Why equity in KM?

Equity in KM for health programs means the health workforce has the information, opportunities, skills, and resources to define and participate in the knowledge cycle—access, creation, sharing, and use—to improve health programs. Embedded power imbalances in global health and KM create unfair differences in the knowledge cycle among groups of the health workforce.

A comparative study of 100 generative AI tools in the context of learning and knowledge management (KM) was conducted and has resulted in a set of 35 KM processes where generative AI¹ has augmented their experience, implementation, and execution. This article (part 9 and the final part in the series) provides the summary and conclusion of the study.

The upside

Generative AI can help in:

• personalizing the learning experience, providing assisted coaching, and suggesting skills for individual career growth
• curating and suggesting relevant learning materials in multiple languages, in addition to transcribing and translating audio & video content for almost instant access for the global audience
• drafting missing articles, reports, and creating knowledge portals
• augmenting the community experience by suggesting topical communities, matching mentor-mentee pairs, enriching the sharing behaviors with sister communities, and generating knowledge narratives and stories
• accelerating the ideation and creativity processes by mapping and connecting ideas and idea authors to innovation campaign objectives
• creating rich content either by drafting missing knowledge base articles or by auto-completing ideas with arguments and examples
• searching and extracting answers from specific document sections after analyzing the context, the sentiment, and the intent of the user query
• improving human-in-the-loop collaboration by suggesting experts based on their activities, involvement, and preferences
• inferring expertise and micro-skills by analyzing content authors and contributors’ behaviors and patterns of engagement, and as consequence, assist in augmenting peoples’ profiles with micro-skills and topics of interests
• automating, extracting, and regenerating knowledge into new formats or schemas
• extracting competitor’s websites and all the spider’s links into a structured data list with their properties and meta-data
• enriching content with meta-data and semantic relationships and generating a text into a knowledge graph heavily relying on natural language processing (NLP)
• impacting the customer’s experience by offering self-service portals, FAQs or recommended relevant content making the customer experience more conversational and interactive
• integrating with multi-modal systems to scale the data infrastructure for a more comprehensive and integrated database by linking it to different sources of information
• automating the data migration process by removing duplicate entries or by combining similar data.

AI has introduced the concept of digital worker who’s a human like clone for specific roles, responsibilities, and tasks. Digital workers are characterized by being curious, collaborative, and capable. Digital workers can be pre-loaded with smart skills which are signature patterns and workflows replicating industry-specific practices.

The downside

AI algorithms require guidance and supervision to define which information and data are most important to the users to lay down the foundation for meaningful insights and best/ direct answers.

It’s important to note that while large language models (LLMs) like GPT-3 are powerful in generating text, they are not inherently intelligent or conscious. They don’t possess an original understanding or awareness of the content they generate and may sometimes produce outputs that are nonsensical or inappropriate. Therefore, they require human supervision – known as reinforced learning and careful application to ensure their outputs meet the desired outcome.

The cost of implementing an internal infrastructure for generative AI / LLM technology deployment can rise significantly based on factors such as the scale of deployment, the complexity of the technology, the size of the organization, and the specific requirements of the project. Additionally, if an organization decides to use cloud based LLM cognitive services, concerns around governance, security and privacy will be a legitimate subject to carefully consider.

Further reading: This article is the final part in the series AI integration strategy for learning and knowledge management solutions. For further reading related to this topic, please see the ongoing artificial intelligence series.

Header image source: Author provided.

Reference:

A comparative study of 100 generative AI tools in the context of learning and knowledge management (KM) was conducted and has resulted in a set of 35 KM processes where generative AI¹ has augmented their experience, implementation, and execution. This article (part 8 in the series) focuses on knowledge analytics and intelligence.

Part 8. Knowledge analytics and intelligence

8.1. Metadata enrichment (classifier, dictionary…)

With the help of generative AI and natural language processing, content can be enriched with meta-data and semantic relationships. The enrichments include entity extraction, sentiment analysis, emotion analysis, keyword extractions, classifier, concept auto-tagging, regular expression, patterns, lexicon, dictionary and more.

• Classifier: define categories by which text in the documents can be classified.
• Dictionary: recognize terms and synonyms for terms that are significant to the user, such as the names of products that the company sells.
• Regular expression: define regular expressions that capture terms of significance, such as that AB10045 is the syntax that is used for the customer order numbers.
• Patterns: Recognize regular expression – terms, that are mentioned in sentences that match a syntactic pattern that you teach AI to recognize. For example, International Standards numbers, such as ISO 45001, ISO 22000.
• Lexicon: A database that describes a human language and the world where that language is spoken, using symbols and their semantic relationships. It is like a huge dictionary that contains hundreds of thousands of semantic relationships.

For example, a streaming service company was able to utilize AI algorithms to enrich the meta-data of movies from several sources and micro-tag movies based on what the reviewers found worth mentioning, their critics, rather than the content of the movie itself. Another example, the organizational learning leader relied on AI capabilities to automatically transcribe video to text, and automatically tagging of content which results in the creation of smarter more personalized feeds and accurate search results – whether that’s finding a course or video or extracting the answer from within the transcript of a video itself.

Intelligent document processing services are designed to analyze and classify documents and offer metadata suggestions based on file contents, existing metadata, and even user behavior. Such services make use of technologies such as text analytics and machine learning to define and maintain document metadata for the user. Intelligent document processing can be coupled with AI Linking which is the process of joining two datasets together using machine learning models when they do not share a common unique identifier. It’s the ability to map messy data in the form of company names, aliases, and domains to do the mapping at scale with confidence. Once mapped to these unique IDs, a database becomes much more powerful. It is linked and cleaned, making data enrichment and deduplication seamless.

8.2. Generation of social/knowledge graph

A knowledge graph also known as semantic network visualizes the relationships among entities. Whereas a social graph visualizes the relationships among social actors – people. They can improve visibility and discoverability of knowledge in the flow of work. With the help of generative AI and relying heavily on natural language processing (NLP), it’s possible to convert text into structured facts about entities, their sentiment, and their relationships for a custom domain of interest. With the help of AI entity recognition services, entities in a text can be identified such as: the companies, people, organizations, events, places etc. – and then connects them to external data sources like Wikimedia and/ or encyclopedia, unlocking new understanding and insights.

For example, drug discovery and drug repurposing processes can be automated using generative AI and NLP. The generation of knowledge graph specialized for life sciences and health care that enables accurate identification and connection of biomedical information such as diseases, drugs, treatments, symptoms, genes, proteins, and other data elements from context.

Business/technology analyst in collaboration with knowledge leaders can leverage knowledge graph technology to build connections between the organizational systems, it will instantiate a knowledge map driven by business taxonomy and ontology designs, adding consistent definition and context to the organization’s web of knowledge assets. The enterprise search engine can be empowered with a knowledge graph that can index, and structure search results.

8.3. Content analysis capabilities (predictive, NLP)

AI analysis capabilities can anonymously aggregate comments to identify trending topics in community discussions, sentiments and emotions and then flag critical issues. Community leaders can consider critical issues as knowledge gaps for further analysis and development. In collaboration with product/ service function, product managers can include the critical issues in their report, estimate an expected resolution timeline and potential cost savings. For example, an airline company has customer community forum where customers are reporting some issues while booking their flights. After going through all the forum posts, the AI assistant could identify the issue and would suggest fixing the ‘Air miles option to book flights with’. Product/service managers can further investigate the issue and take the required actions.

Generative AI can be applied to rapidly accelerate topic identification and reduce time to define focus areas for training and development. Gain deeper insights into what employees are looking for with experience analytics including search activity, clicks, content gaps, and custom events.

AI capability has been pre-trained to understand the common logic of things enabling business analysts to build the topic models in short time and further enabling them to turn texts into meaningful insights directly. “Learn-as-you-go” approach allows to understand context beneath the written words. Identify specific trend aspects that tie to a particular topic model or theme. For example, organizational transformation and change leaders can detect in real-time the unconscious social bias in written communications, so leaders can see exactly how the written language appeals to different employees’ groups, and how they perceive the communicated messages.

For data centers and network managers, AI based content analysis can identify redundant, outdated, and trivial (ROT) content to accelerate migrations, reduce storage costs and improve content retrieval.

For a customer service center, AI based content analysis can help in improving the customer satisfaction score (CSAT). It can understand the cause of customer frustrations and disappointments. A lengthy customer call may indicate that there’s a concern with the customer experience. AI can resonate with the customer context, sentiment and behavior and then works on identifying and prioritizing actions for customer remediation.

8.4. Content reporting & real-time data visualization

Content reports allow the content authors to set and follow up on content contribution goals and usage-based incentives for employees and subject matter experts. For a customer service center, content reporting enables the customer experience leaders to incrementally enhance the knowledge base based on customer support agents’ feedback. Reports created from wrap-up call data help to manage and plan the contact center agents’ training and staffing requirement. In sales and marketing function, content reporting features include drill-down and filtering that help marketers to find prospects information, competitors’ products, lead and closed deals, networks of brand ambassadors and key influencers.

AI algorithms can support real-time data visualization for decision makers, with executive summaries, threat levels, launch dates, pipeline and timeline information and bulls-eye charts. They also provide powerful insights into how different teams use content, what they’re searching for, and how it’s impacting performance. For example, AI based insights for knowledge base articles provide patterns on users contributing to the article category, search topics leading to the article, and negative searches leading to missing articles for development.

8.5. User, community & business analytics

With the help of generative AI and machine learning algorithms, analytics can become conversational, and users can ask about metrics in natural language, simply as if they are asking their colleagues about data and statistics. AI based analytics provides the capability to understand complex data schemas. For example, company specific dashboards can be developed in short time that maps the organizational structure. Offering various stakeholders both a helicopter view and the capability to drive down into the country, division, or team to see where engagement is high and where it isn’t.

AI based analytics can help in measuring qualitative characteristics and providing actionable insights, including conversation analytics, user behavior analysis, sentiment analysis, and performance metrics. In addition to the ability to understand content engagement, spot gaps, and identify the most important topics for content improvement.

The right set of metrics allows talent retention leaders as well as people’ managers to appreciate the people doing things right with recognition for achieving monthly customer experience goals. Some examples of user, community and business analytics are:

• What an individual user searches for, how many times has accessed a knowledge base, and which community is part of?
• Define a target for the number of resolved questions.
• Get on monthly basis the membership trends as well as the learners’ charts.
• Get the % of contributors within the community core members.
• How is the community sentiment evolving over time for a particular topic?
• Measure what topics are consumed by users?
• Most viewing time on articles.
• Solution quality index: % searches were successful (found answers).

Examples of AI-based KM tools for knowledge analytics and intelligence: Expert AI, Diffbot, Freshworks, Inbenta, M-Files, Gavagai, MonkeyLearn.

Next and final part (part 9): Summary and conclusion.

Header image source: Author provided.

Reference:

A comparative study of 100 generative AI tools in the context of learning and knowledge management (KM) was conducted and has resulted in a set of 35 KM processes where generative AI¹ has augmented their experience, implementation, and execution. This article (part 7 in the series) focuses on knowledge-centered services KM processes.

Part 7. Knowledge-centered services

7.1. Customer conversations & ticket resolution

Unlike the traditional one-way ticket resolution process, with the help of generative AI, customers can engage in a conversational experience to resolve their tickets. The customer service experience can be augmented with natural conversation, personalized responses, and recommendations for query resolution. AI-powered recommendations can be integrated in the help desk console to provide agents with the best knowledge article suggestions based on keywords from the incident submitted. In addition, similar tickets suggestion provides a list of answers based on similar, already resolved requests. The help desk console would show who else in the support team is viewing and acting on a ticket in real-time to avoid agent collision and overlaps.

The help desk console should centralize the support channels in a single workstream that builds a single case queue with website, social media, discussion forums, live chat, and email contacts, allowing the customer service agent to track the complete customer journey. All incoming tickets are automatically sorted into categories like customer support, sales, marketing, and billing. AI would help to manage customer conversations from multiple channels like email, social, chat & phone calls in an omnichannel help desk.

Cognitive AI technologies allow users to solve challenges related to efficiency (automatic routing of tickets to the right agents) and related to effectiveness (getting the right answer immediately). With the use of AI algorithms, powered by the cognitive capability to understand the unstructured data fields of new requests, every ticket can be resolved with less time and effort. For example, during call set up, the interactive voice response (IVR) data can be captured to pre-populate questions so that the agent can get a running start on the issue resolution process.

Cognitive AI technologies can give agents line of sight into an experts’ digital journey prior to the service call or ticket submission. Agents can see what content experts have engaged with and can personalize the response.

Some examples of standard customer service KPIs are:

• 15 minutes average resolution time
• 5 minutes average customer wait time.

7.2. Brand community & customer engagement

A brand community is a space where customers can engage directly with peers and experts at any time, for any topic, at a scale. Hosting these conversations help a brand attracts new customer segment and keeps existing ones returning for more news, updates, and releases on latest products/ services. Customers expect near instant answers and authentic engagement for an increasing variety of questions around their products/ services. With the help of AI algorithms, customers can get quick recommendations and relevant answers to their questions. AI algorithms can further engage customers with personalized updates according to behavior and intention. Whether the objective is to reach and convert high value prospects or offer proactive support to existing customers. AI can also propose insights on community engagement and measures the return on relationships for community events. For example, a community manager has organized two different events: meet & greet and lunch & learn. He/ she would like to know which one has the higher return on relationship to expand and grow on their brand community.

With the help of AI, customer experience (CX) leaders can increase the brand awareness and customer loyalty. For example, AI can augment the gamification experience in a brand community by proposing actionable insights for customers to advance in membership tiers. By doing so, customers can unlock new rewards, digital assets, exclusive merchandise, or a promotional code.

Customer experience leaders can predict and prevent customer churn. With the help of AI, CX can understand the experience of their customers by analyzing open-ended Net Promoter Score (NPS) feedback. They can discover and monitor customer sentiment by topic over time. Break down NPS comments by product-specific topics. Understand exactly what bugs or features customers mention most. Fix the bugs and add features to their product roadmap to win more Promoters. Product teams can focus on features that create loyalty by use data-driven analyses of unstructured NPS comments to master what Promoters love and lessen what passives and detractors find lacking.

With the help of AI algorithms and using thematic analysis, customer experience leaders can craft actionable insights from the most important patterns in the customer feedback. They can filter on metadata, and save customer segments, such as customer location or value, to dig deeper into issues or opportunities. For example, if the software team solves the ‘login issues’, it will improve the NPS by 16.6%.

7.3. Self-service portal and FAQs suggestions

With the help of generative AI, employees self-serve by smartly matching existing HR and IT articles to their questions through relevant search results, proactive recommendations, and smart chatbots. The AI chatbot suggests the top FAQs that provide a potential solution to the problem raised by the customer/ employee.

AI-based self-service portal can help customers to avoid waiting for hours or days to get a webform response. When they start to fill in a request web-form, they can receive personalized resolutions in a matter of seconds while keeping the submission process interactive and engaging. The AI will search for the most relevant and adequate answer based on the context and the customer subject line.

7.4. Automatic routing & real-time agent assistance

With the help of generative AI enhanced with machine learning, customer conversations can be automated and/ or augmented with human assistance. Tickets can be automatically routed based on real-time conversational analytics, customer preference and business context. The AI bot virtual assistant has access to the full context of the conversation and can escalate to human-assisted chat. For some cases, there’s ability to perform a step-down transition back to the virtual assistant. AI can act as both a full automation agent or a real-time agent assist to draft the response for the agents to confirm or edit.

AI bot can connect via web app and integration with a long catalog of third-party platforms, enabling to move forward from informational conversations to transactional conversations – ability to perform actions and execute workflows to answer customer requests. With transaction conversational, the AI bot can help in automating transactions, facilitate multipart transactional inquiries, automate repetitive tasks, cut costs, free customer service agents to focus on complex requests and ultimately improve client satisfaction.

7.5. Customization and API integration with systems

With the help of generative AI, visual and interface customization can be semi-automated including themes, logo, favicon, layout. AI algorithms can also help with advanced customization by generating ready-to-use design elements and codes in Liquid HTML/ CSS or other programing languages.

API system integration is the ability to access and interact with a given knowledge system with 3rd party applications. API integration lowers the barrier to interact with the knowledge system by offering multiple user experience and interaction modes proposed by the 3rd party applications. For example, the ability to search wiki articles stored in a Knowledge Base from a messaging/chat system. By doing this, it will increase the adoption and the usability of the central knowledge base.

For some APIs, AI can automate the system integration with 3rd party applications. Below are some examples of applications offering APIs for system integration:

• Google Chrome (browser) add-ons
• Microsoft Teams (chat) integration
• Salesforce (ticketing) integration
• Slack (chat) integration
• Zendesk (ticketing) integration.

Examples of AI-based KM tools for knowledge-centered services: Thematic, Freshworks, Zendesk, Netomi.

Next part (part 8): AI-based KM features for knowledge analytics and intelligence.

Header image source: Author provided.

Reference:

A comparative study of 100 generative AI tools in the context of learning and knowledge management (KM) was conducted and has resulted in a set of 35 KM processes where generative AI¹ has augmented their experience, implementation, and execution. This article (part 6 in the series) focuses on knowledge discovery and generation KM processes.

Part 6. Knowledge discovery & generation

6.1. Content views / topic-based dashboard

With the help of generative AI, emerging topics are showed in customized dashboard view based on what an audience groups are searching for. Views are created based on topics that represent stakeholders’ interests and expected information/ knowledge. Views can also be created based on the role (Level 1, Level 2, etc.) the user can view appropriate levels of information while using different methods to access the knowledge base. AI algorithms can suggest the creation of topical pages to expand digital reach and meet new audience needs and interests. Domain experts can find information and documents specific to their line of work, whether it’s sales, legal, or engineering. AI can personalize the employees’ personal space in a knowledge base with dynamic content suggestions and tailor content based on employee role, activity, topics of interests or specific views. Views can also feature content which is often used to promote time-sensitive communication to increase visibility and reachability.

6.2. Content optimization (accuracy, clarity…)

With the help of generative AI, content goals can be set for content optimization. A goal is a content characteristic which the AI algorithm is going to check the content for. In other words, content goal is a set of guidance that fits the content strategy and helps the contributors/ writers stay aligned. For example, a global-ready content will likely be translated and localized, so content goals such as clarity, consistency, and scan-ability (for easy readability) are important goals. More example on content goals can be insights on gendered phrases, harmful language, age bias, corporate clichés, engaging phrases, brand keywords, fixed mindset language.

AI content optimization algorithms can also measure behaviors and track them over time. For example, a change leader may need to quantify the impact of the inclusion efforts by tracking changes in the entire team’s language over time.

6.3. Content automation & operational efficiency

With the help of generative AI, process managers can build complex workflows and connect them with core systems. As result the AI application is not limited to responding to queries but also take action to resolve them. With the human supervision the AI algorithm can map out the end-to-end processes and know exactly where to step in and deploy content automation modules that support the front-end people and improve their resolution experience with customers. Internally, employees can request services in a centralized interface for any function ranging from IT, HR to finance, facilities, and other lines of business. AI features can make processes to flow seamlessly with a simple drag and drop workflow builder, allowing users to collaborate efficiently with cross-functional teams, and effortlessly scale the operations for efficient resolution of customer inquiries.

6.4. Recommend relevant/confident content

With the help of generative AI and using self-organizing techniques, the knowledge application offers relevant answers to customer requests based on dynamic relevance ranking and human supervision. The most relevant contents are surfaced in one list, regardless of format or where they reside – in specific applications, websites, internal servers, community discussions, blogs, wikis, library and more.

Search results also include federated & unstructured knowledge to provide highly relevant results which increase knowledge re-discovery. An example, in the context of problem solving, a community site may deploy ‘critical knowledge finder’ an application that returns relevant answers that qualify as possible solutions. It returns also similar and related problems for further discovery of potential solutions in case of variations in the problem conditions and context.

The recommendation engine goes beyond keyword matching. It delivers specific, relevant answers by comprehending the context and intent behind queries. With answers rooted in trusted enterprise content, users can trace back the origins of the returned answers, fostering a deeper understanding and trust in the results. The recommendation engine also builds semantic relationships that are established between different data sources and data from one source can be used to enrich data in another source. As a result, the recommendation engine can identify and extract correlations and dependencies between the existing information and provide relevant results on a priority basis.

6.5. Content re-generation & conversational search

With the help of generative AI and using machine learning techniques content re-generation or content extraction into a new format or schema can be simplified and/or automated. For example, content from websites and their spider links, articles, product pages, discussions can be automatically extracted without any business rules for content retrieval. For a webpage, content is interpreted by a machine learning model trained to identify the key attributes on a page based on its type. The result is a website transformed into clean structured data (like JSON or CSV), ready to be imported into business application. Content regeneration can act on specific sections of a long report and regenerate it into a new document based on pre-built questions in specific domain areas. It supports content extraction from PDFs and even extracting content within images using advanced OCR techniques.

Content re-generation using smart document understanding (SDU) enables users to label text so that the tool builds an understanding of critical components inside the enterprise documentation, such as headers, tables and more. With the human supervision, an initial annotation of few documents’ pages, the SDU can teach itself the rest, retrieving answers and information only from relevant content. It transforms unstructured documents into a machine-readable format that organizes the headers, titles, paragraphs, tables, and footers detected within the document in natural reading order. AI algorithms interpret messy page layouts, structuring text into cohesive paragraphs that can then be effectively analyzed and searched.

In conversational search, users may engage in conversations to get educated on a given topic, to solve problems, to troubleshoot an issue or simply feed their curiosity. AI conversational search algorithms examine long-form unstructured documents (like blogs, bios, support articles, product manuals) and returns search results based on the user prompt, intent, and context. Users engage in free flowing & personalized conversations by retaining the context of users’ previous messages. Meta-data are added in the process of text extraction. For example, a new hired engineer may engage in a conversational search to better understand key concepts on designing a wind turbine. The AI search algorithms will offer quick access to technical information that is stored in various external data sources and document formats to answer the questions of the newly hired engineer. Conversational search can also recommend questions to newly hires to start with then they can easily follow up on their own and lower the barrier to an effective onboarding experience.

Conversational search may also be applied to transform boring web forms into friendly conversations, in this way information from abandoned forms are collected for further analysis or follow up with the customer. Conversational search features predictive query suggestions and typo tolerance to ensure employees find the most accurate, relevant, and useful content based on intent rather than exact keyword match. Conversational search can be coupled with cognitive based search on the question, so it finds the most important paragraph of the documents where the topic is explained. It can also be tied with semantic search, so if the user searches for Bank, he/ she will get loan, credit, finance in the results as well.

Examples of AI-based KM tools for knowledge discovery and generation: Textio, Mindbreeze, 3R Knowledge, Kyndi, DiffBot, Enterprise Bot, Yext.

Next part (part 7): AI-based KM features for knowledge-centered services.

Header image source: Author provided.

Reference:

A comparative study of 100 generative AI tools in the context of learning and knowledge management (KM) was conducted and has resulted in a set of 35 KM processes where generative AI¹ has augmented their experience, implementation, and execution. This article (part 5 in the series) focuses on expertise discovery and dissemination KM processes.

Part 5. Expertise discovery & dissemination

5.1. Human-in-the-loop collaboration (experts)

With the help of generative AI, knowledge seekers can locate and connect with experts. They can identify the right experts on their teams and beyond their network to answer questions and verify content is correct. For example, customer service agents can easily pull in colleagues for their expertise in a side conversation. These conversations are always linked to the original customer conversation, so agents can keep a trace of the whole story for later analysis. With human supervision, AI algorithms can be tuned for the relevance of experts’ recommendations based on location, department, skills, or other important attributes. Once the AI algorithm is fine-tuned, it can help in identifying rising stars gaining experience and skills in a particular team, function, or domain.

5.2. Assist in creating people’s profile

With the help of generative AI, people’s profile can be constructed based on a combination of available entity record attributes. The search and indexing engine allow combination of evidence from multiple matching attributes to perform identity resolution. With the help of human supervision application-specific business rules can be implemented by determining what combination of record attributes should be matched and what weights should be assigned to each attribute. For example, community members can collaborate with an overview of who they are, their work, and role in the organization. Members can visualize the community composition, learn about each other via AI-Augmented profiles, and make new connections. These profiles reveal the members’ most unique cognitive, social, and emotional attributes, and how these behaviors translate at work. Helping the community members to belong to the fittest communities based on the profile’s relevance and similarities.

5.3. Inference of expertise and micro-skills

With the help of generative AI, skills and expertise can be inferred by understanding the digital footprint subject matter experts create with publications, email, collaboration tools, and other sources. During the learning phase, AI algorithms make use of historical support request tickets, community discussions, questions and answers, enterprise documentation corpus, compliance logs and audit logs to identify expertise and micro-skills. For example, in a peer-to-peer problem resolution context, the AI application can automatically determine the required micro-skill and directs the moderator to the potential available experts. Helping the community membership to expand and include relevant, related, and similar experts.

5.4. AI digital worker & pre-built smart skills

AI digital workers are not bots. They are human-like clones to perform specific roles, responsibilities, and tasks. They have 3 characteristics:

• Collaborative: seamlessly work with their host team through intuitive user-friendly Human-in-the-Loop collaboration.
• Curious: train quickly and easily on even less-than-perfect datasets and improve continuously from their daily job and repetitive tasks.
• Capable: they go beyond rules-based automation and streamline document-heavy processes with native advanced intelligent document processing capabilities.

Example of AI digital worker:

• Data entry clerk, data analyst, digital content analyst.
• Accountant payable/billing analyst, purchase order specialist.
• HR assistant, recruiting coordinator.
• Customer service representatives.
• Shipping clerk, freight classification specialist.

AI digital workers are connected to their global network of digital workers. They aggregate performance results, allowing their human supervisor to compare their performance to industry benchmarks. This enables the human supervisors to answer questions like “How well is my digital worker doing compared to other workers doing the same task at other companies?” or “Do I need to label more data and train my digital worker to perform at the industry standard or am I already there?”

AI digital workers can be loaded with pre-built smart skills, without starting from scratch, without coding and without the need of a training data set. It’s possible by leveraging signature patterns which are templates created by recognized leaders with specific industry or domain expertise. Patterns may be selected from a library of pre-built conversational workflows and interactive interface make implementing best practices possible and reduce time-to-market.

Example of pre-built industry-specific smart skills:

• Schedule a customized health checkup appointment (healthcare).
• Schedule/ Re-schedule a new blood collection request (healthcare).
• Fetch and provide project details basis user requirement (construction).
• Collect user concern and accept their request to terminate service (telecom).
• Schedule a math class and share class details with students (education).

5.5. Sentiment/intent aware recommendations

Sentiment analysis can detect emotion, intent, and sentiment with natural language understanding (NLU) models that automatically surface people’s underlying needs.

• Sentiment: positive, neutral, negative.
• Intent: issue, opinion, request, book a flight…
• Emotions: joy, anger, trust…

Using natural language understanding of sentiment and intent, leaders and analysts can harness the context and user behavior to proactively discover the meaning in data, documents, discussions, and customer conversations. For example, managers can listen to the workforce across every channel to understand how customer service agents address issues with the right level of emotions, and sentiments in real-time. Sentiment analysis helps to understand and analyze customers’ reviews about products/ services in the brand community. It helps to identify important keywords in customers’ reviews and assigns a positive or negative score based on the modifiers describing the keywords.

Using natural language understanding of sentiment and intent, moderators can monitor topics being discussed on the pages of a company network to find the best pages for each ad. Use sentiment analysis to make sure the ad placements are brand safe and most relevant to the target audience. Help customers to discover products in e-commerce by using customer intent data which will improve conversion rate and revenue.

With human supervision, intent discovery can be tuned using the existing information sources, past chat records and live agent conversations.

Examples of AI-based KM tools for expertise discovery & dissemination: Pymetrics, Loop AI Labs, WorkFusion, Gavagai, OneReach, Haptik, Guru.

Next part (part 6): AI-based KM features for knowledge discovery and generation.

Header image source: Author provided.

Reference:

A comparative study of 100 generative AI tools in the context of learning and knowledge management (KM) was conducted and has resulted in a set of 35 KM processes where generative AI¹ has augmented their experience, implementation, and execution. This article (part 4 in the series) focuses on knowledge retention and reuse KM processes.

Part 4. Knowledge retention & reuse

4.1. Assist in creating rich content (wiki, KB, reports)

With the help of generative AI, creating content and summarizing complex topics can be facilitated and semi-automated. AI algorithms can help in the writing of domain-specific reports (i.e.: financial), removing the risk of error in manual writing to ensure accuracy, consistency, and compliance. AI can enable shared content creation and editorial workflow that enables cost-effective management. For example, it can auto-fill templates for consistent content that’s easier for employees to edit and complete later on. Another AI feature would be “write it with me” – An idea auto completion that generate and complement an idea with supporting argument and examples. For example, a customer call agent would get assistance on creating a robust ticket response with one click based on just a few words typed.

AI can play the role of article planner – ability to plan for new and/or missing support articles based on the history of customer conversations or community discussions. AI can also generate articles from audio & video files making them editable, searchable, and collaborative, later they can be imported into the company knowledge base for future reference and reuse.

4.2. Feedback loops and lessons learned

With the help of generative AI, it is possible to collect relevant input and reactions early in feedback review process. These inputs and reactions provide information that allows mitigation of expensive mistakes later in the process, as well as finding new ways of solving problems. This is often tricky as it might impact who needs to get involved in the work. It has more characteristics of designing a stakeholder map than just studying behavior and data. AI algorithms can help in the identification of relevant stakeholders for review and to collect early feedback in the process. It can also help in clustering similar feedback into groups and themes, making sense of them for improved process design, re-engineering and decision making.

4.3. Content syndication & inline integration

Community content syndication refers to the ability to automatically transfer and deploy content anywhere: websites, intranet, or client collaboration platforms. With the help of generative AI, the community content can be inline integrated with virtual case management, ticketing system or any organizational systems. For example, FAQs from community forums can be embedded into self-service customer portals. AI algorithms can develop the connected Knowledge capability empowering knowledge seekers to find the answers they need directly in their line of sight while handling their job tasks.

For example, a customer call agent can directly consult the community resolved cases within his/her customer call or webchat system. The customer call agent may improve his/her first contact resolution by mirroring and co-browsing content from different communities and data sources without the need to interrupt the flow of work.

4.4. Insights and best/direct answers extraction

With the help of generative AI, employees can gain insights from pattern analysis or perform root cause analysis on a specific issue for an informed decision making. For example, “I want to uncover insights about the decreasing sales of product A”. Research scientists can aggregate information from millions of scientific documents and news releases, providing them with an efficient, one-stop shop where they can discover insights that drive their research. AI can also help product managers generate insights from product comparisons, brand community discussions, up-to-date, accurate financial data, and approved content.

AI can also provide direct answers using an extractive question-answering (QA) system. In extractive QA, a specially trained version of BERT (Google’s open-source neural network which is trained to understand language) helps to identify the excerpts from long documents that best answer the user’s question. If it finds a good answer in the text, the algorithm displays it as a featured snippet. These snippets provide a great search experience and help searchers find the best answers to their queries, in a focused in-line section. AI algorithms require guidance and supervision to define which information and data are most important to the managers to lay down the foundation for meaningful insights and best direct answers.

4.5. Knowledge portal: diffusion of reusable content

With the help of generative AI, knowledge portal / homepage can be drafted with initial layout and structure to curate content, blogs, newsletter, events, RSS feeds, or any topic of interest. Knowledge portals centralize access to knowledge resources from various repositories and personalize the experience for each department and team. For example, AI algorithms can help in drafting self-service portals for HR and IT knowledge, so employees can access enterprise-wide content in one place. AI can also draft a homepage for every employee, so important announcements and real time updates on topics of interest are personalized with AI-suggested content for each user. All relevant interactions are available in one view, with the ability to customize and brand portals for internal teams as well as external customers.

Examples of AI-based KM tools for knowledge retention and reuse: RightAnswers, Guru, Kyndi, InBenta, Yext, Textio.

Next part (part 5): AI-based KM features for expertise discovery and dissemination.

Header image source: Author provided.

Reference:

A comparative study of 100 generative AI tools in the context of learning and knowledge management (KM) was conducted and has resulted in a set of 35 KM processes where generative AI¹ has augmented their experience, implementation, and execution. This article (part 3 in the series) focuses on knowledge co-development and exchange.

Part 3. Knowledge co-development & exchange

3.1. Suggested topical communities: problem solving

With the help of generative AI, community program leaders can get suggestions for topical communities based on taxonomies, most discussed and searched topics, recurrent and related issues and/or customer most frequent incidents. Community members engage in threaded discussions with peers and collectively elaborate ideas, solutions, and propositions. Communities let users subscribe and browse content they are interested in. AI algorithms can provide user-specific content recommendations based on their search activities and subscribed to “communities” (topics of interest). Customer service teams can rely on topical communities for a quick discussion for clarity to answer customer inquiries.

3.2. Augment knowledge sharing behaviors

With the help of generative AI, community members can share their questions, experiences within and across sister communities for additional insights. AI algorithms can identify related communities (sisters) where topics are interfaced, and members’ expertise are complementary. By tapping into the power of connectivity, communities’ members can get further answers in real-time from related community members whom they might not know but their feedback is valuable and relevant to their questions.

3.3. Digital project-based workspace

With the help of generative AI, a digital workspace can be created centralizing customer projects and data sources that can be branded for every customer. AI algorithms can automatically deliver reports based on organizational hierarchies or teams structure making progress tracking easy and shares accountability with HR and executive sponsors. Project follow up on action plan progress can also be automated for different stakeholders empowering them with actionable insights.

3.4. Knowledge narratives and storytelling

With the help of generative AI, extraction of tacit knowledge into explicit can be more streamlined with techniques such as storytelling, knowledge narratives, video interviews and dialogues. We know that intrinsic reward and a higher purpose are significantly more efficient in terms of motivating an organization and creating results. Linking a strong knowledge narrative to what you do, why you do it, and how you do it, will light a spark with your peers and boost the energy levels for engagement and collaboration.

AI algorithms can accurately predict which story will resonate the most with your target audience. AI can generate a story or knowledge narrative after being trained on guidelines, policies, a desired transformation, or organizational behaviors. AI algorithms can surface video interviews to detect key moments, uncover themes and storylines. AI can facilitate the task of creating an engaging story by reordering clip segments and combining coherent narratives that captivate the target audience. The story can be exported and shared automatically in different groups, channels, or communities.

3.5. Ideation and collaborative creativity process

With the help of generative AI, an ideation management platform running on AI-powered software that allows to collect, cluster and link ideas to strategic initiatives via mobile, desktop, and tablet. The creativity process requires collaboration internally and with external parties. It is done best through crowdsourcing. Crowdsource ideas for improvement from employees, support function inclusion or external stakeholders leading to breakthrough vetted ideas to take to development and product/ service success. Collaborate with customers in the brand communities on specific ideas to generate ideas for new products or services. Runs polls and contests to co-innovate with customers.

Ideation and creativity process is a non-linear process and by clustering many ideas and restating refined campaigns, you will end up at places you could never foresee from the start. To succeed with ideation, it is essential to keep idea authors engaged with feedback along the journey, regardless of what is happening with the idea. AI algorithms can help in creating, mapping, and connecting ideas, and idea authors to the campaign objectives. AI-assisted mind mapping can generate a mind map from questions asked to conversational search or can help in enriching the ideation process by suggesting related ideas, questions, topics to expand the mind map with additional branches.

Examples of AI-based KM tools for knowledge co-development & exchange: Squirro, 3R Knowledge, Phrasee, Trint Editor, Innovation 360, Miro.

Next part (part 4): AI-based KM features for knowledge retention and reuse.

Header image source: Author provided.

Reference:

A comparative study of 100 generative AI tools in the context of learning and knowledge management (KM) was conducted and has resulted in a set of 35 KM processes where generative AI¹ has augmented their experience, implementation, and execution. This article (part 2 in the series) focuses on social learning and personal capabilities KM processes.

Part 2. Social learning & personal capabilities

2.1. Personalized learning & assisted coaching

Develop targeted learning and development (L&D) programs to upskill or reskill employees with personalized course recommendations to close any skill gap. AI knowledge sensors can track each learner’s behaviors to unlock hidden dimensions that identify their learning style. They build capability reports with behavior recommendations and learning content. AI knowledge sensors can be utilized to enable adaptive learning paths and improve content governance. Employees can efficiently navigate to the most up-to-date learning assets and can get directions for relevant content for their career growth. For example, gamified learning experiences, interactive videos, and expert-led webinars. Generative AI can provide assisted coaching by giving feedback, insights, and actionable supervision on employees’ performance. For example, insights into learning styles, teamwork styles, and development strategies.

2.2. Skills suggestions for capabilities building

With the help of generative AI, human resources and talent development leaders can explore the inherent soft skills and capabilities of the workforce, highlighting similarities & differences across the organization. The leaders can match potential and current talent to their best fit roles as expected by the organization. AI algorithms can measure learners’ actions through behavior, cognitive, and engagement preferences. They help in identifying the learners’ skills gaps, suggest bite-sized learning modules and adapt content in real-time to accelerate skill development.

AI algorithms can help in identifying capability gaps between high and low performers based on behavioral assessment. They can provide leaders with development reports and insights to elevate 1–1 personalized skills development plans for their own career growth. At team level, AI algorithms can help in identifying winning behaviors by comparing with A-grade teams and then recommending the favorite behaviors. Teams can reduce new hire ramp time, drive methodology adherence, and can increase their team’s performance.

2.3. Automated multi-language learning

With the help of generative AI, audio and video content can be transcribed and translated in many languages, so content can be tailored for a global audience in near real-time. AI can help in automating speech-to-speech translation – a training video developed in English speaking language can be automatically regenerated in French speaking language (or other) making the training video available for new audience in short time. AI translation algorithms can specialize in domain-specific language for higher accuracy, for example banking, finance, legal, etc.

AI algorithms can self-correct and handle the complexities of business audio, including industry jargon, accents, numbers, currencies, product names, as well as the nuances of spoken language, including mumbling, and stuttering. AI algorithms can make the audio and video content searchable and accessible across multiple languages. Search results will return content from the prompt language as well as other language.

2.4. Dynamic creation of curated learning style

With the help of generative AI, learners can augment their learning strategies and curate content specifically for each learner based on their behaviors and preferences. Learners can have their dedicated learning space with a dashboard of activities, progress, helpful tips with the ability to connect with fellow learners. AI algorithms can help learners to find information and learning resources specific to their expertise and skills. Learners can configure role-based learning hubs relying on deep learning AI models. Once configured intelligent curation will prioritize content to watch based on a person’s role, geography, and preferred language. Learners will get recommendations for tutorials, videos, and learning resources.

In context of health and pharmaceutical industry, employees can stay up to date with the latest information as new trials are registered or updated. Healthcare experts can get instant updates and alerts for their topics of interest, scans the landscape for the latest scientific and biopharma news, including drug approvals, trials, conferences and more.

2.5. Suggestion of matching mentor-mentee pairs

Generative AI can help in suggesting a matching mentor-mentee pairs in the context of a community-based knowledge transfer. The mentoring relationship allows engineers to enroll in a learning activity with experts for critical skills development. Unlike traditional programs, community-based mentoring is a one-to-many transfer of critical knowledge which is shared with the whole community. The mentee acts as a mediator to transfer the know-how from the mentor to the whole community members. The AI algorithms can identify the required expertise after conducting a gap assessment for missing critical knowledge and/or expertise. The AI can later locate and map the required expertise to the best fit community and suggest experts within this community. The mentor-mentee pair will capture, formalize, and share the know-how with the whole community.

Examples of AI-Based KM tools for social learning and personal capabilities: Fuse Universal, Zoomi, ZoomInfo Chorus, Textio, Kensho Scribe, Trint.

Next part (part 3): AI-based KM features for knowledge co-development and exchange.

Header image source: Author provided.

Reference:

Artificial intelligence (AI) technology and particularly generative AI¹ has gained mainstream attention, the public media, as well as the business corporation. The global AI market² is valued at $ 142.3 bn as of 2023 and it’s expected to continue to grow into a $ trillion market in the coming years. On another note, the AI, algorithmic, and automation incidents and controversies (AIAAIC) repository³ has reported that the number of incidents and controversies concerning the misuse of AI is exponentially increasing from 123 incidents (2019) to 1,000+ incidents (first half of 2023).

As knowledge management (KM) leaders and practitioners, it’s critical to have an active role in guiding the integration of generative AI into KM areas, applications, and processes. This report is an attempt to provide some guidance on the current state of generative AI integration within the KM context. Specifically, the report is answering the following question:

Where and how generative AI is accelerating and impacting knowledge use cases, areas, and processes?

I have conducted a comparative study of 100 generative AI tools in the context of learning and knowledge management that has resulted in a set of 35 KM processes where generative AI has augmented their experience, implementation, and execution. The 100 KM solutions integrating generative AI are shown above.

AI-based KM solutions by service area

AI-based KM solutions have served multiple areas ranging from market intelligence, sales prediction, customer service, and intelligent search to employee learning and development. The below chart shows the distribution of the 100 AI-based KM solutions by service area:

• Customer services: customer experience, customer call center, customer feedback analysis, incidents, ticket management, self-service customer portals, and customer web chat.
• Cognitive services: intelligent search, semantic search, cognitive search, symbolic search, and insights engine.
• AI infrastructure for KM: AI and machine learning platforms, symbolic and reasoning algorithms, deep learning, large language models, and neural networks.
• Content and collaboration platform: intelligent document and process automation, content automation, content optimization, content editorial, content summarization, content intelligence, enterprise knowledge platform, knowledge sharing platform, and knowledge base.
• Sales and marketing: brand experience, digital marketing, digital advertising, market intelligence, and research intelligence.
• Learning and development: smart skills, digital worker, talent engagement, and social learning platform.

AI-based KM features by process area

The study found 35 KM processes that have been promoted with generative AI technology and features, which we have organized into seven knowledge activities.

Each of these seven knowledge activities and the KM processes under them will be discussed in subsequent articles in this series:

Part 2: Social learning & personal capabilities

Part 3: Knowledge co-development & exchange

Part 4: Knowledge retention & reuse

Part 5: Expertise discovery & dissemination

Part 6: Knowledge discovery & generation

Part 7: Knowledge-centered services

Part 8: Knowledge analytics and intelligence

Part 9: Summary & conclusion.

Next part (part 2): AI-based KM features for social learning and personal capabilities.

Header image source: Author provided.

References: