We're sharing with you here a series of short discussions captured on video between Stephanie Barnes and John Girard about the use of creativity in knowledge management. It came about because of the chapter that Stephanie wrote for the book John and JoAnn Girard created and edited called, Knowledge Management Matters: Words of Wisdom from Leading Practitioners. 

We seem to have spent so much time in the last 100+ years trying to drive efficiency and effectiveness into our processes. How to do things faster, with more quality, with better outcomes, reduce waste, reduce re-work. These are not bad things, but in our push to be effective and efficient many of our organisations have removed time for reflection, for questioning, for considering alternatives out of the process. These chats look at a different motivator for knowledge management: creativity and how it can be used to facilitate innovation. 

There are nine videos in the series and the topics range from how creativity, innovation, and knowledge management fit together to how to enable innovation through diversity and what organisational mindsets are helpful in when innovation is the goal.

We hope you enjoy the series as much as John and Stephanie enjoyed making it. You can see the full set of videos in this YouTube Playlist.  

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Knowledge Management (KM) is fundamental to the effectiveness and success of every organization. A strategic roadmap to maturing an organization’s KM capabilities is what sets apart organizations that leverage their collective knowledge from their competitors who don’t have a handle on it at all. That is especially the case for organizations that are in the business of offering their knowledge to their clients, such as:

• Professional services firms that provide executive and management consulting based on the expertise and experience of their consultants and subject matter experts;
• Financial advisors who recommend the optimal set of investment strategies and tools to increase their client’s portfolio value;
• Legal advisors who need reliable access to laws, regulations, and related matters in order to apply them to their client’s unique situations or mitigate risk for their own organization; and
• Retail companies that have to guide buying decisions for their already informed customers who have direct access to all of their product information online.

Most organizations see the value of KM, but struggle with determining where to start and how to show progress quickly, continuously, and impactfully. In this blog, I’ll share the elements of an Agile KM Roadmap that will allow your organization to take an agile approach to implementing your knowledge management strategy. 

Workstreams, Tasks, and Milestones

The building blocks of an Agile KM Strategy Roadmap are workstreams, tasks, and milestones that are based on a current state analysis and target state definition.

Workstreams

Workstreams are independent, yet interrelated, KM efforts that add value on their own but ultimately create a more holistic solution when combined with other workstreams. 

The classic examples would be taxonomy design and governance, content strategy, and search design and implementation. Each of those workstreams alone will help an organization standardize the way it manages its knowledge and information while improving content findability. The outputs of these efforts can be combined into a web portal that indexes multiple repositories of good, quality content, resulting in optimal access to that content regardless of where it is stored.

Tasks

Tasks are the discrete steps towards producing the deliverables in each workstream. They are defined not only by the activities involved, but also the appropriate methodology for ensuring that the task is completed based on best practices.

Whenever EK designs a taxonomy, one of the critical sets of tasks is Top-Down Analysis, which involves conducting interviews, focus groups, and workshops with stakeholders and subject matter experts to identify primary, also known as “core,” and secondary metadata fields and values that should be included in the enterprise taxonomy design.

Milestones

Milestones mark the delivery date of a deliverable that adds value to the overall effort. This is important because all tasks within a workstream need to be purposeful, leading towards something that can be used. This may seem intuitive, but often times KM practitioners go through many efforts of producing something of value to the organization without ever delivering something that can be used to guide decision making or begin implementing. 

Examples related to taxonomy include the delivery of a report that summarizes all of the top-down and bottom-up analysis efforts and how the inputs gathered have helped to formulate the first implementable version of a taxonomy design. This report will not only summarize the work done, but provide the foundational information and next steps for continuing to improve the taxonomy design.  

Independent vs. Dependent Tasks

The art of crafting an Agile KM Roadmap is based on prioritization of efforts, as well as identifying the areas where tasks are contingent upon one another. In most cases, if you follow the approach recommended above, you’ll have a series of independent tasks that don’t require one to be completed before beginning another. There is a benefit however to sequencing your tasks in order to maximize how much value you add to the organization in the shortest amount of time.

Although not absolutely necessary, identifying and coaching the right group of individuals to lead each workstream is critical when it comes to driving efforts forward without losing steam. Without a sense of ownership and accountability for the outputs of the overall roadmap and each workstream within it, stakeholders assume that someone else is leading the efforts. Being explicit about who is Responsible, Accountable, Consulted, and Informed (RACI) brings transparency into who is doing the work vs. who is guiding the work. The key here is not only conducting a thorough stakeholder analysis that identifies the individuals needed to gain buy-in and adoption, but also hand-picking KM leaders (or those with high potential to become one) based on their interests, capabilities, and proclivities. 

Agile Tools and Ceremonies 

Once you have identified what you are doing, who’s responsible for doing it, and the order in which you’ll do it all within a set time period, such as 6-months or 1-year, you’ll need a way to track progress and a method for building in continuous improvement. Keep it simple to start with and then only build complexity as needed..

You can go low-tech and have a Kanban wall dedicated to the tasks you have To Do, are Doing, and those that are Done, utilizing a post-it per task. There are also no-cost, online options, like Trello, to help manage your tasks. Make sure that you manage your KM team’s “Work in Progress” capacity, meaning you allow them to commit to tasks, rather than assigning them tasks, based on their availability and their expertise-level to complete the task within a time-boxed period (or Sprint).

Set-up recurring meetings like a Sprint Planning session and a Sprint Review session to make sure everyone understands all that’s involved with each task, specifically the “Acceptance Criteria” that will determine whether a task is completed. In your agile cadence, build in time to check in with your team to determine what’s working and what’s not working so that you can identify actions and action owners that will help to improve your process and team dynamics.

A Few Final Recommendations

Now that we’ve covered the basics of an Agile KM Roadmap, here’s what you need to build into your plan to maximize the benefits of your efforts:

• Start with your users: Whether you are undergoing KM efforts to benefit your internal workforce or your external clients, you need to understand what your users need and want. Look at KM from their perspective by asking them questions, watching them work, and requesting information related to past effort that may or may not have worked. If you truly understand who you are doing this for from the very beginning of your effort, you will increase the likelihood of success as soon as you’re ready to roll-out the KM solutions you have developed.
• Define success metrics: Spend some time thinking about what you will measure in order to determine whether or not you’re moving in the right direction. If you’re developing a Community of Practice (CoP), you can measure the number of members in your CoP, how many people attend each session, or how your participants rate the usefulness of the information you provide them on your team site or at the various events you host. Beyond the success of the effort itself, ask what Key Performance Indicators (KPIs) matter to the organization, such as revenue each quarter, and how you can align your success metrics to impact to those KPIs. With the CoP example, perhaps one of your Innovation Challenges leads to a new product that generates unexpected sales when you bring it to market.
• Manage change and communications: KM practitioners make the mistake of focusing on the solution they deliver as the end-goal when, in reality, they should be focusing on whether that solution is even used and adopted by the individuals it was designed for. Integrated Change Management is an absolute must for any Agile KM Roadmap. This is a workstream that needs to be initiated at the start of the KM effort and carried through until the end of the initiative and beyond. People who are impacted by your KM solutions need to know why you’re introducing new technology, processes, and way of doing things. This can often seem like an unnecessary disruption to their already busy workday. If they understand “what’s in it for them” and are involved in the decision-making process as the solutions are being designed, then they are more likely to change the way they do things because they were involved and engaged throughout the process, having input and insight from the conception of the KM solutions to the delivery of them.
• Leverage a combination of technical and non-technical solutions: Technology is often the first and only solution that comes to mind when organizations face KM challenges. Introducing new technologies such as a more robust Content Management Systems (CMSs) or a user-centric enterprise search portal can significantly improve an organization’s knowledge management maturity, however implementing the technology itself is not enough to yield desired results. Technical solutions are an enabling factor to good KM, but it needs to be designed and governed in a way that maximizes adoption and delivers actual business value. Your KM roadmap should include facilitated sessions that allow your users to interact with the designs, prototypes, proofs of concept, and production-ready versions of your technical solutions. It should also include sessions with senior leadership and stakeholders to help them understand how the technical strategy and product align with overall business objectives. By approaching KM with an integrated technical and non-technical approach, your efforts will result in not only an optimal experience, but you’ll also make the most out of the features your technical solution offers.

By taking an agile approach to designing and implementing your KM Roadmap, you can go from not knowing exactly where to find your knowledge and information to having an action-oriented plan for creating, managing, and finding information in a more consistent and reliable way across the organization. Within months, as opposed to years, you can demonstrate that your KM efforts are bringing order to the once chaotic landscape of systems, content, and people. If you’re ready to design your custom KM Roadmap, contact Enterprise Knowledge and our KM experts will guide you through developing a practical approach for improving KM at your organization.

Workplace culture is an intangible concept that can be influenced by a myriad of internal and external factors. One of the most significant factors impacting the development of an organization’s culture is how well it responds to and adapts to change. Many organizations share common business challenges. To overcome these challenges, each business must look to its unique collection of assets. The most valuable asset a business has lies in the collective knowledge of its workforce. Business leaders can deploy knowledge management strategies in order to establish an informed, inquisitive workplace culture.

The advent of knowledge management

Although the concept of knowledge management (KM) has been around since the early to mid-1990s, it has grown in popularity through the prevalence of modern digital solutions. Business leaders use technology to augment their KM strategies. These strategies are geared towards business objectives including:

• Achieving greater employee performance
• Gaining a competitive advantage
• Engaging in innovation programs
• Deploying continuous improvement strategies

Above all, KM enables organizational learning. This is especially valuable to business leaders looking to create an “idea culture.” However, learning cultures don’t develop overnight. They require a long term commitment as well as an eye on the future. Let’s take a closer look at some ways organizations can create a learning culture within their organizations.

Centralize communications

One of the best ways to disseminate knowledge is to centralize the mode of delivery. Internal communication tools and strategies have become increasingly more commonplace in recent years. Inc. named company communication “2018’s Tech Sector to Watch.” According to experts consulted by the magazine, a convergence between different phenomena is changing the way businesses share information. The convenience, mobility, and speed by which employees access information have subsequently influenced their expectations about communication in the workplace.

Currently, many organizations suffer from misalignment due to different departments creating knowledge silos. When information becomes isolated from the larger employee population, productivity goes down. How can businesses expect teams to perform when they don’t have access to basic information that will enable them to do their jobs more effectively? Additionally, a lack of clarity surrounding organizational objectives can lead to a lack of trust among employees. Centralizing communications is one of the best ways to make the best use of organizational knowledge, a core principle in KM strategy. To do this, consider leveraging internal communications tools or establishing a company-wide intranet for sharing information across department lines.

Prioritize employee experience

The experience an employee has within an organization plays a large role in how workplace culture evolves. Moreover, how well an organization molds its culture to prioritize learning and knowledge sharing has a direct link to its ability to engage employees through periods of change. Given the way technology is changing modes of interaction, many businesses turn to digital HR tools to help them facilitate the employee experience. Tools like these offer collaborative, contextual learning platforms where employees can discover and follow top contributors in their organization. This socially-driven learning structure fosters knowledge sharing and helps employees build their reputation and share their own expertise.

To take it one step further, companies can then certify their employees in various aspects of KM. Whether an employee becomes a certified knowledge manager, specialist, or practitioner, they’re developing practical leadership skills that will enable them to succeed.

Workplace culture is a delicate concept that requires constant care and investment in order to thrive. Business leaders looking to innovate and stay ahead of the competition have a vested interest in developing learning cultures. An optimized communication and employee experience strategy leverage emerging technology to create better KM processes.

Everyone's excited to discuss Artificial Intelligence, and even more excited when Deep Learning is brought up. Surely, this is a revolutionary concept. That said, it has surely somewhat dizzied us.

Some seem to think that in the future professions will change and workers will become redundant. Some refer to production workers, others add accountants and other to the list. They are probably right.

Some say that machines will perform many actions previously performed by humans. Driving, for example. They are probably right.

Some say that in a world of AI and Deep learning, Knowledge Management is no longer needed.

They are certainly wrong.

There are many fields for which Deep Learning cannot provide solutions. I believe that these fields' nature is essentially different. Some say that human beings are irreplaceable with regard to human emotion, yet this insight less helps us in taking business decisions in organizations.

I wish to focus on an aspect that is a "black hole" for AI and Deep Learning technologies, the world of small numbers.

The power of Artificial Intelligence is based on deep learning, endless data results analysis, while utilizing neuron network algorithms to learn from the data and information found. The near future will probably feature Deep Learning-oriented machines making decisions better than us. Far better.

However, our business, organizational and personal world involves many decisions that are based on small samples and few stats. Deep learning is less relevant to these areas.

Furthermore, human decisions are not better. As Tversky and Kahneman have taught us in their Nobel Prize winning research, humans systematically tend to make wrong decisions, especially in small scale. Once, when wrongly assuming that behavior in small scale situations is similar to statistic operation typical of large scale. And secondly, when we make mistakes typical of any scale, for example, when we think of a certain solution then suddenly notice that everyone is apparently implementing this solution.

This is where Knowledge Management can come in hand. Knowledge Management reflects the knowledge accumulated through experience and can present us with what has been previously learned. Knowledge Management can serve as a rational anchor by either setting us an insight database, sharing the products of our analyses and activities, or by holding an expert/colleague forum. This anchor can not only shorten processes but also optimize decision making. In areas in which both human intellect and AI cannot be of assistance, Knowledge Management is the natural solution.

Like with the three monkeys we can end with not hearing, not seeing and not talking. But, When AI, human intuitions and Knowledge Management are coupled wisely, these 3 combined factors lead to an optimal solution in almost all situations.

The fourth industrial revolution has arrived. The possibilities of AI and how we will benefit from it is mind boggling and beyond imagination of many. It is said that like second industrial revolution resulted in us getting electrified, the fourth industrial revolution will end in us being ‘cognified’. We are getting into a data and insight driven world and it will be interesting to check the linkage between Knowledge management and Artificial intelligence at this juncture so that we leverage AI in a more meaningful way.

To understand the linkage between KM and AI, let us first understand what exactly organizations do with knowledge. Organizations perform different kinds of tasks and their success and competitiveness depends on the maturity in performing critical tasks, as well as where they stand with respect to industry in this. Tasks are performed by employees and machines, who take input information about the task, process the same based on knowledge (know how and know why) and complete the task. A physician collects symptoms, a professor’s input is what was taught in earlier session of the class, an architect needs requirements from the client etc. Hence for any task there is an input in the form of information, then that information is processed using knowledge and output is created.

In the case of humans, they can process large variation in the input information with respect to a task, even if the input information is not clear, they can remove the noise and if they do not have the relevant knowledge to process the information, they do further study, discuss with others, gain further knowledge and work on the information. They apply both know-how (procedural knowledge) and know-why (causal knowledge) as required. In the case of machines, they are pre-coded with rules (Know-how) on how to process the input information. The types of input information that they can process is very well defined. The knowledge (know-how) created to process the inputs are created by humans and used by both machines and humans.

With advent of AI, this relationship between input, processing and output for machines started changing. AI has enabled machines to create their own know-how to transform input to output. As a result AI can take up a wider range of inputs for a task, create their own know how and give output. Through learning they improve their know how and as a result provide better outputs as they learn. Here do note that, the input range does not change much, but for the given set of inputs, output created improves as a result of learning. 

What does this mean for organizations? As mentioned earlier, success or competitiveness of an organization depends on maturity in performing tasks and how they improve upon it. There is a journey towards efficiency and effectiveness that all organizations are forced to undertake, as a result of market dynamics. Underlying this journey is a continuous decrease in complexity with respect to tasks performed, where more and more variables are identified, their relationships are understood.

How does AI impact the way tasks are performed and the learning cycle?

Positive impacts

• Improved efficiency of tasks: Due to their ability to learn and improve, AI driven technology can help an organization improve its task on a regular basis. Given an approach to performing a task, the AI tools can help reach the most efficient approach must faster.
• Expediting learning: AI based technologies if used prudently can help in fast tracking the learning cycle. This is enabled through generating new data and creating insights from the same in the way tasks are performed.
• Knowledge findability and Employee productivity: One of the most popular use cases with AI has been the ability to find relevant content faster. AI can improve search drastically and give employees the information and the knowledge most relevant to them. This in turn will improve employee productivity and overall productivity.
• Human-machine collaboration and Employee productivity: With AI taking up routine and data heavy activities, employees are able to focus on complex activities, which can directly impact overall productivity of the organization and fast track maturity in performing tasks

Limitations

• Cannot improve effectiveness: AI improvement happens at the know how level and they cannot work with causal knowledge. Hence AI technologies on its own cannot innovate and drastically change the approach to perform a task.
• AI cannot leverage existing knowledge: This is another great drawback of AI. AI is data driven and creates insights from data to improve. It is not able to leverage knowledge generated from other sources, bring them together and create a new know how with respect to the task it is performing.
• Dependency on AI algorithms may at times slow down learning: Because know-how evolved by AI technologies are a mystery when deep learning techniques are used, organizations who extensively use AI in their process, without any clear strategy may find their learning cycle slow down with respect to the specific tasks. This is because they are not able to develop any understanding about the tasks they are performing. They will also become heavily dependent on AI vendors for algorithms to perform those tasks.

Hence for organizations to stay competitive in the long run, we need an approach that considers the strengths and weakness of AI and accordingly leverage knowledge. Unplanned application of AI may actually bring down competitiveness of an organization.