APQC is the world's foremost authority in benchmarking, best practices, process and performance improvement, and knowledge management.   Effective knowledge management (KM) plays a key role in today’s fast-paced environment. KM is more than just a collection of techniques; it’s a strategic approach woven into the fabric of organizational culture. By cultivating a collaborative atmosphere, organizations can encourage employees to share their insights while leveraging technology to enhance the flow of knowledge.  Gain a better understanding of the most important and enduring best practices for KM success in these articles! Understanding Knowledge Management Understanding Knowledge Mapping Understanding Communities of Practice Understanding Knowledge Loss

In this hyper-connected era and ubiquitous computing world, a tsunami of knowledge is being generated and shared by organisations.  The key concern is that knowledge alone cannot work its magic.  Knowledge should be tied to action to deliver real value in the form of cutting-edge innovations and streamlined internal processes. As technologies advance and ways of working change, knowledge management should also be redefined to achieve maximum benefits. Here is a list of some of the notable knowledge management trends that you cannot miss out on. 1. Cloud Continues to Rule Cloud hosting is a great option that is incredibly flexible and secure. To make the most of a SaaS knowledge management system, you need two things – an internet connection and a device (laptop, mobile phone, or desktop). Modern cloud-based knowledge management systems are based on a subscription model where you just pay for the services you opt for. 2.       Friendly User Interface for Effortless Navigation A good user interface facilitates a smooth interaction between the user and the knowledge management system. It is not just aesthetically pleasing but also responsive, uncluttered, and easy to navigate. 3.       Social Media Elements for Higher Engagement There is a reason why people love using social media. It keeps them connected and informed just with a few clicks and swipes. Features like activity streams, votes, likes, comments and instant sharing facilitate the culture of ‘collaboration with a click’. 4.       Information Mobility Mobile technology is here to stay for a long, long time. One major reason behind this is the heightened convenience and accessibility it provides. Today, most of the knowledge management systems are compatible with mobile phones, making information available in a flash, whether your employees are in the office or working remotely, or traveling. This information mobility promises higher productivity, better decision-making, and borderless collaboration. 5.       100% Customisation To offer feel-good experiences to employees, a knowledge management system must literally feel familiar to read and browse. A lot of information can overwhelm readers, but the way it is presented can make all the difference between good and poor employee experience. 6.       AI-Powered Search for Quick Content Discovery A knowledge management system amounts to nothing if it does not have a powerful search engine. That is why AI-powered search that works at the speed of light is a prominent knowledge management trend for this year and all coming years. Unlike a normal search system, AI-supported search produces the most relevant results after analysing the user’s search history and the context of the query.   7.       Support That Never Sleeps Customers are the primary source of revenue for any kind of business. That makes customer support an important area that cannot be ignored at any cost. Besides knowledge sharing and collaboration, a state-of-the-art knowledge management system can even help in customer support. Seeing this possibility, businesses today are employing knowledge management systems for both internal and external use. 8.       Media-Rich Content for Higher Engagement Traditional knowledge management systems consisted of lengthy documents and guides. Today’s knowledge management trends are in line with what employees want – a seamless and engaging knowledge-seeking experience. More focus is now given to content that is a rich mixture of text, images, and videos. 9.       Real-Time Notifications to Keep Employees Updated Every member having access to your KMS will get instant notifications regarding article updates, new sections created policy changes, and much more. 10.   Pragmatic Analytics for Impeccable Experiences Companies today know the tremendous power of data and analytics. Analytics helps you understand your KMS and related employee experiences in ways you cannot even imagine. Right from what your employees frequently search for to the path they take through your knowledge base, analytics decode every little activity. Analytics gives actionable insights into how many employees access your knowledge base, the language they speak and the country they live in, the bounce rate on specific pages, and much more. 11.   Powerful Collaboration Tools A knowledge management system is slowly becoming an all-purpose tool, with companies now trying to use it for both knowledge-sharing and collaboration. One of the major benefits of a knowledge-based management system is that it facilitates company-wide knowledge exchange. 12.   Flexible Management of User Roles & Permissions One of the notable features of a knowledge management system is its ability to streamline user management and define each member’s roles and responsibilities. You want a culture where employees can contribute their knowledge, share suggestions, and receive feedback, but with some level of governance. Flexible user management with you having complete control of what each user is responsible for is one of the most notable knowledge management trends for the future. 13.   Digital Workspaces A knowledge management system is a social platform where information is shared, organised, and stored securely. A digital workspace is a new idea in knowledge management that keeps your intranet segmented and organised for easy reference. If implemented, it can streamline the way knowledge is managed and shared across departments. 14.   Discussion Forums A knowledge management system is incomplete without a discussion forum. Simply coming to the KMS, sharing and retrieving information from the articles written is old school. Modern knowledge management solutions are equipped with a full-fledged discussion forum where employees can ask questions and get a response within seconds. 15.   Knowledge Bots For Prompt Access to Information The ultimate goal of a knowledge management system is to make knowledge-gathering a seamless, uninterrupted process. Knowledge bots help you achieve just that. Knowledge bots deliver relevant answers at the speed of light through chat or voice mediums. Knowledge bots act as personal assistants giving employees everything they need at a moment’s notice.

KM reference (https://www.knoco.com/knowledge-management.htm ) Story-powered communication / business story-telling (https://www.anecdote.com/ ) Real KM - evidence based, practical results (https://realkm.com/ ) Green Chameleon Blog (http://www.greenchameleon.com/ ) APQC (https://www.apqc.org/expertise/knowledge-management ) Knowledge Management Global Network (KMGN) (https://www.kmglobalnetwork.org/ ) Cynefin Co. - making sense of complexity (https://thecynefin.co/our-thinking/ ) The KMedu Hub - The Body of Knowledge for Knowledge Management Education & Training (https://kmeducationhub.de/ ) Gurteen Knowledge Website (https://www.gurteen.com/gurteen/gurteen.nsf/ ) KMWorld (https://www.kmworld.com/ ) Stan Garfield’s KM Site (https://sites.google.com/site/stangarfield ) Step Two Designs (https://www.steptwo.com.au/services/expertise-knowledge-management/ )

Originally published by KMI Dec 06, 2023 | By KMI Guest Blogger Alicia Rother We frequently hear the phrase "knowing more means accomplishing more" in our modern, data-saturated world. Even though organizations possess vast quantities of data, the true challenge does not consist solely of data collection. The true trick is to handle it properly and make sense of it. Thankfully, that's where AI comes in! Artificial Intelligence (AI) is changing the way we store, organize, and use information to better face future problems and gain a competitive advantage. Read on to learn more about how AI is changing Knowledge Management (KM) and the tools that make it happen. Let's see how AI can help! How Knowledge Management (KM) Has Progressed Over Time? In the past, knowledge management relied heavily on manual record-keeping. However, that evolved into digital repositories of knowledge and content management systems. The organized process of producing, gathering, saving, and sharing information within a company is called knowledge management. Conventional methods of knowledge management significantly depended on manual labor, including the setting up of documentation repositories, intranet portals, and databases. But it turned out that these methods required a lot of work, took a long time, and weren't always effective. The digital era has brought up new issues due to the vast amount and complexity of data. It's getting harder and harder for typical knowledge management systems (KMS) to keep up with the fast growth of unorganized data, which makes it harder to access and use knowledge effectively. AI's Role in Knowledge Management AI has changed the way information is managed in big ways. However, knowledge and information management are equally essential to AI. Like in everything else today, this technology is playing an important role here too. If you consider fields like graphic design, AI tools have already taken over conventional methods. Similarly, the data that an AI model is trained for in KM may have a major impact on its performance. The AI is more likely to give accurate responses when it is trained using information that is precise, current, and carefully structured. MIT researchers found that adding a knowledge foundation to a language model improved output and reduced hallucinations. Thus, rather than eliminating the necessity for KM, advancements in AI and machine learning merely increase its importance. The following is a list of 11 different ways that artificial intelligence has been used to solve some of the complex problems that everyone who uses KM solutions has to deal with: ➢ Advanced Analysis: AI can identify patterns and trends in massive data sets and provide useful insights. To do so, AI processes data using statistical models and machine learning methods. By looking at how factors are related to each other, AI can find patterns and trends that people might miss. This is more than just adding numbers together; it's figuring out what the organized data means. KM uses pattern recognition and natural entity extraction to find related information. ➢ Proactive Knowledge Discovery: AI can actively search for fresh, relevant information, guaranteeing that knowledge bases are constantly up-to-date. AI uses unsupervised learning methods to identify patterns in unstructured information, such as association and clustering. This uncovers new insights and goes beyond simple data retrieval. An intriguing example of this use case is how the finance division of a Fortune 500 business uses AI to analyze a variety of economic data to find unusual investment possibilities ➢ Collaboration Tools: Predictive analytics may predict user requirements and offer appropriate papers or meeting schedules based on behavior, enhancing individual productivity. AI teamwork tools let people talk to each other in real-time, share documents, and work together to solve problems. Based on what teams have done in the past, they can get advanced ideas for how to share documents or schedule meetings. ➢ Intelligent Search: AI combines conventional search algorithms with semantic knowledge. It can figure out what the user is trying to say by inferring context from their questions. This makes sure that search results fit what the user wants instead of just matching keywords. Employees may now get accurate, contextually relevant info even when they look for confusing or frequently used phrases. ➢ Content Tagging and Categorization: Artificial Intelligence can automatically tag and classify newly entered data, thus guaranteeing consistency, decreasing redundancy, and eliminating the labor-intensive process of manually classifying data. Using supervised learning, the AI is instructed on pre-labeled data. It is hardly unexpected that KM systems have embraced this feature broadly, as it greatly minimizes the work involved in selecting and organizing content. ➢ Smart Chatbots: To understand what users are asking, chatbots use Natural Language Processing (NLP). These chatbots provide fast access to information, offering essential information on demand. ➢ Expert Systems: AI makes choices in expert systems based on a set of rules that have already been set. The rules come from a human-in-the-loop, which lets the system act like a human expert in certain areas, making sure that accurate information is transferred. When used appropriately, AI-based expert systems can (mostly) replicate human decision-making and transform implicit information into organizational knowledge, which is essential to successful knowledge management. ➢ Recommendations: AI can make suggestions for related content or courses by learning how each user acts, which improves adaptation. With a corporate learning platform, for instance, employees may get recommendations for courses based on their learning history and the preferences of their colleagues in comparable positions. ➢ Virtual Assistants: Virtual assistants employ NLP to interpret user requests and task automation algorithms to perform a range of activities. While these AI-powered tools can process content, set notes, and even summarize long papers, they make KM tools more engaging for users and easier for them to use. ➢ Creating Content: AI can mine datasets, make outlines and reports, and make sure that knowledge bases are always being updated and expanded. It may also use NLP to make sure the content's language is appropriate for the target audience. This feature lets strategy teams automatically make outlines of 50 pages or more documents or a group of documents. The same feature may be used by sales teams for generating battle cards for major rivals or account profiles for mining current clients. ➢ Knowledge Transfer and Sharing: AI may assess user behaviors and propose relevant content to them. This feature could be used by the IT-KM function to automatically offer a new IT training program to workers whose past contacts show they need an update. Tips on How to Use AI in Knowledge Management For organizations to get the most out of AI in KM, they should think about the following strategies: Set Clear Goals: Write down clear objectives for incorporating AI into KM. Having clear goals is important whether you're trying to improve customer service, streamline internal processes, or spur new ideas. Ensure Data Quality: The quality of the data supplied into the system is critical for determining the accuracy and dependability of AI-driven insights. AI models should be updated and improved regularly to make sure they stay useful and effective. Emphasis on User Adoption and Training: Workers should get training on the efficient usage of AI-driven knowledge management systems. To get the most out of AI in knowledge management, people need to know what their job is in this new environment. Prioritize Privacy and Ethical Considerations: Make sure AI systems are fair and neutral and create strict privacy measures. This is essential for trust and data protection. Acknowledge Continuous Improvement: The domains of AI and KM are ever-evolving. To stay ahead of the game, tactics and tools need to be updated and improved regularly. Conclusion There is no doubt that AI will play a big role in the future of KM. By properly incorporating AI into KM plans, firms may achieve unparalleled levels of efficiency, customization, and strategic insight. Getting there will take careful planning and attention to things like data quality, the right way to use AI, getting people to use it, and always being able to adapt to new technologies. The possibilities for growth and advancement are endless as we go forward into the intelligent future of KM.

Principles For organisational knowledge to have value, it must include the employee (human) additions of context, experience, and interpretation. Insight combines explicit and tacit knowledge – balance knowledge codification and internalisation. Create organisational learning cycles of gaining experience and using that experience to create knowledge. Design your knowledge assets with knowledge-distance and reusability in mind. Think creatively what constitutes a knowledge artefact – and use existing tools that you have in your organisation. Application Identify and mobilise your key employees (knowledge assets) in your organisation to develop new knowledge – an “entrepreneurial” mind set. Find your organisational “hook” for creating value from knowledge e.g. innovation. Don’t underestimate the importance and value of experimentation in learning processes. Agile KM - adapt to context and the business environment fast, change and implement your knowledge assets rapidly. Deploy your knowledge capability through employee experience (it answers the question “what's in it for me”).

According to the KMI Institute, some organisations conduct their business as usual, while their KM solutions are accelerating their growth. Major trends such as cloud technology, the hybrid workplace, graph databases, artificial intelligence, and language processing solutions lay the groundwork of a robust KM environment. In a recent presentation on the World Bank’s knowledge programme, Margot Brown, Director of Knowledge Management at The World Bank Group, explained their use of AI to enhance their KM offering. The World Bank’s knowledge management team pinned down the core activity of the bank and its unmet needs: reusing existing knowledge when leading a new project. Over the years the World Bank has run over 22,000 projects. Manually sorting all the information from various projects and offering the top-ten projects to be considered, together with the most relevant information and knowledge in each is a lengthy process. The World Bank Pre-design Knowledge Package (KP) is a compendium of knowledge gathered from multiple sources, focusing on different aspects of past projects through a KM-AI approach. Knowledge identification is done using a combination of advanced unsupervised machine learning algorithms, and manual knowledge curation.

It has been established that knowledge management and collaboration are critical in all organisations – collecting information, managing knowledge, creating new knowledge, and inspiring ideas. Knowledge collaboration and connection is about providing ways for new knowledge to be built through collaboration or uncovered in employees other than the experts and connecting employees in different teams, locations, countries, and domains. Such knowledge collaboration and connection make employees more effective, efficient, innovative, agile, and overall better performers – over and above adding business value! It fosters and builds networks exposing employees to resources, policy, needed skills or abilities, vendor suggestions, best practices, and more. Knowledge collaboration and connection enable the interactive exchange of information that empowers employees and exposes the organisation to new perspectives, and uncover hidden resources in the workplace network. It harnesses the team, department, organisation or group's collective intelligence to build high-quality relationships actively, networks, and social capital while solving real business problems. We need to strengthen the knowledge community through unity and relevance within the sub-continent, becoming a central contact point for knowledge transformation within the public and private sectors, and within academia. Therefore, we need to exchange ideas, advice, connections, knowledge, and experience regarding knowledge collaboration and connection: Impact of disruptive technologies on knowledge connecting and collaborating Knowledge collaboration and connection adding value in organisations Knowledge collaboration and connection role players and human experience design Thinking ahead, KM identifies and globalises knowledge to facilitate access for all collaborators. Organisations depend on a reliable knowledge management system for smooth information sharing and internal operations. Technology has a tremendous impact on knowledge management, inspiring the development of robust software platforms to leverage knowledge management strategies. We need to better understand the impact of digitalisation on knowledge collaboration and connection in the new hybrid world of work we are working in.

Bill Gates once stated, "Most people overestimate what they can achieve in a year and underestimate what they can achieve in ten years” (Alammar, 2023: Online). The same can be inferred about people’s expectations of innovative technologies. These expectations, in most cases, are unrealistic and far-fetched. The last time the tech industry was seduced by a deep learning frenzy, we were promised self-driving cars by 2020. Seeing as in 2023 we are not going to work in one of them, this points to an unrealistic reality. However, Generative AI is nonetheless an exciting and promising field and should not be discarded just because it is not easily understood, implemented, or achieved. After all, nothing substantial is ever achieved quickly. Generative AI can be understood as “a set of algorithms, capable of generating seemingly new, realistic content—such as text, images, or audio—from the training data.” The most powerful Generative AI algorithms are built on the foundation models that are trained on a vast quantity of unlabeled data in a self-supervised way to identify underlying patterns for a wide range of tasks (Boston Consulting Group, 2023: Online). ChatGPT – Chat “Generative Pre-trained Transformer” (GPT) is an AI large language model (LLM) which deploys deep learning and autoregressive (AR) modelling. AR modelling is simply a model that predicts a future outcome in a series, or body of text based, on previously observed outcomes of that sequence. The success and failure of Generative AI depend on your point of view, more specifically, on the cherry-picked demos and reliable use cases used to prove the success of Generative AI. However, some groups have disregarded results from Generative AI because, “the average rate of getting correct answers from ChatGPT is too low” (Alammar, 2023: Online). This is an example of a use case where people expected the model to reliably generate an exact resolution for a complex set of problems. However, there are other use cases (and workflows) where these models are capable of more reliable results. Among them are neural search, auto categorisation of text (classification), copywriting suggestions and brainstorming workflows for generation models. Impressive demos will continuously be introduced, and they are part of a community discovery process to evaluate the limits and new possibilities of these technologies. As with any recent technology, keep questioning the usefulness of the cherry-picked examples, recognise their uncertain timelines, and invest in the robustness and reliability of AI systems and models. Once we think of Generative AI (model/tool) as a component, we can start to compose more advanced systems that use multiple Generative AI models or tools. If we make each of the Generative AIs address a specific area, we can combine multiple Generative AIs to address a larger environment. Another way of thinking of this is to look at the motor vehicle; it is not one thing but how all the individual parts work together to form a tool to transport people. “Generative AI is only possible because larger, better models trained on massive datasets enable AI models to make better numeric representation of text and images” (Alammar, 2023: Online). For creators of Generative AI, it is important to know this enables a wide variety of possibilities, such as neural search. Neural search is a search system that uses language models to improve on a simple keyword search and enables searching by meaning. There are many promising Generative AI and AI developments, but they must be assessed and aligned with your business needs. Sources: Alammar, J. 2023. What's the big deal with Generative AI? Is it the future or the present? [Online]. Available at: https://txt.cohere.ai/generative-ai-future-or-present/ Accessed: 2023.04.02 Boston Consulting Group. 2023. Generative AI. [Online]. Available at: https://www.bcg.com/x/artificial-intelligence/generative-ai Accessed: 2023.04.02

A knowledge audit refers to a systematic process of identifying knowledge assets and their relationship across an organisation. Acquiring a proprietary knowledge audit methodology is not always economically feasible, especially for smaller organisations. A knowledge auditing methodology focusing on core processes includes the following steps [1]: Stage 1: Acquire Organisational Strategic Information This step aims to identify the organisation's mission, vision, and objectives considering its environment, culture, and traditions. Stage 2: Identify Organisation's Core Processes This step aims to identify which organisational processes contribute most to the organisation's overall success and establish criteria for measuring and ranking processes according to their criticality. Stage 3: Prioritise and Select Organisation's Core Processes Core processes with the highest potential impact on organisational performance are selected as the focus of the audit. This does not mean that other processes are omitted from consideration, but some priority level must be established to maximise the use of limited time and resources. Stage 4: Identify Key People This step aims to identify all the people that play a key role in the core processes identified during the previous step. Stage 5: Meeting with Key People Once identified, the key people are introduced to the concept of the knowledge audit. It is crucial to ensure that they understand the processes and motivations behind the knowledge audit and that they feel supported by management. Stage 6: Obtaining Knowledge Inventory During this crucial stage, the existing knowledge assets within the organisation are identified and captured by engaging with key people through questionnaires and interviews. This process can be repeated for processes of decreasing priority until all knowledge assets have been examined and integrated into an overarching inventory. Stage 7: Analysing Knowledge Flow The questionnaires and interviews utilised during the previous steps should include questions regarding how tacit and explicit knowledge flows through the organisation. As with the previous step, the flows identified initially will relate to the highest priority core processes. This process can be repeated for processes of decreasing priority until all knowledge flows within the organisation have been identified and captured. Stage 8: Knowledge Mapping The objective of this step is to create a visual representation of the organisation's knowledge assets (e.g. who has knowledge, where these persons are located, the level of accessibility to them, with who they most often share and exchange knowledge, etc.). The first iteration of the knowledge assets map will focus on the highest priority core processes. This process can be repeated for processes of decreasing priority until a complete map of the organisation's knowledge is created. Stage 9: Knowledge Audit Reporting After a thorough analysis of the knowledge inventory, knowledge flows, and knowledge map of the first core processes, an audit report is presented to management. The report should describe in detail the major findings of the audit and should include the following: The existing status of knowledge assets within the organisation. The knowledge maps generated during the audit. The current effectiveness of the organisation in achieving its business process objectives. Potential knowledge gaps or threats. Opportunities and recommendations for improving the organisation's use of knowledge assets. The final knowledge report forms a basis for implementing KM initiatives going forward. Stage 10: Continuous Knowledge Re-auditing Once the core processes with the highest priority have been audited, the process is repeated on the remaining core processes until all knowledge assets and flows are identified and captured. The organisation should also periodically re-audit its knowledge assets and update any relevant changes to the knowledge inventory, knowledge flows, and knowledge map. In this way, the organisation can measure the performance of its KM initiatives over time and make adjustments to facilitate continuous improvement. 1. Perez-Soltero, A. et al., 2007. A Model and Methodology to Knowledge Auditing Considering Core Processes. The ICFAI Journal of Knowledge Management, 5(1), pp. 7-23.

The terms knowledge audit and knowledge management audit (KM‐audit) are often used interchangeably. However, the two processes are distinct and serve entirely different purposes. The goal of a KM-audit is to evaluate the impact and effectiveness of ongoing KM practices and processes within an organisation, while a knowledge audit refers to a systematic process of identifying knowledge assets and their relationship across an organisation. Knowledge audits help organisations determine what knowledge they currently have, how they utilise knowledge, and what knowledge they will need in the future. The knowledge audit process consists of the following steps [1]: 1. Identify what knowledge currently exists in the organisation or department: (a) Determine existing and potential knowledge sinks, sources, flows, and constraints, including environmental factors. (b) Identify and locate explicit and tacit knowledge within the organisation or department. (c) Build a knowledge map of the taxonomy and flow of knowledge in the organisation or department. The knowledge map relates topics, people documents, ideas, and links to external resources, in respective densities, in ways that allow individuals to find the knowledge they need quickly. 2. Identify what knowledge is missing in the organisation or department: (a) Perform a gap analysis to determine what knowledge is absent to achieve business objectives. (b) Determine who needs the absent knowledge. 3. Provide recommendations from the knowledge audit to management, regarding possible improvements to the knowledge management activities in the organisation or department. Questions to extract the information needed to identify what knowledge currently exists in a targeted area include [1]: 1. List specifically the categories of knowledge you need to do your job. 2. Which categories of knowledge listed in question 1 are currently available to you? For each category of knowledge you specified in question 1 . . . 3. How do you use this knowledge? Please list specific examples. 4. From how many sources can you obtain the knowledge? Which sources do you use? Why? 5. Besides yourself, who else might need this knowledge? 6. How often would you and others cited in question 5 use this knowledge? 7. Who are potential users of this knowledge who may not be getting the knowledge now? 8. What are the key processes that you use to obtain this knowledge? 9. How do you use this knowledge to produce a value added benefit to your organisation? 10. What are the environmental/external influences impacting this knowledge? 11. What would help you identify, use or transform this knowledge more effectively? 12. Which parts of this knowledge do you consider to be (a) in excess/abundance, (b) sparse and (c) ancient/old/outlived its useful life? 13. How is knowledge currently being delivered? What would be a more effective method for delivering knowledge? 14. Who are the ‘experts’ in your organisation housing the types of knowledge that you need? 15. In what form is the knowledge that you have gained from the experts? 16. What are the key documents and external resources that you use or would need to make your job easier? 17. What are the types of knowledge that you will need as a daily part of your job (a) in the short term (1–2 years) and (b) in the long term (3–5 years)? By identifying the knowledge assets that contribute to core processes, an organisation can focus its KM efforts on knowledge assets at various levels of criticality rather than managing everything regardless of its significance. 1. Liebowitz, J. et al., 2000. The Knowledge Audit. Knowledge and Process Management, 7(1), pp. 3-10.

Knowledge production and management are inherently human-centered. Therefore, the most effective roles assigned to generative AI in KM will mostly augment humans rather than replace them. Generative AI cannot make something from nothing – it is trained on existing data and information. Thereby achieving collaborative intelligence, in which generative AI and humans enhance the complementary strengths. Generative artificial intelligence (AI) is artificial intelligence capable of generating text, images, or other media, using generative models e.g. OpenAI’s DALL-E 2 (text-to-image model). We know already that: AI will impact knowledge work, knowledge management and streamlining work, however the pace of the change has critical implications Fast-paced creation of content (in our already information-overloaded workplaces) Generative AI is trained on large corpuses of information, which encode the biases of that material Impact our skills, from generating text to being a good editor, deciding what to keep and what needs to change (e.g. fact-checking AI generated text and spotting visual flaws) Knowledge is at the core of innovation, and enhancing KM practices help accelerate the flow of ideas and collaboration. However, organisations struggle with the time and effort required to capture and maintain knowledge to create a thriving KM practice, drive employee productivity and ensure people can find organisational knowledge. Therefore, moving KM toward agility helps driving its success! Agile in the KM context means rapid implementation and results, being adaptive to culture, context, and the business environment, and focused on changing knowledge sharing mindsets and behaviours.

Newsletter of Knowledge Management South Africa - Autumn 2023