This, of course, is an ongoing challenge, rather than a moment in time initiative. To ensure the important work you’ve done to get your content, data, and other assets AI-ready is not lost, you need governance as well as an operating model to guide it. Indeed, well before any AI readiness initiative, governance and the organization must be top of mind. 

Governance is not a new term within the field. Historically, we’ve identified four core components to governance in the context of content or data:

• Business Case and Measurable Success Criteria: Defining the value of the solution and the governance model itself, as well as what success looks like for both.
• Roles and Responsibilities: Defining the individuals and groups necessary for governance, as well as the specific authorities and expectations of their roles.
• Policies and Procedures: Detailing the timelines, steps, definitions, and actions for the associated roles to play.
• Communications and Training: Laying out the approach to two-way communications between the associated governance roles/groups and the various stakeholders.

These traditional components of governance all have held up, tried and true, over the quarter-century since we first defined them. In the context of AI, however, it is important to go deeper and consider the unique aspects that artificial intelligence brings into the conversation. Virtually every expert in the field agrees that AI governance should be a priority for any organization, but that must be detailed further in order to be meaningful.

In the context of AI readiness for knowledge assets, we focus AI governance, and more broadly its supporting operating model, on five key elements for success:

• Coordination and Enablement Over Execution
• Connection Instead of Migration
• Filling Gaps to Address the Unanswerable Questions
• Acting on “Hallucinations”
• Embedding Automation (Where It Makes Sense)

There is, of course, more to AI governance than these five elements, but in the context of AI readiness for knowledge assets, our experience shows that these are the areas where organizations should be focusing and shifting away from traditional models. 

1) Coordination and Enablement Over Execution

In traditional governance models (i.e. content governance, data governance, etc.), most of the work was done in the context of a single system. Content would be in a content management system and have a content governance model. Data would be in a data management solution and have a data governance model. The shift is that today’s AI governance solutions shouldn’t care what types of assets you have or where they are housed. This presents an amazing opportunity to remove artificial silos within an organization, but brings a marked challenge. 

If you were previously defining a content governance model, you most likely possessed some level of control or ownership over your content and document management systems. Likewise, if you were in charge of data governance, you likely “own” some or all of the major data solutions like master data management or a data warehouse within your organization. With AI, however, an enormous benefit of a correctly architected enterprise AI solution that leverages a semantic layer is that you likely don’t own these source systems. The system housing the content, data, and other knowledge assets is likely, at least partly, managed by other parts of your organization. In other words, in an AI world, you have less control over the sources of the knowledge assets, and thereby over the knowledge assets themselves. This may well change as organizations evolve in the “Age of AI,” but for now, the role and responsibility for AI governance becomes more about coordination and less about execution or enforcement.

In practice, this means an AI Governance for Knowledge Asset Readiness group must coordinate with the owners of the various source systems for knowledge assets, providing additive guidance to define what it means to have AI-ready assets as well as training and communications to enable and engage system and asset owners to understand what they must do to have their content, data, and other assets included within the AI models. The word “must” in the previous sentence is purposeful. You alone may not possess the authority of an information system owner to define standards for their assets, but you should have the authority to choose not to include those assets within the enterprise AI solution set.

How do you apply that authority? As the lines continue to blur between the purview of KM, Data, and AI teams, this AI Governance for Knowledge Asset Readiness group should comprise representatives from each of these once siloed teams to co-own outcomes as new AI use cases, features, and capabilities are developed. The AI governance group should be responsible for delineating key interaction points and expected outcomes across teams and business functions to build alignment, facilitate planning and coordination, and establish expectations for business and technical stakeholders alike as AI solutions evolve. Further, this group should define what it means (and what is required) for an asset to be AI-ready. We cover this in detail in previous articles, but in short, this boils down to semantic structure, quality, and entitlements as the three core pillars to AI readiness for knowledge assets. 

2) Connection Instead of Migration

The idea of connections over migration aligns with the previous point. Past monolithic efforts in your organization would commonly have included massive migrations and consolidations of systems and solutions. The roadmaps of past MDMs, data warehouses, and enterprise content management initiatives are littered with failed migrations. Again, part of the value of an enterprise AI initiative that leverages a semantic layer, or at least a knowledge graph, is that you don’t need to absorb the cost, complexity, and probable failure of a massive migration. 

Instead, the role of the AI Governance for Knowledge Asset Readiness group is one of connections. Once the group has set the expectation for AI-ready knowledge assets, the next step is to ensure the systems that house those assets are connected and available, ready for the enterprise AI solutions to be ingested and understood. This can be a highly iterative process, not to be rushed, as the sanctity of the assets ingested by AI is more important than their depth. Said differently, you have few chances to deliver wrong answers—your end users will lose trust quickly in a solution that delivers inaccurate information that they know is unmistakably incorrect; but if they receive an incomplete answer instead, they will be more likely to raise this and continue to engage. The role of this AI governance group is to ensure the right systems and their assets are reliably available for the AI solution(s) at the right time, after your knowledge assets have passed through the appropriate requirements.

3) Filling Gaps to Address the Unanswerable Questions

As the AI solutions are deployed, the shift for AI governance moves from being proactive to reactive. There is a great opportunity associated with this that bears a particular focus. In the history of knowledge management, and more broadly the fields of content management, data management, and information management, there’s always been a creeping concern that an organization “doesn’t know what it doesn’t know.” What are the gaps in knowledge? What are the organizational blind spots? These questions have been nearly impossible to answer at the enterprise level. However, with enterprise-level AI solutions implemented, the ability to have this awareness is suddenly a possibility.

Even before deploying AI solutions, a well-designed semantic layer can help pinpoint organizational gaps in knowledge by finding taxonomy elements lacking in applied knowledge assets. However, this potential is magnified once the AI solution is fully defined. Today’s mature AI solutions are “smart” enough to know when they can’t answer a question and highlight that unanswerable question to the AI governance group. Imagine possessing the organizational intelligence to know what your colleagues are seeking to understand, having insights into that which they are trying to learn or answer, but are currently unable to. 

In this way, once an AI solution is deployed, the primary role of the AI governance group should be to diagnose and then respond to these automatically identified knowledge gaps, using their standards to fill them. It may be that the information does, in fact, exist within the enterprise, but that the AI solution wasn’t connected to those knowledge assets. Alternatively, it may be that the right semantic structure wasn’t placed on the assets, resulting in a missed connection and a false gap from the AI. However, it may also be that the answer to the “unanswerable” question only exists as tacit knowledge in the heads of the organization’s experts, or doesn’t exist at all. This is the most core and true value of the field of knowledge management, and has never been so possible.

4) Acting on “Hallucinations”

Aligned with the idea of filling gaps, a similar role for the AI governance group should be to address hallucinations or failures for AI to deliver an accurate, consistent, and complete “answer.” For organizations attempting to implement enterprise AI, a hallucination is little more than a cute word for an error, and should be treated as such by the AI governance group. There are many reasons for these errors, ranging from poor quality (i.e., wrong, outdated, near-duplicate, or conflicting) knowledge assets, insufficient semantic structure (e.g., taxonomy, ontology, or a business glossary), or poor logic built into the model itself. Any of these issues should be treated with immediate action. Your organization’s end users will quickly lose trust in an AI solution that delivers inaccurate results. Your governance model and associated organizational structure must be equipped to act quickly, first to leverage communications and feedback channels to ensure your end users are telling you when they believe something is inaccurate or incomplete, and moreover, to diagnose and address it.

As a note, for the most mature organizations, this action won’t be entirely reactive. For the most mature, organizational subject matter experts will be involved in perpetuity, especially right before and after enterprise AI deployment, to hunt for errors in these systems. Commonly, you can consider this governance function as the “Hallucination Killers” within your organization, likely to be one of the most critical actions as AI continues to expand.

5) Embedding Automation (Where It Makes Sense)

Finally, one of the most important roles of an AI governance group will be to use AI to make AI better. Almost everything we’ve described above can be automated. AI can and should be used to automate identification of knowledge gaps as well as solve the issue of those knowledge gaps by pinpointing organizational subject matter experts and targeting them to deliver their learning and experience at the right moments. It can also play a major role in helping to apply the appropriate semantic structure to knowledge, through tagging of taxonomy terms as metadata or identification of potential terms for inclusion in a business glossary. Central to all of this automation, however, is to ensure the ‘human is in the loop’, or rather, the AI governance group plays an advisory and oversight role throughout these automations, to ensure the design doesn’t fall out of alignment. This element further facilitates AI governance coordination across the organization by supporting stakeholders and knowledge asset stewards through technical enablement.

All of this presents a world of possibility. Governance was historically one of the drier and more esoteric concepts within the field, often where good projects went bad. We have the opportunity to do governance better by leveraging AI in the areas where humans historically fell short, while maintaining the important role of human experts with the right authority to ensure organizational alignment and value.

If your AI efforts aren’t yet yielding the results you expected, or you’re seeking to get things started right from the beginning, contact EK to help you.

The post Defining Governance and Operating Models for AI Readiness of Knowledge Assets appeared first on Enterprise Knowledge.

One way we do this at EK is by defining a series of pilots, which are limited-scope efforts, focused on quickly demonstrating value to organizational stakeholders by solving targeted issues and exploring new technologies and practices. Each pilot is intended to validate that the KM Strategy approach we’ve developed will work for the organization and to determine how that pilot can be scaled. These pilots also serve to drive incremental change and excitement for “what could be.” This exercise in defining pilots begs the question though, “How do we know where to get started?” 

Here are 6 questions that help us determine the best approach for an organization to start their KM transformation. 

1. Where is the low-hanging fruit?

A commonly used metaphor, what I mean by “low-hanging fruit” is that we’re looking to identify the simplest activity to implement within an organization that will produce immediate, tangible value. What this means from a practical standpoint  is that the pilot has a low-level of complexity. There are a few ways to judge this:

• The pilot is able to be conducted using solely the internal expertise and experience of the organization’s staff. In this scenario, no external subject matter expertise or consultancy is required. The organization can get started today with the skills and competencies they have in house.
• The pilot involves one department (or business area) or up to two closely-aligned departments. Scoping the pilot to one or two departments allows an organization to test a methodology or process within a specific function before it’s adapted and scaled for the enterprise’s benefit. 
• The pilot is building off and enhancing a pre-existing technology or practice. We’re always looking for examples of “good KM” when we’re conducting our Current State Assessments because we know that there are strengths that can be leveraged. Some of our pilots do just that – they improve something that is already in place that has the potential to be transformative if modified or if the right incentives are in place to increase adoption.

2. What does the organization care about, and what would get them excited?

At the onset of a KM Strategy project, we ask staff at different levels of the organization, “If you had easier access to the people and information you need to effectively execute your daily tasks and responsibilities, what would that mean for you? How would that help you be successful?” Ultimately, we’re trying to understand the downstream effects and business value of KM for the organization. 

In every organization, the downstream impacts and business value of KM can vary depending on the teams and departments whose insights are being solicited. For those in Sales roles, for example, it could be access to accurate, current, and competitive market information that is going to help them pursue and close sales deals. For those in Customer Service positions, it could be having the ability to find customer and account information to provide the right level of service to customers based on what the organization has done for them in the past. For other organizations, it’s ensuring continuity of operations by ensuring that knowledge does not walk out the door when their employees leave or retire. 

It’s these value statements that help us think through what pilots can serve to further these goals:

• Does the organization need a pilot around content clean-up to ensure that when people do come across information, they have confidence that it’s up-to-date and accurate, and they can use it to take action or make a decision?
• Could we come up with a pilot that helps to define what customer-facing staff would want to see when searching for past information on customers and accounts? 
• Do we need to consider a pilot around experimenting with knowledge transfer techniques to support colleagues in sharing what they know throughout their tenure with an organization?

My colleague, Mary Little, discusses the importance of aligning KM with your organization’s strategic goals and this can start as early as the pilot definition phase. 

3. Who is interested in being an early adopter of KM, or is equipped with the capabilities and resources to support a pilot immediately?

If we’re conducting an KM Strategy project at the enterprise level, we always ask to speak with staff who represent different functions and departments with the organization. We do this for a variety of reasons, one being that it helps us understand those pockets within the organization that are acutely experiencing a KM challenge and who are eager to see change. This approach not only helps us brainstorm options for what recommendations and pilots we will define for the organization, but it also helps us identify who might want to be a part of a pilot. Identifying early adopters in the form of a department, group, or team helps the organization drive interest in and momentum for its KM initiatives. This is critical for the long-term adoption and sustainability of a holistic KM program, which will be focused on solving different challenges over time and necessitate changes in how people work. 

Another angle to consider is whether there is a department or group who has the capabilities and resources needed to support a pilot immediately. Part of this involves exploring what skill sets will be needed to perform associated responsibilities and whether the organization can draw on current employees with specific expertise to support the implementation of a pilot. Conversely, it is also important to gain an understanding of an organization’s internal processes around approving funding for projects. It can be beneficial to have these conversations to determine whether departments have their own pool of funding to use at their discretion or whether projects have to go through a more formal review process that happens at different intervals throughout the year.  

4. Is there an existing organizational initiative that we can align a KM pilot to?

In developing a KM Strategy, we look at five different dimensions within an organization: People, Process, Content, Culture, and Technology. Because we’re looking across these dimensions, we often hear about other initiatives that are going on in the organization. We love to hear about these because they can be tangential to what we’re doing and there are opportunities for alignment. In the past, these tangential initiatives have taken the form of:

• Data inventory and governance efforts.
• Enterprise search projects.
• Process improvement efforts.
• Initiatives to consolidate content management or customer relationship management systems.  
• Records management implementations.
• Selection and implementation of a learning management system. 
• Sunsetting legacy knowledge repositories and related content migration efforts.

Just as it can be easier to secure support for a pilot if it’s tied to an organization’s strategic objective, it can be easier to secure support for a pilot if you can communicate how it will support the success of another initiative. By aligning a KM pilot to another relevant initiative, you’re helping to ensure the maximal effectiveness of both.

5. How many people will the proposed pilot impact?

In considering what pilots we recommend prioritizing as part of a KM transformation, we’re thinking about what is going to drive the biggest return on investment. Part of that has to do with how many people will be affected by the proposed change. Early on in our KM Strategy engagements, we request an overview of our client’s organizational structure, their departments, and which departments have interdependencies. This gives us a sense of how big the departments are in relation to each other and which work closely with one another. In return, as we conduct interviews, focus groups, and workshops, we start to understand the degree to which staff are experiencing similar KM challenges regardless of where they sit in the organization, and which KM challenges are most pressing. Armed with this information, we can think through how to prioritize our pilots based on how many people it will impact positively. These pilots often end up being holistic efforts that will benefit all departments over time, as they are scaled. 

6. How foundational is the pilot?

When developing pilots and recommendations, we are also outlining a roadmap across which these can take place. Our roadmaps span different timeframes based on an organization’s needs and resources, but they can include both “foundational” and “advanced” pilots. A foundational pilot is one that helps establish the success of subsequent efforts in the roadmap. This could include, for example, developing metrics to monitor the success of KM pilots, enable alignment across different initiatives, and allow the organization to make data-driving decisions on how to adapt its KM Strategy, as needed. We may also include, if the organization is ready, advanced pilots that lay the groundwork for AI applications – for example, developing a knowledge graph to connect and show meaningful relationships between data regardless of where it is located. While the advanced pilots can sometimes be more “exciting” work, we want to ensure an organization is laying the foundation to explore advanced AI capabilities in the right way and in a way that will be scalable and sustainable. Prioritizing foundational pilots on your organization’s KM Strategy Roadmap is essential to building that infrastructure.

Closing

Regardless of how big your company is, how many millions of documents your organization might maintain, or how widely disparate the processes are between staff to capture critical information, we know it can be overwhelming to contemplate the question “Where do we start?” But it doesn’t have to be. We’re here to help! Contact Us at Enterprise Knowledge to navigate this ambiguity and jump start your KM transformation.  

The post 6 Questions to Help Determine Where to Start Your KM Transformation appeared first on Enterprise Knowledge.

A quick online search of the question “When do we implement change management?” surfaces the age-old response of “the sooner the better.” It’s common advice that change management efforts should run in tandem with the kickoff of any initiative (or technology launch) to ensure sustainability and return on investment. But in the early stages of a knowledge management project, what does change management look like?

EK’s Integrated Change Management approach consists of three phases: Aligning, Surfacing, and Realizing. During the first phase – Aligning – we work with leadership at the project onset to understand their high-level vision for the organization, and begin to shape the tactical approach for execution. This phase is critical for setting the groundwork for the long-term adoption of the change at hand. 

There are 4 Must-Do’s that occur during the Aligning phase. Because the effect of these actions taken together is exponential rather than additive, it’s best to complete all four activities rather than selectively execute only one or two.

Must-Do 1: Establish a Crystal Clear Purpose Statement

Time after time when we ask clients “What is the purpose of this change?,” we hear things like “greater efficiency,” “improved customer experience,” and “culture-shift.” If the change is technology-related, we also hear adjectives about the user experience – phrases such as “one-stop-shop,” “easy to navigate,” “greater search functionality,” and “time-saving.” In both scenarios, while those elements are certainly a part of the change or the platform being launched, the reality is, that kind of language is not clear enough. It is still too “squishy,” and just a jumble of words. While these sentiments are important to capture, they don’t identify a North Star for the project – something every purpose statement should do in the form of a complete statement. A crystal-clear purpose statement should be simple, relevant, and repeatable to generate momentum and widespread trust in the change. It should also act as the judge and jury when the team comes to a crossroads. We should always be able to look to the purpose statement and ask ourselves: “Does this decision move us closer to that direction or further away?” Generating a succinct and easily repeatable purpose statement is a real skill. Your team will move through a few iterations before getting it “right.” Don’t worry and don’t settle. This is important. Make sure you nail it down. 

Must-Do 2: Establish a Change Team

EK’s approach to Integrated Change Management is heavily metrics-based. However, before metrics can be collected and analyzed, it is important to have ‘canaries in the coal mine.’ Every organization has its own language, strengths, and sensitive areas, and you need to manage change with those considerations in mind. We’ve had success with establishing a group of 8-10 individuals who can tell us what is percolating in the organization at the moment, how the organization has dealt with change in the past, and who can utilize their social capital to build both trust and supportive engagement for the change. Selecting the right individuals for this team is key. There needs to be a good mix of people with time to do the work, the ability to accurately convey the sentiments of their colleagues, and those who can engage in healthy conflict. It is also absolutely critical that actual business users are on this team to represent the perspectives of those who will be most impacted. This team needs to be able to challenge one another’s ideas and ensure groupthink doesn’t take over this small, but mighty team.

Must-Do 3: Define Success & ROI Metrics

Developing metrics to track the progress of your change efforts is critical to ensuring that everyone involved in the project stays both output- and outcome-driven. In order to understand the value of change though, metrics must be accompanied with context. A way to easily remember this rule is with an alliteration: numbers and narrative. It’s not enough to simply offer up a positive story about the change – your senior leaders will want to see numbers that demonstrate impact. At the same time, numbers without context are not able to convey the significance and meaning of the work that is being accomplished. At EK, we utilize a distinct methodology to capture both numbers and narrative for reporting ROI that resonates with leaders. 

Metrics and milestones are also necessary anchor points that will enable you to make more data-driven decisions and adapt your change strategy as necessary. Your Change Team will help identify transition activities to support their colleagues in adapting to new ways of working. To gauge success, there has to be a way to determine whether those transition activities are having the intended impact. Defining corresponding metrics that are tracked on an ongoing basis will position you to adjust your change strategy when it is clear something isn’t working as anticipated, and pivot to try a different approach. When developing your ROI strategy, keep your audience in mind and set yourself up to be adaptable.

Must-Do 4: Get to Know the Status Quo

For better or for worse, the phrase “every system is perfectly designed to get the results it gets” holds true. When we’re helping a client make a change, we start by understanding why things are the way they are. It’s during the early stages of a project that we’re looking for answers to a lot of questions, and you should be too. We want to know, who is most heavily invested in how things are done today? What is the role of failure and learning in the organization, and what would make people feel safer when taking calculated risks at work? We want to know, quite simply, who can tell others what to do? Who do people listen to? There will be people in your organization who can influence at both a small and large scale, and it will be important to engage these groups or individuals as part of the change. We also ask about how information flows across the organization and top-down to determine what silos exist and look to understand what type of information is made available versus what is released on a need-to-know basis. There are different ways to go about collecting this data, but don’t skip out on asking these important questions. The responses will inform the development of your change strategy as you consider how best to prevent issues from arising as you’re introducing change into people’s day-to-day.

We know that change management can’t be saved for the tail end of the engagement and isn’t something an organization can just mention and hope for the best – the scaffolding has to be set up at the beginning of the initiative and that begins with Aligning. We encourage you to use these 4 Must-Do’s to set up your scaffolding and to be on the lookout for Part 3 of this series, where we’ll discuss the second phase of EK’s integrated change management approach, Surfacing.

For more information on how to start implementing Integrated Change Management early, consider EK’s One-Day ICM Workshop or contact us at info@enterprise-knowledge.com.

The post Avoiding Adoption Failure: 4 Change Management Must-Do’s appeared first on Enterprise Knowledge.

• Staff can’t easily find useful and relevant information, when they need it.
• There is a lack of trust that the information their staff come across is complete, up-to-date, and accurate.
• Collaboration is hindered by silos.
• Expertise, best practices, and lessons learned aren’t exchanged in a way that could drive innovation and creativity.
• Knowledge is “walking out the door” when people leave.
• Onboarding processes aren’t supporting new staff in getting acclimated effectively.

While many organizations face similar business challenges, how these challenges emerge and look within an organization vary based on its culture and operating environment. In this presentation, Mary Little, Practice Lead, and Kristin McNally-Levitas, Senior Consultant, of Enterprise Knowledge, share methods and proven practices in assessing an organization’s KM maturity, surfacing their primary KM challenges, and defining a roadmap to their KM goals that is framed within the context of their business.

The post Designing an Organization’s KM Journey appeared first on Enterprise Knowledge.

necessary to complete their work). Others see their staff hindered by silos and cultural behavior that doesn’t lend itself to information sharing. And all are trying to keep up with evolving trends in their industry.

At EK, we couldn’t agree more that organizational adaptability is imperative. When we are working on a Knowledge and Information Management (KIM) initiative for a client, we take a holistic view of the organization to understand how people work together, how they share information and resources, and how management processes govern their work. For organizations grappling with this same issue, here are a few ways to build adaptability as a central competency in your organization.

Foster cross-organizational connections and knowledge sharing.

We’ve worked with a range of organizations, where the extent to which their staff collaborate and share information differs greatly. We’ve observed workplace cultures where staff are reluctant to share information for a variety of reasons, and others (including our own) where information is made available in real time to all. Moving towards a collaborative working environment that adopts open, transparent communication practices is not easy, but when information is shared sporadically or solely between team members, what one knows becomes mostly dictated by one’s social network. Organizations then run the risk of duplicating efforts, making decisions without considering how they’ll impact other internal groups, and missing opportunities to exceed the expectations of their customers.

The need for a more collaborative approach to working presents the opportunity to think through how you can build personal relationships among colleagues who don’t normally work closely together and create stronger connections between organizational departments. Try hosting staff-driven town hall meetings, where employees report out on the work they are accomplishing. This format facilitates staff recognition and highlights work that others in the organization might not be aware of. These town halls can facilitate connectivity between staff of different departments, enabling them to collaborate in unexpected and productive ways. Additionally, consider other ways (e.g., open meeting formats, rotational programs, communities of practice) for individuals to see how the business looks from inside another group. As your staff spends more time with people from different departments, they come to understand the larger environment in which decisions are made and understand how each group is collectively contributing to the organization’s purpose. They build a more holistic understanding of how the organization operates and learn to place more trust in their colleagues, making them ultimately more empathetic to other groups’ needs.

Pursue learning and development opportunities.

At EK, we think a lot about knowledge workers – those individuals who apply what they know to creatively solve problems, develop products and services, and add value to their organization. The organizations we work with create, manage, use, and share knowledge and information to enhance their growth. When we’re working with them, we’re looking to identify how to maximize the effectiveness of their knowledge workers in order to help the organization be successful. Critical to maximizing their effectiveness is placing increased significance on education and lifelong learning. Learning and Development (L&D) provides staff with the perspective and skills they need to adapt to evolving trends in their field and within their organization as well as improve their productivity and the quality of their work. Without continual learning, staff’s proficiency on a subject will diminish.

Day-to-day work and organizational barriers can prevent staff from being able to take advantage of L&D opportunities. But, as our CEO Zach Wahl attests, prioritizing your people’s development can be one of the best ways to apply your resources and position your organization for continued growth. At EK, every employee receives an annual training budget to continue their education and professional development. There are numerous other ways organizations can provide support for L&D. For instance, start an internal skills development program led by employees who are passionate about a topic and can tailor learning material to the language and culture of the organization. AT EK, we host bi-weekly knowledge sharing sessions during our all-hands meetings. This technique has proven to be a great way to facilitate the sharing of tacit knowledge (i.e., the knowledge that is held within experts), introduce new concepts, boost morale by recognizing people for their expertise, and help build relationships across our company. Additionally, you can encourage employees to become points of contact beyond their teams by developing the capability to search for people by expertise through a company intranet. We’ve worked with companies on developing “expert finders,” which help people locate colleagues with specific expertise or technical knowledge and either contact them or post questions directly on the platform.

Give people room to adapt.

EK leverages Agile to coach leaders to create a culture of learning, adaptation, and resiliency in their organizations. Agile is a method of and mindset towards continuous improvement, and one of its core principles instructs teams to reflect on how to become more effective, and then adjust its behavior accordingly. On any given project, consistent communication and regular touch points within teams enable employees to ensure they’re on the right trajectory and pivot when needed to new requests, changing requirements, and general feedback. We always recommend that teams hold retrospectives throughout a project to provide feedback on what has gone well and what requires improvement. Though these sessions can easily be skipped in the face of mounting responsibilities, they are critical in streamlining processes, enhancing team dynamics, and improving performance as one of my colleagues has previously discussed. Regular touch points, like retrospectives, give people the space not only to discuss what improvements they want to make on a specific project, but also provide a forum for general discussions. We’ve seen teams use retrospective meetings as opportunities to institute cross-training programs and test out tools. Regardless of how they’re implemented, providing forums and building in time for people to talk with one another is imperative to creating an environment and culture of continuous development.

If you’re looking to create more transparency across your organization, foster a culture of continuous learning and feedback, and build the capability to quickly adapt to changing stakeholder needs, EK can help you transform the way you do business. Contact us to learn more.

The post 3 Ways to Build Adaptability as a Core Organizational Competency appeared first on Enterprise Knowledge.

At EK, we are mindful that for any Knowledge and Information Management initiative to be effectively adopted within an organization we have to ask ourselves two questions: “who will have to do their jobs differently?” and “what new processes and practices need to be put in place to ensure those individuals are equipped to succeed?” Not answering these questions can lead to ignored technology systems, subverted processes, and painful organizational change.

That’s why when we’re developing a Knowledge Management (KM) strategy for a client, we are intent on understanding the organization within which they are operating. Our goal is to help build a more adaptable workforce that is prepared for change – whether it occurs in organization structure, process, or technology – and can sustain their KM strategy over time.

Design Thinking: An Approach and Mindset

Determining how we can support the underlying organization is no small feat. To do so, we often leverage Design Thinking to reimagine how people can work more effectively together. The value of Design Thinking lies in the fact that it is both an approach and a mindset. As an approach to problem solving, Design Thinking necessitates that we seek to understand our end users – those individuals for whom we are designing a KM strategy. We treat our end users as partners and co-creators, discovering what is meaningful to them so we can be sure that we are focusing on what matters. To start shifting behaviors within an organization, it’s critical that we meet them where they are, immerse ourselves in their perspectives, and co-create solutions. We want everyone to be aligned about the challenge we are solving and understand why things need to change.

As a mindset, Design Thinking is about being open-minded and curious. It’s about building empathy and setting aside any assumptions that we might have about the people for whom we are designing. And it’s about being comfortable generating and iteratively working through various possible solutions, knowing some won’t work, but trusting that some will.

Building a New Organizational Model

How do we enable employees to embrace and adopt new ways of working that support a proposed KM strategy? American architect and designer Richard Buckminster Fuller once said “You never change things by fighting the existing reality. To change something, build a new model that makes the existing model obsolete.” At EK, our approach to organization design recognizes that we have to create a future that is different from the present. We have to look at and reimagine various aspects of an organization. When we’re seeking to understand our client’s organization, we are looking to see where they fall on a spectrum of topics, including:

• Organizational Structure: Do employees have formal roles within a fixed hierarchy or is the organization comprised of self-managing teams, which support the creation of more fluid, natural hierarchies?
• Leadership Style: Do leaders manage by command-and-control or do they think of themselves as servant leaders?
• Information Flow and Knowledge Sharing: Does the company adopt practices of transparency, or is information considered power and provided on a need-to-know basis?
• Coordination: How do teams and departments work together? Do people typically work in silos? Alternatively, does the organization support cross-functional collaboration?
• Decision-Making: What guides decision-making – profit, growth, and market share, or values and organizational purpose? Are frontline workers given autonomy to make decisions?
• Mindset and Perspective: Do people in the organization follow established, stable processes (i.e., there is one right way of doing things)? Or is change viewed as an opportunity and, subsequently, people are rewarded for thinking innovatively?
• Performance Management: Does the organization operate as a meritocracy, with people advancing based on their individual talents, or is the focus on a team’s overall performance?

These are just some of the areas that we delve into through a combination of interviews, focus groups, and workshops as we think through how to help an organization successfully harness their knowledge. While every organization is different, we do see common elements in successful KM organizations. Organizations that are adaptable and promote openness and connectivity often have servant leaders, create channels to share information more broadly, empower teams (or departments) with responsibility, hold people accountable, and use a combination of centralized and decentralized decision-making practices. Ultimately, by looking holistically at the structures, processes, communication practices, tools, resources, and incentives that are in place, we can identify where change is most needed in order to help an organization achieve its Knowledge and Information Management goals.

Design Thinking and Organization Design

We approach organization design from a Design Thinking perspective, recognizing that if we want to create the conditions that change behavior, we have to understand our end users’ wants, needs, pain points, and goals, and the system within which they work. Our Design Thinking for Knowledge Management approach (DTKM) – Empathize, Define, Ideate, Prototype, and Test – allows us to do just that.

During the Empathize stage, we meet with people from across the organization of different job levels, tenures, and areas of expertise to look at the problem through their lens, surface pain points that they are facing, and discover unmet needs. Our objective is to understand why things are the way they are and to build a foundational partnership from which we can later co-create solutions.

Once we have a clear assessment of the organization’s current operating state, we move to the Define stage. It’s here that we analyze and synthesize the information collected during our interviews, focus groups, and workshops to identify the core organizational issues affecting the end users, and we visualize a target state for the organization that will support their KM initiatives. It is critical at this stage that we align leadership and staff around the vision for change, why change is needed, and what the risk of not changing is.

With our target state defined, we can start prioritizing where we need to focus – what areas of the organization need our attention the most to support their KM goals. That’s when we begin to Ideate. We work with our end users to explore new ideas and practices that will help nudge behavioral changes. We visualize new ways of working to see where, for example, we can simplify burdensome organizational processes.

Armed with possible solutions, we then move to the Prototype and Test stages. Since our focus is on people and processes and shifting behavior, our goal in this experimental phase is to start small and try out new ways of working through developing minimal viable “products.” From an organization design perspective, this could be testing different approaches to decision-making within a project team. It could also involve rolling out a KM training curriculum to a select group of end users to see whether it could be adopted enterprise-wide in the future. Ultimately, our goal is to see what works and what doesn’t, and iterate based on feedback.

Once we have a solution, it’s important to demonstrate quick wins and identify a KM leadership team and tribe that can commit to sustaining the change and ensure the new behaviors stick. People need to see tangible action that is delivering real value, and they need to see their leadership visibly and actively supporting the recommended efforts in order to participate themselves.

In Conclusion

Each KM strategy we develop and implement for our clients is unique. It is dependent on their needs, priorities, and goals, as well as the people, process, culture, and enabling technologies that comprise their organization. Similarly, there’s never a “one size fits all” approach to organization design. Our Design Thinking for Knowledge Management process is effective in keeping the focus on end users, learning about the context in which people work, and driving recommendations to the organizations that are practical, sustainable, and will enable new behaviors to stick.

Want to learn more about how we use Design Thinking to rethink how people work together in support of a KM strategy? Contact us.

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