Malta’s Innovative Legal Response to Blockchain Technology

KMWlogo_Stacked_Session Description: Malta Digital Innovation Authority & Legal Framework

Ganado has been heavily involved in the drafting of new legislation required for the development of Malta as a financial center, including the revision of the law relating to trusts and the law on legal persons and foundations, as well as on netting, securitization and aviation.  His most recent publication is Legal Personality for Blockchains, DAOs & Smart Contracts.

Speaker: Max Ganado, Senior Partner, Ganado Advocates mganado@ganadoadvocates.com

Slides:  1100_Ganado.PPTX

[These are my notes from the new Blockchain in Government conference, which is part of 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.]

NOTES:

  • Focus on Distributed Ledger Technology (DLT)

    • distributed
    • encrypted (private)
    • audited
  • Focus on Smart Contracts
    • These contracts are composed of smaller executory contracts expressed as a series of “if then” statements (i.e., If this happens that that must happen)
    • Used for determining consequences of flight delays, for example. So if the flight is delayed, you automatically get the agreed financial award.
  • How to approach regulation?.
    • Where there is risk to investors and consumers, make the regulations mandatory
    • Where there is a need to support innovators and investors, make the regulations voluntary
  • Malta Digital Innovation Authority Act
    • provides an enabling structure for digital innovation
    • creates an administrative authority to provide oversight for digital innovation
  • Innovative Technology Arrangements and Services Act.
    • caters to the voluntary application for certification of DLT/Smart Contracts and the registration of systems auditors nd technical administrations
    • issues certifications of validity
  • Virtual Financial Assets Act.
    • Mandatory law: addresses cryptocurrencies — token issuance and intermdiation services
    • regulates exchanges and wallet providers
    • created a test to determine if the financial asset is security, money, something in between, or a utility that is not subject to regulation.
      • if it is “like money” then it is deemed a virtual financial asset and is subject to this law
    • why implement this law? It provides more certainty in an uncertain situation.
  • Is a DLT platform a legal entity?
    • It looks a lot like a partnership that creates, stewards, and uses a key asset = their software.
    • Should the government grant legal personality to this entity?
      • option 1 = use existing legal forms
      • option 2 = vary some rules in existing forms
      • option 3 = design a completely new legal form
  • Legal Organization Qualities.
    • Activities
      • Legal Organization: Centralized
      • Blockchain: Decentralized
    • Governance
      • Legal Organization: has a governing body (e.g., board, annual meeting)
      • Blockchain: has some element of governance embedded in it
    • Accountability & Auditability
      • Legal Organization: governed by applicable
      • Blockchain:  may have accounting and auditability embedded in it
    • Legal Personality?
      • Legal Organization: depends on legal form chosen and registration with government authorities
      • Blockchain: No — it’s just a piece of software!
  • Impacts of Legal Personality.
    • Capacity to contract, to carry out contract, to comply with legal obligations
    • Liability: the right to own assets and liabilities, the ability to limit liability, PLUS recourse for liabilities
  • Proposed Solution.
    • They created a variant of a foundation — a new type of purpose foundation (similar to a civil law foundation BUT not limited to charitable purposes.)
    • The cells within the platform must be bankruptcy remote
  • Are Smart Contracts Treated as a Legal Person?
    • Simple smart contracts will not be considered a legal person with the benefits of legal personality.
    • However, if the contract has broader impacts on society then this complicates the analysis and may give rise for the need for legal personality.
  • Distributed Autonomous Organizations.
    • How are they governed? By people OR by technology?
    • Then you need registered auditors who can periodically monitor and certify operations.
    • What compliance regulation do you need to curtail misuse such as money laundering?
  • Bankruptcy Considerations.
    • Put the software in a bankruptcy-remote (i.e., bankruptcy protected) cell and then treat that software as an asset for which you have fiduciary responsibility
    • Create an asset cell that can pay damages in the event of bankruptcy. It contains
      • insurance
      • sinking fund
      • guarantee/support fund
    • Interpose between these two layers an administrative layer that manages governance and financing.
  • Smaller Countries are Racing to Regulate this Area. They can be more nimble to create legislation but need to balance this with their desire to create an attractive regime that will draw in more economic opportunities to their countries.
Share

What Thought Leaders and Analysts Say about Blockchain #KMWorld

KMWlogo_Stacked_Session  Description:

As with anything new, seeing the woods for the trees around blockchain technology is challenging. It may not even yet be at the peak of the hype cycle, and clearly there are already exravagant clains for its potential competing with damning dismissiveness of it as as inefficient and nothing new. This panel of analysts and experts help us see our way through the noise to get at the signal. Just how much difference can blockchain technology make? Where is it likely to make the greatest impact? And how can we ensure that positive impact is maximized?

Speakers:

[These are my notes from the new Blockchain in Government conference, which is part of 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.]

NOTES:

  • Andrew Young: Blockchain for Social Change.
    • Slides:  0930_Young.pdf
    • He works at the Governance Lab at New York University
      • Their website: http://www.thegovlab.org/
      • See their blockchain websiteblockchan.g
    • They define “blockchange” as the use of block chain to enable social change.
    • They have just launched a new website and resources for blockchange, which includes current projects underway:  https://blockchan.ge/curatedexamples.html
    • Decisions made at the design phase of a blockchain can
      • Permissionless blockchain  — anyone can read it or write to it
      • Permissioned blockchain — only selected people can read it or write to it
      • Public blockchain — available to anyone
      • Private blockchain — available by invitation only.
    • Three types of use cases:
      • Track and trace intangible objects
      • Identify
        • They have released a field report on the use of blockchain for identity
        • Identity is essential for life and for all blockchain transactions
          • 1.1 billion people currently lack a verifiable identity
        • The lifecycle of identify
          • provisioning a new identify
          • authentication of that identify
          • administration
          • authorization
          • auditing/monitoring
      • Smart Contracts = a set of “if this, then that” rules encoded in a blockchain
    • They have created a Periodic Table of Blockchain
      • cross-cutting challenges
        • user interfaces are not uniformallygood
        • technical inefficiencies
        • incentivizing use
        • risks
        • legal and policy uncertainty — e.g., digital signatures are not considered valid in all jurisdictions
        • measuring impact
    • When should I develop a blockchain?
      • Is there a clear problem definition?
      • Are there information asymmetries that would incentivize use of blockchain transparency?
      • Are there existing reliable data and technology
      • Are there feasible and credible alternatives
      • Does the ecosystem support blockchain — do you have the right partners and intemediaries with the right level of coorpation
      • Capacity — do government and business leaders have the technological know-how to manage this change
    • How to manage a blockchain:
      • Governance legitimacy
      • Ethically sound
      • Focus on solutions to actual problems
      • What’s the ecological footprint?
      • Can this be synchronized with existing intitiatives?
      • Is there sufficient interoperability and open standards?
      • How can you secure first block accuracy?
  • Jonathan Lehman: Blockchain in Government.
    • Chief Strategy Officer of the Government Blockchain Association around the world
      • 40 working groups
        • Example:
          • Cybersecurity and authority to operate (Stratus Cyber)
          • Blockchain as a service (Simba Chain)
          • lottery programs
          • energy management
          • organ and blood donation
          • digital identity management
      • 90+ chapters around the world
      • 6000 people attend meetups every month
    • Blockchain offers distributed trust, reputation, and confidence
      • it can reverse the concentration of organizational power
        • peer to peer
        • frictionless
        • more secure
        • more trusted
        • smart contracts
        • decentraclized autonomos organization (DAO)
        • decentralized applications (dApps)
      • result = traditional institutions can resist or embrace the change:
        • governments
        • financial and insurance sector
        • global private sector companies
    • What is required for a paradigm shift to blockchain?
      • generally accepted standards
      • decentralization, trust, reputation
      • incentive-based solutiosn (not zero sum)
      • personal self-sovereign identify (borderless world)
      • banking the un-banked (2 billion people are waiting for this ability to join the global economy)
  • Hugh Logue: Smart Contracts.
    • Director & Lead Analyst, Outsell
    • His interest is in using technology to open up access to justice (access to legal services). (He was a barrister (litigator) in an earlier phase of his career.)
    • He has written a book on this. It will be published by the American Bar Association (to be published in 2019.)
    • His focus is on smart contracts. He believes that the demand for smart contracts will explode.
    • Why the increased demand for smart contracts?
      • The Internet of Things will allow transactions without human intervention.
        • Example: if a driverless car discovers that its parking place can provide energy at a competitive price, it could purchase the necessary energy then and there via a smart contract.
      • The shortening of supply chains
        • thanks to Amazon and others, consumers have become used to purchasing directly (or almost directly) from the manufacturer. They no longer need a middle man.
        • increasing use of smart contracts will reduce the need for middle men
      • The increase of trusted blockchain partners
        • large companies are comfortable working with established vendors (e.g., IBM, SAP, etc.) and may not be comfortable working with new blockchain platforms such as Ethereum.
      • Expedite legal processes (and reduce their costs)
Share

Blockchain for the Non Geek #KMWorld

I’m attending the new BlockChain in Government conference that is running alongside the KMWorld Conference. Euan Semple is chairing these sessions.

Session Title and Description:

Speaker: Euan Semple, Director, Conference Chair, & Author, Euan Semple Ltd

[These are my notes from the new Blockchain in Government conference, which is part of 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.]

NOTES:

  • The Hype Cycle.  We are in the hype cycle of blockchain and there is so much noise about it — especially with respect to cryptocurencies. More quietly, organizations are working on smart contracts and other ways of using blockchain technology to find solutions to persistent problems.
  • It isn’t just another slow database. It’s a ledger.
    • It is a permanent record of transactions identified by and aggregated by a specific hash. Each new transaction is assigned a hash and added to the blockchain bearing that hash. The detail and data relating to each transaction are held outside the ledger.
    • The ledger is held in identical form in multiple places. Therefore, each change to the blockchain is replicated in multiple places and cannot be changed (or corrupted) in one place without raising an alert in the other places.
    • This transparency and constancy helps engender trust. This displaces institutions and professionals who previsously were trusted to authenticate and keep transactional records.
  • The ideology of algorithms. We are allowing a group of geeks to shape society through the algorithms they write without society’s oversight or input. This means they can embed in their code their biases and blindspots. If the rest of society treats the output of these algorithms as value-neutral or omniscient, then society blindly acquieses to this new shape of society.
  • Immutability. One of the pillars of trust is immutability. However, as some parts of society are beginning to assert “the right to be forgotten,” how does that square with a permanent, immutable record?
Share

Solving Real-World Problems with AI #KMWorld

KMWlogo_Stacked_Session Description:

Pittman starts off by discussing the many subfields that make up AI and then looks at how various industries are using it to achieve incredible results. The Raytion speaker shows how machine-learning can be applied for sentiment analysis of unstructured data in the context of social media using an example of a large telecommunications organization. Guarino believes AI is already having a significant impact for the U.S. government (including defense and intelligence community uses cases) and is also providing game-changing capabilities for global enterprises in a range of industries, including financial services, healthcare and all areas of technology. She provides real-world examples of how AI is driving measurable benefits today in a range of industry sectors, discusses the importance of Explainable AI to regulated industries, where being able to justify the reasoning behind algorithmic decisions is essential.

Speakers:Chris Pittman, Principal Security Engineer, Cylance

Christian Puzicha, Senior Solutions Architect, Raytion GmbH

Amy Guarino, COO, Kyndi

Speakers:

[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.]

NOTES:

  • Chris Pittman, Chief Engineer, Cylance
    • Data Breaches are expensive.
      • In 2017, the average cost of a data breach is $11.7M. (Health care organizations have the most expensive data breaches.)
      • Ransomware attacks have doubled in 2017 from 13% to 27%. In 2018, we are see 31%-32%.
      • The average time to resolve a maliious insider’s attack is 50 days .
      • The average time to resolve a ransomware attack is 23 days.
    • How does the Security Industry respond to this?
      • Before AI, they worked to understand how the malware and its creator operate, then the security company created a definition file that effectively “innoculates” your network and devices.
      • With the advent of affordable computing, storage, and processing, now we have the technological power to support AI to quickly identify and resolve data breaches. Then, the more we study the behavior of the bad actors, AI can help predict future breaches and help prevent them.  Because of this 99.7% of malware is prevented (accoding to Cylance security).
  • Amy Guarino, COO of Kyndi.
    • Think big, start small, but just get going!
    • If you’re interested in exploring AI, then start with robotic automation (E.g, blueprism) to hep automate repetitive tasks
    • Small data: How do you create a labeled data set? Kyndi ingests unstructured content and then extracts and classifies data embedded in each document. They combine machine learning with natural language process and knowledge graphs to help understand the concept and its context.
    • Ontologies and taxonomies: increasingly, this can be done by machine rather than by hand.
    • Explainability: as the AI system evolves, it helps me make better decisions. However, we have to be able to explain what happened in the “black box” that led to the ultimate decision.
  • Christian Puzicha, Raytion.
  • Self-leaning social media systems
    • Raytion does Enterprise seach (i.e., Google + security)
    • What happens in a social media disaster? (Example, Kendall Jenner’s ad)
    • Raytion did sentiment analysis of the social media response to the Jenner ad
    • Unstructured information (especially on the internet) has special challenges:
      • multiple languages
      • unbalanced input — usually on the unhappy folks complain via social media
      • lots of typos
      • decoding irony and sarcasm
      • the text does not stay stable — if you train the model set, it works now but will not work in 5 years. So you need to keep retraining.
    • How does magical machine learning work?
      • the bad news is that it involves math
      • the good news is that it works — it uses pretty old old math that has been tested over time
      • first, transfer the text into numbers. Then optimize it.
    • Conclusions:
      • different content sources require different modell
      • different languages require different models
      • features of search engines are useful for social media monitoring
      • success depends on size and quality of training data
      • ethics — what to monitor and should I do it?
Share

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.]

NOTES:

  • 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
Share

Next-Level KM — Produce One Billion in Benefits by 2020 #KMWorld

KMWlogo_Stacked_Session Description:

The KM journey in Shell is heading for a new turn. Manders discusses how KM developed and evolved over the past 15 years and zooms in on recent experiences with implementing a complete set of KM tools, processes and Working Out Loud behaviors. He talks about how KM in Shell realized $300 million value in the last couple of years, how they aim to triple the impact by 2020, and what other qualitative and quantitative impact they have made in Shell’s communities. Learnings, as well as structures and practices shared, as well as the next step in their journey: moving KM under the Organizational Development function in HR, describing the logic behind this decision, objectives, and expectations. Manders discusses future developments envisioned to further improve the KM capability in Shell. To learn more about Shell’s techniques plan to take workshop 16 on developing scenarios!

Speaker:

Willem Manders, Global Head of Knowledge Management, Projects & Technology, Shell

[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.]

NOTES:

  • Shell’s KM journey.
    • In 2013, they tried to scale up their impact by standardizing their approach.
    • Wave #1
      • Dsicipline engineeing
      • Wells
      • Development
      • Capital Projects
    • Wave #2
      • Process Engineering
      • Maritime
      • Exploration
      • Upstream Commercial
    • Wave #3 (they are about to finish this wave)
      • Research & development
      • Projection
      • Safement and  Environment
      • Contracting and Procurement
    • They focused on making KM more useful and relevant in the business
  • How Shell’s KM team delivered more than $350 million of value.
    • Knowledge Management strongly supports the Shell Strategy
      • KM too often focuses on its “cool tools” while the C-Suite focuses on specific strategic chalenges. Now KM focuses on theosestrategic challenges
      • Identify key value areas (based on the work of Etienne Wenger)
        • if you want to sustain the long journey that is KM, you have to show value
        • Short-term value vs long-term value
        • Organization value vs. individual value
        • 2×2
          • short-term value for individuals
            • Performance Support
              • improved productivity
              • increased engagement
          • short-term value for organization
            • Operational Excellence
              • Define it and replicate it
              • improved decision-making
              • create an arean for problem solving and collaboration
              • enable continuous improvement
          • long-term value for individuals
            • Learning & Development
              • enhanced onboarding and role changes
              • shorten the time to autonomy
              • 70% is learning on the job, 20% is learning from others, 10% is formal training
          • long-term value for organization
            • Winning Capabilities
              • conribution to winnign and differntiabing capabilities
              • learning what it takes to be successful
      • Purpose Areas
        • connect people to poeple
          • track leading indicators
          • collect success stories that demonstrate value
          • share those stories widely
        • connect people to content
        • support collaboration
        • capture and reuse lessons learned
      • Then provide KM elements
  • Performance Support.
    • This is “powered by KM”
    • They use “Working Out Loud” to enable people to help each other
    • They connect people in communities in practice to enable sharing of problems and solutions
    • They identify time saved and translate this to money saved.
  • Learning & Development.
    • They focus on “Learning Nuggets” — the smaller the piece of learning, the easier to find it and the easier it is to find.
    • They have their internal version Khan Academiy
  • Performance Excellence.
    • Their focus is on “Replicate don’t reinvent.”
    • Their executive VP for Retail championed the process; he held an awards ceremony for the most successful replications in their retail operations.
    • The cultural change: the engineers in their organizations are often focused on creating new solutions altogether. Now they are being asked to be creative in their replication efforts.
    • Replication allows you to calculate fairly easlly the value of not having to reinvent.
  • Winning Capabilities
    • In addition to after action reviews, use “before action reviews.” This brings organizationational knowledge to the forefront and allows it to shape the way they do business. It helps them do better every time..
    • This is their version of working out loud.
  • Support the Energy Transition.
    • Shell’s KM team is working with their new energy team to accelerate the transition to renewables.
  • How to move from a programmatic KM approach to an embedded KM approach.
    • Some options:
      • in operational groups
      • in IT
      • in Marketing & Communication
      • in Finance
      • in Learning & Development
    • Where KM housed has a huge iimpact on how KM is done.
    • Housing Shell’s KM in its HR function (organizational learning & development), allows them to focus on individual and organizational improvement.
      • Previously, KM, learning, and development were in separate silos. Therefore, they saw potential solutions through their own lenses not through shared lenses.
    • To optimize business reults, they must focus on People, Processes, Structure,  Culture AND Leadership
  • Look for Hooks. Manders looks for hooks in the business that allow KM to accelerate change journeys for the business.
Share

Wilkinson Keynote: Entrepreneurial Skills for Knowledge Sharing #KMWorld

KMWlogo_Stacked_Session Description:

Sharing knowledge for enterprise success requires entrepreneurial skills, new ways of thinking and operating, continuous learning, and change. There are many new tools available to help, but it is the people and the culture of an organization that determines its ultimate success. Wilkinson interviewed 200 of today’s top entrepreneurs, including the founders of Airbnb, LinkedIn, eBay, PayPal, Yelp, Dropbox, Tesla Motors, SpaceX, Chipotle, Under Armour, Spanx, Jetblue, and Revolution Foods, to distill what it takes to go from startup to scale in our rapidly changing economy. As leaders reinvent their approaches to digital transformation for organization survival in this economy, they can learn these fundamental skills, practice them, and pass them on. Join our accomplished researcher and speaker as she shares her framework and provides ways to master the skills that underlie entrepreneurial success.

Speaker: Amy Wilkinson, Founder & CEO, Ingenuity and Lecturer at Stanford Graduate School of Business; Author, The Creator’s Code: Six Essential Skills of Extraordinary Entrepreneurs

[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.]

NOTES:

  • Find the Gaps.
    • Be curious:
      • children ask 100 questions per day; adults ask 2-3 questions per day. This is due, in part,  to our development of expertise, which leads us to create our own silos. This, in turn, becomes our Achilles heel that stops us from becoming innovators and entrepreneurs.
    • Be an architect:
      • Look for the open space: find a green field on which to build.
        • Elon Musk — see a problem and then go back to first principles to solve it. He saw that space shuttles were like airplanes that were thrown away after every flight. He thought this made no sense. So he started SpaceX to figure out how to make cheaper, reusable shuttle.
      • Solve a problem for yourself and then scale the solution for others.
        • Sara Blakely — she solved the pantyhose/underwear problem for herself. Because she was the first woman to tackle this problem, she had a real challenge convincing the men in the industry. So she did it herself: she taught herself how to file a patent application, her mother (an artist) drew the schematic. Blakely kept going until she found a manufacturer with daughters who was willing to listen. She persisted.
    • Be an integrator:
      • Look for opportunities for innovation at the intersection of disciplines, industries, markets.
      • Chipotle: the founder was a classically trained chef. He wanted to create fast meals from fresh food. So he mashed together his classical culinary training with the fast food process. This created a restaurant at which a chef would be willing to eat.
  • Drive for Daylight. In this fast-moving world, act as if you are in a race car. You have to focus on the horizon, not on what is right in front of us. Typically, most businesses focus on what is right around them or, worse still, they focus on the rearview mirror.
    • Avoid Nostalga: you can’t be nostalgic about the past  — especially if you’ve had tremendous success. Netflix had big success with DVD by mail but overcome customer protests to move from that to streaming.
    • Fire yourself: Andy Grove at Intel used to talk about the importance of “firing yourself.” They asked themselves (when they thought they might be fired becuase of the poor performance of their business), what would our successor do? The answer was to get out of the old business and then move into the microprocessor business. This led to extraordinary growth.
    • Be prepared to cannibalize your own products — Apple does this time after time.
    • Focus on “to go” rather than “to date.” This keep your focus forward — on the problem you need to solve, on the product you need to ship. This allows you to meet your near-term goals.
  • Fly the OODA Loop. Observe, Orient, Decide, Act.
    • Eventhough the Russians had better fighter planes, the American air force has maintained
    • Paypal: they merged two businesses and then went through 6 different business models in 18 months.
    • Paypal Mafia: then after they sold PayPal to eBay, they all tried new ventures and have been successful.
      • Jeremy Stoppelman: The first thing you try likely will not work. So look for a “counterintuitive blip of data” that could point to a new, more profitable path.
      • Always have a wingman: this is someone who will question your assumptions and help improve your thinking
    • Startups view business as a form of intellectual debate. This enable fast action. In a large organization, people aim for consensus. However, this can be too slow in a fast-moving world.
  • Fail Wisely. This goes beyond failing fast. Have a failure ratio (e.g., 1/10 things I try won’t work or 1/3 things I try won’t work.) The key is NOT to aim for zero. This means that you are aiming for perfection, which will shut down your innovation and risk-taking.
    • Place small bets:  don’t put all your money on one bet. Place small bets on several opportunities.
      • Stella & Dot: their ratio is 1/3. She counts on her team to make fast decisions on new products: “love it or lose it”
    • Titanic Example: You know you have a catastrophic problem (e.g., you’ve hit an iceberg). You know how many people you have and you know how many lifeboats you’ve got. What do you do?
      • Reframe the problem: switch from saving the ship to saving lives. Then you use the lifeboats as ferries to move people from the ship to the iceberg where they can stay until rescue boats arrive.
      • Repurpose what you’ve got: Look for alternatives that can function like life boats (e.g., anything that floats will work — tables, doors, etc.)
  • Network Minds. We need to focus on cognitive diversity, not just visible diversity. The goal is to harness different points of view and then build on that.
    • IDEO Approach: use space
    • Amazon’s two-pie rule: they want folks to work in teams — but small teams that can be fed by two pizzas. Then they get to know each other and can get things done.
  • Gift Small Goods. Provide small kindnesses to others. Do five-minute favors. This helps amplify your reputation because of connectivity. Then information, opportunities come to you.
    • Generosity enhances productivity — Bob Langer at MIT is always trying to amplify the work of his students in their drive to ending human suffering. The are regrowing human tissue (including vocal chords for Julie Andrews and Larry Page). They produced the nicotine patch. They have developed many innovative delivery mechanisms for cancer treatment.
    • Focus on the “snuggle for existence.” This will help enormously with the struggle for existence.
Share

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.

Speakers:

  • 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.]

NOTES:

  • 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.

Share

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.

Speakers:

  • 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.]

NOTES:

  • 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.

Share

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.

Speakers:

  • 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.]

NOTES:

  • 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.

Share