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Tuesday, October 03, 2017

How to create an agile organization

Transforming companies to achieve organizational agility is in its early days but already yielding positive returns. While the paths can vary, survey findings suggest how to start.

Rapid changes in competition, demand, technology, and regulations have made it more important than ever for organizations to be able to respond and adapt quickly. But according to a recent McKinsey Global Survey, organizational agility—the ability to quickly reconfigure strategy, structure, processes, people, and technology toward value-creating and value-protecting opportunities—is elusive for most.1 1.This definition of organizational agility was given to respondents when they began the survey and reflects McKinsey’s proprietary definition, which is distinct from how we define organizations with agile software-development processes. Throughout the report, we will use “agile transformations” to refer to transformations that focus on organizational agility. The online survey was in the field from February 14 to February 24, 2017, and garnered responses from 2,546 participants representing the full range of regions, industries, company sizes, functional specialties, and tenures. Of these respondents, 207 work at nonprofits and government agencies or departments. But we will use the word “companies” to refer to all respondents’ firms, whether in the private or public sector Many respondents say their companies have not yet fully implemented agile ways of working, either company-wide or in the performance units where they work,2 2.“Performance unit” refers to a part of the organization (for example, a functional team, cross-functional team, or business unit) that is responsible for the delivery of specific performance outcomes. We asked respondents to answer the survey with regard to the performance unit in which they are most familiar. Forty-four percent responded on behalf of a business unit, 31 percent on behalf of a cross-functional team, 23 percent on behalf of a functional team, and 2 percent on behalf of another type of unit. though the advantages are clear. Respondents in agile units report better performance than all others do, and companies in more volatile or uncertain environments are more likely than others to be pursuing agile transformations.

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Few companies are yet reaping these benefits, but that may soon change; the results also indicate that organizational agility is catching fire. For many respondents, agility ranks as a high strategic priority in their performance units. Moreover, companies are transforming activities in several parts of the organization—from innovation and customer experience to operations and strategy—to become more agile. Finally, respondents in all sectors believe more of their employees should be working in agile ways. For organizations and their performance units that aren’t yet agile, the path to achieving agility depends on their starting points. But the results indicate some clear guidance on how and where they can improve, whether they are lacking in stability or dynamism.

Organizational agility is on the rise

Across industries and regions, most survey participants agree that the world around them is changing, and quickly. Business environments are increasingly complex and volatile, with two-thirds of respondents saying their sectors are characterized by rapid change. In such environments, the need for companies to demonstrate agility is top of mind: the more unstable that respondents say their environments are, the more likely they are to say their companies have begun agile transformations (Exhibit 1).

Exhibit 1

To date, though, few organization-wide agile transformations have been completed. Only 4 percent of all respondents say their companies have fully implemented one, though another 37 percent say company-wide transformations are in progress. When asked where their companies apply agile ways of working,3 3.The survey asked which of 12 agile ways of working were currently applied in respondents’ performance units. The 12 options were cross-functional teams, self-managing teams, knowledge communities, innovation hubs, scrums, integrator roles, staffing portals, hackathons, flow-to-the-work pools, Skunk Works, scaled agility frameworks (for example, Scaled Agile Framework, Large Scale Scrum), and holacracy. respondents most often identify activities that are closest to the customer: innovation, customer experience, sales and servicing, and product management.4 4.Innovation includes R&D, new-technology development, and/or idea generation; customer experience includes marketing, branding, campaigns, customer journeys, and/or customer-experience design; sales and servicing includes customer services, sales, and commercial and/or account management; and product management includes product development and/or product engineering. This is not too surprising, since customer centricity is cited most often—followed by productivity and employee engagement—as the objective of agile transformations. Companies are also focusing on internal end-to-end processes. At least four in ten respondents say their companies are applying agile ways of working in processes related to operations, strategy, and technology, while roughly one-third say they are doing so in supply-chain management and talent management.5 5.Operations includes production and/or manufacturing; strategy includes general management, corporate strategy, budgeting, and/or resource allocation; technology includes IT infrastructure and support; supply-chain management includes purchasing, procurement, logistics, and/or product delivery; and talent management includes organizational culture, human resources, and/or capability development.

Looking forward, the results suggest that companies have higher aspirations for agility. Three-quarters of respondents say organizational agility is a top or top-three priority on their units’ agendas, and more transformations appear to be on the way. Of those who have not begun agile transformations, more than half say plans for either unit-level or company-wide transformations are in the works. Respondents across industries also report a desire to scale up agile ways of working. On average, they believe 68 percent of their companies’ employees should be working in agile ways, compared with the 44 percent of employees who currently do. By industry, respondents in telecom and the electric-power and natural-gas industries report the biggest differences between their actual and ideal shares of employees working in agile ways—followed closely by respondents in several other industries: media and entertainment, the public sector, oil and gas, pharma, and advanced industries.

What’s more, the survey also confirms that agility pays off. Eighty-one percent of respondents in agile units report a moderate or significant increase in overall performance since their transformations began. And on average, respondents in agile units are 1.5 times more likely than others to report financial outperformance relative to peers, and 1.7 times more likely to report outperforming their peers on nonfinancial measures.6 6.The survey measured financial performance as the revenue, growth, market share, cost efficiency, and profitability of respondents’ performance units, relative to units at competitors’ organizations that do similar work, and nonfinancial performance as performance units’ development and innovation (that is, of products, services, processes, and/or solutions), responsiveness to customer needs, time to market, productivity, and employee engagement, relative to units at competitors’ organizations.

Agile organizations excel at both stability and dynamism

In previous work, we have determined that, to be agile, an organization needs to be both dynamic and stable.7 7.For more information, see Wouter Aghina, Aaron De Smet, and Kirsten Weerda, “Agility: It rhymes with stability,” McKinsey Quarterly, December 2015. Dynamic practices enable companies to respond nimbly and quickly to new challenges and opportunities, while stable practices cultivate reliability and efficiency by establishing a backbone of elements that don’t need to change frequently. The survey scored organizations across eighteen practices (see sidebar, “Eighteen practices for organizational agility.”), which our research suggests are all critical for achieving organizational agility. According to the results, less than one-quarter of performance units are agile. The remaining performance units lack either dynamism, stability, or both (Exhibit 2).

Exhibit 2

Of the 18 practices, the 3 where agile units most often excel relate to strategy and people (Exhibit 3). More than 90 percent of agile respondents say that their leaders provide actionable strategic guidance (that is, each team’s daily work is guided by concrete outcomes that advance the strategy); that they have established a shared vision and purpose (namely, that people feel personally and emotionally engaged in their work and are actively involved in refining the strategic direction); and that people in their unit are entrepreneurial (in other words, they proactively identify and pursue opportunities to develop in their daily work). By contrast, just about half of their peers in nonagile units say the same.

Exhibit 3

After strategy, agile units most often follow four stable practices related to process and people: entrepreneurial drive, shared and servant leadership, standardized ways of working, and cohesive community. When looking more closely at standardized ways of working, the agile units excel most on two actions: the unit’s processes are enabled by shared digital platforms and tools (91 percent, compared with 54 percent for others), and processes are standardized, including the use of a common language and common tools (cited by 90 percent of agile respondents and just 58 percent of all others).

Among the dynamic practices, process—and information transparency, in particular—is a strength for agile units. Within transparency, for example, 90 percent of agile respondents say information on everything from customers to financials is freely available to employees. Among their peers in other units, only 49 percent say the same. The second practice where agile units most differ from others is in rapid iteration and experimentation. More than 80 percent of agile respondents say their companies’ new products and services are developed in close interaction with customers and that ideas and prototypes are field-tested early in the development process, so units can quickly gather data on possible improvements.

The path to agility depends on the starting point

For the performance units that aren’t yet agile, the survey results suggest clear guidance for how to move forward. But organizational agility is not a one-size-fits-all undertaking. The specific practices a unit or organization should focus on to become agile depend on whether it is currently bureaucratic, start-up, or trapped.

Bureaucratic units

By definition, bureaucratic units are relatively low in dynamism and most often characterized by reliability, standard ways of working, risk aversion, silos, and efficiency. To overcome the established norms that keep them from moving fast, these units need to develop further their dynamic practices and modify their stable backbones, especially on practices related to people, process, and structure.

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The keys to organizational agility Read the article

First is the need to address the dynamic practices where, compared with agile units, the bureaucratic units are furthest behind (Exhibit 4). Only 29 percent of bureaucratic respondents, for example, report following rapid iteration and experimentation, while 81 percent of agile respondents say the same. A particular weakness in this area is the use of minimum viable products to quickly test new ideas: just 19 percent of bureaucratic respondents report doing so, compared with 74 percent of agile respondents. After that, the largest gap between bureaucratic units and agile units is their ability to roll out suitable technology, systems, and tools that support agile ways of working.

Exhibit 4

At the same time, bureaucratic units also have room to improve on certain stable practices (Exhibit 5). For example, bureaucratic units are furthest behind in performance orientation; in agile units, employees are far more likely to provide each other with continuous feedback on both their behavior and their business outcomes. What’s more, leaders in these units are better at embracing shared and servant leadership by more frequently incentivizing team-oriented behavior and investing in employee development. And it’s much more common in agile units to create small teams that are fully accountable for completing a defined process or service.

Exhibit 5

Start-up units

Start-up units, on the other hand, are low in stability and characterized as creative, ad hoc, constantly shifting focus, unpredictable, and reinventing the wheel. These organizations tend to act quickly but often lack discipline and systematic execution. To overcome the tendencies that keep them from sustaining effective operations, these units need to further develop all of their stable practices—and also broaden their use of the dynamic practices related to process and strategy in order to maintain sufficient speed.

First is focusing on a stronger overall stable backbone. On average, 55 percent of start-up respondents report that they implement all nine stable practices, compared with 88 percent of agile respondents who report the same. According to the results, a particular sore spot is people-related practices—especially shared and servant leadership (Exhibit 6). For example, just under half of start-up respondents say their leaders involve employees in strategic and organizational decisions that affect them, compared with 85 percent of their agile peers. Similar to bureaucratic units, respondents at start-up units also report challenges with process, particularly with regard to performance orientation. Within that practice, only 44 percent of respondents at start-up units say their people provide each other with continuous feedback on both their behavior and their business outcomes; 80 percent at agile units report the same.

Exhibit 6

Start-up units also have room to improve their use of dynamic practices, particularly in process and strategy. According to respondents, the agile units excel much more often than their start-up counterparts at information transparency—for example, holding events where people and teams share their work with the unit (Exhibit 7). Moreover, agile respondents are much more likely to say new knowledge and capabilities are available to the whole unit, which enables continuous learning. On the strategy front, the start-up units are furthest behind their agile peers on flexible resource allocation—more specifically, deploying their key resources to new pilots and initiatives based on progress against milestones.

Exhibit 7

Trapped units

The trapped units are often associated with firefighting, politics, a lack of coordination, protecting turf, and local tribes. These organizations find themselves lacking both a stable backbone and dynamic capabilities. In applying the stable practices, the trapped units are most behind on those related to people: specifically, shared and servant leadership and entrepreneurial drive. Just 13 percent of respondents at trapped units say they follow shared and servant leadership, compared with 89 percent of their agile peers. The dynamic practices in which they are furthest behind are process related, especially continuous learning and rapid iteration and experimentation.

Looking ahead

In response to the challenges that the survey results revealed, here are some principles executives and their units or organizations should act upon, whether or not they have already begun agile transformations:

  • Embrace the magnitude of the change. Based on the survey, the biggest challenges during agile transformations are cultural—in particular, the misalignment between agile ways of working and the daily requirements of people’s jobs, a lack of collaboration across levels and units, and employee resistance to changes. In our experience, agile transformations are more likely to succeed when they are supported by comprehensive change-management actions to cocreate an agile-friendly culture and mind-sets. These actions should cover four main aspects. First, leaders and people across the organization align on the mind-sets and behaviors they need to move toward. Second, they role-model the new mind-sets and behaviors and hold each other accountable for making these changes. Third, employees are supported in developing the new skills they need to succeed in the future organization. And finally, formal mechanisms are put in place to reinforce the changes, rewarding and incentivizing people to demonstrate new behaviors.8 8. See Tessa Basford and Bill Schaninger, “The four building blocks of change,” McKinsey Quarterly, April 2016.
  • Be clear on the vision. The results show that agile units excel most at creating a shared vision and purpose and aligning on this vision through actionable strategic guidance. In contrast, at companies that have not yet started a transformation, one of the most common limitations is the inability to create a meaningful or clearly communicated vision. An important first step in deciding whether to start an agile transformation is clearly articulating what benefits are expected and how to measure the transformation’s impact. This vision of the new organization must be collectively held and supported by the top leadership.
  • Decide where and how to start. Respondents whose organizations have not started agile transformations most often say it’s because they lack a clear implementation plan. While the right plan will vary by company, depending on its vision, companies should first identify the part(s) of the organization that they want to transform and how (for example, by prototyping the changes in smaller parts of the performance unit before scaling them up, or by making changes to more foundational elements that go beyond a single unit). Second, they should assess which of the 18 agile practices the organization most needs to strengthen in order to achieve agility, so that the actions taken across strategy, structure, process, people, and technology are mutually reinforcing. Third, they should determine the resources and time frame that the transformation requires, so the effort maintains its momentum but the scope remains manageable at any point in time.
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About the author(s)

The contributors to the development and analysis of this survey include Karin Ahlbäck, a consultant in McKinsey’s London office; Clemens Fahrbach, a consultant in the Munich office; Monica Murarka, a senior expert in the San Francisco office; and Olli Salo, an associate partner in the Helsinki office.

They would like to acknowledge Wouter Aghina, Esmee Bergman, Aaron De Smet, and Michael Lurie for their contributions to this work. Article Actions


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Friday, August 11, 2017

How the demographics of the organisation affect Knowledge Management

The demographics of your organisation determine the distribution of knowledge, and therefore the Knowledge Management Framework


Here's another factor that can affect the way you address KM in an organisation; the demographics of the workforce. Because the demographics are is linked to the distribution of knowledge across the staff, it determines how many sources of knowledge you have, and how many net users, for example:


  • A company with very many junior staff and few experienced staff will have few knowledge suppliers and many knowledge users; while
  • A company with very many experienced staff will have many knowledge suppliers, each of whom is also a knowledge user.

Please note that I am not talking here about whether older people behave differently to younger people - there are many assertions made about these differences in behaviour, few of which seem to stand up to scrutiny.

Take a Western engineering organisation. 

Here the economy is static, and the population growth is stable. Engineering is not a "sexy topic". The workforce is largely made up of baby boomers. A large proportion of the workforce is over 40, with many staff approaching retirement - the blue line in the graph above.

Experience is widespread in the organisation - this is an experienced company, and knowledge is dispersed. Communities of Practice are important, where people can ask each other for advice, and that advice is spread round the organisation. Experienced staff collaborate to create new knowledge out of their shared expertise. Knowledge can easily be kept largely tacit. The engineers know the basics, and a short call to their colleagues fills in any gaps. The biggest risk is knowledge loss, as so many of the workforce will retire soon, and a Knowledge Retention strategy would be a good investment.

Take an Asian engineering organisation. 

China or in India the economy is growing, the population is growing, there is a hunger for prosperity, and engineering is also a growth area. The workforce is predominantly very young - many of them fewer than 2 years in post. There are only a handful of real experts, and a host of inexperienced staff - the red line in the chart above.

Experience is a rare commodity, and is centralised within the company, retained within the Centres of Excellence, and the small Expert groups. Here the issue is not Collaboration, but rapid onboarding and upskilling. The risk is not so much Retention of knowledge, it is deployment of knowledge, although the reliance on a few experts means that they must be given a Knowledge Ownersgip role, rather than using them on projects.  Rather than keeping knowledge tacit, it makes sense to at least document the basics in explicit form (the experts will be too busy to answer so many basic questions), keeping this documentation updated as the organisation learns.

These two demographic profiles would lead you to take two different approaches to KM. The Western company would introduce communities of practice, and use the dispersed knowledge to collaborate on building continuously improving practices, processes and products. Wikis could be used to harness the dispersed expertise. There would be huge potential for innovation, as people re-use and build on ideas from each other. Crowd sourcing, and "asking the audience" are excellent strategies for finding knowledge.

The Eastern company would focus on the development and deployment of standard practices and procedures, and on developing and deploying capability among the young workforce. The experts would build top-class training and educational material, and the focus would be on Communities of Learning rather than Communities of Practice. Innovation would be discouraged, until the staff had built enough experience to know which rules can be bent, and which must be adhered to. Crowdsourcing is not a good strategy, and the "wisdom of the experts" trumps the "wisdom of the crowd".


This is one of the factors that KM must address, namely the amount of expertise in the company, and how widely it is dispersed.



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Leaving lessons in a lessons database doesn't work - an example from NASA.

NASA found out the hard way that just collecting lessons into a database is not enough.

Image from wikimdia commons
5 years ago, NASA conducted an audit of lesson-learning. At the time, NASA spent 750,000 annually on their lessons learning approach, centred around a tool called LLIS (The Lessons Learned Information System).  NASA was at the time (and still is) one of the worlds leaders in Knowledge Management, and they wanted to know if this money was well spent, and if not, what could be done (note of course that lesson learning is only a part of NASA's KM approach, and thanks to Barbara Fillip for bringing me up to speed).

According to the levels of use and application found by the auditors 5 years ago, there was plenty of room for improvement in lesson-learning. Specifically -
"We found that NASA program and project managers rarely consult or contribute to LLIS even though they are directed to by NASA requirements and guidance. 
In fact, input to LLIS by most Centers has been minimal for several years. Specifically, other than the Jet Propulsion Laboratory (JPL), no NASA Center consistently contributed information to LLIS for the 6-year period from 2005 through 2010. 
For example, the Glenn Research Center and the Johnson Space Center contributed an average of one lesson per year compared to the nearly 12 per year contributed by JPL .....  
Taken together, the lack of consistent input and usage has led to the marginalization of LLIS as a useful tool for project managers" 
With minimal contributions (other than at JPL), and with rare consulation, then this system was just not working.


Why did it not work?

The project managers that were surveyed offered a variety of reasons for not using or contributing to LLIS, including:
  • A belief that LLIS is outdated, and is not user friendly
  • A belief that LLIS does not contain information relevant to their project
  • Competing demands on their time in managing their respective projects.  
  • Policy Requirements have been weakened over time. 
  • Inconsistent Policy direction and implementation. 
  • Lack of Monitoring. 
Interesting that three out of these six reasons are directly related to governance. One wonders that, even if a spanking new LLIS were introduced, whether (without better governance) anyone would bother to use it. 

The auditors suggested a number of improvements, including improvements to process, policy and resources, but one of the main issues with a lessons database is that it is a clumsy solution. Lessons should not be stored in an ever-accumulating database - lessons need to be embedded into design, into principles and into process.

Levels of lesson learning

I described, earlier this year, 3 levels of lesson learning, and the approach reviewed by the auditors is Level 1 - reactive capture of lessons in the hope that others will review them and learn from them.

Ideally any organisation should aim for level 2 - where lessons lead to changes in designs, practices or procedures. A lesson is therefore an increment of knowledge, and those little increments are used to build an ever-improving body of knowledge. Once the lesson has been embedded as a practice change, or a design-principle change, or a change in a checklist,  then it can be removed from the database.

Ideally the NASA database would be empty - all lessons would be incorporated in some body of knowledge somewhere. The only lesson in the system would be those pending incorporation.

If this system works well and quickly, then there should be no need for anyone to consult a lessons database - instead they should go to the designs, the checklists, and the design principles.

By relying on a Level 1 lesson learning system, NASA were already making things difficult for themselves. 



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Thursday, August 10, 2017

Free and cheap technology is killing organizational effectiveness

Technologies supporting knowledge work are deceptive, especially for knowledge work shared among groups and teams. The ease of getting started obscures the challenge of learning to be effective. We focus on the details of particular features and functions at the expense of ignoring the cognitive challenges of deep thought and collaborative work.

I’ve been participating in a Slack team with a loose group of colleagues scattered across two continents. I was off the grid for about two weeks and found myself lost when I returned to the conversation that had continued in my absence. My first hypothesis was that Slack was the culprit and that some magically better UX would eliminate the problem. Slack, of itself, isn’t the problem but it is emblematic of the deeper issue that should be tackled.

In trades and crafts, the most experienced and effective practitioners would never invest in cheap tools or materials. Learning to use those tools and materials effectively is the work of years of deliberate practice. The strategy shouldn’t be any different if you are manipulating ideas than if you were manipulating clay. But the marketing and deployment of software rejects these hard won lessons. Software fame and fortune is built on promises of simplicity and ease of use, where ease of use has been interpreted as ease of getting started and minimally productive. We’ve all become facile with learning the first 5% of new tools and services. We’ve been led to believe or we pretend that this is enough. Few among us are prepared to invest in pushing further. Fewer still belong to organizations willing to support this investment.

The payoff from even this 5% has long been sufficient in terms of personal and organizational impact. We’re reaching the limits of the return from this minimalist strategy–it’s even more acute when we shift focus from individual knowledge workers to teams and groups.

To go beyond the 5% we need to modify our expectations and approaches about how we blend powerful tools with powerful practices. We need to adopt the attitudes of those who think in terms of craft and expert practice. Organizationally, we need to provide the time, space, and support to design and invent this new craft.

My hypothesis is that there are models to look to and borrow from. In particular, I believe that the world of software development has the longest and richest experience of dealing with the individual and group production of the thought products of the knowledge economy. Further, there are individual expert knowledge work craftspeople in various other fields; their tools and practices are also worth understanding and reverse engineering.

I don’t have this all figured out yet. Nonetheless, I ‘d like to get a new conversation going about how to improve on this train of thought. Where are good places to look?

The post Free and cheap technology is killing organizational effectiveness appeared first on McGee's Musings.



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Wednesday, August 09, 2017

The Role of Storytelling in a Team of Teams

Team of teams and storytelling

Four-star general Stanley McChrystal became the commander of US forces in Afghanistan in the mid-2000s. When he arrived in the country, he found the American military operating much as it always had, through command-and-control organisational structures. It didn’t take him long to figure out that wasn’t working against an enemy organised in flat networks that could adapt and reconfigure at a moment’s notice. So McChrystal restructured his forces into what he called a Team of Teams, which is the title of his 2015 book on the subject.

The principles of a Team of Teams are grounded in an understanding of complex versus complicated environments. A complex environment is where there are so many interconnected parts with non-linear relationships that it is impossible to predict with any certainty what might happen next. Cause and effect are intertwined and only make sense in hindsight. Complicated environments, on the other hand, can be pulled apart and analysed.

Human systems tend to be complex, whereas mechanical systems, such as the workings of the giant Airbus A380, are complicated. The Cynefin framework makes this distinction nicely. I have a minor YouTube hit with a simple explanation of the Cynefin framework which you might like to take a look at.

Companies have been quick to implement the ideas behind a Team of Teams, although to be frank it’s not easy. It requires culture change, even in modest-sized companies. But if you want to give it a try, here are some of the things to consider when implementing this approach.

Key elements of a Team of Teams

The first step involves structure. You have to move from a fixed hierarchy to a set of teams that interact in the same way people do inside a high-functioning team. This means, at a minimum,

  • having a clear and common purpose across all teams
  • being able to speak your mind with other teams1
  • having the freedom and trust to make decisions and get on with the job
  • having a shared consciousness around values and what ‘good’ looks like
  • trusting the capabilities, track records and intentions of others
  • leading like a gardener, focusing on creating an environment in which good work can happen rather than task-managing.

How stories can help

You need to carry out three jobs to embed the Team of Teams approach:

  1. craft and tell the change story of why a shift to a Team of Teams is necessary
  2. enhance everyone’s story skills, particularly those of your leaders and sellers, to enable people to find and share stories that illustrate the new way of working, foster rapport, and allow influence without hierarchy
  3. develop a systematic and purposeful process of embedding stories across teams that illustrate values and what ‘good’ looks like, and grow a shared consciousness.

 

Story trainagle

Change story

Leadership expert Simon Sinek has shown the importance of starting the change process with the question ‘Why?’ If you answer this question using a story, people quickly get the cause-and-effect and, more importantly, take away the intended meaning. In the first paragraph of this post, for example, I told a short story about why Stanley McChrystal moved to the Team of Teams approach.

Each organisation needs to create its own version of this story. It’s best done by involving as many individuals in your company as possible, so that the maximum number of people understand and own the change story.

Story skills

A few weeks back I worked with a group of executives at a large UK supermarket retailer at their learning academy just outside London. I was reminded of just how much retailers are numbers people who, at the same time, are in positions where they must influence thousands of employees. Everyone was a storyteller, but I needed to help them become systematic and purposeful in sharing their own experiences and those of their colleagues. Within a couple of days, their story switches were on and they were all actively looking for stories to tell.

Embedding stories

If you want to change a workplace culture, you need to change the stories within the workplace. Now you could just leave that to chance and see what stories emerge. Or you could develop a process where, across all the teams and on a regular basis, stories are shared and discussed that illustrate what ‘good’ looks like. The great thing about stories is that they are not directives or checklists. Instead, they illustrate a pattern of behaviour which people can choose to copy and modify for their own purposes.

Of course, this approach is relevant to any change you are introducing. The more stories you get into the system, the more concrete and real the change will feel for all involved. You will quickly move away from corporate doublespeak to people authentically talking to each other and sharing real-life examples.

1. If you want a simple tool to assess how well connected your teams are then I recommend you check out www.evalu8ing.com

To change a culture you need to change the stories told. Learn how Anecdote can help you do this

The post The Role of Storytelling in a Team of Teams appeared first on Anecdote.



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Monday, August 07, 2017

The Hidden Knowledge Problem in Organizations

Having had the opportunity to observe many conversations in organizations, I have come to understand that the greatest knowledge deficit in organizations is not the lack of sharing nor is it poorly designed repositories. Rather it is the inability to hold authentic conversations. What I have too often observed is teams and units where members do not offer their best thinking out of fear; fear of not being viewed as a team player, fear of being seen as incompetent, fear of embarrassing themselves or someone else, fear of acknowledging that they do not understand something. I find team members unwilling to say they disagree with the boss or saying they agree when they do not.  

When I say fear, I don’t mean shake in your boots fear or fear of being immediately fired. I mean the everyday reluctance of individuals to say what they know or believe, because of the threat of embarrassment or a negative response from others.  I see it both at the highest organizational levels and with front line employees. When fear exists, critical knowledge is lost and serious problems remain hidden. The time waste is both enormous and expensive. Rather than speaking honestly to one another, people invent costly workarounds, delay, procrastinate, or make agreements they don’t intend to keep. This problem is so long standing in organizations that people have become resigned to it as, “just the way things are.” We hear that resignation in the familiar phrases people use to explain not speaking out, “You have to choose your battles,” “You just don’t tell the boss that he’s wrong,” or, “Saying that would be career limiting.”  

But at a time when “critical shifts have taken place in the wider culture away from hierarchy towards networks, from top-down to widespread engagement with greater emphasis on innovation and creativity” (Gilmore) the need for making use of the knowledge of all employees has become a major imperative.

Fortunately, along with the knowledge demands made by these critical shifts, has come a richer understanding of how to access this hidden knowledge (Edmondson, Kegan, Turco).  These new perspectives demonstrate that it is possible for authentic conversations to be the norm in organizations or even within a team that is embedded in a larger organization. But they also illustrate that such a deeply embedded deficit does not succumb to quick fixes such as a simple workshop or admonitions for authenticity from the C suite.  Rather to address the problem requires a sustained focus on three elements 1) developing a culture of psychological safety, 2) members’ gaining awareness of their own blind spots, and 3) building learning routines in everyday work.   

It is possible to find case studies of organizations that have accomplished this. They include some that I have written about and some that others have detailed, Kessels & Smit, Bridgewater, Next Jump, Decurion, TechCo (pseudonym in The Conversational organization),  Lake Nona Project, The Defense Intelligence Agency.



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The CKO of Microsoft Services Has a Surprising Perspective on Knowledge Management

I recently had the opportunity to interview Jean-Claude Monney, for a project I was working on for USAID.  He is the CKO of Microsoft Services, the largest Division of Microsoft with over 22,000 people. One of the questions I asked him was, Do you know of research completed or any underway that demonstrates the value or effectiveness of KM to the larger entity – the society, the organization or the project?” 

Screen Shot 2017-07-18 at 2.20.19 PM

His surprising answer was, “Everyone talks about effectiveness as if it’s thae holy grail.  It’s the wrong question! KM is key to providing insights, for example, on how to improve sales and marketing, but KM is not the one to define benefit. You can’t measure the ROI of knowledge.  Like his unique position on effectiveness, Jean-Claude has atypical views on the familiar KM maxim of People, Process and Technology.

In terms of People he says KM should be focused on the changing context of work. Where we used to talk about blue collar and white collar, we now need to add “no collar.”  “These no collars are those contributing knowledge on platforms like Stack Overflow, they are software programmers that work freelance and get engaged as contractors. There is a movement happening between full employment and contractors. Contractors/freelancers want quality of life, they want to go on vacation when they want and to do the kind of work they like. The social context is changing. We need more research to understand the social context of the worker, for example, what are the legal aspects of who owns the knowledge? “Taking a job for 30 years is gone – it is the past”. He notes that most customers that Microsoft works with, have a 2020 project to think about how the Digital Workplace will be changing the way they work.

A second People issue that concerns Jean-Claude is that KM should focus on how individuals can rapidly grow knowledge and skills to move into a new task. He notes that the world is embarking on an effort to drive in apprenticeship, so people can be employable in two years.  He sees on-going learning as the new normal and uses himself as an example. He explains that although he just hit retirement age, he can talk machine learning with any 30-year-old engineer because he is continually learning. People should not be so busy at their jobs that they do not make time to continue learning by reading, talking to colleagues, and reflecting on actions their team has taken. He is grateful that Microsoft has embraced a growth mindset culture that leads to a desire to learn and see failures as essential to mastery, find lessons and inspiration in the success of others and embrace challenges with agility.

In terms of Process Jean-Claude thinks that most of the current KM processes are shallow. He explains that at Microsoft they have engineered the three processes of 1) creating knowledge, 2) reusing knowledge (embedding it in daily work processes), and 3) harvesting, curating and growing knowledge. But under those three are 18 sub-processes, each of which is measurable. He says process must get to that level of detail to be effective. The KM principle he supports, is not only about sharing, it is also about the programmatic application of shared knowledge where the processes define the trust level of that knowledge. 

In terms of Technology, Jean-Claude says we need new research on how AI based technology mixed with cognitive science and neuro science, are impacting knowledge work. He points to law firms that are increasingly replacing paralegals with AI, what he calls “knowledge augmentation.” For example, AI is a better way to update expertise finder systems, because the rate of technology change is so fast that individuals won’t go back and update their profile with “I learned this yesterday.”  He explains that new expertise finders will capture the employees' digital footprints, for example, if an employee has written a white paper or acquired certain expertise during a project, the employee’s profile will be dynamically updated.

Another example of the use of AI, coupled with the knowledge of cognitive science and neuro science, is in healthcare. He explains AI-based cognitive services can detect emotions in the faces of people to learn about how they are feeling. There are translation services and speech recognition that could be used if the patient and healthcare worker do not speak the same language. Today, the free Skype translator version offers simultaneous voice translation in 8 languages, and the text translator is available in more than 50 languages for instant messaging, you can experience it in this video. And he notes that audio, text and video could be used to mine a knowledge base of healthcare conversations between doctor and patient using the new Video Indexer Cognitive Service from Microsoft.  As an aside, Jean-Claude said he was horrified at with how the Ebola crisis was managed – another place where AI and KM could have been helpful.  “Given that humans are the most important part of KM, we need to translate the academic principles of cognitive science and neuro science into real life knowledge management.” 

He believes we need more education on KM at all levels and has joined the Columbia University faculty where he will be teaching Digital Transformation, the Digital Workplace and KM at the Information and Knowledge Strategy (IKNS) Masters in Science Program.

Finally, on an encouraging note he explains that he has been invited to share Microsoft Services' KM initiative at industry events like a Keynote at the World Bank's bi-annual Knowledge Sharing Conference and with more than 100 companies in the last 2 years.  He believes that the interest in KM is springing up in all industries. And with the accelerating advances in AI and Cognitive systems, KM will become an expected managerial capability, like finance, operations or HR.



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Friday, July 07, 2017

Repeatable Processes and Magic Boxes

There is a trap hidden in most efforts to create repeatable processes and systems that try to guarantee predictable results in doing knowledge work. To avoid that trap, you must learn to recognize and manage the magic box.

The promise of repeatable processes is to identify, design, and sequence all of the activities that go into producing a specified output. It is the core of industrial logic. For industrial logic to turn out uniform results with predictable quality, craft must be transformed into proven systems and repeatable processes. Variation must be rooted out and eliminated. To do this, all of the design thinking necessary to produce the product must be extracted from the process and completed before any further work can be done. You get the prototype product done right and then you churn out a million replicas. For this strategy to work, all of the magic must take place before the first process step ever occurs.

Knowledge work cannot be forced into an industrial straightjacket. The essence of knowledge work is to produce a bespoke result. I have no interest in the strategy McKinsey developed for its last client; I want the strategy that applies to my unique situation. I do not need the accounting system tailored to GM’s business; I need an accounting system matched to my organization. And therein lies the rub. I may want a bespoke accounting system, but I’m not willing to pay for it. Forty years ago, in fact, I had to pay for one anyway because off-the-shelf accounting software didn’t exist. All software development was custom; as was all consulting work.

Consultants and software developers are not stupid people. They could see that, despite the necessity of producing a unique product at the end, much of their work had elements of the routine and predictable. To increase the quality of their work, to train new staff, to improve their economics, and to better market their cumulative experience, knowledge work organizations worked to transform their practices into repeatable processes and methods. New industries were created as software developers redesigned their code to segregate what needed to be customized from what constituted a common core.

In this effort to apply industrial logic to what was fundamentally creative work, most organizations were sloppy or short-sighted about managing what was a fundamental tension between industry and craft. What was big, and shiny, and marketable was the packaging of cumulative experience into a consulting methodology, or a software development process, or a customizable software product.

What could not be eliminated, however, was the essential craft work necessary to employ the methodology or to customize the product. What happened was that this craft work was pushed into a box somewhere on the process map or into a line item in the standard workplan.

This is the “magic box.” Whatever its name or label, it is the step where the necessary creative work takes place. This is work that cannot be done until the moment arrives.

Why does it matter to identify which boxes in the process require magic? Because they determine the quality of the final result. The other boxes only matter to the extent that they set you up to succeed in the magic box.

How do you recognize the your are dealing with a magic box? What are the clues that differentiate it from among all the other boxes? Sometimes you must approach this by process of elimination; many boxes—“conduct field interviews”, for example—are more easily identified as not possibly containing magic. As for positive identifying features, look for language that suggests design thinking steps or analysis that isn’t tightly specified. “Develop market segmentation approach”, or “design chart of accounts” are examples of possible magic boxes.

Understanding which boxes are which in a process is essential to managing the process effectively. Regular boxes can be estimated and managed more tightly than magic boxes. Data collection, for example, is usually straightforward; analyzing it, requires more flexibility and adaptability. Collapsing these related, but distinct, activities into a single step would be a poor project management decision. Just because creative work cannot be controlled in the same predictable way as industrial work does not relieve managers of their responsibility to make effective use of limited resources. Being clear and isolating the magic boxes from the ordinary ones is essential to making those resource deployment questions.

Repeatable processes are often marketed and sold as “proven approaches” that eliminate the trial and error that the uninitiated risk if they strike out on their own. This has enough truth to be dangerous. Traveling in new terrain is safer with an experienced guide. The guide may help you get to where the underlying geology is promising, but cannot guarantee that you will strike gold. Honest guides will emphasize the distinction. But it is a distinction that is only meaningful to those who can hear it.

Trial and error is an unavoidable feature of creation. A serendipitous error is often the seed of a creative solution. Understanding where the magic needs to occur helps you distinguish between unproductive and potentially productive error. Unproductive error is an opportunity for learning and process improvement; it should be carefully reined in. Potentially productive error must be permitted and encouraged. That can only be done effectively by understanding where the magic needs to occur.

Some related links:

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Wednesday, June 28, 2017

The Big Shift From Engagement to Passion

For decades now, companies have been relentlessly tracking levels of employee engagement. Every large company I know has an employee engagement survey it regularly administers. Is it possible that they’re tracking the wrong thing? I’ve come to believe that engagement is a distraction from the real issue – the lack of worker passion. Let me explain.

Employee engagement

Employee engagement is a concept that is used widely and somewhat loosely. If I step back from all of the employee engagement studies and surveys that I’ve seen, the concept broadly focuses on three elements:

  • Do the employees like the work they do?
  • Do the employees like the people that they work with?
  • Do the employees like and respect the company that they work for?

Why has employee engagement become such a significant issue for companies? It’s not just because it’s the “right thing” to do. It’s because more and more research suggests that engaged employees are substantially more productive than employees who are not engaged in their work. One widely quoted study showed a 21% increase in productivity if employees are engaged in their work. There’s a significant bump in productivity that can be fostering by creating more employee engagement.

In a time of mounting performance pressure, it’s completely understandable therefore why companies are so focused on worker engagement. A more productive workforce can be a significant competitive advantage in the marketplace.

So, why is employee engagement a distraction? Because it has limited impact. True, it generates a substantial improvement in productivity, but it’s a one-time improvement. The research shows that an engaged employee is more productive than an employee who is not engaged. But I’m not aware of any research that shows that engaged employees become more and more productive over time.

In a world of mounting performance pressure, that’s not enough. If you’re not accelerating performance improvement over time, you’ll become increasingly marginalized. You may buy yourself some time by expanding employee engagement, but it won’t be enough to keep up with markets that are demanding more and more rapid performance improvement.

Passion of the explorer

That’s the reason we ended up exploring arenas where sustained extreme performance improvement is required. We went into many arenas far removed from business, ranging from extreme sports to online war games. What did we learn? We found that, despite the great diversity of these arenas, they all had one common element: all the participants were deeply passionate about their quests.

Now, passion is another word that’s used widely and loosely. We found that the participants in these arenas had a very specific form of passion, something that we call the “passion of the explorer” and that we’ve written about here. This form of passion has three components:

  • A long-term commitment to achieving an increasing impact in a domain
  • A questing disposition that creates excitement when confronted with an unexpected challenge
  • A connecting disposition that motivates the individual to systematically seek out others who can help them to get to a better answer faster when confronted with an unexpected challenge

That’s a powerful combination. People with the passion of the explorer are never satisfied or happy with what they have accomplished. What excites them is the next challenge on the horizon – it’s an opportunity to achieve more of their potential and take their impact in the domain to the next level. They are constantly seeking out those challenges and connecting with anyone who can help them address the challenge.

What drives passionate people is the opportunity to do better – constantly. Can you see why I’ve become so focused on passion as the key attribute for employees in a world of mounting performance pressure? These are the people that will be accelerating performance improvement over time, rather than just yielding the one-time productivity improvement that comes with engagement.

Who has passion?

Now, here’s the bad news. Our latest survey of the US workforce confirms that employee passion levels are remarkably low – far lower than employee engagement levels. Our best estimate based on our survey is that only 13% of workers have the passion of the explorer.

Why are passion levels so low? This isn’t an accident, but the deliberate product of the scalable efficiency model that all of our institutions have adopted. In a scalable efficiency world, workers are expected to deliver results predictably and reliably, performing highly specified and standardized tasks that are tightly integrated.

In that kind of work environment, passion is deeply suspect. Passionate workers don’t keep to the script and they’re constantly taking risks to get to that next level of performance – something that’s anathema in the scalable efficiency world where “failure is not an option.”

That’s why our school systems have been systematically designed to take students who are curious, creative and imaginative and train them to listen carefully, memorize what they’ve heard and repeat it back reliably on exams. The message is that, if you have a passion, that’s what playgrounds are for but, when you’re in the classroom, you need to focus on the assigned task. Our schools seek to make us successful in a scalable efficiency world.

I’m often told that it’s unrealistic to expect everyone to develop and nurture a passion in work. A common view is that passion is restricted to the select few and that most of us just want to be told what to do and receive a reliable paycheck in return. My response is to take those skeptics out to a playground and watch children 5-6 years old. There’s not a single one who isn’t passionate and curious and creative. Something happens between that age and the age that we’re at now – and my belief is that it’s our experience with the institutions that teach us to leave our passion outside.

If the schools don’t squeeze the passion out of us, our work environments surely will. But, here’s the challenge. As I’ve written before we’re moving from a world where our institutions are driven by scalable efficiency to a world where our institutions will be driven by scalable learning. And passion, which is so suspect in a scalable efficiency world, becomes a prerequisite in a scalable learning world.

People with passion will learn faster and accelerate performance improvement much more effectively than those who lack passion. I’ve written elsewhere about the need to redesign work environments with the primary design goal of accelerating learning and performance improvement. There’s a lot that can be done to apply design thinking and methodologies in this context to our work environment. But, if the people in those environments lack passion, they won’t be able to harness the full potential of those environments.

Another issue with employee engagement

As indicated earlier, employee engagement is characterized by happiness with the work and work environment. While this is certainly a laudable goal, it does have its downside. If the employee is really happy with the work that they’re doing and the people they’re working with, what’s likely to be their reaction when faced with the prospect of fundamental change? There’s a risk that they will resist the change – they’re happy with what they’re already doing. Why mess with a good thing? Engagement can breed conservatism and resistance to change, something that could be dangerous in a world where fundamental change is becoming more and more necessary.

In contrast, passionate employees welcome change, provided it can help them get to the next level of impact. In fact, they’re often frustrated with the current environment because they can see all the institutional obstacles that are undermining their ability to increase their impact. Even more, they get bored and restless if the environment isn’t changing and providing them with more opportunities to have even more impact.

The dilemma of engagement

Here’s something that I’ve never seen discussed. If employee engagement is so important and companies are spending so much money over decades to drive engagement levels higher, why have engagement levels remained so low?

I suspect that the answer might be troubling to institutional leaders. Maybe the reason that engagement levels remain so low despite so much focus and spending is that scalable efficiency environments are simply not conducive to engagement, much less passion. Maybe we humans don’t particularly like to be put into environments where we are expected to perform tightly specified and highly standardized tasks day in and day out. Maybe that’s not what humans were meant to do with their lives.

Maybe this is yet another reason to step back and question some basic assumptions. Perhaps the shift from scalable efficiency to scalable learning is not just necessary for our institutions, but essential for us as humans to achieve more of our potential and to feel that we are in fact needed as individuals, rather than just cogs in a well-oiled machine.

What can leaders do?

For those who recognize the imperative to catalyze and amplify passion within the workforce, what is to be done? I develop this in much more detail in our new research report, but here’s a high level view of the opportunity to drive change:

Lead by example. If you as a leader are not passionate about the work you do, all the words in the world will not inspire others to pursue their passion. Celebrate those who are passionate (remember there are 13% of the workers who already have this kind of passion) and who are taking risks in addressing challenges that will help them, and the organization, get to higher and higher levels of performance.

Provide focus. The most effective leaders in a scalable learning environment are not those with all the answers, they’ll be the ones with most inspiring and high impact questions. These questions help employees to focus on the challenges that matter but they also highlight the opportunity to get to new levels of performance. If the leader is excited by the questions, it will help to generate excitement within the workforce.

Create the environment. We can do a lot to create environments that will help catalyze and nurture passion. Provide platforms that can help people who are excited by high impact questions to find each other, connect with each other and learn from each other as they take on the challenge of getting to the next level of performance. Deploy experimentation platforms that invite workers to test out new approaches while managing the risk associated with those new initiatives.

The bottom line

In a world of mounting performance pressure, we need to shift our focus from employee engagement to employee passion. This is an imperative not just for our institutions, also for all of us as individuals. We have an opportunity to create far more value and achieve far more of our potential than we ever imagined possible. But to harness that opportunity, we need to navigate through the big shift from scalable efficiency to scalable learning.



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Monday, June 26, 2017

The allure of socialism

The urge to improve the world is a powerful one.  We see suffering and deprivation and stunted lives, and we want a world in which as many as possible can live decently and aspire to live fulfilled lives instead.  We think like this because we are human and share what Adam Smith called 'sympathy' with our fellow human beings.  Today we would call that 'empathy,' and it is what drives us to improve the lot of others if we can.

Some people yearn to replace this imperfect world with a better one conceived in the imagination, and in their mind they echo the lines of Fitzgerald's Rubaiyat of Omar Khayyam:

 

"Ah Love! could thou and I with Fate conspire

To grasp this sorry Scheme of Things entire,

Would not we shatter it to bits -- and then

Re-mould it nearer to the Heart's Desire!"

 

F A Hayek called it "The Fatal Conceit" to suppose that we can, with our limited mental resources, think up a better world than the one created by the input of countless people over aeons of time.  It is part of the allure of Socialism, which in theory proposes a world in which we are all more equal, and in which we do things collectively for the common good.  Socialism in practice has always been different, involving oppression, deprivation, blighted, limited lives, and often torture and mass murder.  Its practical record has barely diminished the enthusiasm its acolytes accord its theory.  Many of them become apologists for the atrocities committed when it is applied in practice.

The spontaneous order produced when people are allowed to interact freely with others contains more knowledge than any individual mind can hold.  It is faster to react to changes that could affect it adversely, and it does not involve forcing people to conform to the lifestyles that others would have them live.  It gives men and women space to improve their lives by pursuing their own aspirations rather than any goals that others would have them follow.

If it is folly to suppose that this world can be replaced by one dreamed up in the imagination, it is certainly not folly to suppose that it can be improved.  We can address its perceived shortcomings, experimenting with ways to overcome them, and persisting with those that achieved the desired results in practice.  The last 250 years have seen spectacular improvements in the human condition, and the last 25 years have seen many of those improvements rolled out on a global scale.  Advances have been made by virtually every measure of the human condition.  People live longer, no longer prone to diseases that ravaged their predecessors.  Fewer women die in childbirth, fewer children die in infancy.  Fewer starve or are malnourished.  More are literate, more educated.  It is a record of achievement unparalleled in the history of our species.

Karl Popper referred to a process of "piecemeal social engineering" by which we seek to improve the world by judicious inputs targeted at its failings, a process of evolution rather than the revolution that Marx sought and which his latterday followers still seek.  It is an empirical process that concentrates on practical improvements.

It may be true that young people are less patient, and more inclined to embrace idealistic schemes of total change than are older people, some of whom have lived through the catastrophes brought about when ideologies have been imposed upon the real world.  It seems paradoxical that many young people, the ones who cope more readily with a world of flux and change, should embrace an ideology whose goal is a settled world.  It seems equally paradoxical that many older people, who are supposedly ill at ease with churn and change, should embrace the system of markets and trade that is characterized by constant innovation.  It might be experience of reality that explains this apparent paradox.

Many advocates of socialism suggest that the tyranny introduced by socialist regimes in practice is an add-on that distorts and perverts ‘true’ socialism, but it seems more likely that compulsion is an evil lurking at the very heart of socialism.  It requires people to behave in ways which, given a choice, they would not freely choose.  Therefore they must be constrained to behave as all good citizens of the new utopia must…



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Monday, May 15, 2017

Working as knowmads: How to stimulate knowmadic working in organisations?

Imagine, you work in an organization and you are convinced of the importance of knowmads. You know this is the future, and knowmads are needed as crucial to drive innovation in a learning organization. You also know what skills are necessary as a knowmad. You already working as a knowmad yourself. But organizations need more employees who work knowmadic to be innovative. How do you stimulate a movement ... how do you create a collective of knowmads?

Typology of professionals in use of technology in relation to work

The participants of our Dutch MOOC 'Help there's a knowmad in my organization thought about this challenge with the starting question "How do you stimulate a move toward knowmadic work?" The above model from our book Learning in Times of Tweets, Apps and Like was provided as thought provoker. In this model we describe four types of professionals. They differ in the way in which they employ social technology in their work, depending on the motivation to develop the subject and affinity with social technology. The typology of professionals was recognizable to the participants. The online exchange led to the following strategies to initiate a move towards a more knowadic work and learning climate in organizations:

Start with the knowmads The most logical choice seemed to be focus on knowmads. "Knowmads make your adrenaline flow" is the experience. Finding and combining knowmads can trigger an oil leakage action, with more and more people joining and working on new ways of working. This group can also develop further.You may use the Seek-Sense-Share model to work on sharpening individual practices. You may also pay attention to professional identity. If you show yourself online - what's your identity? These are, for example, questions you can discuss in a knowmad café (see the interventions at the end).

Connect knowmads and googlers  Another strategy is to link knowmads to googlers. Form duo's where the knowmad shows the googler new ways of working. Working with googlers keeps the knowmads realistic and prevents them from getting too far ahead from the troops in the organization. It may earn them some recognition too (and avoids frustration).

Focus on googlers and hobbyists  A large number of MOOC participants intend to focus rather on googlers and hobbyists. You can appeal to Googlers by talking about their field of work. They are likely to be interested in additional possibilities of working knowmadically to keep up with their field of expertise and networking. When you show this, you awaken their curiosity. Hobbyists are already handy online but do not put it at work within the context of their function yet. There may be several reasons for this. Knowing the reason is key to change. Perhaps they have learned to participate in and adjust to the way of working within the organization? For example, let hobbyists help short-term projects to help others get the right supportive media.

Koppel googlers en hobbyisten A number of MOOC participants would specifically choose to link the googlers and hobbyists - a strong combination because they can learn a lot from each other - on an equal footing. The hobbyist learns about the subject and the googler about smart online networks and tools. Think reverse mentoring.

And how about the followers? Few MOOC participants choose to focus on followers, although it is important to continue to encourage and guide this group. They may need, for example, a low-threshold helpdesk.

About the model
The 'Typology Professionals in the Use of Technology in Relationship to Work" model is intended to look at professional behavior. A bad use of the model would be to put people in the boxes. It should lead to a discussion about behaviours. Emphasize that people can change or at some level show google behavior and on another level knowmad behavior. It is important to emphasize that there is not one correct blueprint way of working, but that everyone has to develop his own unique way that suits him or her. Maybe there are offline knowmads who read paper magazines and share knowledge at meetings. "It's not all internet that is blinking". Ultimately, it is about finding an effective way of working, learning and contributing to professional development, not about online or offline. The model is especially helpful in reflecting on the right interventions to stimulate collective know-how work and to differentiate it into types of professional behavior. With a googler, you may not have to talk about blogging right away, with a hobbyist that's not a problem.

Mariëlle van Rijn wrote a nice blog geschreven using more detailled profiles and designing interventions. The Networker for instance is given the task of adding two new people to their network every month who can contribute to the organization and present this on the intranet.

Walk the talk, organize a knowmad café and share success stories
Apart from thinking about who you are going to focus on within the organization, it's equally important to think about your intervention strategy. Many MOOC participants intend to work on a shift in organizational culture. Hereby, the management style (space) and digital skills are important elements to work on. The following strategies emerged:
  • 'Practice what you preach'. Make sure that you work as a knowmad yourself, but also show that you can deal creatively with technology: put up Padlet during a meeting or brainstorm ideas via Socrative. This will help people get used to technology as aid. 
  • Do not focus on individuals but on groups /creating a movement. It's unpleasant if you're alone as a knowmad in an organization. A dynamic movement can attract new people and grow slowly.
  • Organize a workgroup around this theme. Ensure to have  mix of all types of professionals represented in the working group. Or work with ambassadors. Of course, you can find plenty of ambassadors among the knowmads.
  • Start experimenting with this working group. Get started. Don't remaining in policy making or talking modus but ensuring good implementation. For example, a practical experience of a participant is that the toolset in his organization changed too much and technical support was scarce, which made all initiatives fail. 
  • Harvest and share success stories. For example, organize a knowmad café to share these stories. Success stories can trigger googlers in particular. They are already interested in the subject matter and if they see successful new ways to learn and connect, they become enthusiastic. 
  • Engage executives. If knowmadic work is part of the official strategy, this gives you space to experiment and invest.
  • Look closely at the context within the organization to define your strategy. Sometimes a community at the interface of various organizations is easier because it offers more space to innovate. Find a burning issue within the organization and link to it to make it important. 
  • And last but not least - look also at knowmad behavior during the selecting process for new employees. The more knowmads, the more they can invoke a turning point. 
 

Do you read Dutch? This blog is one of six blogposts about 'Werken als knowmad':

  1. De expertise van dokters vs internet. Over de invloed van online op de rol die kennis en expertise speelt in ons werk.
  2. Hoe werkt het in de praktijk? Een verkenning van knowmadisch werken, toegepast in de praktijk van organisaties en netwerken.
  3. Zonder gist geen pizza, zonder technologie geen knowmad. Over vaardigheden die je nodig hebt om knowmadisch te werken.
  4. Een wereld vol knowmads in 2020. The future is here!
  5. Hoe vervlecht je oud en nieuw?  Met mogelijkheidszin en progressiecirkels


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Monday, April 24, 2017

Review – Only Humans Need Apply


Only Humans Need ApplyOnly Humans Need Apply: Winners and Losers in the Age of Smart Machines
Thomas H. Davenport, Julia Kirby

In his most recent book, Tom Davenport, along with co-author Julia Kirby, provides an excellent entry point and framework for understanding the evolving relationship between smart people and smart machines. There’s a great deal of hand-wringing over technology encroaching on jobs of all sorts. This is hand-wringing that arises with every new technology innovation stretching back long before the days of Ned Ludd. Davenport and Kirby avoid the hand-wringing and take a close look at how today’s technologies—artificial intelligence, machine learning, etc.—are changing the way jobs are designed and structured.

They articulate their goal as

“to persuade you, our knowledge worker reader, that you remain in charge of your destiny. You should be feeling a sense of agency and making decisions for yourself as to how you will deal with advancing automation.”

In large part, they succeed. They do so by digging into a series of case histories of how specific jobs are re-partitioned, task by task, between human and machine. It’s this dive into the task-level detail that allows them to tell a more interesting and more nuanced story than the simplistic “robots are coming for our jobs” version that populates too many articles and blog posts.
Central to this analysis is to distinguish between automation and augmentation, which they explain as

“Augmentation means starting with what minds and machines do individually today and figuring out how that work could be deepened rather than diminished by a collaboration between the two. The intent is never to have less work for those expensive, high-maintenance humans. It is always to allow them to do more valuable work.”

They give appropriate acknowledgement to Doug Engelbart’s work, although the nerd in me would have preferred a deeper dive. They know their audience, however, and offer a more approachable and actionable framework. They frame their analysis and recommendations in terms of the alternate approaches that we as knowledge workers can adopt to negotiate effective partnerships between ourselves and the machines around us. The catalog of approaches consists of:

  • Stepping Up—for a big picture perspective and role
  • Stepping Aside—to non-decision-oriented, people centric work
  • Stepping In—to partnership with machines to monitor and improve the decision making
  • Stepping Narrowly—into specialty work where automation isn’t economic
  • Stepping Forward—to join the systems design and building work itself

Perhaps a little cute for my tastes, but it does nicely articulate the range of possibilities.

There’s a lot of rich material, rich analysis, and rich insight in this book. Well worth the time and worth revisiting.

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The depreciating value of human knowledge

Automation is just one facet on the broader spectrum of AI and machine intelligence. Yes, it's going to affect us all (it already is with the increasing emergence of intelligent agents and bots), but I think there is a far deeper issue here that - at least for the majority of people who haven't become immersed in the "AI" meme - is going largely unnoticed. That is, the very nature of human knowledge and how we understand the world. Machines are now doing things that - quite simply - we don't understand, and probably never will. 





I think most of us are familiar with the DIKW model (over-simplification if ever there was), but if you ascribe to this relationship between data, information, knowledge and wisdom, I think the top layers - knowledge and wisdom - are getting compressed by our growing dependencies on the bottom two layers - data and information. What will the DIKW model look like in 20 years time? I'm thinking a barely perceptible "K" and "W" layers!

If you think this is a rather outrageous prediction, I recommend reading this article from David Weinberger, who looks at how machines are rapidly outstripping our puny human abilities to understand them. And it seems we're quite happy with this situation, since being fairly lazy by nature, we're more than happy to let them make complex decisions for us. We just need to feed them the data - and there's plenty of that about! 

This quote from the piece probably best sums it up:

"As long as our computer models instantiated our own ideas, we could preserve the illusion that the world works the way our knowledge —and our models — do. Once computers started to make their own models, and those models surpassed our mental capacity, we lost that comforting assumption. Our machines have made obvious our epistemological limitations, and by providing a corrective, have revealed a truth about the universe. 

The world didn’t happen to be designed, by God or by coincidence, to be knowable by human brains. The nature of the world is closer to the way our network of computers and sensors represent it than how the human mind perceives it. Now that machines are acting independently, we are losing the illusion that the world just happens to be simple enough for us wee creatures to comprehend

We thought knowledge was about finding the order hidden in the chaos. We thought it was about simplifying the world. It looks like we were wrong. Knowing the world may require giving up on understanding it."

Should we be worried? I think so - do you?
Steve Dale




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Monday, April 10, 2017

The museum to markets – The Museum of Failures

As regular readers will note around here we tend to like markets. On the grounds that they generally - except where they don't - work. But it's important to understand what it is that markets generally work at and that's not success, not at all. Markets work well because they work well at failure.

Which is why we're rather tickled by this new Museum of Failure.

LEARNING IS THE ONLY WAY TO TURN FAILURE INTO SUCCESS

That's their tagline and we'd quibble a bit with it even though we agree with the general idea. Rather, as we'd put it, you can only succeed if you work out what's failing. Some of the ideas, like that Coke Blak, could have, might have, succeeded. They didn't. Others it's a bit more mysterious why they didn't succeed:

Bic For Her pens are also on display. The supposedly female-friendly pink and purple pens launched to widespread derision and mockery in 2012. "I mean, you know that women can't use regular pens. You need special pens for their delicate hands," West said. "And they're double the price of regular pens because they're specially for women." 

Quite why that didn't work is unknown, pink razors do cost more than blue as we're so often told they do. The important part of it though is this:

West told The Local.: 'You can fail at any point during the process. It's better to have a lot of cheap mistakes early in the process, than to do so on a large scale. Then it costs billions.'


That's why market systems work better than planned ones. What it is possible to do, what people want to have done, is an ever moving feast. The technology with which we can do things is always changing and so are personal tastes. We want thus some method of sorting through what can be done and what people want to have done. And the finest way yet discovered of doing this is for every lunatic to try. We, the rest of us, will then sort through what is available and decide upon which of these possible things that can be done add utility to our lives.

Imagine wandering into GOSPLAN one day to explain that we need an overlay to the telecoms network so that people can swap cat pictures with each other. Mr. Zuckerberg would have been laughed out of the room and yet 2 billion people later that experiment he cooked up in a dorm room seems to add utility to some number of lives.

And thus the glory of that Museum of Failure, it's a museum to why markets work. Precisely and exactly because so many innovations get absolutely nowhere - that's how we find out which ones we want.



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Thursday, March 23, 2017

How Innovative is Your Firm, Really?

Many businesses (including law firms) tout their innovation capacity. They use the right buzz words (agile, design thinking, rapid prototyping, etc.) and they display trendy props (innovation labs, informal gathering spaces, and lots and lots of post-it notes on walls). But is that enough to make a firm truly innovative?

Ideo says no. And Ideo should know.

Katharine Schwab, writing for Fast Company’s fastcodesign.com, reports that Ideo, the world-famous design firm, has studied its own 26-year old archive of client projects (as well as some external resources on innovation) to determine how best to measure innovation in an organization. For Ideo, “the most important element is the organization’s ability to adapt and respond to change.”

Through this research, Ideo “identified six basic vectors that it says are instrumental to an innovative, adaptive company”:

  1. Purpose: “A clear, inspiring reason for the company to exist — beyond just making money.” What is your law firm’s mission? You claim it is to serve the client. Is this actually borne out in the way the firm behaves internally and externally? Is it reflected in every decision the firm makes? Ideo has found that when leaders clearly articulate the company mission and then walk the talk, “projects and strategic solutions succeed 20.40% more often”.
  2. Experimentation: “Trying out new ideas and making evidence-based decisions about how to move forward.” Even if your firm is willing to experiment, does it have the discipline to make truly evidence-based decisions? (Note: many decisions that are described as evidence-based are actually pre-determined and then papered over with appropriate “evidence.”)
  3. Collaboration: “Working across business functions to approach opportunities and challenges from all angles.” In my report, Optimizing Law Firm Support Functions, I found that some of the most successful support functions were the ones that had learned to punch above their weight by collaborating productively with other administrative departments and with fee-earners. Is this type of collaboration the norm at your firm or is it unusual?
  4. Empowerment: “Providing a clear path to create change in all corners of the company by reducing unnecessary constraints.” How much change is your firm willing to tolerate? Can it handle the type of wholesale change contemplated by this vector?
  5. Looking out: “Looking beyond the company’s walls to understand customers, technologies, and cultural shifts.” How plugged in is your firm? Does your firm have the type of close relationships with clients that enable robust two-way communication about the things that matter to the client? Do you keep abreast of technological changes or is your firm a card-carrying technology laggard? Is your firm in tune with changes in the industry? Or is your firm fully occupied with its navel-gazing?
  6. Refinement: “Elegantly bridging vision and execution.” In other words, to what extent is your firm able to successfully execute new ideas? Do you have the right people with a bias toward action? Do you have the right methodology to support them as they transform ideas into reality? Do you have a robust change management approach?

Next, Ideo created a survey that clients can use to measure these vectors and the related behaviors.  Along with the survey results comes “feedback on tangible ways to become more innovative.” Ideo is finding that this self-reporting by teams, coupled with the feedback, demonstrably leads to better innovation performance.

Bonus: Ideo’s New Insights 

Thanks to the survey, Ideo “has definitive data to back up its hypotheses about what behavior actually drives” a team’s aptitude for innovation. Here are some insights from the data:

  • More is better: Do not limit your team to too narrow a range of innovation options at the beginning. “Instead, when teams iterate on five or more different solutions, they are 50% more likely to launch a product successfully.”
  • Command-and-Control systems squelch innovation success: “When a majority of team members who took the survey said that they felt comfortable challenging the status quo and acting with autonomy, the chances of a failed launch decreased by 16.67%.”
  • Your mission and underlying priorities must be in sync and stable: This alignment and stability provide a strong foundation that supports and cushions the naturally disruptive activities of innovation.

If your firm is ready to accelerate its innovation program, take a closer look at Ideo’s assessment and dashboard tool: Creative Difference. It might provide the data and insights your firm needs to truly become more innovative.

[Hat tip to Alessandra Lariu who pointed me to this article.]

[Photo Credit: Alexas Fotos]

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