Ensuring the adoption of new knowledge management programs, systems, and tools requires thorough planning well in advance of actually launching a new initiative. It also takes an agile approach to designing your solution so that you can adapt what you deliver based on what your employees truly need to help them get their job done.

In this presentation, you’ll learn how to develop, refine, and execute the following critical plans, which will ultimately maximize employee engagement with the “new way of doing things.”

• Charter and Project Plan
• User and Stakeholder Analysis Plan
• Communications and Change Management Plan
• Training Plan

Based on her experience managing a successful initiative to design and implement a content management tool for the communications department of a large manufacturing organization, Mary Little will share step-by-step guidelines for improving the adoption of your knowledge management solutions.

Mary gave this presentation to the October 16-20 CKM Class in Tysons, VA.

Please click here to view her full video presentation...

Effective knowledge sharing is the lifeblood to knowledge-based organizations. High-performing organizations are ones that have mastered the art of empowering their newer employees with the knowledge and experience of the more-tenured, highlighting new ways of thinking and doing, and fostering a broad set of avenues for knowledge to flow up, down, and sideways throughout the organization.

Through my years of KM consulting, I’ve noted that too often an organization will focus on trying to fix the tools and technologies around knowledge capture, management, sharing, and finding, without addressing the behavior, processes, and culture that feeds mature knowledge sharing organizations.

Within this framework, the “if you build it they will come,” concept is blatantly false. Most team members won’t gravitate toward the shiny new tool just for the sake of it. A new technology may attract a small subset of early adopters or may generate an initial burst of interest, but without a focus on instilling and maintaining a culture of knowledge sharing in your organization, these tools can only do so much.

In my experience, there are three primary reasons people don’t share their knowledge, especially in the context of tacit knowledge capture in online KM Systems like communities of practice, micro-blogs, and threaded discussions.

• Lack of Priority – When we speak with knowledge holders we often hear phrases that include, “I don’t have time,” or “I’m too busy.” This often gets diagnosed as a lack of interest in sharing one’s knowledge, but at EK we often find the root cause to be a lack of priority stemming from management. If an organization doesn’t stress the value and importance of sharing knowledge, enterprise knowledge sharing can’t be woven into the fabric of the institution.
• Worry About Being Replaced – Everyone has heard the phrase, “Knowledge is Power.” Unfortunately, many people tend to consider this as job security. We often encounter individuals uninterested in knowledge sharing because they want to be the person with the answers. For them, empowering others with that knowledge means they’re less essential to the organization. We commonly see this in highly competitive organizations or functions and industries already experiencing high turnover.
• Fear of Getting in Trouble – In more heavily regulated organizations, individuals often have it drilled into them that anything digital is discoverable in court. This can sometimes lead to a negative loop, where individuals avoid documenting their knowledge. Even in less heavily regulated organizations, certain organizational cultures punish the squeaky wheel and instead encourage their employees to keep their heads down and get the job done the way it has always been done.

Though developing and sustaining a culture of knowledge sharing in your organization requires a broad array of techniques and tools, there are several keys that I find to be critical to overall success.

• Start at the Top – Knowledge sharing culture, like most organizational culture change, starts at the top. The leaders of an organization can invigorate or kill a knowledge sharing initiative based on the support they give it and whether they themselves use it. If an organization’s management sets knowledge sharing as a priority, it will be so. I recently had a CEO tell me he would write the first micro-blog for the company’s new wall and commit to visiting the space at least once a week. That type of leadership doesn’t always exist, but where it does, you’re likely to see a much easier transition to effective knowledge sharing at all levels.
• Reward and Honor Knowledge Sharing – Organizations that are the most effective at knowledge sharing are those that treat their experts like rock stars. The holders of knowledge should be rewarded not just for having it, but for sharing it. Effectively rewarding and honoring knowledge sharing can take many different forms. It can include tying knowledge sharing metrics to real incentives (bonuses, positive reviews, etc.), but certainly doesn’t have to. Simply recognizing individuals as experts and broadly thanking them is oftentimes enough. One organization with whom we’ve worked has begun providing unique online badges and titles to those who have shared their knowledge energetically and effectively, and the results have been excellent, with more in the organization seeking the same recognition and wanting to participate.
• Protect Your Knowledge Sharers – Ensure you’ve established appropriate governance, workflows, and training for knowledge sharing. This goes beyond saying “No, Stan, this is not the place for you to share pictures of your eight cats wearing matching bow ties.” Depending on the industry and framework of your organization, you need to protect your employees by putting the appropriate controls in place so they leverage their knowledge sharing tools in the ways for which they’re intended. Mistakes will happen, so having the right level of reviews and shepherding of content is also a critical investment to ensure these systems trend towards “better” instead of “worse.”
• Think About Email – People use email because it is easy, familiar, and fast. As you’re designing your future knowledge sharing systems and processes, recognize that it takes no more than 45 seconds to send an email with an attachment. Design your knowledge sharing system to allow someone to share in 45 seconds or less. That means sacrificing some level of granularity for the overall usability, but the level of participation will increase as the barrier to entry decreases. 
• Provide Context – Knowledge sharing systems without context quickly stagnate. If you’ve defined a broad and shallow community of practice for “Innovation” around your organization, don’t expect a lot of conversation. Knowledge sharing formats, especially at first, work best with more specific topics and context from day one. The fastest buy-in for knowledge sharing tools happen when the conversation has already begun. To that end, in advance of deploying a tool…
• …Seed Your Content – A critical step in the design and deployment of a knowledge sharing system is mapping what I call the “Eaters” and “Feeders” in the organization (those who will primarily consume content, and those who will primarily supply content). Recognize, too, that a Feeder on one topic is a potential Eater on another. Prior to rolling out a new tool, make sure you’ve enlisted a key number of your Feeders to begin conversations and use these tools in order that, by the time the Eaters get to see it, there’s something for them to consume.
• Communication Goes Both Ways – As with any KM initiative, two-way communications are critical for success. Help your users understand the importance and value of knowledge sharing, but also continuously seek their guidance and feedback on how to make it easier and better for them. If you’ve got a knowledge sharing tool or are planning on rolling one out, I strongly recommend you create a specific forum for ideas on how to improve the tool!

Take a moment to remember how you felt the last time you were surprised at work with an announcement of a big change. No, really – pause for a moment and think about it.

Whether it’s new technology, a change in process, or a complete reorganization, it’s really jarring for most people to hear about a substantive change which impacts your work very late in the process. Unfortunately, we see this scenario all too often – both in Agile and Knowledge Management (KM) projects. In our experience, adoption of new technologies and processes suffer due to poor or insufficient change communications.

In this blog, I’ll outline the key techniques we employ at EK to engage people earlier using skillful change communication techniques to improve adoption and to nip resistance and confusion in the bud.

• Feedback, feedback, feedback: Involving people in true co-creation of change – even in small decisions – is the best way to promote adoption. In terms of communication, this means soliciting feedback and having an authentic dialogue. Feedback can take many forms: focus groups, crowdsourcing on online platforms, self-selection events, or even quick voting exercises. Some sensitive types of change are more “top down” by nature, and may involve less overall employee direction. This is no excuse to send a long email as your only “change communication.” After all, leaders can still employ dialogue in town hall or Q&A formats and change agents can have informal conversations to alleviate confusion.
• Understand and share the value: There are two types of “value” in change communication: value to the business and value to people being asked to change. Often overlooked, the first step is making sure there is agreement at the executive level of why the change is being made. For example, are there clear objectives for an organization’s KM project? Next, communicate the “WIIFM” – what’s in it for me – for individual employees to adopt the change. This is going to vary based on role, individual motivation, and even personality, and the best way to find out is to talk to people being asked to change – not to assume. Once patterns have been established – share them! Even better, ask individuals not on the change team to share – this will be a more authentic message. 
• Show vulnerability and build trust: At EK, we’ve worked with a number of leaders going through complex transformations who hesitate to communicate in times of uncertainty. This is completely understandable, but if executives are feeling unsure, employees generally feel much more lost. It’s even more important to communicate during these times, even if the message is, “I’m listening,” or “I don’t know the exact answer now, but we will discover it together.” This message may make leaders feel vulnerable – but it will help stop rumors and will build trust with teams who might otherwise be suspicious that decisions are being made without their input.
• Don’t be afraid to repeat yourself: The average worker receives nearly 100 emails a day – is it reasonable to expect them to pay attention to a single email announcing a change? This is why employing multiple modes of communication is so important. People also may have individual preferences for modes of communication like a chat tool, in-person meeting, or even physical artifacts, so what may seem like repetition to you is often just effective communication. As a general rule, the bigger or more emotionally charged a change is, the more frequently communications should happen. Frequency in this case is a way to alleviate fear, as rumors are more likely to start when a change impacts an individual’s role or could potentially result in job loss.

While it may seem like change communication is more of an art than science, following these four techniques will go a long way to establishing trust and making an organizational change go more smoothly. Struggling with the messaging around your organization’s next big change in KM or Agile? Reach out to us at info@enterpriseknowledge.com.

In part one I examined the connection of KM and AI and how this connection has led the way for cognitive computing; while in part two I examined those industries that will or are soon to be disrupted by Cognitive Computing.  In this post I will examine those technologies that will lead in the disruption brought to many industries by the way of cognitive computing.

Cognitive computing is the simulation of human thought processes in a computerized model. Cognitive computing involves self-learning systems (Artificial Neural Network machine learning algorithms) that use data mining, pattern recognition and natural language processing to imitate how humans think. The goal of cognitive computing systems is to accelerate our ability to create, learn, make decisions and think.

According to Forbes, “cognitive computing comes from a mashup of cognitive science and computer science.” However, to understand the various aspects of this mashup we must peel back the various components of cognitive computing. These components are centered within AI and KM. The components of cognitive computing enable these applications to be trained in order to recognize images and understand speech, to recognize patterns, and acquire knowledge and learn from it as it evolves producing more accurate results over time.

Cognitive Technologies

Cognitive technologies have been evolving since I started developing AI applications (Expert Systems and Artificial Neural Networks) in the late 1980’s and early 1990’s. Cognitive technologies are now a prominent part of the products being developed within the field of artificial intelligence.

Cognitive computing is not a single technology: It makes use of multiple technologies and algorithms that allow it to infer, predict, understand and make sense of information. These technologies include Artificial Intelligence and Machine Learning algorithms that help train the system to recognize images and understand speech, to recognize patterns, and through repetition and training, produce ever more accurate results over time. Through Natural Language Processing systems based on semantic technology, cognitive systems can understand meaning and context in a language, allowing deeper, more intuitive level of discovery and even interaction with information.

The major list of cognitive technologies solutions include:

Expert Systems, Neural Networks, Robotics, Virtual Reality, Big Data Analytics, Deep Learning, Machine Learning Algorithms, Natural Language Processing, and Data Mining

Various cognitive technologies or applications are being developed by many organizations (large, small, including many startups). When it comes to cognitive technologies, IBM Watson has become the most recognized. IBM Watson includes a myriad of components that comprise the Watson eco system of products.

Companies Delivering Cognitive Solutions

Here are a few companies delivering cognitive solutions that take advantage of the cognitive technologies mentioned above as well as the industry they focus on.

Industry: Healthcare

Welltok: Welltok offers a cognitive powered tool called CaféWell Concierge that can process vast volumes of data instantly to answer individuals’ questions and make intelligent, personalized recommendations. Welltok offers CaféWell Concierge to health insurers, providers, and similar organizations as a way to help their subscribers and patients improve their overall health.

Industry: Finance

Vantage Software : provides reporting and analytics capabilities to private equity firms and small hedge funds. The company’s latest product, Coalesce, is powered by IBM Watson’s cognitive computing technology. This is an example of a company developing a software platform and using IBM Watson’s API’s to provide cognitive capabilities. This product addresses the need to absorb and understand huge volumes of information and use that information to make split-second, reliable decisions about where and when to invest client funds in a highly volatile market.

Industry: Legal

One of the major impediments to quality, affordable legal representation is the high cost of legal research. The body of law is a growing mountain of complex data, and requires increasingly more hours and manpower to parse. Lawyers are constantly analyzing data to find answers that will benefit their clients. For law firms to stay competitive they must find ways to cut cost and streamlining legal research is one way to do just that.

ROSS Intelligence: software is built on the Watson cognitive computing platform, ROSS has developed a legal research tool that will enable law firms to slash the time spent on research, while improving results.

AI & Blockchain

Detailing AI, KM and Cognitive computing would not be complete without adding blockchain to the technologies that will disrupt several industries. Functionally, a blockchain can serve as “an open, distributed ledger that can record transactions between two parties efficiently and in a verifiable and permanent way. The ledger itself can also be programmed to trigger transactions automatically. AI & Blockchain come together when analyzing digital rights. For example, AI will learn the rules by identifying actors who break copyright law. The use of AI applications will be extended by incorporating blockchain technology. When blockchains scale to encompass big-data, AI will provide the query and analysis engine to extract insights from the blockchain of data.

Cognitive technology solutions can be found in a number of applications across many industries. These industries include but are not limited to legal, customer service, oil & gas, healthcare, financial and automotive just to name a few. Cognitive technologies have the potential to disrupt Every industry and Every discipline — Stay Tuned!!

Similar to the old adage, “you can lead a horse to water, but you can’t make him drink,” you can deliver a solution that uses the most cutting-edge technology and beautiful design, but you can’t guarantee that your stakeholders will embrace it. This blog offers practical tips on how to maximize and measure user adoption to ensure that your new tool or process is fully embraced by those for whom you’ve designed it.

To deliver a project success story backed with quantitative and qualitative data to support it, you should take an objective-first approach to change management. This requires a shift in focus from what the change is (e.g. the implementation of a new tool or process) to what you aim to achieve as a result of the change (e.g. increased productivity or improved work satisfaction). Rather than only highlighting the features of the new technology, you’ll want to focus on the benefits the users will gain from using it. Taking this approach is particularly critical for Knowledge Management initiatives, which are initially often met with skepticism and a broad sense of concern that there’s not enough time in the already busy day to acclimate to another new tool or process. By following these guidelines, you’ll be able to say “our users love the new tool and they are so much more effective and efficient as a result of it…” and “here’s the data to prove it.”

The way to accomplish this is by setting “SMART” objectives at the start of your project and developing an anaytics strategy that will help you measure your progress towards achieving those objectives. These objectives should clearly express desired changes in user behavior and the impact these new behaviors are expected to have on overall productivity and effectiveness. In the words of Stephen Covey, “start with the end in mind” so that all your efforts are aligned towards achieving your expected results.

Let me put this into context using one of my current projects. I’m working with a global manufacturing organization to design and implement a tool that will help the communications department work in a more centralized and collaborative way. The team is responsible for delivering content about events, programs, and news items to internal employees as well as external stakeholders. The team is used to working in silos and each team member uses different tools for storing, sharing, and finding information such as a basic team site, email, and desktop file folders.

From the very beginning of the project, change management has been a priority. We knew that if we wanted the communications department to adopt the new tool, we had to think of ways to encourage them to do so well in advance of them even having contact with it. Here are ways to apply what my team has done to your change effort to help you maximize and measure user adoption:

Step 1: Align your metrics with desired outcomes

To encourage a more centralized and collaborative way of working for the communications department, we’re using Microsoft O365 tools such as MS Teams, MS Planner, and modern SharePoint team sitesas a platform for the new system. We chose this suite of tools because it offers various features that, if used, could save the department a lot of time, reduce wasted effort, and ultimately elevate their role to a more strategic partner within the organization.

Here’s how we’ve expressed our primary objective:

“Increase the team’s efficiency by managing all campaign content, including digital assets, in the new tool within 90 days of launch.”

When content is stored in various places, not everyone has access to the latest versions. This causes a lot of confusion and re-work. The challenge is that people defer to the processes they’re most used to, which is often saving information in their local drives and sharing it via email. The new behavior we wanted to encourage was saving information in a centralized location (in this case a SharePoint team site), so that everyone has access to the latest version, edits are being made to the same copy, and there’s a tracking history of the edits, as well as who made them.

The objectives you identify will vary depending on the challenges you’re trying to solve, so your success metrics should be aligned accordingly. In this case, defining our objective leads us to what we should measure: the percentage of campaign content that is stored and shared in the tool vs. outside of it.

Step 2: Capture baseline metrics and keep it simple

In order to be able to tell a story about the impact of a new tool, you need baseline metrics for comparing your results. For this project, we had three categories of metrics and different approaches for capturing each:

• Satisfaction Level: We deployed a survey that measured how useful users found their current system.
• Proficiency Level: We deployed another survey that measured their self-rated proficiency levels with basic SharePoint functionality such as uploading and sharing documents.
• Usage Level: We tracked activity on the system after launch. This includes number of active users, number of documents and multimedia files saved and shared via the tool, and number of interactions in the conversations space.

The key here is to keep it simple. We designed the surveys to be short and to the point, and only asked specific questions that would help inform the decisions we made on the project. We also didn’t measure everything. We kept it basic to start and the longer the users had to engage with the system, the more sophisticated our metrics became.

Step 3: Take actions that lead to measurable improvements

Our satisfaction survey, along with in-depth user analysis and testing, informed the features we included in our new tool. As we were prioritizing the features, we kept our objectives in mind. It was critical for us to ensure our tool had a separate space for managing content for each campaign. This space had to make it easy for the team to upload, edit, share, and find content, including text-based and multimedia assets.

Our proficiency survey helped us to design the training for the new tool. Had we made the assumption that our users were already familiar with SharePoint’s basic functionality, we would have gone into our training sessions ready to introduce all of its advanced features. Knowing that the team members were not as confident in their SharePoint abilities led us to design a basic SharePoint prerequisite training session for those that needed it. Meeting users at their proficiency level and guiding them towards the level they need to be to make the most of the new tool’s features prevents them from being so discouraged that they abandon the new tool prematurely. (Get more helpful tips on user training by watching Rebecca’s video, Top 5 Tips for Using Training to Promote Adoption).

This is important because we planned to deploy the satisfaction and proficiency survey again once we launched the new tool. Taking actions based on the results of the baseline survey created measurable improvements in how much the users liked the new tool(s) they were using and how confident they were in using it.

Step 4: Measure again once you’ve implemented your solution

This may seem like common sense, but let your users know that the tool is now available for them to use and train them how to use it! Often, the team members heavily involved in the project assume that users know it exists and will intuitively learn how to use it on their own. The team building the tool has spent the past few months or so immersed in the tool, so they are likely to overestimate other people’s awareness of the tool and underestimate the learning curve associated with it.

In our case, our baseline usage level was 0 team members because the tool was brand new. Our goal was to increase usage level to all 30 team members. Our strategy for getting all 30 team members to use the tool, rather than relapsing back to their old habits and systems, was the deployment of “early and often” messages about the tool, along with thorough training for each team member we expected to use it. Long before the tool was launched, we built excitement and awareness around the new tools via a teaser video, Yammer posts, emails, and messages from leadership during team meetings. Once the tool was launched, we conducted live training sessions and delivered helpful resources and guides.

Along the way, we were asking:

• What percentage of the team watched the teaser video?
• How many team members saw the Yammer posts? How many “liked” it, replied to it, or shared it?
• How many of the team members heard and saw the presentation?
• Did the team members react positively or negatively to the messages in the video, posts, and presentations?
• How many of the team members completed the optional pre-work and basic training?
• How many of the team members attended the live training sessions?

All of these metrics were indicators of the degree to which the users would adopt the new tool. You can then validate these indicators by measuring actual adoption, e.g. user activity within the tool and their satisfaction in using it.

Step 5: Give it some time, then measure again

As we were building the tool, the project team discussed how we were going to tell our success story. But, that really depended on how we defined our success. For us, did success mean that we launched the new tool on schedule and under budget? Or, did it mean that the communications team members were embracing the new tool and way of working? The latter for us was much more important so we developed a timeline for capturing feedback: one week after launch, one month after launch, 3 months after launch, and 6 months after launch. During these set time periods, we would capture metrics around how satisfied they are with the new tool and its impact on their work and how proficient they felt with their new skill sets. In addition to self-reported data, we would also track usage metrics such as what percentage of the team actively manages their campaign within the tool vs. outside of it.

Summary

Organizations invest large amounts of money on new technology with the intentions of improving employee productivity. The key to getting a significant return on these investments is to make sure your project team has what it takes to define, drive, and measure success. If you want to make sure the next solution you roll-out maximises user adoption and produces measurable results, contact Enterprise Knowledge at info@enterprise-knowledge.com.