Keynote: AI Transformation – Competing in the Age of AI #KMWorld

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Speaker: Marco Iansiti, Professor of Business Administration & Coauthor, Competing in the Age of AI , Harvard Business School

Session Description: Join Marco Iansiti as he shares insights on the revolutionary impact AI has on operations, strategy, and competition beginning with a look at the core of the new firm, a decision factory he calls the AI factory. All the more relevant in the age of COVID where we have seen digital transformation move at an accelerated pace, the AI factory is where analytics systematically convert internal and external data into predictions, insights, and choices, which in turn guide and automate operational workflows. As digital networks and algorithms are woven into the fabric of firms, industries begin to function differently and the lines between them blur. The changes extend well beyond born-digital firms, as more-traditional organizations, confronted by new rivals, move toward AI-based models too,” says our speaker. With insights from the co-author’s revised preface, gather ideas to meet the challenges of a new reset world and find the correct strategies to harness AI for your organization.

[These are my notes from the KMWorld Connect 2020 Conference. 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.]

Rembrandt and a developer walk into a bar…

AI creates the next Rembrandt

The developers on this project created a 3D-printed canvas that looked a lot like a Rembrandt original. The response was mixed: some people were delighted by the potential of AI to enrich the arts but others thought it was a travesty. Jonathan Jones, one of the leading experts on Rembrandt’s art, described the digital Rembrandt as “a new way to mock art, made by fools.”

Regardless of what you think about this particular work, Iansiti points out that with the help of AI, you no longer have to be a genius to produce something that could possibly pass as a Rembrandt painting.

That is the power of AI.

AI Basics

  • Definitions:
    • Weak AI: “Any activity computers are able to perform that humans once performed”
    • Strong AI: “Machines that can think or act in a way that matches or surpasses human intelligence”
  • Relatively simple AI, coupled with effective algorithms and good data, can do remarkable things.
  • You don’t always need strong AI to make a meaningful difference.
  • Weak AI can improve a wide range of operations across your organization and across the economy. Some examples:
    • Customer intelligence and recommendations
    • Market intelligence and forecasting
    • Diagnosis and treatment systems
    • Fraud analysis and investigation
    • Business process automation and internal bots
    • Predictive maintenance and resource optimization
    • IT automation
    • Adaptive learning
    • Research and discovery
    • Intelligent search
  • As you move labor and management off the critical path of these key operations you change the basic nature of the firm. AI transforms the firm.

Rethinking the firm

  • To take advantage of the real possibilities of AI, we have to think about the firm differently.
  • It no longer makes sense to have a traditional, siloed structure. Rather, it makes more sense to build firms with a “softer core” based on a platform of data analytics, coupled with people creating algorithms and enabling smarter automated processes as quickly as possible.
  • To rethink the firm, you have to look at both its business model (i.e., value creation and value capture) and its operating model (i.e., how they deliver value via scale, scope, and learning).
  • AI-savvy firms are able to offer a broader and richer array of products and services than traditional organizations, and they do it with far fewer people.

Ant Financial Case Study

  • Iansiti says that Ant Financial has about one-tenth the number of employees of Bank of America.
  • The core of Ant Financial is a data lake exploited by a huge range of algorithms. They use their vast data to identify consumer preferences and then create products and services to meet those needs. Then they can scale and personalize these very quickly.
  • At Ant Financial, traditional human-centered “processes are digitized to connect with market opportunities at near zero marginal cost. Operational bottlenecks are digitized.”
  • Firms like Ant Financial use AI to drive digital scale, scope, and learning.
    • As the data accumulate, they drive even faster experimentation, improvement, innovation, and personalization.
    • This enables an even greater number of profitable products and services.

The AI Factory

  • The core of a company like Ant Financial is an AI Factory.
  • The AI Factory feeds data and models systematically into the software-enabled operating layer of the firm.
  • This requires more than a collection of Excel spreadsheets supporting traditional human analytics.
  • You have to “industrialize” the process of data gathering, cleaning, normalizing, integrating, and use.
  • This then feeds the Operating Model Core: data, software components, APIs, and applications.
  • There are humans involved with designing, monitoring, and managing operations but they are not on the critical path. They do their work from the perimeter of the operating core.

The Economics of the AI-Enabled Firms

  • AI-enabled firm create relatively little value until they reach scale. They create more value as they get bigger.
  • By contrast, traditional siloed firms tend to create less value as they get bigger because it is hard to manage large human organizations. They become bogged down by silos, red-tape, complexity, administrative overhead, and other operating inefficiencies of size.
  • This means that AI-enabled firms have the potential for unlimited value creation while traditional firms face diminishing returns.

From Disruption to Collision

  • As more AI-enabled firms emerge, they are colliding with traditional players in their industries. They are fighting for the same customers but their operating models are completely different:
    • Ant Financial vs HSBC, AirBnB vs Hilton, Waymo or Uber vs Ford, Moderna vs Merck.
  • As these collisions occur, they fundamentally change their industry and force traditional firms into digital transformation.

Digital Transformation Creates New Responsibilities

  • With the expanded use of AI comes new ethical concerns
    • Increased data collection triggers
      • cyber security issues
      • Privacy issues
    • New focus on algorithms — how they are created and their unintended consequences:
      • Increased issues of inclusiveness and inequality
      • Algorithmic bias

Thanks to Covid-19, this Change is Accelerating

  • Digital Transformation is no longer an option — the coronavirus pandemic has forced even the most hidebound organization to become digital and distributed.
  • After the pandemic, some companies will see the benefits of the digital virtuous cycle and will build on their learnings and technological gains. Others will jump at the opportunity to “return” to their pre-pandemic status quo.

AI-Enabled Responses to the Pandemic

  • We are the midst of a period of huge uncertainty in science, logistics, and policy. This requires enormous agility.
  • In a period of uncertainty, anyone with a successful model is celebrated.
  • Moderna
    • It was built on a powerful software and data platform. It has one system of record, an enormous data lake that combines all their data ranging from research, to clinical tests, to company financials.
    • It uses a very innovative approach in a very traditional industry
    • Their vaccine was created in 25 days — this is extraordinarily fast
  • Massachusetts General Hospital
    • It is on the other end of the spectrum from Moderna.
    • They had an old-fashioned ERP. [NOTE: members of the audience disputed this point. They say that MGH spent $1.2B (by public admission) on Epic. They don’t believe Epic should be considered a legacy ERP.]
    • They went through years of effort to achieve any digital transformation — and then they stood up telemedicine in a matter of weeks.
  • IKEA.
    • Ikea has an innovative but fairly traditional approach to retail
    • When the pandemic began, they shut down all their stores for three days so they could implement a whole host of planned digital transformation projects.
    • They transformed their web presence and made real progress on digitizing their supply chain.
    • The key to their success was that they were far along their planning process so they primarily faced an implementation challenge. They did not have a standing start.

Amazon Case Study

  • Amazon started a process of digital transformation in the early 2000s after they realized they were almost dying under the weight of complexity.
  • Prior to 2002, Amazon was built like a traditional company. By 2002, they were essentially coming apart at the seams.
  • So Jeff Bezos issued an API mandate that fundamentally changed their course.
  • In response, they re-architected their operating model using service-oriented architecture (SOA), which is a way to make software components reusable via service interfaces. This enabled Amazon to become a Platform.
  • The rest is history.

Microsoft Case Study

  • They are transforming from a traditional approach, where the organization is siloed and then develops its IT within those silos, to an AI-first approach — where the entire organization operates from a shared foundation of a single AI factory.
    • Microsoft now operates from a single data lake.
  • Once the data is consolidated, they then deploy agile teams to build processes across the organization in response to needs.
  • Satya Nadella: There is too much promise in AI to trap it in the IT department. The entire organizational now must be involved.

Call to Action

  • Understand and actively anticipate the transformation of our economic and social environment.
  • Drive business and operating model transformation consistently and from the top down.
  • Invest heavily in data pipeline and architecture: encourage strategies grounded on experimentation, analytics, and digitization
    • Don’t do it on spreadsheets — invest in the technology
  • Use pilots to build internal capabilities. Make sure you have a plan and process to implement and scale.
  • Build inclusion, privacy, transparency, and security from the ground up. Focus on the ethical implications — digital transformation is creating new ethical obligations for organizations and their leaders.
  • Help the growing part of the population facing greater need.

Advice for those beginning the digital transformation journey

Congratulations! The good news is that you have the advantage of the latest technology. From a technology perspective, there is no longer any excuse for not being in the cloud. The bad news is that you will have a LOT of work to do to transform your organizational culture and processes.

Your organization MUST change.

The last few months have been mainly about reacting to changes in the environment. Now, we can take a step back and re-imagine a better, more thoughtful, planned approach for our organizations.

The research has shown that the deployment of AI and KM tools will affect the full-range of jobs. Everyone becomes a knowledge worker to the extent they embrace the new technology and approaches. This makes it more critical, as a policy matter, to make sure the workforce is up to the task. The workforce needs to be able to respond quickly to changes. What does the workforce look like when everyone is able to use these new tools?

We are at an inflection point. The pandemic is accelerating enormous changes. Within 5-10 years, every organization will be run differently. Those who invest in it will do just fine. Those who do not will be left behind. It’s time to really do this!

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The Silver Lining of a Virtual Conference #KMWorld

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We’ve all experienced many losses this year. While we have done our best to create moments that matter via video conference rooms and chat, we know it is not quite the same thing as being together in a shared physical space. But take heart. In this year of loss, I’m here to tell you about one particular silver lining.

Last month, the organizers of the KMWorld Conference hosted a remarkably successful gathering, KMWorld 2020 Connect. Although we could not be together physically, we were able to attend a wonderful array of educational sessions and interact with an even more wonderful array of attendees virtually. I attended several of these sessions and made my notes available to you on this blog. However, given my lack of a personal clone (or three), I could not attend all the sessions that interested me.

This is where the silver lining appears: every public session of the conference was recorded. Better still, anyone who registered for the conference has access to these recordings until March 1, 2021. This means that I can now fill in the gaps in my reporting by revisiting sessions I previously attended but could not blog at the time. In addition, I can now attend sessions I could not attend earlier. Of course, this also means that there may well be a few more KMWorld blog posts here!

I’m looking forward to learning more from the knowledgeable speakers at KMWorld 2020 Connect. I hope you’ll ride along with me.

An offer to my readers: Take a look at the conference schedule and let me know if there is a particular session you would like to learn more about. If there is sufficient interest (and the topic is reasonably within my wheelhouse so that my notes are likely to be useful), I’ll attend the session and post my notes here. (NOTE: this offer is available only for sessions on November 16-19.)

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Gratitude Creates Better Managers

Saying thank you seems like the simplest, most basic form of courtesy. Yet so often we forget to thank others for the big and small gifts we receive.

This year, I am profoundly grateful for the gifts of life and health. Further, 2020’s challenges have especially driven up the value of the gifts of family, friends, and teammates. The wonderful people in my life have made all the difference.

(Give thanks. Photo by Simon Maage.)

The positive impacts of gratitude at work

If all of this isn’t enough, here is another reason to up your gratitude game: practicing gratitude makes you a better manager and creates a better workplace.

Courtney Ackerman has collected in one helpful place 28 benefits of gratitude and the most significant related research findings. Items 16-20 on her list concern the positive impacts of gratitude in the workplace:

  • Gratitude makes us better managers: “Gratitude research has shown that practicing gratitude enhances your managerial skills, enhancing your praise-giving and motivating abilities as a mentor and guide to the employees you manage (Stone & Stone, 1983).”
  • Gratitude makes us less impatient and improves our decision making: this applies to both financial and health-related decisions (DeSteno, Li, Dickens, & Lerner, 2014).
  • Gratitude helps us find meaning in our work: “Gratitude is one factor that can help people find meaning in their job, along with applying their strengths, positive emotions and flow, hope, and finding a ‘calling’ (Dik, Duffy, Allan, O’Donnell, Shim, & Steger, 2015).”
  • Gratitude contributes to reduced turnover: “Research has found that gratitude and respect in the workplace can help employees feel embedded in their organization, or welcomed and valued (Ng, 2016).” In addition, the Kelly Services Global Workforce Index (Kelly Services, 2011), found that “feeling unappreciated is the top reason why people leave their jobs, suggesting that workplace gratitude may aid in retention and in creating a stable work environment” (Dik, Duffy, Allan, O’Donnell, Shim, & Steger, 2015).
  • Gratitude improves work-related mental health and reduces stress: “Finding things to be grateful for at work, even in stressful jobs, can help protect staff from the negative side effects of their job.”

What comes next?

Taking this advice to heart, I’ll be sending friends and colleagues notes of gratitude this week. And, because this works best when gratitude is a regular practice rather than an occasional event, I’ll commit to conveying my thanks more often to the people who touch my life. In the process, research shows that I’ll rewire my brain for the better, become happier, and, most importantly, contribute some joy to others.

If you’ve made it this far, thank you for support of this blog. I appreciate each and every reader. You make this effort worthwhile.

I wish you and your loved ones a safe and happy Thanksgiving holiday!

[Photo Credit: Simon Maage]

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Keynote: Responsible AI – Ethics and Inclusive Design #KMWorld

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Speakers: Jean-Claude Monney, Board of Directors Member, Keeeb; Phaedra Boinodiris, IBM Academy of Technology, Executive Consultant, Trustworthy and Responsible Systems; and Steve Sweetman, Customer & Strategy Lead, Ethics & Society Engineering, Microsoft

Session Description: Join our exploration into the future of AI and other emerging tech as it transforms the knowledge sharing, collaboration and innovation in our organizations. Responsible AI, ethics and knowledge management definitely intersect and are routed in culture change and business transformation. Our experts share a lively discussion with the audience and will leave you thinking about what’s next for AI, KM, and our world in 2021 and beyond!

[These are my notes from the KMWorld Connect 2020 Conference. 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.]

Intro

  • Why are these speakers here today?
    • Phaedra has been interested for a long time in both technology and social justice. Her new role at IBM is to work in the trust and AI practice. She is focused on how to reduce bias and increase trust in tech systems.
    • Steve had an aha moment on March 23, 2016 when Microsoft’s friendly chat bot had been poisoned by hackers and turned into a racist and vicious bot. This taught him that ethics were no longer academic. They needed to make ethics real in their tools so that they can build responsible AI.
    • JCM taught students in the Columbia M.S. in Information & Knowledge Strategy about ethics in connection with digital transformation. The students quickly realized how critical this issue is.

Technology

  • KM’s basic concept is to provide relevant information for reuse. When this is enabled by AI, where is the bias in the system? (See below!)
  • For the last 20 years, we’ve been teaching people how to enter data into computers and then work with that data. With the advent of AI, we are teaching computers how to consume data and work with it. But the great dilemma of AI is that we don’t understand how the system reaches a specific conclusion. So how do we trust it?
  • 4 questions to ask before purchasing an AI system
    • What are the intended uses of the system that you’ve built it for and trained it for?
    • What are the unintended uses that you haven’t built it for and trained it for?
    • What makes it perform? What makes perform well?
    • What are its limitations?
  • Other questions you should also be asking:
    • Is it fair? Is it biased?
    • Is it easy to understand and explain to non-technical stakeholders, users or administrators?
    • Is it tamper-proof?
    • Is it accountable? Does it have acceptable governance standards?
  • How can organizations mitigate bias?
    • There are a lot of tools. For example, IBM has donated Fairness 360 to the Linux Foundation.
    • Culture is a big issue. How are teams made up? Consider employing red team vs green team tactics (borrowed from the cybersecurity world).
    • Governance: make sure you have published standards that explain your company standards to the market and your employees. Do you have a diverse, inclusive AI ethics board? Do employees have a way to submit anonymously their ethics concerns?
  • Education is a big challenge
    • Why are we not teaching AI and ethics in high school and even middle school?
    • Current leaders in organizations and government do not seem to understand AI. So they cannot understand the true impact of this technology. All leaders should be at least fluent in AI because it will affect every part of their organizations.

Ethics

  • Recommended reading: Brad Smith, Tools and Weapons
  • If is not a question of IF we have bias in our systems. It is a fact that we DO have bias in our systems.
    • bias comes from the lack of diversity among developers and executives
  • Do NOT attempt to determine AI ethics this alone. It is not something for data scientists to do by themselves. You must involve different stakeholders who bring different points of view to the discussion.
  • The diversity prediction theorem = the more diverse and inclusive the crowd, the closer you get to ground truth.
  • Warning signal: major lack of diversity leads to diminished fairness in AI systems
  • Forensic technology = the tools you can use to create responsible systems. They help address fairness, explainability, transparency
  • How to find bias in your AI systems?
    • Ask if your models would keep you from offering the same service to people? Do you discriminate on a false basis?
    • Do you have fair representations of the services you are recommending? Do different people get the same outcomes?
    • Are we stereotyping? Are we using labels, for example, that reflect inherent bias?
  • How to mitigate?
    • Correct the existing data
    • Collect more representative data
    • Look at all the models across your systems — work to improve all of them and track your progress
  • How to address bias in AI used for hiring and promoting?
    • It is rare to find a bias-free system. Be hyper aware of hidden bias. There are many types of bias beyond race and gender.
    • Pay attention to the training data set. There may be bias in that set — for example, if successful people in a specific job were historically white and male, then the historic data used to train the AI will be biased in favor of white males.
  • Microsoft has a sensitive use protocol. Not all AI systems have the same impact. When AI systems could have a disproportionate impact on peoples lives, then you need to slow down development to ensure they are fair, safe, and trustworthy. Examples of high impact systems:
    • hiring, lending, admission to school
    • system misfires could result in injury to someone
    • system could diminish a person’s human rights)

Culture

  • Microsoft
    • You need to create internal standards that you will live by: put ethics and fairness at the same level as security and innovation.
    • Ensure diversity of teams at every level from ideation to design to development to market delivery sysems
  • At IBM
    • Culture — big focus on diversity and inclusivity; advocacy for ethics in technology
    • Forensic Technology — donating tools to the open source community to tackle fairness, explainability, transparency
    • Governance — shaping global standards on technology governance

Resources

  • https://www.ibm.com/thought-leadership/institute-business-value/report/ai-ethics
  • https://www.ibm.com/artificial-intelligence/ethics
  • https://hbr.org/2020/11/how-ibm-is-working-toward-a-fairer-ai
  • https://www.ibm.com/watson/assets/duo/pdf/everydayethics.pdf
  • https://aka.ms/RAIresources
  • https://azure.microsoft.com/en-us/services/machine-learning/
  • https://docs.microsoft.com/en-us/azure/machine-learning/concept-responsible-ml
  • https://docs.microsoft.com/azure/architecture/guide/responsible-innovation
  • https://github.com/fairlearn
  • https://aka.ms/FairLearnWhitepaper
  • https://docs.microsoft.com/azure/machine-learning/concept-fairness-ml
  • https://github.com/interpretml
  • https://aka.ms/InterpretMLWhitepaper
  • https://docs.microsoft.com/azure/machine-learning/how-to-machine-learning-interpretability
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Keynote: The Role of Knowledge and Information in Crisis Management #KMWorld

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Speaker: Dave Snowden, Chief Scientific Officer, Cognitive Edge

Session Description: Crisis management has moved from planning to a day-to-day reality. However organizations are ill equipped to manage a situation where we are dealing with unknown unknowables or have to deal with multiple Black Elephants (something that changes everything!) competing for resources and attention. What is the role of knowledge and information in a crisis? How do we gain attention to weak signals where anticipatory actions would reduce downstream risk and increase overall resilience. Shifting from Just-in-time. Just-in-case sounds like a good idea but it is far from simple and in a resource starved environment may simply not be possible. For the last few decades we have based practice in industry and government on an engineering metaphor, focusing on efficiency. This approach is, to quote Lincoln, Inadequate to the stormy present. Are there better approaches that we can adopt by treating the organization and society as a complex ecology? Would such a metaphor shift allow us to do more with less? Last year’s conference ended with a rousing discussion of creating resilience in organizations and society. They discussed transforming and revolutionizing the way we do business as we move into an uncertain future, how we satisfy our clients in an ever-changing technological age, and how, in our complex societies, we provide value, exchange knowledge, innovate, grow and support our world. Our popular, and sometimes controversial, speaker Dave Snowden has again assembled a group of experienced thinkers and doers who are capable of reimagining a future based on uncertainty.

[These are my notes from the KMWorld Connect 2020 Conference. 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: [This is a long read but it contains a lot of food for thought.]

Intro

This talk explains how effective knowledge management can be a vital aid in a crisis. Snowden’s approach draws on his earlier work, especially Complex Acts of Knowing. This article was one of the first articles to focus on (1) levels of abstraction and (2) the role of informal networks as “a highly energy-efficient form of knowledge transmission”.

Current Projects

  • He is working on a European Union handbook on how to manage in a crisis. It includes a five-step process for getting out of a crisis and how to use distributed networks and your own employees to do that.
  • They are also working on post-conflict reconciliation. Given the current political climate around the world, they believe this will be necessary to create a stable market.

What’s Wrong with KM? (Part 1)

  • KM’s Core False Assumption: if we just surface the information (by asking them to write down what they know, contribute to a shared repository, generate lessons learned, participate in a community of practice, etc.), then magically knowledge will flow throughout the organization.
  • Knowledge management professionals have been trying this for the last 30 years but it doesn’t work.
  • Why doesn’t it work?
    • They assume information flows automatically between people without thinking first about the nature of the information itself and how it works.
    • They are ignoring the impact of levels of abstraction.

Levels of Abstraction

  • The highest level of abstraction happens when you have a conversation with yourself. There is lots you understand and do not need to specifically explain to yourself because you share your own education and experience. So you can effectively communicate in shorthand. There is little cost of codification. Any notes you write do not require elaboration because you know what they mean.
  • The lowest level of abstraction is triggered when you want everyone to know what you know. The cost of codification becomes infinite becomes you have to provide to everyone the same education and experience. To achieve this, you must communicate your knowledge in the simplest, most concrete and comprehensible way.
  • In any information flow, you must first determine the upper and lower levels of acceptable abstraction.
    • The higher the level of abstraction, the richer the conversation but the fewer the number of people who can participate.
    • The lower the level of abstraction, the thinner the conversation, the greater the cost of codification and maintenance, but the more people who can participate.

Maps and Taxi Drivers

  • The following section relates to work Snowden did with Max Boisot.
  • Snowden and Boisot did some work together based on the work of Michael Polanyi. Snowden extends Polanyi’s observation: “We know more than we can say, and we say more than we can write down.”
    • This contrasts two extremes of knowledge: tacit and explicit. (He doesn’t like these terms and prefers not to use them.)
  • Boisot observed that highly abstract but highly codified knowledge will diffuse to large populations fairly quickly. Examples: a map versus a taxi driver.
  • A map. It contains highly abstract symbols (e.g., symbol for type of church), which he has learned over time and is able to use to navigate easily.
  • A London taxi driver’s “Knowledge.” They have to know all the possible routes by memory, including all major landmarks along each route. The qualifying exam is rigorous and has only a 40% pass rate. People who pass tend to be highly adaptive (and, apparently, highly ethical). Interestingly, their training also enlarges their hippocampus to enable them to hold the additional new spatial mapping. (It takes about 2 years for this enlargement to occur.) This is very low abstraction, very low codification, and very low diffusion.
  • Both types of knowledge are valuable. However, in a competition between a map user and a taxi driver, the taxi driver will win every time. This is because using the OODA loop (Observe, Orient, Decide, Act) to plan the route is highly explicit and slow for the map user but intuitive and very quick for the taxi driver. And, if something goes wrong, the taxi driver can adapt to changes in the terrain more efficiently. (Maps fall out of date and they contain assumptions that may not be explicit. Example, the map may show a route but it likely won’t tell you if it is safe at night.)
    • NOTE: Most KM databases are highly abstract and highly codified (like maps) and make assumptions about what other people know. If those assumptions change, then the database is less useful.
  • So when you are thinking about the kind of knowledge you have and how it should be shared and used, first ask if you need a taxi driver or a map. Don’t automatically assume you need a database (i.e., a map).
  • The taxi driver takes time to train but then becomes highly adaptive and resilient. The map user takes no time to train, but is not nearly as adaptive or resilient. Both are useful, but in a crisis you need taxi drivers. However, because you don’t have time to train them in the crisis, you must invest in training them before the crisis begins.

Narrative-based Knowledge

  • Micro-Narrative or Narrative-based knowledge: humans historically have used stories to share knowledge. These stories are not highly planned and polished, they are more spontaneous natural. They are “wild anecdotes rather than tame stories.”
    • These stories surface weak signals, they surface outliers (e.g., people who are thinking differently).
    • These stories are a way of surfacing attitude: attitude to safety is a leading indicator while compliance is a lagging indicator
  • Side note: don’t run a workshop to ask people what they know. Instead, assess how they know things. The best way to do this is by eliciting their stories. The stories that tell you what is really going on are stories of failures not success.
  • The stories people value are the stories of failure. It is these stories that teach us the most.
    • “The brain registers failure faster than success because the avoidance of failure is a more successful strategy than the imitation of success.”

What’s wrong with KM? (Part 2)

  • We have stories, taxi drivers, and maps. And we need all of them in combination and in the right balance. However, most KM programs focus too much on maps (e.g., structured, explicit knowledge). If they do include narrative, it tends to be highly structured narrative, which is almost as bad as maps.

Informal Networks

  • One of the principle components of a modern KM system is the effective management of informal networks.
  • Done right, informal networks sustain the formal systems
  • When he was working at IBM in the Institute of Knowledge Management with Larry Prusak and others, the ratio of formal to informal networks was 1:60 — and that was counting only the people using specific technology.
  • Informal networks are an efficient way of spontaneously determining the level of abstraction necessary for knowledge diffusion without central planning or control.
    • Informal networks are composed of people who have chosen to participate.
    • Over time, they built a community of trust. Because of this trust, they were willing to admit their failures to each other. This ramped up the collective learning of the informal network.
    • NOTE: We share failures only with people we trust
  • When IBM saw the value of the informal networks and tried to formalize them, most of the useful informal network activity moved into an external collaboration environment beyond IBM’s reach.
  • Larry Prusak: If you have $1 to invest in KM, invest 1 cent in information and 99 cents in connecting people.
  • Human connectivity creates trust.
  • Dense connectivity between people enables knowledge to flow at the right level of abstraction for the context.
  • Direct human interaction is a low energy cost solution for knowledge management.

Stimulate Social Networks

  • One useful technique for increasing direct human interaction is to stimulate social networks
    • Allow people to self-assemble into teams.
      • When people are allowed to choose their teammates, they tend to have higher commitment to each other than when they are assigned to teams.
    • Provide guidelines, a set of heuristics or enabling constraints, that improve team potential by ensuring that you work with people you haven’t worked with before (e.g., a new employee, people who do not report to the same manager, someone who has a degree in anthropology or philosophy, etc.)
    • Give them a series of intractable problems to solve and offer an irresistible reward such as a three-month sabbatical
  • If you ran this exercise every six months, then within 18 months you have a widespread network of people who are within two degrees of separation based on having worked together in a trusted environment.
  • This is a much better investment than spending 18 months building a knowledge base or AI-based search system because you have a dense human network that can assimilate new information quickly and diffuse it rapidly at the right level of abstraction at low cost.
  • They have extended this technique to address mental health concerns.
  • They expect a mental health crisis in early 2021 in response to the Covid-19 pandemic, triggered by the realization that this situation will not be going away quickly. However, the official systems will not be able to cope with a mental health crisis of this magnitude.
  • In response, they are trying to rapidly build peer-to-peer support networks. For example, they created a series of trios in Scotland composed of a student, their parent, and their teacher. These trios overlap and support each other.
  • Next they created additional trios composed of teachers, social workers, and police.
  • This is called “entanglement around points of coherence”:
    • The coherent points are the formal roles that have access to the formal systems.
    • Then you interconnect them in multiple three-way combinations that create a dense overlapping network that contains a narrative learning system that enables a peer-to-peer flow of micro-narratives and the ability to have conversations.

KM for Decision Support

  • If you create this healthy ecosystem of overlapping networks then good things will happen even when you don’t control it directly.
    • “I don’t know what I know but I know that I will know it when I need to know it.”
  • This addresses the biggest organizational challenge of the “unknown knowns” (i.e., the thing the organization knows but the decision makers don’t know)
  • Informal networks that are tightly connected can feed into the formal systems
  • Distributed Decision Support
    • There are two functions of knowledge management: improve decision making and support innovation.

KM for Innovation

  • Use KM to create the conditions for Innovation
  • Inattentional blindness = when people are asked to focus on one thing and do not see something else that is right in front of them (e.g., the gorilla).
    • This is not something you can train against because we evolved to make decisions quickly based on partial information absorption that privileges our most experience. This is called conceptual blending.
  • Conceptual blending: scan the 4-5% of the available information, which triggers a series of brain and body memories, and then blend those brain and body memories to respond to the situation quickly. (We evolved this way to avoid predators.)
    • [Look! A Tiger! RUN!!!]
    • “We do not see what we do not expect to see.”
    • During conditions of extreme change, this is even more dangerous because you are looking in vain for a world that looks like the world of 2019.
  • Micro-Narrative Approach is one way of addressing both inattentional blindness and conceptual blending
  • EXAMPLE: don’t send out an employee satisfaction survey. In surveys and interviews, people tend to provide the answers the think you want.
  • Instead, present (or ask them to bring) a picture of what it is like to work around here. Then give them a series of triangles on which the can index their own narrative about that picture.
    • For example, one of the triangles will say that in the story, the manager’s behavior was altruistic, assertive or analytical. These are three positive qualities so the respondent will have to balance the three.
    • This pushes the respondent out of autonomic response and into novelty receptive processing (i.e., out of fast thinking into slow thinking), which makes them go deeper.
  • Note: Most consultancy methods are context-free but the world of their clients is context-specific.
  • “We live in the tails of a Pareto distribution not the center of a normal distribution.”

Mass Sense

  • Mass Sense — when an executive needs to make a decision quickly but doesn’t have the necessary information, doesn’t have time to research the issue, and doesn’t know what to do, how to proceed? Present the situation (via an infographic, a video, text, or some combination) and then ask everyone to interpret it using the same triangles. This is commonly known as “wisdom of crowds.”
  • The resulting data can be plotted on a probability map, a “fitness landscape” (Stu Kaufman) that shows the various patterns in the responses. This will show you the range of thinking within a network. You can see where the consensus is and who the outliers are.
    • This is real-time knowledge management for decision support
  • This approach can be used in peace and reconciliation work. Start by presenting a set of data to people who are in conflict with each other and ask them to interpret it. Then go down one level to see where there are points in common (where you can bring them together) and points in conflict (where the differences really exist).
  • This is “knowledge management hitting the road.” It’s not about building processes. It’s creating a dynamic, network-based, highly visualized response.
  • This approach provides the “wisdom of the network” and, crucially, it helps your workforce participate and feel involved in decision support. This is critical for good mental health during a crisis.
    • It enables weak signal detection
    • It also enables exaptation
  • Exaptation is critical for innovation. Exaptation is a concept from evolutionary biology: when something is originally adapted for one function but under conditions of stress exapts to another function. This produces an innovation.
  • The history of human innovation is “radical re-purposing” or exaptation.
  • In a crisis, the single-most important thing you should do is take what you do well and apply it to a novel situation. It is a form of improv.
    • It may not occur naturally so use mass sense making to associate problems with existing knowledge capability at a level of abstraction.
  • Art and music come before language in human evolution. They are also ways by which we become highly resilient as a species. Why? Art and music are abstract, they distance you from reality and allow you to make novel connections. Similarly, the fitness landscape maps allow you to see new connections.
  • This is another example of real-time or organic knowledge management.
    • Don’t try to organize knowledge in anticipation of need.
    • Instead, create the mechanisms by which the knowledge can assemble in context at the moment of need.

Aporetic Technique

  • Aporetic Technique introduces paradox
  • An Aporia is an unresolvable problem. In a crisis, you should create more of these because they force people to think differently. This is a major part of their forthcoming EU handbook.
    • The handbook includes the 5 steps to get out of a crisis
    • What parts of the problem do you hand back to experts
    • What if you have conflicting experts? Use ritual conflict techniques.
    • What if you have multiple hypotheses? Set up parallel testing.
    • How to know if you have covered the necessary hypotheses? Use the mass sense techniques.
  • The key thing in a crisis is to have a set of simple processes that enforce diversity.

Conclusion

  • Knowledge management becomes even more relevant in a crisis:
    • We need narratives, taxi drivers, and maps.
    • “We also need the ability to rapidly connect people and things in novel contexts so that we can create new knowledge on the fly.”
  • “Knowledge is a dynamic act of knowing not a static act of storage.”

Bonus: Responses in Q&A

  • There is a new approach to Strategy: Apex Predator Theory
    • When radical disruption occurs, the old dominant predators rarely survive because they were optimized for the old environment rather than the new one.
    • What matters is having the low energy cost of fast adoption.
      • Example: IBM is replaced by Microsoft, which is replaced by Apple.
    • This is because they failed to recognize early enough the weak signals of approaching radical change
    • When their environment changes rapidly, apex predators have two big challenges/opportunities:
      • the exaptive moment: effective exaptation on the fly (i.e., quickly repurpose what you do)
      • competence-induced failure point: where they fail, not because they are incompetent but because they are too competent as per Clayton Christensen. They have a very narrow window for change at this point.
  • How to increase serendipitous discovery of novelty?
    • Say: if I knew the answer to the problem, I would interpret it like this.
    • Then, ask: Who else is interpreting it this way?
    • This widens your lens and increases the chance of serendipitous discovery — particularly across domains and disciplines.
  • The challenge for KM: “switch your focus from taxonomy to typology.”
    • KM doesn’t get this. They think in terms of taxonomies, which gives you boundary conditions. By contrast, typologies give you multiple perspectives.
    • This new focus enables trans-disciplinary work (which is different from interdisciplinary work).
    • In a highly uncertain world, trans-disciplinary work means survival.
  • KM has gone too far down the technology route. We would do better by increasing human connectivity.
  • Narrative-enhanced doctrine:
    • This work he did at Westpoint and elsewhere.
    • They enriched documents with hot links to stories from a variety of people about what that document meant. These documents/stories were socially generated over time.
    • Then you can search using some or all of the underlying stories to gain different perspectives.
    • “Narrative enhances documents; documents enhance narrative.”
    • The only thing that worked in Iraq was field commanders blogging.
      • People wanted immediate real-time experience not manicured databases.
  • We are past the fad cycle of AI. We are now working with the computer-human interface. KM should be part that conversation but it isn’t right now.
  • Technology helps us scale knowledge. However, we need to rethink the way we use technology otherwise we will reinforce inequalities in the current system. (This is a matter of epistemic injustice.)
  • Snowden: I don’t want to be a Jeremiah, but I don’t believe is the worst pandemic I will see in my lifetime and I’m 66. Covid is God’s gift to humanity, an opportunity for us to get our act sorted out and get ready for the big one.
  • Without technology, we couldn’t scale. But right now, technology is an unbuffered feedback loop. Basic complexity science tells us that an unbuffered feedback loop will always be perverted. We need to introduce human buffering into that feedback loop. That is our challenge.

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Digital Workspaces of the Future: Industry Insights #KMWorld

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Speakers: Clint Patterson, Senior Solution Consultant, Simpplr; Dave Dumler, Head of Product Marketing, Panopto; Eric Storm, VP, North America, Starmind; and Sarah Dobson, New Business Account Manager, Starmind

Session Description: Using real-world examples, our industry leaders share how their KM solutions are driving smarter, better business interactions with top-notch knowledge flows in their client organizations. Storm from Starmind shares how unlocking employees’ collective intelligence and expertise at Accenture and SwissRe supercharges productivity, innovation and career development. Patterson discusses engaging employees and aligning knowledge management with an intranet so that content is organized, well structured and connects employees across the organization. He shares how Flexcare Medical Staffing, a nationwide leader in travel nursing and allied staffing services for top healthcare facilities, developed an intranet content management strategy to engage and align the workforce. Dumler discusses why is video now an essential ingredient for the future of knowledge worker productivity. While we may be relying heavily on video conferencing for live communication today, Dumler shares why video conferencing alone won’t be enough to achieve long term goals. He highlights how a number of notable organizations are achieving higher productivity and lower communication costs by creating and sharing on-demand video for training, meetings, and communication.

[These are my notes from the KMWorld Connect 2020 Conference. 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:

One of the key features of an industry conference is the Exhibit Hall. While KMWorld does offer a virtual Exhibit Hall this week, they also provided sessions in which key sponsors could explain and demonstrate their products. This session features some of KMWorld’s platinum sponsors.

Starmind – Identify Experts and Capabilities through AI

  • Expertise location is critical
    • The average Fortune 500 Company spends $500M annually on technology. And they spend a comparable amount on upskilling and reskilling their employees. In addition, these organizations spend billions on digital transformation efforts.
    • Organizations develop silos to make work in a large organization manageable. These silos can be expertise-based, geographic, cultural, etc.
    • Although people feel they are constantly connected, they actually are primarily connected within only their own micro-silos. They do not have sufficient connection across the organization. They don’t know who else is out there or what those colleagues know.
    • Expertise location is critical for organizational success. Remember: while Kodak had some key patents for digital photography, they did not have the necessary internal expertise to take the technology to market.
    • Starmind uses AI to surface experts and expertise and then connects them with colleagues in need.
  • PepsiCo Case Study:
    • Institutional knowledge rarely is held by the institution itself. Usually, it is in the heads of a handful of long-term or wise colleagues. PepsiCo viewed this knowledge as critical but did not have faith in analog knowledge management techniques to retrieve and share the knowledge.
    • Hard Knowledge = what PepsiCo calls documented knowledge
    • Soft Knowledge = what PepsiCo calls knowledge that lives inside the heads of your brilliant colleagues [tacit knowledge]
    • PepsiCo uses Starmind to “data mine” their Soft Knowledge
  • How Starmind works
    • Builds and maintains automated, real-time, updated skill profiles
    • Learns company jargon, concepts and terms
    • Learns sources of expertise and patterns of query response
    • Is able to route queries to the right expert

Panopto – Being Prepared for the Future Communication Needs of Your Organization

  • What’s happening in workplace trends?
    • In 2018, Gartner reported that within four years only 25% of meetings would occur in person.
    • Before the pandemic, 30% of workers worked remotely. During the pandemic, 60% work remotely.
    • Gartner believes that even after the pandemic is over, 48% will be working remotely
    • At the beginning of 2020, most organizations were not prepared to support widespread remote working. This is despite the fact that remote working was already pretty significant and likely to rise. (See Gartner report above.)
  • The University of Washington was better prepared than most. Before the pandemic they already had in place the key tools required to support remote work.
    • Necessary tools: asynchronous tools (e.g., email, documentation, text-based tools, etc.) and synchronous tools (e.g., chat and video conferencing).
    • New tool: recorded video (e.g., Panopto) bridges the gap between text-based communication and video.
  • How Recorded Video is useful
    • Forrester: “1 minute of video is worth 1.8M words.”
    • Employees are also recording their video conferences. This yields a “document” that helps participants remember (and others learn) what was discussed and agreed to.
      • 20 minutes after a meeting has ended, participants have retained only 58% of the meeting’s content.
    • Videos enhanced by search allow workers to find and retrieve critical knowledge quickly.
  • Client experience
    • Synaptics found that recorded video helped them save 7000 hours over the course of one year.
    • Qualcomm’s Wireless Academy enables engineers to use training videos after the training session is over as easily retrieved technical documents.
    • PerkinsCoie uses recorded video to keep their lawyers updated on changes in the law.

Simpplr

  • The Reality of Intranets
    • Gartner finds that 90% of intranets fail to achieve their goals
    • Forrester ranks intranets lowest on the satisfaction scale
    • Top 10 reasons intranets fail
      • Process Related
        • Process governance
        • Unclear purpose
        • Unengaged executives
      • Feature Related
        • Poor use experience
        • Stale, outdated content
        • Not personalized
        • Multiple sources of truth
        • Technical resource dependency
        • Search didn’t work
        • Failed deployment
  • Simpplr:
    • Simpplr is AI-powered in the background to make the intranet more efficient
    • In the Forrester Wave, Simpplr is in the leader wave — and it is the only buy (vs build) option offered in that category
  • Auto-governance: They have created an auto-governance feature that enables the system to automatically flag out-of-date content and remove it if the user does not take any action when the flag appears.
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Keynote: Revolutionizing KM with AI & Document Understanding #KMWorld

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Speaker: Paul Nelson, Innovation Lead, Search & Content Analytics, Accenture

Session Description: Intelligent document understanding is drastically changing the search and knowledge management landscape thanks to AI technologies. As 80% of all enterprise data is unstructured, document understanding delivers tangible benefits across industries and business functions saving time, money and resources. Paul highlights how one of the world’s leading pharmaceutical companies leveraged this solution, along with innovative AI technologies and assets, to automate KM for the purpose of detecting sensitive IP within unstructured enterprise content. He shares how this solution is helping other clients, including Accenture, to improve compliance and risk management, increase operational efficiencies, and enhance business processes.

[These are my notes from the KMWorld Connect 2020 Conference. 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:

  • He will focus on the three practical KM scenarios to make abstract AI real
  • Scenario #1: Customer Support — we started with individual customer support representatives who use their knowledge to respond to customer request, then we documented the usual queries and responses (FAQs), then we added some search for efficiency. But as the system scaled, we needed a document management system to organize all then content. Then, we superimposed AI to monitor data streams, summarize the patterns, and determine what topics should be addressed by technical writers and included in the document management system. But, with the advent of a new neural network (GPT-3), which can recognize new tokens, now we can replace a technical writer by a robot writer to create a fully automated customer support system.
  • Scenario #2: Research Reports — initially we relied on individual researchers to run tests and produce research reports. But sometimes, this resulted in duplication. So we interposed a gatekeeper who received research requests, checked to see if the work has already been done, and then either distributed the earlier report or commissioned a new report. Now, a robot can replace the gatekeeper. It can understand the incoming request, search the database, understand the content of the research reports, and send the appropriate responsive report — even if it contains words that are different from those in the initial request.
  • Scenario #3: Proposal Writing — how do you respond to RFPs or RFIs? No one person knows enough to respond to the entire proposal request by themselves. Currently, various experts complete the section of the proposal related to their expertise. To increase efficiency, we have tried to create templates and snippets that can be used as necessary. Now, the proposal writing is happening in collaborative sites. And key sections are tagged with relevant data. Going further, you can slice and dice the sections into Semantic pieces that can be sent into a semantic search of a neural network to find relevant information that can be reused automatically. Further, the search can identify cross-correlations, which will find the pieces that usually go together for a specific kind of proposal. After the proposal has been assembled and sent to the potential customer, the system can use cross-correlation between the proposals and the CRM system to determine which proposals were successful and which were not.
  • Closing Remarks
    • AI is more than just classification and entity extraction
      • It can find new topics
      • It can connect content by meaning — Semantic Search
      • It can evaluate content quality
      • It can summarize and rewrite
    • Successful projects will improve existing knowledge flow
      • [This is similar to the old KM adage: pave the cow paths]
      • Don’t fight the existing process. Instead, work within it.
    • Successful projects will include targeted AI
      • Focus on solving the problems you can solve today via AI
      • Gather the data you need to solve the problems of the future
    • You still need a great search engine
      • The results of AI feed and enhance the search engine
      • Search engines are the only way to scale
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Keynote: Not Knowing #KMWorld

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Speaker: David Weinberger, Author, Everything is Miscellaneous, Too Big to Know & his latest, Everyday Chaos: Technology, Complexity, & How We’re Thriving in a New World of Possibility

Session Description: How We’re Thriving in a New World of Possibility Through stories from history, business, and technology, philosopher and technologist David Weinberger finds the unifying truths lying below the surface of the tools we take for granted–and a future in which our best strategy often requires holding back from anticipating and instead creating as many possibilities as we can. As a long-time KMWorld magazine columnist, Weinberger has often shared his views of knowledge flows and knowledge sharing as well as the technologies enabling transformation. In this talk, he helps us understand the possibilities that machine learning and other forms of AI are creating and how to harness the power of these breakthroughs to improve knowledge flows in our organizations.

[These are my notes from the KMWorld Connect 2020 Conference. 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:

  • Dominant technology forms us. The Net conditioned us to chaos. AI now shows a way to make sense of the chaos.
  • The Internet
    • Before the Internet, most of us followed the “paleolithic” strategy: You plan for a future event and then take action.
    • The Internet Effect — For the first 19 years of production, Henry Ford sold the Model-T car without making many visible changes. He anticipated his customers’ needs so well, he did not need to change his product. By contrast, Dropbox launched with a minimal viable product and committed to make as many changes as dictated by the market. Dropbox did not anticipate the future and prepare for it the way Henry Ford did. Rather, Dropbox simply followed its customers.
      • The iPhone launched with a basic phone. The key to future sales was the App Store, which allowed others to increase the functionality of the phone.
      • Minecraft similarly allowed others to extend the game in new, and sometimes unexpected, directions.
    • The Internet is built on the principle of interoperability. So that allows an enormous number of unpredictable possibilities.
    • The final results could never have been anticipated by the originators of the tool. In fact, they didn’t want to anticipate. They wanted to watch and learn.
  • Two Opposing Strategies
    • The paleolithic strategy assumes that the future has an immense number of possibilities and our best approach is to narrow our options, choose our path, plan and act.
    • By contrast, the post-Internet environment is about widening the possibilities in the future. So you start narrowly and then enable a broadening of options in unpredictable ways.
    • The impact of abandoning the paleolithic strategy is that we have organized our lives to make life more unpredictable.
  • Machine Learning
    • Machine Learning — gives us a way of understanding the chaos we have created.
    • Traditional programming asks a developer to predict the relevant factors and then implement the logical relationships among these factors using code.
    • Machine learning consumes the data (the underlying factors) but is not given the logic regarding the relationships among the data. Instead, the machine iterates until it surfaces relationships. [This leads to results beyond the knowledge or planning of the developer.]
  • The Black Box Problem
    • The earth itself is a black box. We don’t fully understand how it works.
    • That said, while we cannot understand how a black box system made a decision, we can test that decision to determine how correct those decisions prove to be.
    • The natural state of machine learning is to be a black box. This is a problem because machine learning systems are based on data from a culture or an organization. These data reproduce the biases in that culture or organization and then machine learning amplifies those biases. Unfortunately, this process is hidden from our view because of the black box nature of machine learning.
    • The black box nature of machine learning is resulting in a moral panic because “We don’t know how it works!” But the deeper anxiety is that “It works!” It produces results beyond anything humans can produce. Machine learning systems work in such incredibly complex ways that we cannot replicate without machines.
    • Our brains made machine learning systems but our brains are insufficient to comprehend our creatures and their work.
  • Five Ways We Can Respond.
    • #1. Strategies are outdated.
      • Plato was the first to separate strategy and tactics. Tactics were comparable to what we call logistics. Strategy is more like improv or making music.
      • They require some stability for planning and execution. In an environment of unpredictability, strategy is less effective. For example, look at The Black Swan and Rita Gunther McGrath’s work that tell us that strategies are too broad and lead us away from a more productive, narrower focus. So we need to shift our thinking to a Minimum Viable Strategy, which is the minimal amount of planning necessary to allocate our resources sensibly.
    • #2. Rethink Corporate Knowledge Flows.
      • Corporate hierarchies were created to filter information until the ultimate decision maker had just the right information to make the right decision. Therefore, people lower down in the organization spend their time discarding whatever they consider to be irrelevant information. With the current explosion of information, this means that they need to ramp up their exclusion tactics and may, in the process, throw out valuable information.
    • #3. It takes a Network to Make Sense.
      • The smarter approach is to set up a series of sensors who are able to notice and report on butterflies (the key insights that are valuable). The wider and more open the network, the more effective it is. They also need to be rewarded.
    • #4. Make more possible.
      • Increase open source and access. Increase the opportunities for learning in public. Make it interoperable.
    • #5. Reify Knowledge.
      • To reify is to make something abstract more concrete or real.
      • Reify knowledge means “turn knowledge into a thing.” Although everything is constantly changing, we have believed that there are universal laws that govern and survive. This is where truth resides.
      • Now truth resides in linked open data.
      • Models are the new Body of Knowledge.
        • Traditionally, knowledge is content. But a software program is not content. AI models are not representations (in the same way) of knowledge. They learn by being used. This is new for a body of knowledge — it is a body of knowledge that can “eat” and grow.
        • Before machine learning, a body of knowledge (a domain) could only be extended by human effort.
        • There a price to this efficiency. We may end up reifying bias. Because a body of knowledge becomes a thing, it can be owned. And accessed can be limited or made expensive.
  • Chaos is the Truth.
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Leveraging KM, Collaboration, & Communication Techniques in the Virtual World: Optimizing Virtual Work Hubs #KMWorld

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Speakers: Kim Glover, Director, Innovative Learning & Knowledge Mgmt., TechnipFMC; and Tamara Viles, Innovative Learning & KM Program Manager, TechnipFMC

Session Description: A few months ago, we thought the virtual workplace was a bullet train. But the COVID-19 crisis has upgraded our journey from bullet train to supersonic jet. Working optimally in the virtual environment went from being a nice to have to an absolute must. This session addresses head-on the challenges of working virtually, including trust and communication issues, isolation, variable productivity, and accountability, and provides practical and creative techniques for addressing the challenges so that teams cannot just survive but thrive in the virtual environment. It shares specific and candid examples that leverage KM, collaboration best practices, and communications tools and techniques to help remote teams ensure productivity, stay connected, build trust, and safeguard business continuity. Take home immediately implementable strategies for leading a strong, productive team in the new virtual work world, whether to lead a team, a project, or just your own self-directed work.

[These are my notes from the KMWorld Connect 2020 Conference. 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:

  • 2020 Realities — this year, the Learning & KM team at TechnipFMC had to pivot to focus on how to optimize virtual work.
    • Their organization wants virtual EVERYTHING.
    • Learning & Knowledge are viewed as “first responders” within the organization.
    • They are seeing increased partnerships with back-office functions (legal, communications, finance, etc.)
    • Given the increased demand, the Learning & Knowledge team could not serve each internal customer individually. Instead, the team focused on “teaching people how to fish.”
    • “Knowledge is the currency, and virtual is the exchange system”
  • Tips for Leading Virtual Teams
    • Provide more structure and co-create rules. Keep focused on the things that enable effective work.
    • Establish consistent practices.
    • When developing metrics, focus on results rather than standard operating procedures.
    • Mitigate ambiguity (and anxiety) by explaining the big picture, as well as individual roles and why they matter
  • How to establish structure and rules
    • Over-communicate, elaborate and anticipate needs
    • Create a team charter collectively with your team.
      • This process will create energy and focus. The content varies from team to team. At a minimum, it should cover team goals, values, and expectations.
    • Make sure your charter includes communications expectations when the team is working virtually.
  • Cultivate accountability through visibility
    • Performance
      • Virtual team members needs to take more responsibility to meet deadlines so accountability is critical
      • Set expectations and define success clearly
      • Use results-based metrics
    • Trust
      • Trust elevates team performance — a team that trusts is less anxious and more productive
      • Task-based trust is vital to virtual teams
    • Signs of low trust
      • silos within sub-groups
      • low credibility in the commitments of others
      • virtual leader or other team members micromanage
      • low productivity or missed deadlines
      • open negativity
      • unresolved conflicts
      • information hoarding
    • Ways to increase visibility, accountability, and trust
      • Use dashboards to increase transparency
      • Use a Kanban board or another tool to share what you are working on
      • Use a daily huddle in which each team member discusses what worked yesterday, and what they are focused on today.
      • Use alternative tools to increase knowledge-sharing and fun. Kahoot is a great example of such a tool.
      • Keep your calendars open to your team (including your personal appointments). This helps team members get to know the “whole” you.
    • Increased communication
      • Increased communication reduces anxiety and isolation
      • Connect with your teams 3 times more than you would when co-located
      • Establish regular team meetings
      • Team leaders need to be even more visible and accessible
      • Use one-on-one meetings to keep abreast of work progress and to check on well-being. Being on camera is critical for this.
      • Use the chat function for informal check ins
      • Ask for feedback — try survey tools or just simple conversation
      • Managing conflict is more important — the physical distance can make it more difficult to interpret tone/mood and also can make it easier to ignore issues that are brewing
      • Try virtual coffees and happy hours to keep team spirit alive.
      • Start the virtual practice of “popping your head in the door” by which team members can ask a quick question that will get immediate response.
      • Keep a blog to motivate the team.
    • Foster Community
      • Communities create a sense of belonging, increased trust
      • Virtual water coolers — allow social chat
      • Virtual ceremonies — help you celebrate wins and acknowledge successes
      • Encourage team members to share their own stories
      • Ice breakers help team members learn more about each other
      • Open calendars give more information about team members
      • Identify and share leadership opportunities
      • Have a team nickname
    • Additional Activities
      • Team Workshop on Leading a Team Through Adversity:
        • Start by showing the common challenges of 2020.
        • Then ask individuals how they are responding and rising to the challenges.
        • Finally, play the UNESCO video, The Next Normal.
      • Use a canvas-based tool to build your team charter
      • Ask team members to take a virtual leadership self-assessment. They don’t need to broadcast their scores. But they should use the exercise as an opportunity for self-awareness and reflection.
    • How to have a better virtual meeting
      • use avatars for team members who aren’t physically present
      • schedule consistent team meetings but rotate time zones
      • assign roles and start with a roll call
      • send agendas and materials in advance
      • send questions in advance so that introverts can “pre-ponder” them and respond in writing if they prefer
      • use a “round robin” to develop understanding and consensus
      • everyone should “be on camera” and limit distractions (to prevent multitasking and enable people to “look each other in the eye”
      • make sure all team members know how to use the communications technology and ensure that it works well across all geographies

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Creative & Agile Techniques to Facilitate Change #KMWorld

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Speakers: Felicity McNish, Global Knowledge Management Leader, Aurecon; and Sue Stewart, Global Knowledge Culture Leader, Aurecon

Session Description: Change is not one size fits all; it’s dependent and interdependent on the environment, the market, the organization, the strategy, the culture and the individuals involved to be prescribed in a cast-iron process. Compounding the change challenge are the constraints of time, resources, budget, client commitments, motivation, leadership expectations, and, in some cases, pandemics. Irrespective, there are constants; people need a clear purpose for change, the motivation to support, the knowledge to understand, the tools to act, and the reinforcement to sustain. And the change approach needs to be adaptive and responsive to the needs of both the people and the organization. Speakers discuss the critical factors for sustained change and share practical and creative approaches, fusing together elements of change theory with psychology, communication, marketing, advertising, branding, storytelling, and good old-fashioned manners. They share their experiences and outcomes implementing cultural, knowledge, and operational and technical transformations in different organizations over the last 20 years.

[These are my notes from the KMWorld Connect 2020 Conference. 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:

  • Audience Poll: What’s the greatest obstacle to success in your organization?
    • Organizational Culture
    • Too much change or lack of prioritization
    • Lack of leadership by sponsors
  • This presentation is not intended to be a “how to” discussion. Rather, it focuses on “what is possible.”
  • Case Studies: The presenters have been working together for 20 years. They will be presenting stories and lessons from where they have worked: Woods Bagot, Unispace, and Aurecon.
  • Creativity
    • Creativity is critical to change because it helps you focus on what is not yet but might be someday.
    • See Tina Selig video that lays out the key elements of the creative approach to change
    • Attitude – make sure that your attitude has a positive impact on the people affected by the change. You will need to design and stage their experience.
      • Understand and listen to the people you are working with. Ask them to tell stories about themselves: tell us about yourself and your family, where you work, your best and worst change experience, and your superpower.
      • Show your appreciation to the people working with you by showing up prepared, thanking them during the session, and following up with thanks after the session.
      • Failure is data — use failure as an opportunity to collect data on what worked and what didn’t work. Don’t lose sight of the small victories — even in the midst of failures. Look for the bright spots.
    • Imagination
      • Chindogu — a Japanese term for coming up with useless ideas. Why bother? Because it cranks your brain into gear.
      • Use warm-up cards with provocative questions that help participants begin to think more creatively about connecting critical knowledge.
      • As you ideate, identify what must happen, what should happen, and what absolutely won’t be acceptable.
    • Knowledge — you can’t be creative without knowledge about the area / problem / opportunity you are trying to address
      • Going in prepared helps prevent a bad experience. “A single negative experience has four or five times greater impact than a single positive one.”
      • MLP not MVP — instead of focusing on minimal viable product, focus on delivering the minimal LOVABLE product.
    • Habitat / Environment
      • Create a warm, welcoming, FUN environment.
    • Culture
      • Edgar Schein’s model explains how culture forms and is maintained: artefacts < espoused values < underlying assumptions. The artefacts are the things and behaviors you see. They are based on harder-to-see values and assumptions.
    • Resources
      • Allies — when resources are constrained, identify and work with allies across the organization
      • Innovate with what you’ve got
  • Agile — enables innovation without sacrificing reliability
    • Team
      • Stable teams outperform temporary teams
      • Recruit T-shaped team members who have or can build relationships across the business. Stay away from Lone Stars
    • Time
      • Time is has two elements: the time spent working on the innovation PLUS the time spent waiting for others
      • Understand that people need time to learn and embrace change. Further, that learning time will be a period of reduced productivity, which can be exhausting.
      • Make sure people have some recovery time so that they can absorb and integrate the learning.
    • Attention — they use three key elements to capture attention in an 8-second era
      • Engage with stories
      • Anticipate the needs
      • Show the return
      • Use Images — still photos and video
      • explain things using the same visuals every time
    • Barriers
      • You do not need to be a scrum master. It is more about mindset.
      • Address the elephant in the room such as lack of budget or abundance of bureaucracy.
      • Understand your sponsors: What are they looking for? What is their ability to actual lead through this change?
  • Recommended Resource: Jason Fox, The Game Changer

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