racing to build AI capabilities.<\/p>\n\n\n\n

Almost all economic consulting firms now have\u00a0big data<\/a>\u00a0analytics capabilities (in-house or in the cloud), most offer\u00a0data visualization<\/a>, and around half have used some AI, broadly defined. <\/span>Among those using AI, the most popular application was Natural Language Processing<\/a> (NLP) for semantic or sentiment analysis. Likewise, around a third of firms have performed ML-based<\/a> optimizations or simulations or offer predictive analytics. Out of the 12 firms I reviewed, four mention generative AI<\/a>, three mention geospatial analysis<\/a>, and just one mentions Bayesian<\/a> methods.<\/p>\n\n\n\n

Cornerstone<\/a> recently started the first applied research group in this area, and, at present, just three firms – Cornerstone, Brattle<\/a>, and AG<\/a> – have material AI capabilities. However, none appear to fully understand the very large and rapidly expanding AI space. Keystone<\/a>‘s recent aggressive hiring might change things over the next few years, either directly or (more likely) by spurring competition, but it is starting as one of the smallest firms. For now, the best economic consulting AI group is still learning to use modern methods; they are far from contributing to their development.<\/p>\n\n\n\n

Ranking<\/span> Economic Consultants’ AI Capabilities<\/span><\/h2>\n\n\n\n

The table below ranks 12 US economic consulting firms using a subjective score from one to five to measure their AI capabilities in litigation support. (See the Methodology section for more information). The table doesn’t include NERA<\/a> or HKA<\/a> because I couldn’t find any information about their AI efforts in any context.<\/p>\n\n\n\n\n\n\n\t\n\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t
Rank<\/th>Score<\/th>Firm<\/th>Est. Employees<\/th>Group\/Center<\/th>\n<\/tr>\n<\/thead>\n
1<\/td>4.8<\/td>Cornerstone Research<\/a><\/td>700+<\/td>Data Science Center<\/a>, Applied Research Center<\/a><\/td>\n<\/tr>\n
2<\/td>4.7<\/td>The Brattle Group<\/a><\/td>500+<\/td>Data Analytics <\/a><\/td>\n<\/tr>\n
3<\/td>4.5<\/td>Analysis Group<\/a><\/td>1,200+<\/td>Data Science & Statistics <\/a><\/td>\n<\/tr>\n
4<\/td>4.2<\/td>Compass Lexecon<\/a><\/td>800+<\/td>Data Science<\/a><\/td>\n<\/tr>\n
5<\/td>4.1<\/td>Econ One Research<\/a><\/td>100+<\/td>Data Analytics <\/a><\/td>\n<\/tr>\n
6<\/td>4*<\/td>Berkeley Research Group<\/a><\/td>1,200+<\/td>AI\/ML<\/a><\/td>\n<\/tr>\n
6<\/td>4*<\/td>Keystone Strategy<\/a><\/td>150+<\/td>Core AI<\/a><\/td>\n<\/tr>\n
8<\/td>3.8<\/td>Bates White<\/a><\/td>300+<\/td>Data Science & Statistics <\/a><\/td>\n<\/tr>\n
9<\/td>3.7<\/td>AlixPartners<\/a><\/td>2,000+<\/td>Artificial Intelligence<\/a><\/td>\n<\/tr>\n
10<\/td>3.3**<\/td>Charles River Associates<\/a><\/td>800+<\/td>Analytics<\/a><\/td>\n<\/tr>\n
11<\/td>2.7**<\/td>Secretariat<\/a><\/td>300+<\/td>Data Analytics & Strategy<\/a><\/td>\n<\/tr>\n
12<\/td>2.5<\/td>Edgeworth Economics<\/a><\/td>100+<\/td>Data Analytics<\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n\n

*Score is based mainly on private knowledge.
**Score is driven primarily by the lack of readily available material online.<\/p>\n\n\n\n

The subjective scores fall somewhat naturally into four groups:<\/p>\n\n\n

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