AI Tech + Humans = Improved Bottom Line #KMWorld

KMWlogo_Stacked_Session Focus:  Semantic AI — how assisted intelligence improves human productivity. Metadata drives insight.

Session Description:

The AI hype is rapidly exploding into C-suites of organizations around the world and with good reason; the promise is compelling. The convergence of AI, robotic process automation (RPA), machine learning and cognitive platforms allows employees to focus their attention on exception processing and higher-value work while digital labor manages low-value, repetitive tasks. While the debate as to whether digital labor will add or eliminate jobs is ongoing, what’s important in today’s enterprise is how digital and human labor can be integrated to improve efficiency and drive innovation. Using real-world examples, this session covers how machine processing, when guided by human knowledge, curation and control, provides assisted intelligence (AI) to organizations which want to streamline processes, reduce operating costs, and improve ROI.

Speaker:Jeremy Bentley, CEO & Founder, Smartlogic

See Also:Artificial Intelligence Hits the Barrier of Meaning” by Melanie Mitchell.

[These are my notes from the KMWorld 2018 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.]


  • How to Improve AI’s contribution to business value.  How semantic classification, harmonization, extraction, and enrichment makes AI work and deliver values.
  • What is real about AI?
    • the promise is real
    • the ability to automate higher-order work functions using digital rather than human labor at a fraction of the costs will disrupt global markets
  • What AI are we talking about?
    • Machine learning
      • Deep learning
      • Supervised/unsupervised
    • Natural language processing
    • Expert systems
    • Vision
    • Speech
    • Planning
    • Robotics
  • Myth #1. “You just give the machine the data; it learns and then delivers useful insight.”
    • If you are the machine, how would you look around the room and decide to classify/sort it?
      • a group of humans
      • organized by gender, hair color, handedness, whether people are wearing glasses
    • Whether you are a human or a machine, you need context in order to understand the data you are observing.
    • METADATA helps convey context. This is what machines need to gain and provide insight.
  • Myth #2. “We will  quickly assemble the training set, then pour it into the machine and it will deliver actionable intelligence.”
    • it’s actually not just one set:
      • a training set
      • a validation set = a control to compare the resulting output
      • a testing set = to test the output
    • You need to create this trio for each data set in which you are interested.
    • A one-dimensional classification is binary. It separates data into two groups.
      • if you ask the machine separate the humans into two groups: one group = people wearing black or white shirts, the second group = people wearing all other colors.
      • to do this exercise, the machine must first:
        • exclude everyone who is not wearing a shirt
        • learn the concept of color and how to differentiate among colors
        • learn the concept of shirt and how to deal with blouses and tops
    • It takes a lot ot time, effort, and specialist knowledge to assemble these training sets. The more data dimensions you include in the training set, the more likely you will need to include a mathematician in your team. This is another barrier between the business experts and the machine.
  • Myth #3. “Machines are going to be autonomous decision makers soon.”
    • Today, AI is not mature enough to provide full authonomous decision support
    • It can automate the next level of repetivive work, which is compelling from a cost and efficiency perspective
  • Takeaways.
    • the fewer dimensions, the better the quality of the machine’s output
    • subject matter expertise is paramount
    • humans provide knowledge and context
    • meaning is referenced from ontologies and then encoded in metadata
    • there should be an interplay between human and machine
    • we should play to respective strengths: humans do creative work, machines do repetitive work
    • metadata is how you encode context for the machine
  • AI Business Value Continuum.
    • Automated Intelligence — this is increasingly common
    • Assisted Intelligence — we have begun using assisted intelligence
    • Augmented Intelligence — we should have achieved this in the next 10 years
    • Artificial or Autonomous Intelligence — this won’t happen any time soon

Engaging Lawyers, Clients and Vendors to Adopt AI into Workflows

This session focuses on the White & Case LLP Practice Innovation Group and how it engages lawyers, clients, and vendors to adopt AI into workflows.


  • Oz Benamram, Chief Knowledge Officer, White & Case LLP
  • Monet Fauntleroy, Senior Manager, Practice Innovation, White & Case LLP

[These are my notes from the 2018 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.]


  • Drivers
    • Improving client service
    • Managing peer pressure
    • Finding global solutions
    • Solving a real problem
    • Engaging associates
  • Current Use Cases
    • Document review (eDiscovery, due diligence)
    • Contract analysis
    • Drafting automation
    • Legal research
    • Matter analytics
  • It takes a LONG time
    • Training the machine typically takes 6 months.
    • In law firms, machine training takes an additional 6 months. There will be a further 6 months if lawyers need to do the training.
  • Learnings
    • The more the lawyers understood what the tool could do the better they could help.
    • Don’t fall into a people-pleasing mode that causes you to stretch the tool to do an extra 20%.
    • Strike the right balance in involving partners in the effort. Sometimes associates are a little closer to the workflow.
    • Create an onboarding process early. Make sure you involve the right people from the vendor (e.g., a success lead)
    • Create appropriate workflows to support the innovation (e.g., can you handle questions through the existing help desk ticketing system?)
  • Innovation
    • When clients say “innovation” they mean “exactly the same, but faster and cheaper.”
    • When KM says innovation, they understand that there is a significant risk of failure.
  • Governance
    • Taxonomy may not be sexy but it is critical for good governance maintenance
  • Awareness
    • External awareness: as a community we should talk to each other and to our communities about the real story behind the tech press releases.
    • Internal awareness: you need to keep making internal presentations about your existing tools and services. This is particularly the case with your AI efforts. The lawyers and clients want to know.
    • Client awareness:
      • bring in exciting speakers and invite clients
      • host ideation days for your clients — this give you insight into client problems and frustrations

Session Description:

Oz Benamram, will walk through the Firm’s Practice Innovation team journey to implement LawGeex, an AI-empowered tool for reviewing contracts. The challenges included introducing a new type of client and use case to the vendor (who was used to dealing with in-house counsel) as well as keeping the legal team excited about the project throughout the vendor selection, training and evaluation phases… not to mention ultimately changing how the team works.


Avoid the Shiny Objects

Avoid the Shiny Objects–  Why the Use Case is More Important than the Technology

Key Takeaways:

  • Be as proactive as possible in a reactive environment.
  • Focus on the goal not on the tool.


  • Scott Rechtschaffen, Chief Knowledge Officer, Shareholder, Littler Mendelson P.C.,
  • Chris Boyd, Chief Knowledge & Talent Officer, Wilson Sonsini Goodrich & Rosati,
  • Richard E. Robbins, Director of Knowledge Management, Sidley Austin LLP,
  • Anand R. Upadhye, VP of Business Development, Casetext

[These are my notes from the 2018 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.]


  • Use Case: A software and engineering term that descibes how a user will use a system to accomplish a goal.
  • Don’t fall for the Buzzwords
    • Don’t be snowed by the slick sales pitch filled with buzzwords
    • Keep your vendors honest — ask them to unpack their sales pitch, explain what their technology really does
    • You and your team will have to drive adoption. Make sure you are ready to make your pitch for this tool with your lawyers. And make sure you are ready to partner with this vendor for a while.
    • The best vendors are not just vendors — they are partners who help you lower costs and improve outcomes.
  • Be Proactive
    • YOU are the one who should define the appropriate use cases
    • YOU should think early about what you should be doing for your firm and what your firm should be doing for its clients.
    • Practice group plans and firm strategy are the best sources for important use cases.
  • Build vs. Buy Decisions
    • These seem to be highly cultural. Some firms really believe that they can do the best job. Other firms say that they are not in the business of creating and maintaining software, they believe others can do it better.
    • Remember that you don’t always use what you have.
    • Once you have identified your use cases and prioritized them, see whether you can use a pre-existing tool. If not, see whether you can buy and then later build, if necessary.
    • Littler has a neither build nor buy option:
      • Their Knowledge Desk required neither a build nor buy decision. They used existing personnel to provide a knowledge concierge service.
    • Wilson Sonsini has a neither build nor buy option:
      • They have created a knowledge base populated by pardon-the-interruption emails. They tweaked Outlook to create an automated outbound/inbound email to collect/distribute questions and answers.
  • Takeaways
    • Before you contact vendors, know what you need to accomplish.
    • Avoid solutions in search of problems at all costs.
    • Hold yourselves accountable for your vendor arrangements. If you haven’t demonstrated adoption, then get out of the contract.
    • Focus relentlessly on the goal, not the tool.
    • Be proactive not reactive.

Session Description:

Avoid the Shiny Objects: Why the Use Case is More Important than the Technology

Law firms and those responsible for driving innovation within have been inundated with pitches and proposals from legal tech companies, particularly start?ups, offering them the latest and greatest solutions to their latest and greatest problems. But what are those problems and how credible are those solutions? Which legal technology solutions are truly valuable and address real?life problems and which are nothing more than concepts, or worse— vaporware that neither exist nor solve actual problems? This lively discussion will assist those responsible for driving innovation within their firms to better understand the importance of identifying their most pressing use?cases before deciding upon a particular legal technology solution. Choosing a technology solution before identifying the use?case will always be like choosing a solution in search of a problem. Law firm innovators are now confronted with a myriad of legal technology solutions and could greatly benefit from a paradigm for helping choose between multiple technology options. In this discussion, panelists will cite specific examples of use?cases and the technology solutions adopted.


Assessing the Value of AI and Other Technology to the Law Firm

This session examines key issues in the approach law firms take to techology projects, whether sexy AI projects or more mundance technology infrastructure projects.


  • Philip Bryce, Global Director of Knowledge Management, Mayer Brown,
  • Kingsley Martin, President & CEO, KMStandards LLC and Chief Contract Scientist, Akorda,
  • Patrick Dundas, KM Associate, Schulte Roth & Zabel LLP, Dean Sonderegger, VP Legal Markets, Innovation, Wolters Kluwer Legal and Regulatory US

[These are my notes from the 2018 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.]


  • Comparing Law Firms and Technology

    • Kingsley Martin:
      • Following Susskind, we are supposed to be moving from bespoke to standardized work product.
      • Lawfirms tend to focus on low-volume, high-margin, bespoke work.
      • LegalZoom focuses on the high-volume, low-margin standardized work.
      • Kingsley Martin is focused on the b2b market, where he needs to provide highly efficient work product to businesses and governments. This is a space that most law firms are ignoring
      • Meanwhile, technology is moving from broad-based to sophisticated and nuanced. In other words, moving from standardized to customized.
      • He codes in multiple computer languages, however, he no longer codes. Rather, he trains computers to code as needed. This is the more efficient approach. Machine learning is faster than human learning. Law firms have not figured this out.
  • We operate in a trust model
    • Kingsley Martin:
      • Clients come to us because they trust us. But we don’t have good metrics based on good processes that can validate their trust.
      • We know that we need to move to simpler processes and documents if we want to take full advantage of techonological opportunities. However, there is real resistance to simplification within law firms.
        • This resistance is based on their perception of risk.
        • They believe that a simpler process/document may entail greater risk. However, they do not always have reliable data to support this perception.
        • So it is critical to take a closer look at the true risks involved and then quantify those risks.
        • Then you need to addess those risks. Some firms are moving to insurance/self-insurance models to cover those risks.
  • Prioritizing Projects
    • Phil Bryce
      • How to choose AI solutions?
      • Remember the difference between point solutions and platforms. And keep in mind the critical integration points between your existing technology and the new tools.
        • They must work together seamlessly.
        • Don’t be so enamoured by a new tool that you allow it to create a content silo unconnected to the rest of your tech resources.
      • He uses the classic McKinsey Value vs. Effort 2×2 grid to prioritize projects.
        • He works this grid with his management committee. They have a conversation about the placement of the various projects on the grid and then agree on the final placement of the projects. As part of this conversation, they agree which projects will be done first and which ones likely won’t be done.
        • He works this grid with the partners who supervise practice support lawyers (PSLs) to ensure they are aligned on the PSL’s priorities.
  • How should you choose your tech vendors?
    • Patrick Dundas
      • Most firms choose vendors on the basis of a good demo. However, demos almost always go well. That’s the point!
      • A better approach:
        • Start with a clear and well-articulated understanding of the problem you are trying to solve, as well as the associated requirements.
        • Get a good list of the relevant stakeholders. Understand their specific needs.
        • Be clear about your desired timeline.
        • Ask for RFPs from vendors.
          • Ask them to respond to your matrix of requirements. Their ability to do so appropriately is a good early signal of their ability to work well with you.
        • Evaluate vendor responses
          • How well did they respond?
          • Highlight/flag the responses that distinguish particular vendors
        • Check vendor references — be sure to use a script for these conversations to ensure you cover the key points.
        • Joshua Fireman recommends that you record your demos and your technical deep dives. This will help you remember the details.

Session Description:

In today’s legal tech environment, Knowledge Management professionals have a rich tapestry of tools to choose from to help drive firm success. Each vendor typically will provide ROI calculations as part of the sales pitch, but the onus still sits with the firm to choose wisely in a budget (both me and cost) constrained world.

This panel will explore the technology value chain—from efficiency to outcome to optimization of business processes, the characteristics of each step in the value chain, where different types of solutions fit, and the impact to the firm from different categories of solutions.


What Blockchain Can Teach Legal About Service Models

John Alber believes that law firms are headed to extinction. Drawing from patterns in nature, he sees similar patterns in law firms. He is concerned that there are very few inflection points at which law firms can adapt sufficiently to lead change. He suggests that knowledge management professionals can find a path to useful change by learning from the example of blockchain.

  • John Alber, Practical Futurist, Intitute for the Future of Law Practice.
  • A detailed session description is at the end of this post.

[These are my notes from the 2018 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.]


  • How serious is the Extinction possibility?
    • Looking at nature, we see that species die but sometimes leave behind elements that can give rise to new species. In the law, practices evolve and die. Sometimes they die but leave behind elements that can spawn a new practice. Often they die before they can be replaced by vibrant new practices. Without the option of adaptive practices, law firms will die.
    • Document review used to be the sole preserve of law firms. Now LPOs are taking over that business and there is no obvious substitute business for law firms.
    • A big clue about the potential for extinction — look for ways of doing things that have not materially changed for a long time. In Alber’s view, the legal industry’s approach to contracting is exactly this kind of extinction-ready practice.
  • Nick Szabo:
    • Nick Szabo is an earlier mover in blockchain. He is a computer scientist, legal scholar and cryptographer known for his research in digital contracts and digital currency. (ome believe that he is really Santoshi Nakamoto.)
    • He developed the concept of “smart contracts.” He has analyzed deeply what contracts are, how they work, and how they could optimally be digitized.
    • For him, building contracts on blockchain makes the most sense.
  • Benefits of Blockchain-like Tech for Contracting

    • Institutionless: it does not depend on whether we trust the institution (or law firm) involved. It exists viably separate from specific institutions.
    • Collective: moves away bespoke contracting to contracting by a collective consensus. This leads to less variability and more predictability in the contracting process.
    • Rules-based rather than words-based: this makes it easier to digitize the contracts.
    • Simple: we cannot digitize our contracts without first simplifying them.
  • Peter Drucker Wisdom:
    • “In a period of upheaval, such as the one we are living in, change is the norm. … But unless an organization sees that its task is to lead change, that organization … will not survive.”
    • Law firms are ignoring the fact that they need to lead change in the legal industry. They are too focused on the work of today so they seem to ignore the work of tomorrow.
  • How do we get the necessary skills?
    • Think about design-thinking differently. It is a super-skill to acquire.
      • Take a course, do some reading, get smarter about design-thinking.
    • In his view, design-thinking goes beyond the user interface, it goes beyond making things “pretty”.  Its true value is that it helps us understand more deeply the nature of the problem.
    • Once you have a better understanding of the problem, then work to gain influence in your firm so that you can share your understanding and move the firm toward sensible change.
  • KM Professionals Could be Influential
    • We are interdisciplinary so we have a broader view of the problems and possible solutions.
    • However, we need to move beyond thinking of ourselves experts in library sciences. Otherwise, we will not be able to make an impact on our firms.
    • We cannot afford to be passive.
  • Others are innovating while law firms are largely stagnating
    • There are lots of new legaltech vendors and new legal providers that are innovating technology and processes.
    • They are moving at a much faster pace than law firms are.


Session Description:

Blockchain is all the news now in legal. It is said to be transforming trust rela onships in everything from land tles to securities transactions. And smart contracts are the talk of the town. But shouldn’t blockchain also teach us something about what we missed along the way? How we record, transact and enforce agreements has been a constant almost since the inception of the common law. Yet we let the digital age be born and grow to maturity without ever considering that perhaps our paper?bound and extraordinarily inefficient service model for managing agreements might need changing. It took computer scientists to reimagine how to make agreements concerning digital assets. With the digital age exploding around us, what else about the law needs reimagining? Everything?


Robotic Process Automation: What CIOs Need to Know #ILTACON18

Session Description: Robotic Process Automation (RPA) gives CIOs the chance to help their firms rethink its business model. Beyond the cost savings, automation offers high value in the form of improvement in process efficiency, cycle time, productivity, quality, scalability, and governance and regulatory compliance. The value is easy to understand but there are important things to know as you move to automation in order to get it right and achieve the expected value. This session gives perspective on the value, goals, and best practices of RPA.


[These are my notes from the International Legal Technology Association’s 2018 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.]


  • What is Robotic Process Automation?  Software that can be easily configured to do basic tasks across applications just as human workers do. RPA software is designed to reduce the burden of repetitive, simple tasks on employees. (Source: Investopedia)
  • It automates the actions of everyday users.
    • carry out repetitive processes within applications
    • configured by business users (no development or coding required!)
    • scalable workforce to meet variable demand — you can build more bots to satisfy increased workload, you can take them down when workloads decline
    • work within existing IT infrastructure — no integration required — just trigger a bot by emailing that specific bot (they each have their own email addresses at Seyfarth.)
  • What does RPA look like?
    • Every RPA implementation is different but there are common elements:
    • central management software for bots: blue prism, automation anywhere, UI path
    • Built for processes (time-consuming repetitive tasks)
    • Tackle time-consuming repetitive tasks
    • Bots do more than a macro/script — they can tackle an entire process
    • You get the most value when you deploy bots on an organization-wide basis. (You may want to start within a department first.)
    • You can create off-the-shelf bots or custom bots; you can layer bots on top of each other.
  • How do you identify and measure ROI?
    • Any high-volume, business-rules driven, repeatable process qualifies for automation
    • ROI factors
      • Processing time — start time/end time of a process
      • Productivity — length of time a human worker versus a bot takes to complete the task/process
      • Reduction of error rates — accuracy of bot output — neither bots nor humans are error-free but bots have a lower rate of error and can be stopped easily when they encounter trouble.
      • Redeployment — when bots can handle “reactive” processes, then the humans can focus on more proactive work
  • How is RPA different from AI. Automation technologies speed up or replace human decision making.
    • RPA and AI are on different ends of the continuum. RPA involves less complexity than AI.
      • On the RPA end = RPA and Rules Engine (where the rules are explicitly provided) — primarily works with structured data
      • On the AI end = machine learning (rules deduced by statistical techniques), natural language processing, deep learning, computer vision (using input from sensors) — primarily works with unstructured data
  • RPA is being used across all departments in all industries.
    • New business intake
    • Sending calendar reminders
    • Tax automation
    • IT asset management
    • Employee lifecycle (HR)
    • Finance/Accounting (help automate processes that transfer, aggregate, and report on data)
    • PDF creation for estate tax reporting purposes
  • Gillian Power: The inability of a bot to handles process ambiguity is an opportunity to clarify your process.
  • Seyfarth Shaw’s RPA experience.
    • Launched a RPA Center of Excellence. (This sits outside the IT department.)
    • They got the idea from seeing bots used in other industries and organizations
    • Deployed in Finance, Marketing, IT and Client-facing technology (e.g., extranet)
    • Utilized by various practice groups — initial proof of concept was in their immigration practice. They were able to convert a 25-minute human process into a 4-minute bot process.
  • Other things to consider
    • Security — the bots need credentials to get into your system so they are storing that information. What level of encryption protects this?
      • Be sure to work with your IT security team
    • On-going management, changes, staff, etc.
      • help the displaced humans shift to higher-value work
    • Negotiating strong agreements with vendor
      • work collaboratively with your IT department so you evaluate the new software and vendor in a systematic way
    • Protecting IP
    • Lessons learned

Blockchain 101: It’s not just cryptocurrency #ILTACON18 #G009

Session Description: It’s the big buzzword now, but what are the basics that you need to understand to evaluate blockchain as a technology platform for you and your firm or department? Join us to learn about what blockchain is and why it matters. Learn why the importance of blockchain for the legal industry extends far beyond cryptocurrency. We will provide guidance about resources you can draw on to learn more about blockchain and to explore and develop your ideas for use cases.

Slides: [will be available after the conference]


[These are my notes from the International Legal Technology Association’s 2018 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.]


  • What is Blockchain?  It is another form of a network comprised of software, servers and databases. (It’s just a bunch of code.)
  • Decentralized.
    • no central location for information — this isn’t running on Amazon Web Services
    • network = thousands or millions of computers and databases and users
    • uses the computing power of all computers to transmit (=speed) and store information (=volume)
    • If any one computer goes down, the network does not. Each computer is a “node.”
  • Immutable.
    • Information in a blockchain cannot be altered (except in specific circumstances) = it’s NOT immutable
    • it serves a largely permanent digital record of information
    • digital identity verification and authorization tool
    • provides transaction authenticity and a trusted transaction records
  • Public or “permissionless” blockchain.
    • completely open allowing anyone to join and participat (rea, send transaction to and expect to see them if they are valid)
    • to participate, all you have to do is download the relevant software. All this software is opensource.
    • the security of the information tends to be greater in the public blockchain than in a private blockchain
  • Private/hybrids or “permissioned” blockchain.
    • each of these have their own rules of the road
    • these rules determine how immutable the records really are — what proportion of members must agree before a record may be edited.
    • the smaller the blockchain, the higher the likelihood that it might fail
  • Differences.
    • Privacy
    • Scalability
  • Use Cases in the Legal Industry.
    • smart contracts — these are a series of “if, then” statements that automatically trigger agreed actions without further human action
    • financial services
    • supply chain management
    • identity management
    • voting — blockchain can help ensure one person/one vote by time stamping a record of voting in an immutable form
    • data/asset registries
    • any situation that involves a lot of data, a lot of parties (that may not trust each other completely), the need for accurate records of each transaction
    • early uses in legal
      • internal contract automation
      • contract and deal negotiaon (and auto-updating)
      • calendaring
      • document authentication
      • client identifiy management
      • transaction recordkeeping
      • automated billing
      • service of process verification
  • Examples of Platforms in Legal.
    • OpenLaw (running off Ethereum platform). They are hoping to create a GitHub for contracts
    • The Agreements Network
    • Intergra’s Blockchain for the Global Legal Industry
  • Legal Working Group Examples
    • Wall Street Blockchain Alliance
    • Ethereum Enterprise Alliance
    • Chamber of Digital Commerce — Smart Contracts Alliance
  • Government spending on Blockchain
    • States are moving to use blockchain for government and recording
    • Federal government blockchain spending is set to rise for the third straight year
  • Challenges — this is still a very young technology. The Bitcoin Whitepaper came out in 2008.
    • interoperability
    • regulations
    • scalability
    • energy
    • security risks
    • investment decisions
  • Legal Industry Impact
    • Delaware blockchain initiative — law allows creation/maintenance of corporate records on blockchain
    • West Virginia pilot tested election voting by deployed military — they are using biometrics to validate identify
    • Vermont law approves blockchain data as court admissible
    • Illinois Blockchain initiative
      • medical credentialing process project
      • blockchain in government tracker
      • birth registration pilot project
    • Clients in the Logistics Industry are already pursuing blockchain (Blockchain in Transportation Alliance)
    • Store deal records on the blockchain (not on CDs or in bound volumes)
    • Typical legal functions and the vendors/technologies that use blockchain to support these functions
      • Document management system – -NetDocs, Integra Ledger
      • Document assembly — Thomson Reuters Contract Express, Integra Ledger
      • Document templates for smart contracts — OpenLaw, Ethereum
      • Contract management using smart contracts — Monax’s Agreements Network
      • Document execution, existence – -Basno, Blocksign
      • Notary services — SilentNotary, Ethereum
      • Service of Process — ServeManager
  • Groups working on legal industry opportunities
    • Global Legal Blockchain Consortium (Association of Legal Administrators)
      • ALA has developed the universal process billing codes
      • Standards of Alliance for the Legal Industry (SALI)
    • OpenLaw — they are creating learning tools to help any lawyer develop smart contracts
      • this is a Consensus Project
    • Accord Project
  • Top 10 Industries impacted by Blockchain
    • Banking (FinTech)
    • Healthcare (e.g., processing insurance claims)
    • Government
    • Real Estate
    • Legal
    • Security
    • Politics
    • Rentals and Ride Sharing
    • Charities and Aid Organizations
    • Education
  • What are the Big Four Doing?
    • Deloitte says that if your company is not already looking at Blockchain then you are planning to fail
    • PwC has developed an audit tool for blockchain
    • Accenture is viewed as the  third largest blockchain vendor behind IBM and Microsoft
  • What are the prospects? Gartner says by 2030, this will be a $3.1 trillion industry

When IT is Dangerous to Your Health

If you have worked in an organization, you have undoubtedly dealt with its information technology or information services department. On good days, they provide the technology platform that allows employees to work efficiently. On bad days, when the system is slow, your computer is acting up or the software you must use is not intuitive, the IT department seems unconcerned with the practical realities of employee life. That’s when employees talk about about their Information “Dis-Services” department.

Inevitably, these disruptions of service lead to employee stress. And we know that too much stress can be bad for us.

But there’s another type of stress that may be even worse: the stress physicians experience when dealing with uncooperative IT. Why worse? Because stressed out doctors cannot provide the quality healthcare we need.

How bad is this problem? A recently published longitudinal study of doctors in Finland revealed that they suffer from considerable stress related to their information systems. The reasons for the stress are predictable (and likely are similar to your workplace):

  • the information systems are slow and unreliable
  • they do not adequately support the physician’s daily work or the reality of multi-professional teams
  • usability problems
  • system failures
  • poor documentation
  • difficulty in retrieving data
  • time-consuming data entry
  • interoperability challenges

One might expect stress to decline over time as doctors became more proficient with their systems. But that is not the case in this study. A possible reason for this is that the information systems are constantly changing as part of an improvement effort. Unfortunately, the user experience during the transition can be challenging. Another possible reason for growing stress is that, in fact, the information systems are still too complicated and confusing.

Lest you think this is a problem only in Finland, a 2017 article mentions recent studies in the United States and Switzerland that indicate that electronic medical records are driving higher levels of stress and burnout among doctors. This is due, in part, to the growing burden these information systems place on doctors:

  • physicians spent 27% of their work day on patient care and 49.2% on electronic medical records and clerical work
  • physicians spent approximately two hours on clerical work for every one hour of direct patient care

When our doctors are increasingly focused on clerical tasks rather than patient care, then we know the system is broken. When that broken system ratchets up stress levels and burnout among medical personnel, then we have a situation that is dangerous to patients.

Strikingly, the advice in both the Finnish and US articles is the same: involve doctors more closely in the development and deployment of their information systems so they can help improve usability and stability. In addition, the US article recommends that doctors have a say in determining if the workflow dictated by these information systems makes sense. They should look for ways to streamline processes and push clerical work to people who do not have medical licenses. Finally, consider appointing dedicated scribes to relieve the clerical burden and computer liaisons who work directly with doctors to help them learn and practice smarter ways of working with their IT.

If you think you are off the hook because you don’t work in healthcare, think again. How many of these IT-related stressors exist in your organization? And what is your Information SERVICES department doing about them?


[Photo Credit: Pixabay]




ILTACON Video Killed the Radio Star

Video Killed the Radio Star

In 1978, Trevor Horn, Geoff Downes, and Bruce Woolley wrote a catchy earworm…ahem…jingle that focused on “promotion of technology while worrying about its effects” and “concerns about mixed attitudes towards 20th-century inventions and machines for the media arts.” (Wikipedia)

Doesn’t this sound remarkably contemporary?

The song’s video went on to become “the first music video shown on MTV in the United States at 12:01am on 1 August 1981, and the first video shown on MTV Classic in the United Kingdom on 1 March 2010.” (Wikipedia)


I really did intend to provide a recap of some ILTACON highlights as soon as I returned from Vegas. However, life intervened and I found myself on the road again. Therefore, I’ve decided to focus on some highlights that happened outside the formal ILTACON sessions but were captured by ILTACON TV. Thanks to the wonderful folks at ILTACON TV, I was able to have extended conversations with the conference’s keynote speakers: Pablos Holman, Brian Kuhn, and Shawnna Hoffman. All of the conversation ran overtime. However, we do have a bit of video to share with you now.

Below you’ll find my interviews of Pablos, Shawnna, and Brian. In addition, I’ve linked all the rest of my ILTACON blog posts so you have them in one place. Finally, I’m including a video of the infamous song that started this post. Unfortunately, it doesn’t appear to be the original video but it certainly is enough to induce a bit of nostalgia among my more seasoned readers and, perhaps, introduce millennial readers to a classic.

Pablos Holman



IBM Watson Legal with Shawnna Hoffman and Brian Kuhn


A Roundup of my ILTACON 2017 blog posts


Video Killed the Radio Star (1979)

There you have it. And I bet you won’t be able to stop humming the song. <Sorry!>

While these ILTACON videos may not kill a radio star, they certainly have lots of interesting information for you to chew on over the long weekend. Hopefully, their lessons will endure nearly as well as the one cheesy song that started this post.

Have a great long weekend!


Beyond the Hype: Putting AI to Work at Liberty Mutual #ILTASS18 #ILTACON

Session Description: Neota Logic and Liberty Mutual will share details of the design and implementation of Neota Logic’s AI-driven expert system platform to automate document creation and drive internal efficiencies at Liberty Mutual.


[These are my notes from the International Legal Technology Association’s 2017 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.]


  • It’s just software.  According to Kreutzer, “It’s only AI when you don’t know how it works; once it works, it’s just software.”
  • Why did Liberty Mutual even consider using AI?
    • As a life long geek, Jeff thought that AI was a cool thing to explore. The purpose of this exploration was to try to reduce some of the cognitive load, some of the lower-value tasks that slow the legal department’s lawyers down. Ultimately, it was intended to allow the lawyers to focus on higher-value tasks.
  • Forget the pixie dust. With all the hype about AI, it is easy to be dazzled by the “pixie dust” aspects of the technology. However, in Marple’s view, 90% of the work in an AI deployment project involves capturing, organizing and cleaning the relevant data. Without this essential work, you cannot get to the pixie dust.
  • Start with small, low-risk projects. To prove the value of the technology, they started with their internal non-disclosure agreement (NDA) process. (As with many companies, the NDA process was more onerous and time-intensive than the corporate legal department — and their internal clients — would like.)
  • Their timeline to deployment. The bulk of the time was spent exploring the technology options and identifying the right use case for their pilot. The actual process of creating and deploying their instance of Neota Logic took two to three weeks.
  • Their new toolbox. They are creating a toolbox that includes Neota Logic to be deployed throughout the organization. Because Neota is a business-facing tool, the business folks can use Neota to improve processes without involving a single developer.
  • What skills and competencies best support AI deployments?
    • Legal Engineers are perfectly placed to support AI deployments. They can be lawyers or business analysts. However, they need an understanding of the law and they must be willing to “lean in to technology.” Plus, they must have a healthy curiosity about the technology.
    • Some law schools are training their students in legal technology by using Neota Logic in their courses. Faster than we know it, most law schools will be training students in legal technology. And, when they enter law firms, they will simply practice law in a tech-enabled way.