AI Practice and Ethics for KM #KMWorld

Speakers: Dave Snowden (Chief Scientific Officer, The Cynefin Co) and Phaedra Boinodiris (Trustworthy AI leader, IBM)

Session Description: KMWorld has been exploring the future of AI and other emerging tech as it transforms knowledge sharing, collaboration, and innovation in our organizations. Responsible AI, machine learning, ethics, and knowledge management definitely intersect and are rooted in culture change and business transformation. Our knowledgeable speakers consider the basic differences between human and machine intelligence; the impact of creating training datasets, especially in light of the recent Google controversies; how to assess models; and more as they look at what’s next for AI, KM, and our world in 2022 and beyond.

[These are my notes from the KMWorld Connect 2021 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:

Dave Snowden

  • AI Causes Loss of Human Capability
    • There is a story of someone who blindly followed their satnav (GPS) and inadvertently drove off a pier because the GPS told them that if they took the recommended route they would be driving over a bridge.
    • If humans don’t use a capability, they lose that capability within a single generation
    • Will we all be looked after by “machines of love and grace”?
  • Technology is creating a dangerous unbuffered feedback loop
    • There is a lack of human judgment and empathy in algorithms
    • What you have in Facebook is an unbuffered feedback loop that elevates extremism.
    • Snowden believes that this is not a problem we can rely on the free market to solve. Rather, it is government’s responsibility to fix.
    • Snowden advocates “human buffering.” This mean interposing human judgment and human empathy in the feedback loop.
    • We need to increase the amount of empathy between humans so that they don’t leap to negative judgments and action.
  • How to increase empathy between humans and across human systems?
    • You “entangle” people so that empathy jumps across systems rapidly. [This means that you deliberately create situations in which different people meet and interact. Then you switch the members of the teams around so that each person meets and works with a wide variety of people. This expands their exposure, experience, and empathy.]
  • The lack of ethical training in software developers.
    • This is a criminal omission for which we are paying the price now and will continue to pay for the foreseeable future
  • Improve the training sets for algorithms
    • This is critical for ameliorating the negative effects of algorithms run wild.
    • If the training sets are biased, the algorithm results will be biased.
  • Star Trek Lessons:
    • Horta (The Devil in the Dark) episode: “Silicon-based life forms are not carbon-based life forms.” AI is silicon-based.
    • Darmonk episode: they meet a species that speaks only in metaphors derived from experience. (Remember that art comes before language. Art allows us to think abstractly first and then bring it down to the concrete level through language.)
  • KM should focus more on judgment than on information.

Phaedra Boinodiris

  • Trust in AI
    • What does it take to create trust in AI?
      • This is not a technological challenge. It is a sociological challenge.
  • Diversity improves AI models
    • Without true diversity in your development teams, it is extremely hard to avoid bias in your AI models.
    • Without a wide range of people involved, it is difficult to create “explainable AI” that works for and can be explained to a variety of populations.
  • How to help people think about empathy systemically?
    • They have tweaked design thinking to ensure they consider at the planning stage the primary effects, secondary effects, and tertiary effects of an AI model. It is the tertiary effects that pose the biggest problems because they are usually unintended and potentially harmful. Once these tertiary effects are identified, then her colleagues engage in ethical hacking to remove the negative effects and replace them with positive effects.
  • Scaled Data Science Method
    • This is a collaborative platform that reminds participants, at specific milestones in the AI development life cycle, that they should bring in people with complementary expertise in psychology, sociology, etc.
    • This ensures that more perspectives and expertise are brought to bear during the development process. They are no longer relying entirely on developers to ensure the integrity and positive impact of the AI models.
  • Teach Ethics Early and Often
    • We should be teaching Ethics in Data Science and Ethics in AI to more people early and often — we should be teaching this as early as high school.
    • Snowden Response: it’s not enough to teach ethics. We also need to create processes that train people to BE ETHICAL.

Conversation

  • Snowden:
    • They are doing Children Ethnography
      • This is about seeing the world through the eyes of kids
        • In Wales, there is legislation that requires every new project to be evaluated from the perspective of the children who will have to live with the consequences.
      • By asking children to tell their own stories and to gather stories from their own communities, Snowden’s team are able to generate valuable abstracted data overlaid with human-applied meaning.
      • They are working through schools, churches, and sports clubs to recruit and train the child participants.
      • This child ethnography approach helps them see patterns across communities and allows them to actually consult with children to get real-time data as situations arise.
  • The Deficits in our Education Systems
    • Snowden: Our current problem is that education is privileging STEM over the humanities. We should teach empathy and design thinking, and art, etc. We can teach students coding later.
      • Because of the bias in the education system that favors expertise over general knowledge, there are relatively few generalists under the age of 60. This means there are insufficient people able to connect the dots across domains of expertise.
      • Instead, we are taking an engineering approach to human education: learn a module, then take a test. Pass the test, then tackle the next module. This rigid approach does not leave enough space and time for curiosity or creativity.
    • Boinodiris: It would be wonderful to involve school-age children in societal decision making but some significant problems in our public education system make that difficult. Because children today are being taught in silos, they are less able to make wise decisions.
  • How to Improve Diversity and Inclusion
    • Boinodiris: rethink the role of the chief DEI officer. In addition to recruiting a wider variety of people, they should ensure that the organization has true diversity in a variety of functions ranging from development to the governing ethics board and beyond.
    • Snowden: we have got the diversity agenda wrong. We’ve been focused on visible diversity while ignoring attitudinal diversity. (Attitude is a leading indicator but practice is a lagging indicator.)

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Current Landscape and Future Outlook for AI in KM #KMWorld

Speaker: Anthony Rhem, CEO/Principal Consultant, A. J. Rhem & Associates

Session Description: What are the leading AI programs and technologies that are facilitating knowledge sharing and management today? What can we expect from machine learning in the future? Our knowledgeable speaker, Rhem, shares a look at the current landscape and future outlook for AI in KM.

[These are my notes from the KMWorld Connect 2021 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:

  • He is currently working with startups that are immersed in both AI and KM
  • His focus is “how to infuse KM with AI.”
  • Tenets of AI-Infused KM
    • connects tacit and explicit knowledge through ontology management and knowledge mapping
    • addresses the reality that tacit and explicit knowledge are constantly updating in your information lifecycle management system
    • AI enables predictive analytics and knowledge flow optimization
    • provides dynamic, accurate, and personal knowledge delivery
      • dynamic = as the content updates, it connects with the appropriate subject matter experts who can provide additional insights on that updated knowledge
      • accurate = helps identify the authoritative voice and authoritative source for that knowledge
      • personal = personalized knowledge is tailored to what an individual needs to make a decision and is presented in a way that is consumable by that person.
  • Why KM needs AI
    • Volume = there is so much information and we need to make sense of it efficiently — humans can’t do this unassisted
    • Timeliness = delivers knowledge in a timely manner
    • Access = improves access to the collective knowledge of the organization
    • Personalization = workers need knowledge tailored to their individual needs
    • Productivity = increases productivity by helping workers operate more efficiently and effectively
    • Innovation = depends on effective collaboration and knowledge sharing.
  • Current Landscape of AI in KM
    • Predict trending knowledge areas/topics to meet knowledge worker needs
    • Improve content by leveraging machine learning to identify what content will be best suited to address the situation
    • Identify which content will be most useful to your knowledge workers based on real- time engagement and content consumption
    • Improve search relevance, precision, and efficiency
    • Personalize knowledge based on individual preferences
    • AI more effectively interprets user intent, enabling it to provide more useful search results. This is based on AI’s enhanced understanding of the intended use of the content.
    • Chatbots with Natural Language Processing (NLP) provide cognitive capabilities to understand, interpret and manipulate human language. This enables bots to anticipate the needs, attitudes and aspirations of users to aid in decision making and improve outcomes
  • Leading AI programs facilitating KM
    • Knowledge-as-a-Service (KaaS) incorporates AI into the access and delivery of knowledge. (Typically implemented through a Digital Workplace). Examples:
      • IBM Digital Workplace Hub
      • axero
      • Sift
    • Search with Natural Language Processing (NLP) and Machine Learning(ML) Semantic Search engines are incorporating NLP and ML in understanding the intent and contextual meaning behind the search query. This, in essence, is figuring out what a user means when executing search. Examples:
      • Enterprise NLP
      • Ontology Builder
      • elastic
      • lucid works
    • Intelligent Chatbots with NLP/ML provide cognitive capabilities to understand, interpret and manipulate human language in a way that enables the bots to anticipate the needs, attitudes and aspirations of users. Examples:
      • Landbot.io
      • Microsoft
      • IBM Watson
      • ManyChat
  • Future AI/KM Paradigms and Technologies
    • Ethical AI in knowledge delivery — ideally, the resulting decision making will be ethical and free from bias.
      • [But as with other ethical AI considerations, we should not assume that the knowledge selected and delivered by AI will be free from bias. The devil is in the developer and design details.]
    • Cognitive Digital Twins (CDT) — a digital twin is a digital replica of a physical system and runs in parallel with that physical system. Adding cognitive ability provides enhanced predictive capabilities regarding the digital twin and, ultimately, the physical system.
    • Augmented Intelligence — where AI assists human reasoning and decision making rather than replacing it.

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Walmart’s Content Management Journey #KMWorld

Speakers: Amber Simpson (Senior Manager, Learning & Development, Walmart) and Todd Fahlberg (Senior KM Consultant, Enterprise Knowledge)

Session Description:

[These are my notes from the KMWorld Connect 2021 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:

  • Initial Challenges
    • They deeded efficiencies to create, maintain and sunset content
    • BUT there was a lack of accessibility and awareness of existing content, they saw a lot of unnecessary time and money spent recreating, manually managing, duplicating, searching for content. PLUS there was an overliance on the “powers” of Excel. They were using Excel to do more that it should.
    • How to create associate experience and learning data as a service
      • enabling the delivery of learning to Walmart associates before they know they need it
      • providing recommended content and personalized eperience
      • needing a single centralized location for indexing learning content
    • How to align their taxonomies so that machine learning could ingest and parse the language(s) of Walmart.
  • Lessons Learned
    • “If we’re going to do anything, we first must be able to show value and how we’re giving back to the organization.”
    • “Have to stop referring to KM as a separate entity, should instead align with Ways of Working.”
    • “Keep things Sesame Street Simple”
  • Their Initial Content Management Strategy Roadmap had several work streams:
    • LCD MVP & taxonomy implementation
    • Findability (Solr implementation)
    • Knowledge Graph implementation
    • Analytics reporting platform
    • Business process improvement
    • Content governance
  • Their Simplified Implementation Roadmap:
    • Product discovery and analysis
      • define and validate product vision
      • conduct environment analysis
      • update architectural design
      • update content data model
    • Agile adoption
      • consolidate requirements and backlog to Jira
      • Modify agile processes to fit Walmart culture
    • UI/UX Improvements
      • conduct user interview
      • update site map and consolidate application screens
      • design and develop new search interface
    • Findability (Solr implementation)
    • Taxonomy (PoolParty implementation)
  • Digital Library — Their Learning Content Database (LCD)
    • Learning content database
      • Their LCD became the one source of truth. This enabled them to deliver high-quality learning content in the most efficient and reliable way, which resulted in time and cost savings.
      • Their LCD connects skill badges to learning content so they can track associate development, provide targeted learning to upskill employees, and identify gaps in content.
    • PoolParty
      • The LCD is their metadata hub
        • User-contributed metadata (manually added)
        • Derived metadata (harvested metadata)
        • Provenance & lineage metadata (metadata added by users and technology) — this is GOLD — it shows relationships, change history, and dependencies
      • PoolParty automated tagging, taxonomy/ontology management
        • the controlled vocabulary is stored in the LCD and applied automatically by PoolParty
    • Solr
      • indexes content from Walmart’s repositories
      • provides customizable options
  • Future Vision
    • They are transitioning from a relational database to a graph database
      • Their goal is to provide a semantic search experience. Solr, powered by Walmart’s Knowledge Graph and taxonomies, provides US Learning and its internal and external facing systems a Google-like search experience.
  • Recap of Lessons Learned
    • Keep it simple. What is understood is supported
    • Meet people where they are. Explain WIIFM — what’s in it for me — for them
    • Training, training, training.
    • Don’t try to boil the ocean
      • Clean data is a must but waiting for perfection will delay your start
      • Find a place where you can make an impact. Identify the problem and jump in.
    • Identify a trust partner/advisor who is willing to teach, mentor, and provide guidance
    • Stakeholder, business, and tech alignment
    • Know your organization’s culture and understand your customer
    • Show yourself GRACE!
  • Agile Content Management — They use agile methodology to ensure the millions of content items in their Learning Content Dabase are regularly reviewed and updated.
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Intelligent Search in Action #KMWorld

Speakers: Brendan Heck (Senior Manager, Knowledge Management Platform Modernization, Charles Schwab & Co., Inc.) and Gloria Burke (Director, Knowledge Management & Field Communications, Charles Schwab & Co., Inc.)

Session Description: Hear how one organization used intelligent search to enable frontline professionals to streamline access to knowledge and information  reducing average handle time of client service requests and increase speed to proficiency for new hires.  Our speakers share the steps to determine a vendor, the path forward of implementation and successes since the launch.

[These are my notes from the KMWorld Connect 2021 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:

  • Engagements with two major consulting firms identified knowledge management as an area of need in their company. These firms were able to convince management of the need to invest further in KM.
  • Intelligent Search is one part of a wider transformation of their KM capabilities. They are building a Knowledge Center of Excellence.
  • Objectives of their Intelligent Search Project
    • improve time from search-to-click (68 seconds)
    • first call resolution
    • reduce average handle time
    • “Google like” search experience
  • Vendor selection
    • Scope: internal, external (involving clients), or both?
    • Buy or build? Configure or customize?
      • if you are working in a core competency of your business, then build. If not, buy from a vendor who is committed to providing and maintaining a best-of-breed product.
    • What primary connectors to content sources do you need? (They cost extra!)
    • Calculate your ROI with your leadership
    • Start with search or with content?
      • while they understood that the “garbage in garbage out” principle would suggest that they start with content, they decided to start with search on the theory that it would give them a better sense of their content landscape and needs.
    • They worked with Gartner and Forrester to find the best-of-breed vendors
      • they identified several key evaluation areas they would use to choose a vendor and then created a questionnaire to send to vendors
      • they used spider charts to graph the questionnaire responses from candidate vendors. Interestingly, they decided not to go with the vendor that seemed to have the most well-rounded responses because they did not seem to be as good a fit with Schwab.
    • They narrowed the list of candidate vendors to the top two and then ran a 3-month proof of concept.
    • They created a system usability scale to score both vendors.
    • They chose Coveo and their intelligent search
  • Implementation
    • Setup (system access, identifying content source, connecting to content sources, crawling and indexing those sources)
    • Limited scope of initial rollout to their Service Teams because that would give the best results for clients and best return for the company
    • Think about bringing search to where your people are working — this reduces the need to swivel from where they are working to a separate search system.
    • How can search work better for you? Are there data in other systems that can enable search results before a user does anything? (They did this by folding in data from their customer call and response system.)
    • They took 6 month to implement and release to production (but believe it could be done in as little as 3 months if all coordinated systems were ready to go.)
    • They ran the user interface through their UX lab to make sure what they designed would work well for their users.
    • Beta testing with a subset of users in prduction
    • They helped the system by adding specific “featured results” and synonyms specific to their business.
    • According to Coveo, approximately 30K interactions are needed before machine learning will kick-in properly. However, Schwab saw benefits far earlier.
    • They established new baselines so that they could collect and check data periodically to make sure they were heading in the right direction.
  • Results
    • Reduced average handle time by 11.25% during the beta test (as compared to the non-beta users who were doing similar searches outside the intelligent search system.)
    • Their initial average click rank baseline was 1.8. This means that the correct answer the user was looking for was within the top 2 search results. (Their goal was top 3.) Since general release, the click rank has improved to 1.6.
    • They reduced search-to-click time by 71% initially. Now the actual reduction is 84%, which translates to real savings of time and money for the company.
    • Adoption: currently they have 47% adoption within their initial audience. They will be broaden access and expect their adoption rates to get higher then.
  • What’s next?
    • They are remediating content and meta data tagging, both working in concert with the new search capabilities
    • They plan a scaled expansion to the enterprise
    • Implementing Coveo Q&A Snippets: this is similar to the Google function in which they provide a short answer to the user’s query above the links to the responsive web pages.
    • A/B Testing to examine proposed improvements before they roll them out. This testing approach enables data-driven decision making regarding proposed enhancements
    • Refinement
  • Things to Remember
    • Objectives — know where you are headed
    • Be clear about your ROI — and be able to articulate it for your leadership
    • Choose a vendor that fits your needs and will help you meet your objectives
    • Eat the whale one bite at the time — you can’t to the whole project at once, so do it in sensible phases
    • Measure your results — “You can’t manage what you don’t measure.”
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Snowden Keynote: Rewilding Knowledge: Sense-Making in a World of Uncertainty #KMWorld

Speaker: Dave Snowden, Chief Scientific Officer, Cognitive Edge

Session Description: We’ve been talking about resetting after the global pandemic, but our popular KM speaker looks at rewilding. Rewilding has been described as the optimistic agenda for halting the decline in biodiversity and restoring a balanced suite of ecosystem services. Snowden has been focusing on organizational knowledge ecosystems and how to optimize them for innovation and success. Hear his latest ideas about KM-related rewilding using the concepts based on decades of ecological, physical, and socio-economic research, as well as conservation experience. It’s going back to basics, not a return to the original, but a rebalancing of knowledge management to recognize the key role of humans, including distributed intelligence and sense-making. Be sparked to experiment with new practices for knowledge sharing in your organization.

[These are my notes from the KMWorld Connect 2021 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:

  • Background
    • In his 20s he moved from a finance role into building decision support systems for large companies during the early days of computing.
    • Then he moved into logistics software to manage inventory. He dynamically adjusted buffer stock, thereby saving the company a great deal of money.
    • In those days, there was a huge naïveté about what computers could do. Therefore, people did not know what was possible and what they should ask for. So it made little sense to ask them what they wanted from their computers.
    • Even today, the capability of technology to do things for people is huge and we don’t fully understand it. In fact, the technology can now do things we may not want but we don’t know how to manage it.
  • Knowledge Management
    • While he was at IBM, knowledge management started emerging from multiple sources (Ikujiro Nonaka, Bob Buckman, Leif Edvinsson, Tom Stewart). It was not like fad where there was a single truth from a single source.
    • The purpose of KM is NOT to manage knowledge. Its purpose is to provide decision support AND to create the conditions for innovation.
    • However, too many people took an engineering approach: capturing and collecting knowledge.
    • This mechanistic approach did not match how people actually seek knowledge.
    • Most people are not looking for a document, they are looking for a name of person they can talk to for information and guidance.
  • Formal vs Informal Knowledge Systems
    • At IBM, they studied how knowledge actually flowed within the organization. This study turned up both formal and informal knowledge systems. (The formal systems were set up by IBM; the informal systems were created under the radar by the employees themselves.) Their study revealed that there was good knowledge flow BUT the ratio of formal to informal knowledge systems was 1:64.
      • The enormous number of informal knowledge communities was driven by the people themselves and supported by flexible tools that had been provided by the organization without too many constraints. (In this case, they were using Lotus Notes in novel and interesting ways to meet their needs, as they understood them.)
    • Much of the value of KM is in enabling and supporting informal networks, which are much more resilient and reliable in a crisis than formal networks.
  • The Key Difference Between Systems Thinking and Complexity Approaches to Work
    • Systems Thinking:
      • Where are we?
      • Where specifically do we want to be?
      • What specifically must we do to get there? (The gap analysis informs the action plan.)
    • Complexity Theory:
      • Where are we?
      • What is the next best action that will take us in a direction we like? (He recommends the film Frozen 2, which he described as a great complexity movie. In particular, pay attention to the song in it regarding doing the next best thing.)
  • Field Ethnography
    • He has used field ethnography to understand how the work really happens and how the knowledge really flows.
    • His approach was to join a work team and actually do the work with them (rather than simply observing them or asking for a report). This gave him an understanding of the actual work they do rather than what their job descriptions say. He also learned how they used informal channels to share knowledge effectively.
    • Once he established trust, he could talk to them about how to share their knowledge more broadly.
  • Rewilding
    • Rewilding is about restoring the balance in nature by reintroducing both prey AND predators in an area.
  • Rewilding KM
    • We’ve lost balance in KM by focusing on technology at the expense of people and how people prefer to work.
    • The free exchange of information and knowledge happens in the context of a conversation as opposed to a formal KM program.
    • So we need to rebalance by using technology to support people’s natural way of working.
  • How to develop a KM Strategy
    • Don’t start with a vision of the kind of organization you want to be
    • Start by understanding what kind of organization you are
    • Then create your guiding counterfactuals
      • gather negative stories on the shop floor of what kind of organization you DO NOT want to be
      • it is easier to gain consensus on negative stories than on positive stories
    • Then choose a set of possible new directions (trajectories not targets) for the organization that are opposite to those counterfactuals, and launch a set of experiments to explore these various directions in parallel. When there is a successful result, amplify it; when there is an unhelpful result, disrupt it.
    • Build systems and use your employees as a sensor network
      • Larry Prusak: “If you have $1 to spend on KM, spend 1 cent on technology and 99 cents on connecting people.”
    • Remember: You can never really capture knowledge. You can only create systems in which knowledge flows.
  • How to change People and their Mindset
    • It is hard to change a person’s mindset
    • It is easier to change the people they come into contact with. As they connect with new people, their thinking will change.
    • They have been using “Entangled Trios” in which they assemble project teams based on roles (e.g., a district nurse, a district police officer, a parent). Over the course of the project, they switch around the members of these triads, thereby creating new connections and surfacing new knowledge. By the end, every participant will be within two to three degrees of separation of each other participant, and collectively they will have formed a highly functional and resilient knowledge network.
    • Similarly, the way to break down knowledge silos is not to compel knowledge sharing. Rather, it is to increase the opportunities for connection and interaction between people across silos.
  • KM for Innovation
    • Most innovation comes from extending existing knowledge to new areas/problems
    • The KM system should enable the flow of information at the level of granularity necessary to make it easy to make knowledge from one domain translatable to another domain.
  • The Three Rules of Knowledge Management
    • Knowledge can only be volunteered. It cannot be conscripted.
      • So you must create the conditions for the voluntary sharing of knowledge.
      • He has seen time after time that people share their knowledge in response to a genuine request for help. By contrast, few people respond fully when asked: “What do you know.”
    • We always know more than we can say and we always say more than we can write down. (Adapting Polanyi).
      • The best knowledge is developed through experience over at least two to three years.
      • Most knowledge is stored in STORIES, especially stories of failure.
    • We only know what we know when we need to know it.
      • Human knowledge requires a contextual trigger to be released and shared.
      • This is why we often want to “sleep on things” in order to respond more fully to the trigger.
      • Therefore, if you can create the right context, you have access to a wider range of human knowledge.
  • Resources:

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Keynote: The Future of Work: Dealing with Digital Disruption #KMWorld

Speaker: Tracey Wilen, Author, Independent Consultant

Session Description: Everything we do is impacted by technology—how we communicate with others, connect at work, learn at school, and live our lives. We are accustomed to and dependent on technology. The accelerated pace of technology and competition is causing workplace environments to become more technical, diverse, and in need of disruptive leaders. This new landscape requires innovative styles of leadership and new techniques of managing organizations.  Drawing on over twenty years of research and the latest happenings in our world, Wilen discusses the key forces impacting the future of work, industries, leadership styles, skills, and education with a focus on how to remain relevant in an ever-increasingly complex digital world. Hear about the latest trends in a disruptive world, get practical advice about innovative best practices, case examples, as well as pragmatic tips and pointers.

[These are my notes from the KMWorld Connect 2021 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:

  • 3 Forces Impacting Organizations
    • Extreme Longevity: babies born today could live for 115 years. Science has made this possible. What are the implications of this for work?
      • Work until 85 in order to fund a longer retirement. This implies a 60-70 year career.
      • Multi-generational workforce (maybe as many as 5 generations at once). McDonald’s youngest employee is 14.5 years old. Walmart’s oldest employee is 102. (He is a gardener.)
      • Each generation has different expectations, work goals, motivations, learning styles, view of leadership, and points of view. And they have differing appetites for technology. The organization will have to accommodate all of these differences simultaneously.
    • VUCA: We live in a VUCA world. VUCA = Volatile, Uncertain, Complex, Ambiguous. The effect of this is to make us constantly feel like first responders.
  • 5 New Skills Needed for our New World
    • Multimedia literacy = the ability to absorb and use information from multiple content inputs: words, images and sounds
    • Data literacy = being able to aggregate, sort, and parse available data. PLUS the human element to understand the meaning/implications of these data.
    • Computational thinking = think like a computer
    • Virtual Collaboration = remote work can be a fabulous way to work but you must establish new ways of connecting, creating trust, communicating, supporting digital work processes, meeting organizational objectives.
    • Novel and Adaptive Thinking = borrowing trends to solve a problem with new technology
      • 3D printing is well-established for use in industrial manufacturing. Now a doctor is using liquid collagen to “print” a new ear for a patient.
  • 3 Points for Leaders
    • Interconnected Knowing = have access to broader real-time knowledge so that you keep on top of your business and the world. (She reports that Rupert Murdoch has to consume 6 hours of content per day in order to manage his business.)
    • Customer and Vendor Guidance = you need to integrate your knowledge from one end of the supply chain to the other. How can you make your customers and vendors part of your executive decision making?
    • Workforce Innovation = hiring innovative talent, harvest employee ideas, find diverse talent/idea people, employee advisory teams, new board members, etc.
  • 3 Points for Employees
    • You are not a robot — Remember that your manager did not hire you to do a robot’s job. The elements you personally add to the way you do your job will fundamentally change that job.
    • Become the CEO of your job — do what you need to do to make a success of your current position AND to enable you to move to the next level.
    • Everyone should take a “career selfie.” This involves doing periodic career planning. One key is writing down your goal and the next steps to get there. The act of writing it down moves your mind/will to action.
  • Takeaways
    • DISRUPT YOURSELF!!
    • Cast your net widely for new ideas. Remember that good ideas can come from anywhere — even from a 13-year old.

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Leveraging the New Normal to Drive Adoption of Enterprise Search

Speakers: Ari Kramer (Knowledge Management Officer, Robert Wood Johnson Foundation) and Sara Teitelman (Co-Founder & Principal, Ideal State)

Session Description: When the Robert Wood Johnson Foundation (RWJF) was just starting to roll out a new enterprise knowledge management function, enterprise search was part of the plan, but RWJF expected to approach it gradually. Then COVID hit, and everything changed as remote working became the norm. RWJF capitalized on this through a rapid-cycle user research process and enterprise search prototype that garnered broad organizational awareness and support. RWJF is now firmly on the path to implementing an enterprise search experience that combines enhanced use of available in-house technology capability with targeted new supporting roles to help it continually identify and elevate the kinds of information RWJF staff most want and need. Learn how RWJF has been able to leverage the “new normal” to drive dialogue around the importance of effective internal search capability, as well as the kinds of staff engagement, cross-departmental collaboration, and leadership support necessary for enterprise search to succeed in the long-term.

[These are my notes from the KMWorld Connect 2021 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:

  • When the lockdown began, they shifted from a general KM strategy to focusing primarily on implementing enterprise search. Here are the steps they took
    • establish a strong baseline understanding of the organization’s current and emerging needs relating to internal search and discovery
    • recommend technology to address immediate search needs and establish a foundation to guide future development
    • identify governance and supporting resources necessary for the effective management and maintenance of enterprise search
  • Discovery Stage
    • Interviews: they talked to staff to understand the needs generated both by the staff and by the foundation’s grantees. (The foundation is a network of networks, so there are many stakeholders.)
    • Mapping: they mapped the content types and locations. They found that their content lived a large variety of places. So what to keep inside the search experience and what to leave out?
    • Key themes:
      • confusion about where to search
      • duplicate content across multiple systems
      • workarounds used to index key resources (e.g., parallel system of Google Docs “link farms” that were shared from generation to generation of staff members)
      • people as “systems” of record
      • difficulty searching key systems
      • use of different naming conventions and standards
      • use of G suite (and later Google Suite) for file storage, sharing and collaboration
      • difficulty in finding staff, their expertise, and their projects
  • Philosophy: They decided to use what the organization had, rather than bringing in any new technology. The key existing tools were Microsoft 365 for email and content creation (although not much SharePoint use), and G-Suite for document collaboration and resourcing.
  • Process:
    • They started with a lightweight prototyping exercise.
    • Leveraging the native search in these existing technologies, they built a lightly customized search experience. This was done in parallel on Microsoft 365 and G-Suite so that they could choose the best technology for the organization. Then they configured two or more search connectors to index select data from their system and grantee/partner websites. Their goal was to generate excitement among staff for the possibilities of enterprise search.
    • They selected Microsoft 365 as the basis of their enterprise search engine
    • They selected Raytion in Germany as their implementation partner. (Raytion was familiar with both Microsoft 365 search and G-Suite search.)
    • They created a project team and staff search liaisons who could provide depth and reach in terms of knowledge and support.
  • Architecture: Their working architecture for Phase 1 of enterprise search involves indexing a fixed set of materials via Microsoft Search Index. Then they used SharePoint User Profile Service to collect content from their Oracle data warehouse and Azure active directory, and SharePoint Online Search Index to reach content in SharePoint and Google Docs.
  • Custom Development: They believe that there is a lot of promise in what Microsoft Search is planning to roll out. So they will first take advantage of what Microsoft offers before committing to a lot of customized development.
  • Extending the Impact:
    • the search project has allowed them to expand KM-related relationships with Program, Learning, IT, HR
    • they have created a stronger connection with other key organizational initiatives such as their Internet redesign
    • they have elevated the value of key related KM work areas (e.g., metadata, governance, etc.)
  • Takeaways for an enterprise search project:
    • establish and maintain strong alignment with organizational strategy
    • leverage user research as a chance to connect with staff across the organization
    • identify champions and staff in natural positions to play supportive roles
    • find the right balance between search rollout priorities and long-term opportunities
    • engage key stakeholders as much as possible in ongoing decisions and work
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Augmented Intelligence for the Enterprise #KMWorld

Speaker: Igor Jablokov, CEO & Founder, Pryon

Session Description: Named an “Industry Luminary” by Speech Technology magazine, our speaker founded the world’s first high-accuracy, fully automated cloud platform for voice recognition which served as the nucleus for follow-on products such as Alexa, Echo, and Fire TV and which was the precursor to Watson when he was with IBM. In this session, he discusses knowledge ops, an AI-enabled, process-oriented methodology used by business units, knowledge managers, and application developers to improve the quality and accessibility of knowledge through the enterprise. It’s an emerging, additive approach to KM challenges, including continuously capturing and making available everything the company knows across all departments in near real time so decisions can be made and actions prioritized based on the best possible information; knowing what is valuable to a person it has never interacted with; where all this knowledge should be collected and who will keep it organized as it grows and grows. AI plays a massive supporting role here, orchestrating and fusing data from any information, content, or knowledge place and file format. Our speaker emphasizes how augmenting agents with a state-of-the-art language model that understands natural language queries can help organizations eliminate steep learning curves so new agents perform like pros on Day One.

[These are my notes from the KMWorld Connect 2021 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:

  • Augmented Intelligence vs Artificial Intelligence
    • He prefers to focus on Augmented Intelligence rather than Artificial Intelligence. People are concerned that artificial intelligence will replace humans. But he believes the better approach is to use Augmented Intelligence, which will not displace humans but will help them work more productively.
    • [All references to AI in this post are intended to be references to Augmented Intelligence.]
  • Why focus on Augmented Intelligence?
    • AI transforms raw data into consumable knowledge
    • Once the AI is trained to be “literate”, it can “read” across all categories
  • What is possible with Augmented Intelligence? To see the current capability of AI, look at the consumer world. Consumers are comfortable using Alexa and Siri.
    • Next, they will expect to have similar capabilities at work.
    • BUT the enterprise has to manage this at scale within typical organizational constraints (e.g., security, competitive, regulatory, etc.). So the enterprise is slow to meet this demand.
  • Imagine the future of work in 2030
    • Wall Street 2030 will reward the AI winners and losers
      • Companies that left employees out of their AI strategy got left behind
      • Companies who moved from narrow to unified platforms were winners (by working more comprehensively rather than limiting themselves to small use cases)
    • 2030 org charts will include humans + AI
      • humans will manage a team of AIs doing the rudimentary work, which will free humans up to deal primarily with more challenging and interesting issues
      • Companies will need to increase capacity by 10X but will not be able to increase their staff by 10X. So they will make up the difference in new AIs that can plug the gap.
    • AI Assistants
      • AI can predict, read, summarize, find answers and experts. With this out of the way, their human colleagues can focus on the more abstract, conceptual work.
      • Big tech is making heavy investments in AI assistants. Those assistants will become the dominant interface between the users and the 278 apps (and their unique passwords) on your phone. The AI assistant becomes the agent that “harmonizes” your experience of the various apps.
      • Assistant to Assistant Collaboration: Soon, you will be able to say “Have your AI call my AI.”
  • Present Day Capabilities
    • If knowledge is power, then knowledge visibility is a superpower.
    • Companies win with enhanced knowledge visibility. Key examples:
      • Amazon
      • Netflix
      • Uber
    • Full knowledge visibility requires powerful AI to continuously read, organize and retrieve all the content in your systems. It also provides next generation experiences to employees and customers via natural language processing that is automated as much as possible.
      • NO code, no skills required to create apps. You need to democratize access to knowledge and move the engineering to the edge.
  • Benefits of full knowledge visibility
    • with a knowledge “fabric” that is centralized and easy to access, you will be able to achieve better business outcomes and improve efficiency.
  • Legacy Knowledge Management has failed us:
    • Deloitte found that 91% of organizations do not have knowledge management capabilities and systems ready to take advantage of AI.
    • Knowledge in an organization is scattered and siloed.
    • Companies often either do custom development tailored to their content or they give up and just create a people-centric model in the hope that connecting people will help them keep up with the rapid growth in content.
  • How AI solves Knowledge Management
    • The first mile: it more comprehensively gathers ALL the content — it can gather, unify, normalize the content and then fill in the missing gaps in knowledge.
    • The last mile: it uses NLP to deliver content that is tailored to the individual needs of users.
  • Ambient Computing will leave current capabilities in the dust. Ambient computing will predict what you want and deliver it to you BEFORE you even ask for it.
  • Farm-to-Table AI: look for vendors who can offer creative AI to you as soon as it is available rather than waiting for large tech companies to select, standardize and “cook” the technology for you first.
  • Modern AI changes your knowledge base strategy. He suggests that we should no longer create knowledge bases. Instead, he believes that modern AI can create a better knowledge base for you, and do so more comprehensively and accurately.
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Tomorrow’s KM Leaders: Education for KMers #KMWorld

Speakers:

  • Daniel Ranta, President, DR Consulting
  • Dennis Pearce, Collaboration Strategist, Start Early
  • Ed Hoffman, CEO, Knowledge strategies, LLC
  • Kendra Albright, Goodyear Endowed Professor in KM, School of Information, Kent State University
  • Kim Glover, Director, Knowledge Management & Social Learning, TechnipFMC
  • Stan Garfield, Knowledge Management Author, Speaker, and Community Leader (Moderator)

Session Description: The demand for KM professionals continues to increase; according to many popular job sites, KM-related jobs are likely to increase substantially in the next 2 years. The present demand for various levels of experienced KMers has created debate about whether formal graduate education, i.e., a master’s degree, or alternative and varied experiences provide the best preparation and pathway to KM employment. Our panel discusses professional competencies needed and master’s degrees from various schools of information as well as other pathways to KM leadership roles.

[These are my notes from the KMWorld Connect 2021 Conference. I’m publishing them as soon as possible after the end of a session so they may contain the occasional typographical or grammatical error. Please excuse those. To the extent I’ve made any editorial comments, I’ve shown those in brackets.]

Notes:

  • What undergraduate programs best prepare you for a knowledge management career?
    • There is no single preferred path.
    • The panelists have a wide range of backgrounds: business, engineering, journalism, computer science, psychology, organizational development, leadership development, interdisciplinary studies, etc.
  • Why should someone get a graduate degree in knowledge management?
    • their organizations want them to have additional depth in the subject, as well as a credential
    • they are personally curious about how and why things work the way they do in organizations
    • they have work experience that has opened their eyes to reality in the workplace, to issues that they want more tools to address
  • What are the KM capabilities that organizations are looking for in a person who gets a formal KM education?
    • the ability to help people to learn, to share knowledge
    • great communication skills (oral and written) and the ability to sell an idea
    • mastery of the human aspects of being successful at implementing change
  • Balancing theory and practice: Dan Ranta is teaching a graduate course on developing a KM program in an organization. He and his students appreciate that the course presents a real “view from the trenches.” It is more practical and actionable than theoretical.
    • Kim Glover: “‘balancing the relationship between theory and practice’ is a critical component for setting up KM students for success in the business world.”
  • Alternative Paths: An alternative path to KM leadership is to consciously recognize the KM skills and methods you are using in your work — even if your title does not include KM.
    • You may well be “doing KM without knowing it” as part of your current responsibilities
    • Many of the panelists fell into KM by accident
  • How have the requirements and capabilities of the KM field changed over the last 25 years?
    • Dennis Pearce thinks that knowledge has “turned from a solid to a liquid”:
      • 25 years ago, KM was more focused on formal document capture and collection. The skill required then was the ability to persuade people to actually document their knowledge and then share that document.
      • Over time, we have come to understand that knowledge is more fluid and, therefore, requires more informal ways of sharing knowledge. As a result, people are writing more than ever, but in a less structured way. So the skill necessary now is the ability to find the useful, actionable knowledge buried in the constant stream of messy, informal written conversation.
  • Is there an advantage in pursuing a Ph.D.?
    • At Kent State, they offer a Ph.D. in Communications with a specialization in knowledge management. It gives the KM practitioner a broader knowledge basis and training to do further research.
    • It helps with your personal brand and credentialing.
    • Do it for the love of learning.
    • If you are a generalist, a KM degree gives you a way to get expertise that is widely transferable across a variety of domains.
  • How to weigh a KM certification vs a KM degree?
    • Dan Ranta believes that both give you a credential that separates you from the uncredentialed KM professional.
  • How to persuade your employer to pay for your graduate degree?
    • much depends on the culture of your organization, independent of KM.
    • does your company have a program for advanced degrees?
    • if you see problems in the organization that could be helped with KM, you could pitch the degree as a way to gain the expertise necessary to solve the problems.
  • Is there a change in KM capabilities that we should focus on in our KM education?
    • Kim Glover sees the strong need to go back to basics: focusing on people, behavior and culture. We have to strike the right balance between the technological capabilities and “reigniting the human capabilities for learning and sharing.” [Amen!]
  • Additional Resources:

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Learning Strategy & KM #KMWorld

Speakers: Anders Sjolokken (Vice-President, Learning & KM, TechnipFMC) and Kim Glover (Director, Knowledge Management & Social Learning, TechnipFMC)

Session Description: Collaboration is critically important in leveraging what an organization ‘knows’, and that’s why KM is a key element in TechnipFMC’s enterprise learning strategy. The company is invested in supporting ‘enterprise dialogue’, both cross-functionally and within communities of practice as an enabler for social learning, innovation and better business outcomes. Connecting people across the organization provides continually increasing value as employees co-construct ideas and experiences into new solutions and better ways to solve problems as a collective, while individuals also benefit as they gain new knowledge and skills, thus ‘leveling up’ the skills of the organization. The ways in which TechipFMC utilizes KM and social learning tools and techniques to underpin learning programs and fuel collaboration are explored in short case studies, and the cultural aspects are shared, as healthy dialogue requires psychological safety and trust, as collaboration is established as an expected, normative behavior.

[These are my notes from the KMWorld Connect 2021 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:

  • Trust is Critical for Enterprise Dialogue
    • trust that someone is listening
    • trust that someone has an answer to my question
    • trust that my question won’t be career-limiting
    • trust that knowledge sharing is valued by the enterprise as a whole and by my colleagues individually
  • Interpersonal Trust
    • has to be built — it requires openness to learning and a willingness to have difficult conversations that establish honesty.
  • Connection before Collaboration
    • Personal connection is critical before true collaboration
    • “Turtaking” is a Norwegian for taking turns in which you stop to listen deeply to your colleague to understand what they are really saying (not just what you think they are saying)
    • You need to “co-construct meaning together” so that no single person is the sole author. [Instead, the finished product reflects the aggregate expertise of the collaborators. Ideally, it will be greater than the sum of its parts.]
  • Develop a holistic perspective: View the enterprise as a holistic ecosystem that includes KM methods/tools and Learning methods/tools.
    • then mix, match, and deploy the tools for optimal learning
  • Culture: Establish a Learning and Collaboration Culture at the Individual and Enterprise Level
    • enable collaboration, knowledge sharing and learning at the individual level. Individual adoption supports personal development
    • leader-driven deployments to support business strategy and the individual’s work.
  • Learning Ecosystem:
    • 4 Interrelated Platforms
      • A competency model that connects directly with the learning system
      • Learning hub (formal learning curriculum and a catalog for employee-driven learning)
      • The collective knowledge base built by the employees to support their exchange in the communities of practice
      • Business-sponsored communities of practice
    • Integrate KM into Learning: Knowledge resources are fully integrated into the learning strategy
      • knowledge sharing webinars
      • podcast about their people and business
      • facilitated collaboration (virtual workshops + a platform for crowd-sourcing ideas)
  • Putting it into practice
    • Once the Learning & KM Strategy is in place, you have the tools to deeply enrich knowledge sharing and learning. Their channels:
      • The Well = their document knowledge base
      • Overview course = eLearning course
      • Illuminate = their podcast
      • The Stream = video materials
      • The Bridge = their communities of practice
      • Experts Explain = another online training opportunity
      • Practitioner course = online class
      • Supportive champions / mentors
    • The speakers gave the example of their Inclusive Leadership / Unconscious Bias Learning Plan. They were able to push learning and knowledge resources through:
      • Setting the stage: Podcast with a senior executive + external resources (relevant articles and TED talks)
      • eLearning produced by the company
      • self-awareness assessment
      • understanding impact on others (using LinkedIn Learning and TED talks)
      • Engage with Technip FMC leaders in live webinars
      • Continuous Learning through additional resources coupled with self-assessments

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