Search Outside the Box #KMWorld

KMWlogo_Stacked_Session Title and Description: Thinking and Searching Out of the Box

Our industry helps people retrieve information by searching, browsing, and visualizing the data stored within their content management systems. This endeavor is inherently introspective in so far as it focuses on the close analysis of an enterprise’s internal content. This talk is an exercise in thinking outside of that box. Clarke explores ways in which an enterprise’s internal content can be mined for information, even when the answers don’t always exist within the data we are querying. He discusses the use of natural language processing and semantic query expansion techniques, demonstrating the power of ontologies and machine reasoning to interrogate internal content in new and powerful ways.

Speakers:

Dave Clarke, CEO, Synaptica
Maish Nichani, Co-founder, Ola Search

[These are my notes from the KMWorld 2016 Conference. I’m publishing them as soon as possible after the end of a session, so they may contain the occasional typographical or grammatical error. Please excuse those. To the extent I’ve made any editorial comments, I’ve shown those in brackets.]

NOTES:

  • What is the “Box”?  The box is your content collection.
  • What’s the difference between search inside and outside the “Box”? The speakers assert that you can do better job searching your internal content if you first map your content to external content.  For example, if you type in a general query or a minimalist query, the search engine needs to understand the concepts implicit in the query. If the search engine does not have the required information, the search engine will (at best) return a rather general response that may not contain the desired results. By contrast, if you map externally after the search, you can see how a similar search is handled externally. That exercise will help enrich the query, thereby giving the search engine more useful information to work with.
  • Do not think small about Search. Search is not just about locating specific content. It is also (and increasingly) about finding answers to specific questions. Google is learning that users increasingly want answers to questions (e.g., how to treat the common cold) rather than particular documents or videos.
  • Start by mapping. When you map internal content to the external content, this helps you understand better what is inside your content collection. It finds and validates information that is not already in your content collection, but that can be used to enrich both the initial user search and the results the search engine brings back.
  • How does this fit with your taxonomy?  Taxonomy and search belong together. Make sure your search engine does not ignore the taxonomy that you have built so carefully. Equally, sometimes your taxonomy does not encompass everything you need for an efficient search. So searching “outside the box” can help enrich the taxonomy and search.
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How to Innovate #KMWorld

KMWlogo_Stacked_Session Title and Description: Hacking KM or How to Innovate!

Speakers: Jeffrey Phillips, VP & Lead Consultant, OVO Innovation Co-Author, OutManeuver: OutThink, Don’t OutSpend

[These are my notes from the KMWorld 2016 Conference. I’m publishing them as soon as possible after the end of a session, so they may contain the occasional typographical or grammatical error. Please excuse those. To the extent I’ve made any editorial comments, I’ve shown those in brackets.]

NOTES:

  • Maneuver.  
    • We need to learn how to compete by increasing agility and responsiveness.
    • Business tends toward standardization and efficiency. This makes it possible for an organization to “do a slow grind” but does not allow an organization to pivot quickly or course correct.
    • The secret to success is to outmaneuver your competition rather than trying to compete with them head on.
  • Ways to Attack your competitor.
    • Preemption
    • Dislocation
    • Disruption
  • How to compete. Find the weaknesses in your competitor’s business and then compete there.
    • What are the vulnerabilities in your competitor’s strategy?
      • is your competitor locked into a particular way of working? For example, AirBnB needs a supply of privately owned bedrooms. Can you make it difficult for them to operate that way? Can you operate that way, but better.
    • Are there tangible business requirements you can compete against?
      • Does your competitor need specific laws and regulations? Is your competitor violating specific laws and regulations? Can you hold them accountable for this? (This is a tactic some cities (and hotels) are using against AirBnB.
    • Are there intangible business requirements?
      • can I beat you by creating a better culture, hiring better talent, etc.
    • Can you compete strictly on product? This is the most common arena for competing.
  • Maneuver Tools.
    • Temporal competition. If your competitor brings our new product on a fixed schedule, can you come to market on a different schedule, at a different time? Can you compete on speed?
    • Psychological competition. Can you use psy
    • Positional competition. Does your competitor have a position (or market share) I need to take or can I make that position/market share irrelevant? Amazon and Netflix made the prime real estate location of Barnes & Noble and Blockbuster irrelevant.
    • Informational competition. How do I use information more effectively than my competitors? Can I use information to beat my competitors? We need insight = see opportunities before they emerge
  • Maneuver Strategy Relies on Speed, Agility, Insight and Innovation.
    • Speed. Can I act before my competitors do? This is pure speed. (Most companies are set up for the slow-grind, not the fast sprint.)
    • Agility. Can I course correct at speed? This agility.
    • Insight. Insight is the ability to see opportunities before they emerge, before they are obvious to your competitors.
    • Innovation. Can I bring to the market something new that has commercial value?
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Artificial Intelligence Use Cases for Law Firms #ArkKM

Title: Artificial Intelligence: Use Cases in Law Firms

Session Description: Artificial intelligence is in mainstream and legal media headlines daily – and often with much hype. What’s real and what’s not? And what exactly is AI anyway? And are law firms really using it? Today, there are as many questions about AI as there are headlines. In this session, we will answer some of the key questions. These law firm use cases will illustrate what problems they are trying to solve and/or what benefits they create with AI. As well, the corresponding software providers will also explain how their products work and fit into the broader AI picture. Attendees of this session will also hear what it takes to create a working AI system, who might use it, how to encourage adoption, and where AI is likely headed within law firms.

Speakers:

  • Jonathan Talbot, Director, IT Enterprise Systems, DLA Piper LLP,
  • Marlene Gebauer, Director of Knowledge Solutions, Greenberg Traurig,
  • Steve Obenski, CMO, Kira Systems,
  • Ryan McClead, VP, Client Engagement & Strategy, Neota Logic
  • Moderator: Ron Friedmann, Partner, Fireman & Company

[These are my notes from the 2016 Ark Group Conference: Knowledge Management in the Legal Profession.  Since I’m publishing them as soon as possible after the end of a session, they may contain the occasional typographical or grammatical error.  Please excuse those. To the extent I’ve made any editorial comments, I’ve shown those in brackets.]

NOTES:

  • What led the law firms on the panel to AI?
    • At Greenberg Traurig, they were looking for ways to automate processes and become more efficient. They wanted to adopt new technologies that would provide greater capability. This led them to Neota Logic.
    • At DLA Piper, their due diligence group wanted to improve and automate their due diligence process. This led them to Kira Systems.
      • They are using this across several practice groups.
      • Clients are outsourcing due diligence work to DLA Piper. This is an expanded source of business for the firm. (They support this with their low-cost service centers.)
    • At Norton Rose Fulbright, Ryan McLead used the platform as a prototyping tool. He could automate a process and show his internal clients a prototype in just a handful of days.
  • What’s Kira Systems?
    • It is a machine learning tool for taking unstructured content in contracts, and then structuring it in order to expedite document review.
    • Their platform ingests contracts, OCRs them, analyzes them, does entity extraction, and then enables reporting.
    • Some firms are using Kira to digest and analyze outside counsel guidelines.
    • Kira encourages potential clients to compare the results of their own due diligence processes against the results from using Kira Systems.
  • What’s Neota Logic?
    • It is a platform upon which you can build algorithmic expert systems = knowledge builders. It is engine that produces repeatable, reliable and consistent results. It makes your knowledge exponentially scalable. It is no longer trapped in one head.
    • It can do risk analysis.
    • Some law firms use the platform for document automation — although they are not a document assembly tool.
  • This is not magic! Humans need to put in the time and effort to create the models.
    • Plus, both tools aim to provide some transparency regarding how they operate and make decisions. They are trying to dispel the anxiety of the “blackbox.”
    • Who should be allowed to train these systems?
      • Each firm needs to make this choice carefully. Do not simply give this job to the most junior person (on the theory that they are young and therefore must be the most tech-savvy???). It is wise to have an internal vetting process.
    • The person who builds the expert system often becomes the expert.
  • What are the related human issues?
    • Help them understand the extent to which this new tool might (or might not) make them redundant.
    • Help them understand the extent to which the tools augment what they do, allowing them to do more value-added work. “Kira does not practice law. YOU practice law.”
    • Give them sensible incentives to participate.
  • These tools leverage good process
    • This means that you need to know and understand your processes.
    • This also means that you need to ensure that your processes are smart ones. (Don’t automate a faulty process!)
  • Key Lessons Learned
    • If you understand the lawyers’ process, it is not hard to show the value of automation. And they will get it.
    • Even the most entrepreneurial law firms are VERY conservative.
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KM, Artificial Intelligence and Information Security #ArkKM

Title: Data Security is Required. KM is Demanded. AI is Here: Armageddon or Utopia?

Speaker: Peter Kaomea, Chief Information Officer, Sullivan & Cromwell LLP

[These are my notes from the 2016 Ark Group Conference: Knowledge Management in the Legal Profession.  Since I’m publishing them as soon as possible after the end of a session, they may contain the occasional typographical or grammatical error.  Please excuse those. To the extent I’ve made any editorial comments, I’ve shown those in brackets.]

NOTES:

  • “When powerful forces collide, you can get either great devastation or great beauty.”  Here are the big forces that are coalescing now:
    • Knowledge Management
    • Artificial Intelligence
    • Information Security — this one is top of the agenda now because of cybersecurity concerns.
  • The PERFECT song to describe the degree to which our lives are surveilled or disclosed is “Every Breath You Take” by the Police.  (See video below)
    • Peter Kaomea then did a fantastic “dramatic reading” of the lyrics of the song to show how all the behaviors described in the songs are now being done by technology. We ARE being watched and analyzed with every breath we take.
  • Security Challenges:
    • Hackers are more sophisticated. There is a group that monitors mergers & acquisitions transactions and then “injects” itself into the email traffic.
    • Hackers inject themselves into transactions in order to redirect payment into their own accounts.
    • Ransomware — now even on smartphones (which contain a great deal of sensitive personal information)
  • Pressures: Clients want additional protections on their information
    • Client external law firm guidelines contain a huge number of restrictions on the way data about them can be stored and used.
    • Law firms have to change their behavior to comply with these guidelines.
  • Perfect Storm Approaching
    • The hardware, software and data handling tools are reaching the point where enormous and dangerous security breaches will be regular events.
      • By 2025, you will be able to buy the computing power of a human brain for approximately $1000.
      • By 2045, you will be able to buy the computing power of all human brains for approximately $1000.
  • How can KM help Security?
    • Help information services people manage the client data security contracts they are required to sign.
    • Focus on how to protect information even as we are trying to share it.
    • Purging information once you have finished using it.
      • The less you have, the less you have to protect
    • Put super-sensitive information in an “Enclave” — offline repository that requires the user to go a physical place to retrieve it in person (an updated version of using microfiche)
  • Thinking about using Security to improve precision AND recall.
    • There is a mathematical way to calculate how to achieve precision and recall. For example, removing unnecessary data makes it easier to find the useful data
    • This is a nice way of switching perspective: don’t see information security concerns as a handicap for KM, see them as enablers/opportunities.
  • How can KM and AI work together?
    • Profiling content
    • Profiling users — e.g., what’s the ratio of send to receive? Has that behavior changed? What could that change indicate?
    • Automating taxonomy creation
    • Automating knowledge workflows
  • How do Information Security and AI work together?
    • Anomaly detection programs watch the traffic over the system. Is there a spike in traffic that correlates to the work day in Russia or China? Could it indicate possible infiltration?
  • “Too big to know”
    • What happens when you join data sets that have never before been joined? This can turn up valuable insights. It can also expose information that was considered hidden/secure.
  • How should you converge KM, AI and Info Security?
    • Entity extraction can be helpful to understand your content. Can you also automatically delete those entities to achieve quick document sanitation?
    • Can you use auto-classification so that fewer people need to handle sensitive materials?
    • Can you use auto-purging processes to strengthen security?
    • If you are watching activity anyway, can you create behavioral analytics and then use those insights?
    • Can you use these three areas of expertise to improve access to justice?

 

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Rebooting KM with Purposeful Collaboration #ArkKM

Title: Rebooting KM with Purposeful Collaboration, Silo-Busters, and Ambient Knowledge

Speaker: Stuart Barr, Chief Strategy Office, HighQ

Session Description: Traditional KM has focused on accumulating and organizing knowledge that you know people need and trying to make sure it’s available when they need it. But what about what is known but not documented? Or the knowledge trapped in silos that are completely unstructured and inaccessible? In this session, Stuart Barr will explore how to break down traditional barriers to knowledge sharing, capture knowledge as people get their work done and automate knowledge extraction to drive new insight from your historical data.

[These are my notes from the 2016 Ark Group Conference: Knowledge Management in the Legal Profession.  Since I’m publishing them as soon as possible after the end of a session, they may contain the occasional typographical or grammatical error.  Please excuse those. To the extent I’ve made any editorial comments, I’ve shown those in brackets.]

NOTES:

  • Traditional Approaches to KM
    • Collecting knowledge
    • Connecting that knowledge to people
    • Tying that knowledge to the organization’s productivity systems
    • Automating knowledge systems
  • Challenges to Traditional Approaches to KM
    • They usually are manual processes
    • They are siloed — both the repositories are siloed and the processes are siloed
    • They often are concentrated on “known knowns” — mainly the obvious knowledge is “hunted down and captured.”
    • People are not always motivated to contribute
    • You need to connect the knowledge to people more effectively
      • connect with experts
      • enable people so they can ask their questions in the open — this openness spreads knowledge and emboldens people to ask the questions they might have been afraid of asking.
    • We are stuck in very old ways of work = Ineffective Collaboration
      • Email is a massive “Black Hole” of knowledge. It is where knowledge goes to die.
      • Most firms have not found a way to collaborate. They do not realize that email was not designed for true collaboration.
  • Why is Social Collaboration Useful?
    • Assuming it is implemented correctly, it can provide a “peripheral vision” or “ambient awareness” of what is happening within an organization. This makes a knowledge worker much more plugged in and effective.
    • It provides passive access to information (e.g., the activity stream, group conversations, etc.)
    • It also enables active collaboration (e.g., shared workspaces)
    • It helps people share information actively, for example, by @ mentioning someone to draw their attention to an issue or to specific content.
  • Digital Transformation can drive KM. That said, KM should be at the heart of your digital transformation strategy. When done properly, digital transformation changes the way people connect, communicate and work.
  • What comes next?
    • Analyzing the data that are captured through your knowledge tools and social collaboration tools.
    • Coupled with machine learning, you can understand what content is important. In fact, you could provide digital assistants that can help knowledge workers find the content they need.
  • Conclusion
    • We need to keep doing traditional KM
    • But we also need to use more social ways of
    • We need to connect our systems of record to our systems of engagement
    • Collect and analyze the data about our work behaviors so we can make our systems and processes better
    • Use machine learning & AI to take these insights and enable digital assistance at the point of need
  • Audience Discussion:
    • How social collaboration helps strengthen law firm information security:
      • Meredith Williams (CKO, Baker Donnelson) noted that phishing is one of the biggest information security vulnerabilities for law firms. Often the dangerous emails masquerade as internal emails. (She estimated that 20% of emails are purely internal.) If you move those internal conversations into a social platform, you reduce the number of emails that can be used for phishing schemes.
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#ILTACON from the Bridal Suite

2016_ILTACON_logoPeople attend the International Legal Technology Association’s annual conference (ILTACON) for a variety of reasons. Whether you are looking for outstanding educational sessions, insightful conversations with your peers or informative encounters with leading technology vendors, ILTACON has it all.

In prior years, my strategy has been to attend as many educational sessions as possible so that I can drink from the amazing firehose of useful information provided at ILTACON. (I have then tweeted or blogged those sessions so the rest of you can benefit from those sessions as well.)

This year, however, I found that instead of sitting in the scheduled educational sessions, I was sitting in the Bridal Suite.

To set the record straight, I was not in the Bridal Suite for any reasons having to do with a wedding. Rather, the Bridal Suite was ILTACON TV’s studio and I was fortunate enough to be one of the interviewers. This meant that I had the privilege of participating in wide-ranging conversations with some of ILTA’s impressive thought leaders.

If time permits, I’ll be revisiting those interviews and blogging some of their key content. In the meantime, here are links to the interviews I conducted that are now available. (I’ll update this post as more interviews become available.) Plus, there is a bonus link so you can learn from the conversation between Todd Corham (CIO, Saul Ewing) and Jeffrey Brandt (CIO, Jackson Kelly).

John Alber (ILTA’s Futurist, formerly Strategic Innovation Partner at Bryan Cave):

ILTACON 2016 – ILTACON TV – John Alber from ILTA on Vimeo.

 

Katie DeBord (Strategic Innovation Partner, Bryan Cave) & Jay Hull (Strategic Innovation Partner, Davis Wright Tremaine):

ILTACON 2016 – ILTACON TV – Katie DeBord and Jay Hull from ILTA on Vimeo.

 

Michelle Mahoney, Executive Director, Innovation, King & Wood Mallesons):

ILTACON 2016 – ILTACON TV – Michelle Mahoney from ILTA on Vimeo.

 

Chris Emerson (Chief Practice Economics Officer, Bryan Cave):

ILTACON 2016 – ILTACON TV – Chris Emerson from ILTA on Vimeo.

 

Keith Lipman (President and Co-Founder, Prosperoware):

ILTACON 2016 – ILTACON TV – Keith Lipman from ILTA on Vimeo.

 

David Michel (Director of Technology Services, Broad and Cassel):

ILTACON 2016 – ILTACON TV – David Michel from ILTA on Vimeo.

 

Lida Pinkham (Technology Training Manager, Ice Miller):

ILTACON 2016 – ILTACON TV – Lida Pinkham from ILTA on Vimeo.

 

BONUS: An interview between Todd Corham (CIO, Saul Ewing) and Jeffrey Brandt (CIO, Jackson Kelly):

ILTACON 2016 – ILTACON TV – Jeffrey Brandt from ILTA on Vimeo.

 

 

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When Clients and Law Firms ACTUALLY Collaborate #ILTACON

2016_ILTACON_logoSession Title: A New Approach to Aligning the Objectives of Outside Counsel, In-House Legal, and Corporate Business

Session Description: The past few years have brought a lot of discussion about how to better align the interests of law departments and their outside counsel through alternative fee arrangements, but the discussions generally end there. What if there was an approach that aligned outside counsel and legal departments in their pursuit of better business outcomes that extended beyond pricing? How can the strength of that relationship help demonstrate the value that the legal department brings to the organization as a whole? Come hear a case study exploring how one legal department and its panel of law firms have partnered differently and how their holistic approach to solving legal problems has the power to transform the way the department delivers value to the business.

Speakers:

  • Chris EmersonDirector, Practice Economics Bryan Cave, LLP
  • Bryon KoepkeSVP, Chief Securities Counsel Avis Budget Group, Inc.
  • David A. RueffShareholder and Legal Project Management Officer, Baker Donelson Bearman Caldwell & Berkowitz

Session Slides: Available through the ILTACON website

[These are my notes from the International Legal Technology Association’s 2016 Conference. I’m publishing them as soon as possible after the end of a session, so they may contain the occasional typographical or grammatical error. Please excuse those. To the extent I’ve made any editorial comments, I’ve shown those in brackets.]

NOTES:

  • The Avis Story.  They undertook a convergence effort to reduce their legal panel from 700 law firms to seven firms globally. The winning seven firms are guaranteed work, provided they maintain expected quality levels. In addition, these firms work within a “target” (or fixed) fee structure. This convergence provided several benefits for the Avis legal department and their related business clients:
    • reduced administrative burden
    • cost certainty
    • legal risk mitigation
    • increased efficiencies in legal services
    • improved business outcomes
  • The New Way in which Avis wanted to work with its panel firms:
    • focus Avis resources on tiers of work that were created based on business risk and complexity
    • foster collaboration between Avis and its panel firms, as well as among the panel firms themselves
    • provide incentives to panel firms to invest in innovations that result in better business outcomes for Avis
  • The Work. Avis did a wide-ranging risk assessment and then asked their panel firms to bid on the work. Avis identified 3 categories of work:
    • Cream — high-risk work that requires high levels of legal expertise
    • Core — moderate-risk work that requires moderate levels of legal expertise
    • Commodity — low-risk work that does not require much legal expertise
  • Activities in Preparation for RFP. Avis asked 130 law firms to complete a “pre-qualifer” (PQ) that was quite similar to a law school exam. The questions in the exam reflected the real issues Avis faces. Each question was multi-disciplinary.
    • Law firms had a tight timeframe (about 10 days) within which to respond. Plus Avis sent this challenge out during Spring Break, which put added pressure on the law firms. This was a way of gauging responsiveness.
    • Of the 130 firms invited, only 80 responded.
      • 50 firms did not respond. Some thought Avis was trying to get free legal advice. They were wrong; Avis already had the answers to the questions in the PQ.
      • Some of the firms that did not respond thought their relationship with Avis was so solid that they did not need to go through these hoops. They were wrong; they were eliminated from the Avis panel
    • Some of the firms provided responses that simply were wrong.
    • All firms were asked to tell Avis how they would staff these matters and what they would charge.
    • Avis also asked how they answered these questions, whom they involved?
  • The RFP. They invited 45 law firms to participate in the RFP. (This was 45 out of the 80 firms that responded to the PQ.) There were three areas of emphasis:
    • Legal expertise
    • Pricing
    • Universal Requirements (Operations) — focused on actual examples of innovations these firms had developed to better the firm or outside clients. According to Avis, they were looking for Jetsons firms (firms that were innovative on behalf of themselves and their clients), not Flintstones firms that are stuck in the Stone Age.
  • How did the Firms respond to the RFP?
    • They researched the business goals of Avis so they could align their responses better to Avis’ needs
    • They managed tight turnarounds on drafts of the RFP as they involved a wide range of firm personnel in the RFP process in a very short period of time
    • Bryan Cave took a divide-and-conquer approach. They put the legal questions in the hands of the lawyers and kept the Universal Requirements in the hands of the legal operations team. The Bryan Cave legal ops group had the expertise to discuss the range of technologies they had invested in (or were contemplating) to improve firm and client outcomes, as well as completed or contemplated process improvement efforts.
  • The Semi-finals. During the semi-finals, Avis invited 17 firms to meet with the company. Each firm was asked to bring four partners of their choice and their legal operations person. During the interviews, the partners expected to lead the conversation. Instead, Avis said they would review their slides later. Then Avis asked to begin the conversation with the most important person in the room — the legal operations person.  (Partner jaws dropped!) Avis started with legal ops because they were serious about understanding the technology and innovation potential of each firm.
  • The All-Star Team. Avis invited the final seven firms to a Summit at which they met with business and legal department leaders of Avis. At that meeting, Avis made it clear that the chosen firms were stars that now had to find ways to work together as if they were on an All-Stars Team. This meant not just solo excellence, but collaborative excellence as well.
  • The Legal Ops Bounce. Crucially, the legal ops folks from the law firms met with the legal ops folks from Avis. This combined client/firm legal ops group has unleashed powerful tools and methodologies for the benefit of Avis (and the panel). Further, the emphasis that Avis has placed on legal ops gave the law firm legal ops teams greater confidence and enthusiasm in the work they do.
  • Avis Success Factors. Panels are graded on Key Performance Indicators (KPIs). These KPIs are assessed on a matter-by-matter basis and, in the aggregate, feed quarterly and annual performance assessments. Here are the KPIs:
    • how well a Matter Assessment Form (MAF) was filled out. (The MAF helps the firm and Avis scope out a potential matter very quickly.)
    • submission of MAF in 3 days
    • the number of changes in scope requested per matter
    • the firm’s ability to accurately predict legal spend and outcome
    • the ability of the firm to avoid surprises to business units
    • how the firm uses technology to improve accountability and efficiency
    • value-adds the firm provides to the Avis engagement
  • How Baker Donelson revised its processes to meet the Avis KPIs:
    • They created an intranet Client Site that tracks the Avis engagement
    • They created workflow to ensure they can turn around an MAF within the required 3 days. This workflow is managed via their  Avis client site
    • They use budget and project monitoring tools internally so that they can notify Avis before something happens. This allows them to meet the critical KPI of avoiding surprises.
    • They created workflow to manage changes in scope and budget
    • They developed an external communication plan for the Avis engagement
      • monthly case management updates
      • quarterly reports to in-house counsel
      • annual reports
      • how to deal with emergency issues
    • They developed an internal communication plan for the Avis engagement
      • phase and task code requirements for matters
      • training regarding the initial budgeting, MAF and change processes
      • training regarding regular updates on budgets and contingent liability
      • training on and communication of Avis’s outside counsel guidelines
  • How Bryan Cave has invested to improve the Avis engagement:
    • they developed new internal processes and technologies
    • they trained attorney teams on this new way of working
    • they created and provided to the Avis legal department training on alternative fee arrangements (AFAs):
      • how law firms construct AFAs
      • the types of AFAs and their typical uses
      • how to frame AFA requests to obtain responses that support business objectives
    • they worked with the Avis legal department to build a dynamic technology platform that
      • facilitates the MAF process for Avis and for all panel firms
      • capture critical data points in structured format
      • leverage workflow tools to enforce operational standards
      • integrate with Avis’ e-billing system to automatically open matters
      • display actionable information to all users via flexible dashboards
      • provides dynamic authoring tools to create/update forms within minutes/hours rather than days/weeks
      • stores information in structured databases, but can generate documents in formats attorneys are used to reviewing (e.g, Word or PDF)
    • Who reviews, tests and suggests improvements to the technology?
      • Bryan Cave engineers, business analysts and other operational professionals do the initial work
      • Avis attorneys and legal ops professionals advise on integrating the panel’s technology with Avis’ e-billing, advanced workflow reporting and alerting, dashboard structure and key metrics
      • Baker Donelson (and other panel teams) provide recommendations on U/I enhancements and how to integrate the shared technology with the proprietary technology platforms of the panel firms — this eliminates duplication of effort and strengthens their shared common sources of record
  • The Collaboration is Growing.
    • Now panel firms share Avis work with each other if they believe this approach will benefit the client.
    • If Bryan Cave creates new technology, Baker Donelson  will do acceptance testing. When Baker needs automated data feeds, Bryan Cave provides it. Both firms confer with each other (and the other firms) to find solutions that benefit the client.
    • The collaboration among the panel firms has generated new ideas and approaches to matter intake and AFA construction
    • The technology used by the panel firms has improved because these firms now have a reason and the ability to share ideas as never before
  • Next Steps. Both Avis and its panel firms have ambitions for growing and improving their collaboration.
    • On Avis’ list of next steps: Creating metrics to measure and dashboards to communicate progress in key strategic areas of operations.
    • On the panel firms’ list of next steps: Creating metrics to measure and dashboards to communicate progress by the panel firms in helping Avis manage its legal issues.
  • Results. This collaboration has been an unqualified success for  Avis and for its panel firms.
    • Avis: Thanks to the collaboration, the Avis legal department has now established itself as a critical business partner of the larger organization. Through its pioneering work in this collaboration, the legal department has modeled better ways of managing liability and expenditures that can now be applied across the company. Further, the work of the legal department has become a source of competitive advantage for the company.
    • Panel firms: Their experience with Avis has demonstrated how non-attorney professionals can be critical to the selection of the firm for a legal panel, as well as the on-going relationships between the firm and its client. The panel firms now have clear confirmation that their investments in innovation, project management, and process improvement have enabled them to differentiate themselves in a competitive market. Finally, these firms now see the benefits of not only collaborating with the client but also with the other panel firms. The Avis collaboration has become a significant win-win situation for these firms.
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Keynote: Measure Twice, Cut Once: Solving the Legal Profession’s Biggest Problems Together #ILTACON

2016_ILTACON_logoSession Description: Clients and law firms need to work together to solve the biggest problems facing them both, including the optimal overall data strategy for collection, hoarding, preservation, measurement and metrics; data we should collect today to help answer the questions of tomorrow; leveraging current technologies to improve data measurement and regularization and overall collaboration; measuring the performance of and determining fair value for legal work; how to create appropriate micro-incentives to further innovation; startup technologies being developed that are worthy of consideration; and where we’ll find the staff to manage it all. Together, we’ll measure twice, cut once and start solving legal’s biggest problems.

Speaker: Daniel Katz, Associate Professor of Law, Chicago-Kent College of Law.  Dan is a scientist, technologist and law professor who applies an innovative polytechnic approach to teaching law, meshing litigation and transactional knowledge with emerging software and other efficiency-enhancing technologies to help prepare lawyers for today’s challenging legal job market. His forward-thinking ideas helped earn him acknowledgement among the Fastcase 50, which “recognizes 50 of the smartest, most courageous innovators, techies, visionaries and leaders in the law.” He was also named to the American Bar Association Journal‘s “Legal Rebels,” a prestigious group of change leaders in the legal profession. Professor Katz teaches civil procedure, e-discovery and entrepreneurial lawyering at Chicago-Kent and spearheads new initiatives to teach law students how to leverage technology and entrepreneurship in their future legal careers.

[These are my notes from the International Legal Technology Association’s 2016 Conference. I’m publishing them as soon as possible after the end of a session, so they may contain the occasional typographical or grammatical error. Please excuse those. To the extent I’ve made any editorial comments, I’ve shown those in brackets.]

NOTES:

  • How to build a more perfect supply chain.  This requires that we move more parts of legal practice from the “art” column to the “science” column. This, in turn, requires that we measure our work more rigorously.
  • Economics of the law.
    • What’s a lawyer’s value proposition? What do lawyers solve for? If you get too far from that value proposition, you become irrelevant.
    • the value proposition:
      • help people manage complexity
      • help people manage enterprise risk
    • Lawyers as Complexity Engineers: In the face of growing legal complexity, we have applied greater and greater numbers of human experts to solve problems. (We throw more and more labor at the problem.) This, in turn, creates a huge opportunity for disruption. Do we really need all these people? Can some of what they do be done by a machine. Better yet, can we remove some of the complexity?
    • Where are the large-scale complexity-filled opportunities in law? Banks! They do very complex transactions and need increasingly efficient ways to cope with the legal complexity of their transactions.
    • Paul Lippe’s insight on three types of lawyers:
      • Mediocre lawyers play whack-a-mole, seeing legal risk around every corner
      • Clever lawyers find solutions to those legal risks
      • Great lawyers help design systems that can balance risk and then price risk correctly.
    • Why do we have law firms?
      • Every client has to decide whether to make or buy.
        • Law firms help offset peak load by helping law departments from having to staff up when unusal issues arise.
        • Firms provide high value by providing expertise that is rarely used or hard to acquire
    • The problem of agency costs:
      • If I am the principal and hire an agent to act on my behalf, the agent often has more information than the principal. This creates agency cost. Therefore, this relationship is always slightly antagonistic.
  • Moving from Artisinal to Industrial.
    • Across many industries, we are moving things from the artisan column to the industrial column. This requires standardizing and measuring business processes. The result is reliable and repeatable.
    • This does not need to mean a loss of quality. We do not need to move from being Sal’s artisanal pizza to Dominos. We need to produce artisinal- quality pizza with the economies of industrial processes.
  • Creating a Data-Driven Enterprise.
    • Our understanding of our processes is imperfect. We rarely appreciate how complicated they really are. We rarely see how simple they really should be.
    • The first step is process mapping. When process mapping, don’t make it so granular that it turns into a #ridiculogram.
    • Don’t merely aim for understanding the average cost/effort. Focus on the variability. Understand what moves things out of the left tail and into the right tail.
    • Transparency is the glue that builds relationships. But in the legal industry, we hate transparency. If we want real change in the industry, the transparency has to work both ways. Both law firms and clients must share their data. Only then will we have a clearer understanding of actual opportunities and risks.
    • Data-driven outcomes = using data to underwrite legal risk.
    • Data-driven transactional work = using data to determine the value of particular negotiating and drafting approaches. If we don’t know how much risk is being avoided or created, how can we choose the right approach?
    • We need a better understanding of the actual drivers of risk. A mediocre lawyer sees risk everywhere. A great lawyer has the data to explain the actual risks.
    • Human are good at pattern detection. A high-frequency trader may take only 60 seconds to identify a pattern, but in that minute the arbitrage opportunity will disappear because computers are faster.
    • Predicting case outcomes using data. When it comes to forecasting, there are only 3 ways to predict: experts, crowdsourcing, and algorithms.
      • However, “experts” don’t really need to be expert in law in order to predict well. Take the example of Jacob Berlove, an actuary who lives in Queens, New York, who is one of the best predictors of supreme court case outcomes.
      • Yet there is something that can outperform a high-performing individual. A Human + a Machine always outperforms either a Human OR a Machine.
  • Legal Analytics & Machine Learning as a Service (MLaaS). Law is a relatively small vertical. And it has a great diversity of expertise. Therefore, it is unlikely that the big players such as IBM will focus on the legal vertical. However, we can take IBM’s general offerings such as Watson and conquer the last mile, which is to figure out how to adapt it to the legal industry.
  • Fin(Legal)Tech.
    • If you are offering alternative fee arrangements, you are self-insuring because you are assuming the risk. However, most firms do so without the necessary data or models. Crazy!
    • Fintech is about removing socially meaningless friction and then characterizing and pricing exotic risks. Once we understand the data under the legal system and use that data to characterize and price legal risk, we will create Fin(Legal)Tech.
  • Practical Steps You can Take:
    • Improve your early case assessment. Collect and understand the data your have in your firm regarding litigation cases. Then build a predictive model.
    • Improve your transactional predictions. Collect markups on every deal document. Understand what they are and why they are done. Then you can predict the next negotiating/drafting tactic of opposing counsel. And you can assess the costs of every move.
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The Knowledge Supply Chain #KMWorld

KMWlogo_Stacked_Session Description: The business value of knowledge is to enable the knowledge worker, and support them in making the most effective and efficient decisions. Knowledge is as much a raw material for the knowledge worker as parts and tools are for the manual worker. We can therefore think of KM as being the supply chain for knowledge, providing just-in-time knowledge to support  the front-line knowledge worker. This allows us to take models and insights from other supply chains in order to improve how KM works, including the “elimination of 7 wastes” from Lean Supply Chain theory, and the clear focus on the knowledge user.  Hear about the supply-chain view of KM, its implications, and ways to develop and/or improve a KM Framework.

Speaker: Nick Milton, Director & Founder, Knoco Ltd

[These are my notes from the KMWorld 2015 Conference. I’m publishing them as soon as possible after the end of a session, so they may contain the occasional typographical or grammatical error. Please excuse those. To the extent I’ve made any editorial comments, I’ve shown those in brackets.]

NOTES:

  • Slide Deck
  • Peter Drucker.  The biggest management challenge of the 20th century was to increase by fifty times the productivity of manual workers in manufacturing. The biggest management challenge for the 21st century is to increase by fifty times the productivity of knowledge work and knowledge workers.
  • Manual Work Productivity.
    • The manual worker. Nick’s grandfather was a blacksmith, a manual worker, a craftsman.  When he made something, he made every part of it. His workmanship was superb.
    • The manufacturing worker. Management achieved a productivity increase in manual work by moving from the solitary manual worker to the manufacturing worker who made only one part of a finished product. By dividing labor, factories could make products significantly faster.
    • Other causes of increased productivity in manual work:
      • mechanization
      • a ready and available supply of the materials needed to create the product
  • Knowledge Work Productivity.
    • The knowledge craftsman is the expert who knows it all
    • The knowledge worker.  Today, however, the knowledge worker no longer needs to know it all. Knowledge is held collectively by the community and the network. In fact, an expert is almost always outperformed by a network. This is the knowledge work equivalent of improved productivity through the division of labor. However, in this case, it is a division of knowledge.
      • this requires a cultural shift = a fundamental change from knowledge as personal property to knowledge as collective property
        • this is challenging to some people because they believe that knowledge gives them worth and security
    • Automation: The knowledge equivalent of mechanization is automation.
    • Knowledge supply change — if you no longer own/have all the knowledge you need, then you need a reliable supply chain that gives you the knowledge you need when you need it.
  • Lord Browne of Madingly
    • Lord Browne was a former CEO of British Petroleum.
    • In Unleashing the Power of Learning (an interview published in the Harvard Business Review), he stated that if a company wants to gain and keep a competitive edge it must learn better than its competitors and then must apply that knowledge faster and more widely than its competitors do.
    • In the same interview he also stated that anyone who is not directly involved in profit-making activities should be fully occupied in creating and sharing knowledge that the company can use to make a profit.

Knowledge Supply Chain.

A supply chain is “a sequence of processes involved in the production and distribution of a commodity.” A knowledge supply chain is a sequence of processes involved in the production and distribution of knowledge. In other words, a knowledge supply chain provides “the right knowledge at the right time to the right people, so they can make the right decisions.”

To have an effective knowledge supply chain, we need the following:

  • We need a set of knowledge processes:
    • Knowledge creation
    • Knowledge capture
    • Knowledge synthesis
    • Knowledge seeking
    • Knowledge application
  • We need the related knowledge roles.
    • Knowledge managers
    • Knowledge engineers
    • Practice owners
    • Knowledge workers
  • We need the supporting technology .
    • Lessons learned management systems
    • Community portals
    • Discussion
    • Knowledge bases
    • Search engine
  • We need Knowledge Management Policy = Governance for this set of processes, roles and technologies.
  • Attributes of good supply chains:
    • They are user-focused (focused on the profit-maker)
    • Everyone in the organization is committed to this system
    • The supply chain must be reliable — when someone seeks knowledge, it should be there
    • The supply of knowledge should be high quality
    • Efficient
    • Pull-driven
    • Lean
  • Lean = a systematic method for the elimination of waste within a manufacturing system, and a focus on value add
    • Waste #1 = overproduction:
      • Info overload
      • Technology complexity overload
      • Producing more than and /or ahead of demand = a massive oversupply of knowledge
      • Davenport & Prusak, Working Knowledge: “Volume is the friend of data and the enemy of knowledge.”
    • Waste #2 = waiting = clock speed = the speed of learning
      • Waste = knowledge that is waiting to be used
      • Huawei has the Rule of 3 Ones:
        • you should be able to find something in one minute
        • you should get an answer to a question in one day
        • you should circulate new project knowledge within one month of the close of the project
      • Read Atul Gawande’s Checklist Manifesto
        • they use checklists to speed learning
    • Waste #3 = unnecessary transport = unnecessary steps or handoffs
      • This usually is the result of too much bureaucracy/hierarchy
      • You can eliminate this by allowing people to connect directly/horizontally with each other
    • Waste #4 = inappropriate processing = doing more work than is necessary
      • When knowledge is in a jumble, everyone who needs that knowledge will need to sift it and sort it every single time. The way to eliminate this form of waste is to sift and sort the content once on behalf of everyone.
    • Waste #5 = unnecessary motion = going to multiple places to get your knowledge
      • Some organizations have too many collaboration tools (e.g., yammer, jive, slack, etc.) — this is waste
      • In some organizations, every division has its own lessons management system
      • Schlumberger has provided only one tool for each knowledge function. They built a successful expertise locator. Later, when they deployed SharePoint, they turned off MySite because they believed it would function as a duplicate expertise locator.
    • Waste #6 = excess inventory
      • A lessons management system is helpful provided it has just enough lessons to cover the work being done. One lesson on an issue is good. Ten lessons may be better. However, 100 or 1000 lessons constitute an oversupply. A knowledge worker will never be able to read and apply all of them.
      • Don’t give users too much — give them just enough. Overproduction constitutes waste.
    • Waste #7 = defects = the cost of wrong knowledge
      • this arises when you fail to clear out of your knowledge systems old or outdated materials
  • Best approach to lessons learned
    • Complete the project or activity
    • Identify, document, store the new lessons learned, best practice, cases
    • Review, validate, take action >> update the practices and training
    • Access the database and apply lessons learned
  • The Knowledge Supply Chain
    • Raw materials = experience
    • Supplier = team members
    • Manufacturing = creation of lessons
    • Distribution = lessons management
    • Assembly plant = improved process
    • Consumer = knowledge re-use and application
  • How to incentivize knowledge seeking and re-use?
    • Make it easy
    • Promote success stories
  •  Questions:
    • If you view your own KM system as a supply chain, where is the waste?
    • How will you eliminate the waste?
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Transferring Critical Knowledge When Speed Matters #KMWorld

KMWlogo_Stacked_Session Description: This session looks at the rate of knowledge (i.e., expertise) transfer as a critical KM issue and shares research which looks at expertise transfer through the lens of personas. The creation and perspective of knowledge worker personas provides a breakthrough in the identification, construction, and delivery framework for expertise transfer. Well-established methods of knowledge sharing and transfer, such as communities of practice, provide the capability for expertise transfer, but they do not always address the concept of speed in their structure. Similar approaches, such as the transfer of best practices, or self-service models, also do not take into consideration the need for speed based upon a specific situation, a specific knowledge need, nor a specific role. The persona lens on expertise transfer ensures that all three perspectives are taken into account when designing a knowledge management framework and methodology. Speakers describe the thinking behind the persona perspective, and give attendees an opportunity to test their expertise transfer needs through hands-on experience.

Speakers:

Darcy Lemons, Senior Advisor, Advisory Services, APQC
Jim Lee, Senior Advisor & KM Practice Area Lead, APQC

[These are my notes from the KMWorld 2015 Conference. I’m publishing them as soon as possible after the end of a session, so they may contain the occasional typographical or grammatical error. Please excuse those. To the extent I’ve made any editorial comments, I’ve shown those in brackets.]

NOTES:

  • Slide Deck
  • Sustained focus on knowledge transfer
    • Their 2015 KM Priorities Survey results show that
      • knowledge capture/transfer is among the top 3 KM approaches to implement in 2015, with 37% of survey participants saying their organizations plan to create new processes to capture and transfer critical knowledge over the coming year.
      • approximately 48% already have knowledge capture/transfer processes
  • Known approaches to knowledge transfer.
    • knowledge capture/transfer approaches can range from systematic (formal, structured) to organic (informal, unstructured)
      • Formal KM approaches
        • knowledge elicitation interviews
        • knowledge mapping
        • retiree knowledge ransfer
        • knowledge continuity processes
        • after-action reviews
      • Learning & Development approaches:
        • lunch-and-learn
        • webinars
      • Content repositories:
        • content portals
        • wikis
        • blogs
        • Internal videos
        • lessons learned databases
      • Network-based approaches:
        • communities of practice
      • Person-to-Person approaches
        • forums
        • meetings
        • conversations at the watercolor
    • 3 key questions
      • Explicitness: How easily can the knowledge be captured?
      • Audience: Is the knowledge recipient known?
      • Stability: How fast is the knowledge evolving?
  • Techniques to support knowledge transfer.
    • APQC’s 8th KM Advanced Working Group
      • this group addresses new issues for which there are no established best practices
      • the group is made up of organizations and top KM experts
      • member organizations
        • ConocoPhillips
        • Phillips66
        • EY
        • Praxair
        • Intel
        • NASA
      • These organizations want to know about speed — how to transfer knowledge quickly
      • Setting the Scope
        • Who: when needs the knowledge? what role do they perform? What do they do?
        • What: what knowledge do they need? How complex is it?
        • How:
        • When: when do they need it?
    • They recommend a knowledge mapping approach
      • start with a known/agreed process
      • follow with a knowledge map (a simple spreadsheet)
      • They started with a process-centric knowledge map: 1st column is process steps, 2nd column is activity, 3rd column = what knowledge is needed, 4th column = who has the knowledge, 5th column = is it tacit or explicit…
        • chart the knowledge that is needed for each step of the process
          • what knowledge is needed?
          • who has it?
          • when is it needed?
      • Then they developed a role-centric knowledge map — defining the following items by Who  (person, group, team) and What (type of knowledge). Here are the column headings:
        • type of knowledge needed
        • rate of speed
        • created by whom
        • identified by whom
        • collected by whom
        • reviewed by whom
        • shared with whom
        • accessed how
        • used by whom
  • Determining HOW to transfer knowledge.
    • If you need it immediately
      • Methods: Yammer, Communities of Practice, videos, discussion groups
    • If you need it in the mid-term
      • Methods: Wiki, SharePoint library, webinars, lunch-in-learns, search and find
    • If you need it in the long-term
      • Methods: formal training, conferences, knowledge elicitation interviews
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