Please End the Misery

It was a headline guaranteed to grab my attention: “Email is making us miserable.” And the subtitle really hit home too: “In an attempt to work more effectively, we’ve accidentally deployed an inhumane way to collaborate.” This is how Cal Newport began his recent article in The New Yorker on the misery of email.

Work Harder (Jordan Whitfield)

In this article, he argues that email exploits and perverts our fundamental human need for connection. In the first place, it lures us in with the promise of meaningful relationship. Then it threatens us with deprivation of those relationships if we have the temerity to limit our time on email. Of course, toss in a little hit of dopamine now and then for the rare occasion good news arrives via email, and you have created a system that makes nearly willing slaves of us all.

But it also makes us miserable. Newport cites a University of California (Irvine) study that found that “The longer one spends on email in [a given] hour the higher is one’s stress for that hour.” Unfortunately, the common recommendation to limit time on email by batching messages is not always a good solution. Other researchers found that “batching e-mails actually made them more stressed, perhaps because of worry about all of the urgent messages they were ignoring.” Finally, a 2019 study of Swedish workers came to another troubling conclusion: “They found that repeated exposure to “high information and communication technology demands” (translation: a need to be constantly connected) were associated with “suboptimal” health outcomes.”

But how do we reduce the misery? Part of the problem is the unrelenting pace of email, coupled with the haphazard nature of its messages. It’s as if we are running an endless marathon through a minefield.
Unless you want to feel like Lucy and Ethel in the chocolate factory (see below), the first step is to reduce the volume and pace of the flow. This means:

  • Reducing your own contributions to the volume:
    • Make a conscious choice NOT to use email if another, more effective communication method is available to you. Of course, this means that you have to get educated on those other options.
    • Keep the email messages you write short, sweet, and to the point. And make sure your subject line contains the gist of your message.
    • Indicate near the top of your message what action is required.
    • Do not cc and bcc a ton of people. Copying people simply adds unnecessary work (and stress) to their day. (Do unto others as you would have them do unto you!)
    • Do not engage in email volleys. If it goes beyond two rounds of responses, pick up the phone and talk it out.
  • Shutting down behaviors that add to the volume:
    • If you are on the receiving end of lots of copied email, ask the sender what they are hoping to achieve. If it is a defensive move on their part, you likely have some miscommunication or trust issues that must be addressed.
    • If you see something, say something. When email threads get out of control, suggest that the conversation be taken offline. If necessary, convene the offline conversation yourself.
    • When you see people using email like a Swiss army knife, gently point them to the better tool. For example, if they are trying to be collaborative, they might have more success on a platform specifically designed for collaborative work. (For the record, that platform is NOT email.)
  • Setting expectations for your team:
    • Set common expectations on response time. Is replying within 24 hours sufficient? If not, what is the right period? People stress out when they worry that they haven’t responded quickly enough. (For the record, an immediate response should not be necessary. If it is an emergency, call or text.)
    • Set common expectations on the right communications medium. Email is fine for information that is not time sensitive. However, it is suboptimal for emergency communications, scheduling, shooting the breeze and, above all, collaborating. Make sure you have better options available to your team and then make sure they are trained and able to use those better options well.

As you’re reducing the volume, you also need to remove the mines in the minefield. This means:

  • Create more trust in your work relationships so that you have the assurance that your colleagues will bring important matters to your attention in a timely fashion.
  • Establish with your colleagues preferred ways of communicating bad news. If you can, choose face-to-face conversation (or, at least, a phone call) over email.
  • Get to know your colleagues better. The more you know them and their work, the less likely you are to be unpleasantly surprised.

Unless you have magical powers, you cannot reform your entire organization. But making these changes within your team could materially reduce the misery inflicted on all of you by email. Wouldn’t that be a change for the better?

Lucy and Ethel at the Chocolate Factory (I Love Lucy)

[Photo Credit: Jordan Whitfield]


From Skunkworks to Subsidiary: how to make innovation happen at your firm

How is innovation delivered in your firm? This panel takes us on a four stage journey from (1) the solo innovator working skunkworks-style, to (2) the implementation of firm-endorsed innovation incentives, to (3) the funded innovation function and program, and finally to (4) the establishment of a separate innovation/legal tech entity. In addition to describing how to make each stage work well, our panelists will share how they transitioned from one stage to the next and the highs and lows of the journey!

These are my notes from the Strategic Knowledge & Innovation Legal Leaders’ Summit (SKILLS 2021), is a private gathering of large law firms. As with all live-blogging, there will be inevitable errors so please excuse them. My editorial comments are noted in brackets.


Skunkworks innovate under the radar without the heavy burden of bureaucracy. Skunkworks attract the enthusiastic amateur as well as those who just think they know better and can do better.

KM can help by providing support and resources for these solo innovators and small innovation teams. And, when the project is ill-advised, KM can withdraw support and resources. Knowing which projects to support depends on having a structured, strategic approach to innovation for the firm. This helps filter out the potential innovators who are loud and demanding as opposed to those who are quieter but working on more strategically important projects.

Firm-endorsed Innovation Incentives

Firm-endorsed innovation incentives are a way for the firm to back innovation with real muscle. This involves providing “good citizen” or billable hour credit for participating in nonbillable projects for knowledge management and innovation. Create a firm committee to collect, triage/prioritize and then monitor the innovation projects. Invite partners to get involved and even provide oversight for these projects.

Also consider providing cash incentives and competitions to encourage teams to implement, use, and document the results of innovative tools. This will generate helpful ROI data.

Above all, create a community of innovators across the firm. By connecting them, you help provide moral support for them and also cross-fertilize innovation across the firm. This creates a fly-wheel effect within the firm.

Key learning:

  • Make sure you manage and monitor the innovation projects
  • When you get the monitoring data, use them to build momentum and support for increased innovation.
  • ROI can be identified with respect to documented time reduction and efficiency gains.
  • Be sure to publicize the wins. It provides important recognition and reward.

Funded-Innovation Function

A formal innovation function is useful for bringing a measure of structure and discipline to the innovation effort. It helps focus on strategy and outcomes. In addition, it can help guide the creation of an innovation culture within the firm.

To give the function influence within the firm, give it a budget of dollars and a budget of billable hours to be allocated to lawyers involved in innovation projects.

In terms of staffing, considering hiring an innovation solutions architect with strong analytical capabilities. Another potential source of staffing is the group of professional support lawyers who may be looking for new ways to demonstrate their value to the firm.

Connect the innovation effort to your learning and development function. In this way, you can embed innovation in the most recent hires and then reinforce this throughout their professional development.

Innovation Subsidiary

To the extent that there is a “journey” through the innovation stages, it does not need to end up in a subsidiary. That said, an innovation subsidiary that is wholly-owned by the firm can provide real benefits.

A fully built-out innovation platform needs three horizons: (1) how to make today’s practice of law more efficient. This deserves about 70% of your attention and effort. (2) how to create new revenue for the firm. This deserves about 25% of your attention and effort. (3) seeing the next new thing before it hits you. To do this, invest in identifying the model that will break the current model. This may mean simply monitoring developments; for other firms it may mean shaping the new model. This deserves about 5-10% of your attention and focus.

The benefit of creating a wholly-owned innovation subsidiary is that you can focus on creating and then developing products and services in a disciplined fashion. Above all, the subsidiary can create and sustain an effective marketing and sales channel. (Law firm partners are too expensive to work on lead generation and customer calls.) Introducing a different way to market products has been a real benefit to the subsidiary and its affiliated law firm.

If you need to bring in specific legal expertise from your affiliated law firm, you will have to compensate them. Sometimes, compensation is in the form of billable hours. Sometimes it is in the form of a success fee.

Why choose a separate legal entity? Running a startup company is very different from running a traditional law firm partnership. It is very hard for a start-up to predict its revenue — particularly in the absence of historical data. This makes many lawyers extremely uncomfortable. In addition, some regulatory frameworks frown on law firms providing non-legal advice, products, and services.

Main lessons learned:

  • Stay close to the client and let client demand drive what you build. Ask the client — they like to be asked.
  • Listen to the partners — but only to a reasonable extent. They know the law but do not know marketing.
  • Never build a product without partner support.
  • Say close enough to the firm to maintain firm support.
  • Stay nimble — don’t get bogged down in red tape.
  • Stop giving things away for free. If you have “free stuff,” make sure you understand the benefits to the firm and to the client.
  • Finding new sources of revenue for a firm is a critical part of a fully built-out innovation platform.

[Photo Credit: Johannes Plenio]


New Frontiers in Data Analytics

Data analytics has been hot in large law firms for several years. Much of the activity has been focused on improving law firm business, for example, better budgeting and pricing. Can we use data analytics to improve the advice we give to clients? To reduce the risk of malpractice? We will examine new data analytic frontiers with three short case studies. Case studies will include uses of firm, client, third-party, and public case law data that drive better outcomes and surprising insights.

These are my notes from the Strategic Knowledge & Innovation Legal Leaders’ Summit (SKILLS 2021), a private gathering of large law firms. As with all live-blogging, there will be inevitable errors so please excuse them. My editorial comments are noted in brackets.

Three Types of Data

There are three key types of data: firm data, client data, and third-party data.

  • Firm data enables operational efficiency.
  • Client data combined with firm data provides matter intelligence.
  • Firm data combined with third-party data provides environmental intelligence (i.e., how we conduct our business and benchmark it against various industry sectors).
  • Client data combined with third-party data provides tactical insight and enables client service enhancements.

Critical First Steps

  • Win hearts and minds within your firm to gain a commitment to data cleansing. This requires committed investment and focus. You will not obtain useful insights unless the data is usable.
  • There will always be tactical pursuits and quick wins for data analytics. But make sure they are aligned with your long-term vision for data analytics in the firm.
  • Use the PPDAC Framework:
    • Problem
    • Plan
    • Data
    • Analysis
    • Conclusion

Recommended Data Practices

  • Don’t rush to data analytics. Start with data mining. Then build your data models.
  • Model explainability is key. Use a combination of text and excellent visuals to make your models more comprehensible to decision makers.
  • Data Trends to Watch
    • Natural language processing (e.g., text processing, text generation, Legal BERT)
    • Differential privacy — more effective than anonymization for masking identifying data. It works by “adding noise” to the data, thereby obscuring the critical data.
    • Client delivery

Learnings from Case Studies

  • Data analytics allow more sophisticated matter pricing and budgeting.
  • Data analytics can drive legal strategy — what patterns emerge across matters? How do these patterns inform business choices and client choices? (Clients now expect to be able to get this kind of data from their law firms.)
  • Predictive analytics / machine learning: Some examples are using advanced machine learning algorithms to tackle complicated business problems; using statistical analysis to identify impacted groups; doing predictive pay analysis for groups that have experienced discrimination.
  • Using data to manage the workplace: this allows clients to mitigate risk in their own workplace. This could involve combining legislative updates with focused analysis of the client’s own data.
  • Create tools that help clients identify trends that are critical to their business and workplace.

[Photo Credit: Franki Chamaki]


Your Roadmap to KM Success in 2021 and Beyond

This panel will share their advice and experiences for growing and sustaining a KM program in the virtual world. They will touch on a range of topics including engaging with internal clients and successfully driving knowledge initiatives when informal and in-person interactions remain limited. The panel will also look at how managing, mentoring and motivating their teams has changed and what they are doing to ensure their team members are continuing to grow and develop the skills they need to support the evolving KM strategy.

These are my notes from the Strategic Knowledge & Innovation Legal Leaders’ Summit (SKILLS 2021), a private gathering of large law firms. As with all live-blogging, there will be inevitable errors so please excuse them. My editorial comments are noted in brackets.

The pandemic has reinforced the importance of the traditional knowledge management focus on people – their well-being and productivity. January is not too early to think about how to keep your team engaged and energized.

Staying Connected

  • Firms that are email-centric need to find other ways to stay connected in meaningful, human ways.
  • You can’t jump straight into business. You need to start meetings with a moment of social connection.
  • In the early days, we forced everyone on camera. Now we are focusing on doing more screensharing.
  • Pay attention to the latest research on Zoom fatigue.
  • Team members with young children have a great deal of stress to manage — and their stress affects the entire team.

Find Your WHY then stay Positive

  • Your KM team should be very clear about its WHY.
  • This helps you sort your priorities and keep your focus.
  • Don’t read every bit of COVID news. One firm provides regular updates on the good, as well as the concerning, COVID news and suggests that its people focus on those updates and ignore the rest of the information provided by the relentless 24-hour news cycle.
  • Be sure to share the positive news wherever it occurs in the world. One firm shared pictures of new babies born in the firm. Others share good news from other countries.

Managing a Large Team

  • Remote working can lead to a sense of insecurity — especially when people feel untethered from their teammates, from the various practice groups, or from the firm generally.
  • Developing a core competency model helps your KM team members focus on concrete next steps. It gives them a sense of being connected to their own career path.

Stay Connected to the Business

  • Stay visible — attend practice group meetings, stay in touch with individual fee earners.
  • Communicate value — even if there has not been a recent breakthrough in your area, is there a breakthrough in another practice that would be useful to share?

Branding & Marketing

  • Lawyers express interest in KM resources and opportunities but they get distracted when billing work comes in so their interest is not always sustained.
  • One firm does internal marketing by interviewing a single member of the firm who has found a better way to deliver client services and meet client needs. They then send a written update to the entire firm sharing their learning. This provides recognition to the interviewee and sparks the interest of others in the firm.
  • Remember: you have to communicate a message seven times in seven different ways.
  • Taking the time to craft a high-level presentation to the firm regarding your function helps your own team bond and get a better sense of what it does and why it does it.

KM After the Crisis: What’s Next?

  • The biggest mistake we can make is to revert to the three-year plan we were using before the pandemic. So much has changed.
  • People in the firm do not want to go back to the office five days a week. So the KM team needs to think about what hybrid work arrangements look like. How does KM support critical functions such as training and integrating new lawyers?
  • Think and read more broadly about what might happen after the pandemic. For example, what happens when we can move about and socialize freely. Will productivity drop radically?
  • Think about lawyer technological proficiency. Lawyers need to be proud and able to be their own help desk.
  • Boundaries will be an issue: just because you know everyone is online after business hours does not mean you should be touch. People need boundaries so that they can have some personal time.
  • Training will look different going forward. Large-group training sessions are less useful than small-group or one-on-one training.
  • Employers will shift from managing an employee’s work experience to managing an employee’s life experience.

Reimagining the Future

  • Looking beyond the immediate crisis, ensure that you have corporate legitimacy. Pay attention to external training, credentialing, and standards (e.g., new KM ISO standard).
  • Find more ways to exchange knowledge with people outside the firm. The resulting learning will keep you at the cutting edge.

[Photo Credit: Kevin Bhagat]


Let 2021 Be A Year of Mistakes

We are finally at the end of a consequential year that few of us will forget soon. Now what should we do?

Standing on the precipice of a new year, it is easy to fall back on the usual greeting card good wishes:

  • Out with the old, in with the new: may you be happy the whole year through. Happy New Year!
  • May this year bring new happiness, new goals, new achievements, and a lot of new inspirations on your life. Wishing you a year fully loaded with happiness.
  • Wishing every day of the new year to be filled with success, happiness, and prosperity for you. Happy New Year.

But, given the year we’ve just come through, I think a different approach might be in order. While it would be only natural to wish for a return to “normal,” a return to life pre-Covid-19, that is neither realistic nor wise.

So what is the better approach? Follow Neil Gaiman’s advice:

I hope that in this year to come, you make mistakes.

Because if you are making mistakes, then you are making new things, trying new things, learning, living, pushing yourself, changing yourself, changing your world. You’re doing things you’ve never done before, and more importantly, you’re Doing Something.

We were given a tremendous gift in 2020 to experience a way of working and living that was inconceivable to most of us (and our employers) in 2019. But the transformation is not finished. Now we have to experiment further to find an even better way of working and living. For that, Gaiman’s advice is absolutely right.

As long as we make intelligent mistakes, and then learn from those mistakes, we can ensure that 2022 will be even better than 2021.

To that end, I wish you a fabulous 2021 filled with great mistakes!

[Photo Credit: Annie Spratt]


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


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!


The Silver Lining of a Virtual Conference #KMWorld


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.)


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]


Keynote: Responsible AI – Ethics and Inclusive Design #KMWorld


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.]


  • 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.


  • 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.


  • 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)


  • 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



Keynote: The Role of Knowledge and Information in Crisis Management #KMWorld


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.]


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.


  • 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.