Beyond the Hype:Artificial Intelligence in law firms

and what the future holds

Multilaw – Lisbon 20 October 2017

Beyond the HypeArtificial Intelligence in law firms and what the future holds

Peter Wallqvist

VP of Strategy

Co-founder of RAVN

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Today’s Discussion

Business environment

to foster change

Technological progress Practical examples of AI applications

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Motivations for efficiency increases are maturing

Increased pressure for AFA

Regulators as change agents

New competition

Clients wield more power

© Copyright 2017 iManage. 5

Source: 2017 Altman Weil Survey

Although There is no Doubt we are at Peak Hype…

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…There are Still Concrete Examples of “Practical AI”

BLP enlists AI to shoulder process work burden – and so far the lawyers like itBy Dan Bindman

BLP’s Whalley: Using AI for processing is just the beginning

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AI in a Nutshell: What can you see?

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In consideration of the grant in Paragraph 3 above, Party A agrees not to distribute The Product to any for profit third party without prior written consent of Party B. Party A agrees to ask whether the purchaser is a for-profit entity, and if so identified, advise the purchaser that it must first obtain a use license from Party B. Party B shall notify Party A of its written consent within (10) days of execution of any use license for Party A. It shall be the sole responsibility of Party B toobtain and enforce any use licenses.

AI in a Nutshell: What is the Obligation?

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AI in a Nutshell: Reduce Domain – Increase Accuracy

Platform philosophy

Increases Accuracy

Reducing Domain

Narrow domain means better outcomes

‘Out of the Box’ models serves as a starting point

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• Reducing the domain:

specific, clearly defined tasks

in a professional’s daily routine

which can be augmented / automated

• These tasks are normally the most repetitive, costly and boring

AI in a Nutshell: Routine Cognitive Tasks

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Lesson: AI is Most Powerful When Embedded into Tools Used Daily

A focus on practical uses and consumability

Work Product Management

• Centralized Repository

• Document, Email, Records

• Governed, Secure

• Where Work Gets Done

AI Current Applications

• Point Solutions

• High Value

• Used for a Specific Purpose Within a Matter

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Content Aware Work Product Management

• Intelligence Inside core tasks• Tasks get smarter• Saves time, adds Insight • Manages risk

Key AI capabilities

Predict

• Forecasts based on past examples

• Examples:

- Cost of matters

- Suggested clauses based on history

Extract

• Reads, understands documents

• Extract key terms- Clauses- People- Dates- Obligations

Insight

• Full text, taxonomy and conceptual search

• Federated search

• Across repositories

• Identify experts via social graph

Classify

Automated classification

Predictive Coding (tagging)

Intra-document analysis

Time card narratives

Work Safer

• iManage Records Manager

• iManage Security Policy Manager

• iManage Threat Manager

• iManage Comply

Work Smarter

• iManage Insight

• iManage Classify

• iManage Extract

Work More Productively

• iManage Work

• iManage Share

iManage – Transforming How Professionals Work

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iManage Cloud On Premises

OR

iManage RAVN Engine – Content Intelligence and AI

iManage Platform – Administration ∙ Common Services ∙ Security

ClassifyAdding Structure to Chaos

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• Document tagging Automated classification

Automated clustering

• Intra-document analysis Document & Clause level tagging

Adding structure to documents

Deviation analysis

• Out of the box capabilities Document type

Clause type

Classify: Adding Structure to Chaos

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RequirementIdentify potential Precedent and Knowledge Base content

Legal Document Classification

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Business DriverReutilise tacit knowledge within the firm

SelectionItems considered and selected based on training data set

OutcomeContent is automatically selected, classified and made available through search for rapid re-use, improving legal service delivery

CRMFiles

DMS

Knowledge Identified

Training Data

ClassifyAuto ClassificationMachine learning based auto classification of content

LOB Solutions Extract

Refine

TimeCard Narrative Classification

Business DriverClassify activity for billing, pricing, project mgmt, budgeting

SelectionUse correctly assigned records to teach learning engine

OutcomeTime records are updated and made available for additional analysis by the firm. Proactive processes can be engaged to continuously monitor and correct new records.

Billing Systems Time

Entry

ClassifyAuto ClassificationMachine Learning based auto classification of content

Knowledge Identified

Training Data

Refine

Tag

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Analyze

Attorney Client Privilege

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Training Data

Manual ReviewACP ContentNot ACP Content

↓ 75% workload

Business DriverReduce time and cost of identifying Privileged content from review

Machine LearningAttorney tagged content used as training set for applying to large corpus of content for review

OutcomeAccuracy in excess of manual review. Massive reduction in time and cost of processing. Defendant’s representative agreed to innovative approach. Smaller volumes (hence cost) in review platform

Adjustable Sensitivity can be adjusted. Increases false positives but ensures privileged content includedACP robot

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Government Department Documents processed: 30m • Employees:300

Challenge:

• Manual identification process too lengthy & costly

• Inconsistent review

• Important information could be missed

iManage Classify automatically identifies privileged and non-privileged information

iManage Solution:

• AI techniques to automatically read through documents

• Automatically determines whether items are subject to LPP

• Classifies privileged and non-privileged information

Benefits:

• Increased efficiencies

• Increased productivity

• Removed human error from review process

• Increased accuracy

“The robot technology is able to learn and bolster its own knowledge base to help identify privileged and relevant material and is more efficient and more accurate than human intervention.”

— David Green, Director at SFO

ExtractTransforming unstructured into structured

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Name

Address

Dates

Concept

Company

Extract: Transforming unstructured into structured

• Automatically understand

Type of document

Structure of the document

Meaning

• Multi-purpose

AI trained by the end users

Data scientists

• Out of the box

Out of the box models

Re-use of the client models

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M&A Due Diligence

RequirementExtract KPIs to support due diligence exercise on M&A

Business DriverClient responsiveness, efficiency and margin improvement

OutcomeAutomated clustering of M&A document types followed by detailed key terms extraction for each to spread sheet.

Provision of contextual ad-hoc query platform over entire corpus

Deal Room

Party Termination

Change ofcontrol Benefits

Parties Amount

Interest Rate Repayment

terms

Extract

Analyze

Employmentcontracts

Facility Letters

Corporatedocuments

Clustering

Commercialcontract

Benefit800 hours removed from a 1000 hour billable exercise

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Extracted Data

Property Portfolio Due Diligence

RequirementIdentify ‘gold standard’ lease and extract KPIs and provide variance tools to support lease DD review

Business DriverClient responsiveness, efficiency and margin improvement

Deal Room

Leases

Title Deeds

Portfoliodocuments

Clustering

EPCReports

Tenure Registered Owner

Charge Holder Covenants

Extract

Review

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Extraction & Automation Robot

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RequirementAutomation of production of Light Obstruction Notices (LONs)

Business driverEfficiency, speed and competitive advantage

Outcome• Automated production of 100s of LONs

• >80% reduction in overall time taken

• Massive increase in margin (fixed price deals)

• System delivered as a service

BenefitLabour intensive task previously taking many hours can now be completed in seconds at agreed rate per notice. Delivers significantly higher margins for each instruction and highly responsive service for clients

Connection

OCR

Classification

Hot Zone Detection

Extractors

Review

Enrichment optional

Output

Corporate Contract Analytics

analyse

Business DriverGovernance & Risk: Contract Discovery, structuring and analysis

Revenue & Efficiency: Contract Assurance process automation

Corporate Data

Repositories

ResultContract estate now under management

£ Millions recovered in revenue ‘leakage’

£ Millions saved in resource effort

£ Millions opportunity applying charge rate changes correctly

Regulator fine risk mitigated across estate

Addition of further contract sets through business acquisition present no challenge to assurance team

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International Law Firm • Employees: 500+ • Partners: 200+

Challenge:

• Lack of resource and time to review documents in order to meet tight deadlines

• Low morale in team due to manual review

• Inconsistency and inaccuracy of extracted data

BLP uses iManage RAVN to allow lawyers to focus on higher value work for clients

iManage Solution:

• iManage RAVN AI powered contract review

• Key data points extracted and exported to excel for further review

Benefits:

• Reduce costs over time

• Increase in efficiency

• Increase in accuracy

• Improved staff morale

“The robot has fast become a key member of the team. It delivers perfect results every time we use it. Team morale and productivity has benefited hugely, and I expect us to create a cadre of contract robots throughout the firm.”

— Matthew Whalley, Head of Legal Risk Consultancy at BLP

Extract: Endless Applications

Lease review

ISDA / CSA

M&A due diligence

Structured finance

Commercial contracts

Employment contracts

InsightSurfacing the Knowledge in Your Organization

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Insight: Surfacing previously hidden knowledge

• Universal search Documents

People, matters, projects

Relationships matter

• Represent knowledge as a graph People, documents, matters, projects

Relationships

• Learn what is relevant User feedback

External factors

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© Copyright 2017 iManage. Information may be confidential. 32

Project / Deal Expeditor

RequirementEfficiently locate deal related materials

Business DriverCompetitive advantage, efficiency and margin improvement

OutcomeLawyers can efficiently access relevant deal information across a wide variety of repositories.

Crawling Multiple repositories –

DMS, Deal bibles, CRM etc.

Unified Search Interface

Expose different types of knowledge

Identify Knowledge

BenefitIncreased efficiencies, more responsive, make more informed decisions

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Expertise Locator

RequirementEasily identify subject matter experts, their skills and area of expertise Business Driver

Get to the information they need as efficiently as possible to remain competitive

OutcomeLawyers can find the most appropriate person and expertise to move forward on a client engagement

BenefitIncreased efficiencies, more responsive, make more informed decisions

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Search

Further Refine Search

Subject Matter Experts Identified

Identify Experts

Clause Knowledge Bank

RequirementEfficiently locate specific clauses Business Driver

To understand how accurate president documents were compared to live content

OutcomeLawyers can efficiently access and compare clauses across different versions of documents.

BenefitIncreased efficiencies, more responsive, make more informed decisions

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Extracted Clauses

Search Specific Clauses

Compare Clauses

Mid-Tier Law Firm• Partners: 53 Staff: 350

Challenge:

• Had to look in several locations to locate information

• Research process too lengthy & disjointed

• Needed in keep existing legacy systems

Howard Kennedy achieves efficient information retrieval with iManage Insight

iManage Solution:

• Enterprise search

• Knowledge Management

• Unstructured data mining

Benefits:

• Quick & efficient access to knowledge via a single source

• Safe & secure

• Supports all core functions of the business

• Allows them to increase competitive advantage

“Howard Kennedy needed an innovative solution and iManage Insight has allowed us to implement our vision of providing integrated, secure and effective access to our knowledge resources. For us one of the key benefit is that we continue to manage our resources through our DMS whilst iManage provides a sophisticated layer above this”

— Robin Hall, Head of Knowledge Management at Howard Kennedy

Routine legal processes: The present

Routine legal processes: The future

The Way Professionals Work