AI technologies

Develop knowledge management use cases with generative AI

Artificial intelligence is revolutionizing many areas of business. The potential of the technology is particularly evident in the areas of customer service, talent and application modernization. According to IBM’s Institute for Business Value (IBV), artificial intelligence can improve contact center records,improving customer experience by 70%. Additionally, AI can increase workforce productivity by 40% and application modernization by 30%. One example is reducing workload by automating ticket management across
IT departments.While these numbers highlight the transformation opportunities for businesses, scaling and operationalizing AI has always been a challenge for businesses.

Without AI there is no artificial intelligence
AI is only as good as the data it produces, and the need for a suitabledatabase has never been greater. According to IDC, the amount of data stored is expected to increase by 250% over the next 5 years.

As data is stored in cloud and on-premise environments, it becomes difficult to access it while managing governance and controlling costs. To make matters worse, the way data is used has become more diverse and companies are having to manage complex or low-quality data.

I have conducted extensive research and found that enterprise data scientists spend 80% of their time cleaning, integrating, and preparing data, dealing with multiple formats such as documents, images, and videos.Overall placing emphasis on establishing a trusted and integrated data platform for AI.


Trust, AI and effective knowledge management
With access to the right data, it is easier to democratize AI for all users by using the power of foundation models to support a wide range of tasks. However, it’s important to factor in the opportunities and risks of foundation models—in particular, the trustworthiness of models to deploying AI at scale.

Trust is a leading factor in preventing stakeholders from implementing AI. In fact, IBV found that 67% of executives are concerned about potential liabilities of AI.Existing responsible AI tools lack technical capabilities and are limited to specific environments, meaning customers cannot use these tools to manage models on other platforms. This is concerning because generative models often produce results that contain toxic language –including hate, insults and profanity (HAP) – or reveal personal information. Companies are increasingly facing negative press about the use of artificial intelligence, which is damaging their reputation. Data quality has a major impact on the quality and usability of the content produced by an AI model, highlighting the importance of addressing data-related challenges.

Increasing User Productivity: Knowledge Management Use Cases
A new generative application of AI is knowledge management.Thanks to the power of artificial intelligence, companies can use knowledge management tools to collect, create, retrieve and share relevant data to gain organizational insights. Knowledge management applications are often deployed in a central system or knowledge base to support business areas and activities, including talent, customer service, and application modernization.

HR, talent and artificial intelligence
HR departments can use AI to perform tasks such as content creation, advanced search generation (RAG), and ranking. Using content generation, you can quickly create a role description. Augmented Generation Research (AGR) can help determine the skills required for a position based on internal HR documents. The evaluation can be used to determine whether acandidate is a good fit for the company based on their application. These activities optimize the processing time of a
request from the time it is submitted until a decision on the request isreceived.

Customer Service and Artificial Intelligence
customer service can take advantage of AI by applying RAG, synthesis and classification. For example, companies can integrate a customer service chatbot on their website that uses generative AI to provide better conversations and context. Advanced search generation can be used to search for company-internal knowledge documents to answer a customerquestion and generate personalized results. A summary can help employees by providing a brief description of the customer’s problem and previous interactions with the company. Text classification can be used to classify customer sentiment.These activities can reduce manual work while improving customer service and hopefully customer satisfaction and loyalty.

Application Modernization and Artificial Intelligence Modernizing
applications can also be achieved through content generation and aggregation activities. By unifying business insights and business goals, developers can spend less time gathering the information they need and more time coding. IT staff can also create a ticket summary request to quickly resolve and prioritize issues found in a support ticket. Another way for developers to use generative AI is to communicate with extended language models (LLMs) in human language and have the model generate code.

To prepare data for AI, data engineers must be able to access all types of data from a large number of sources and hybrid cloud environments from a single entry point. A data lake with multiple query and storage engines can enable team members to share data in open formats. Additionally, engineers can clean, transform, and normalize data for AI/ML modeling without duplicating it or creating additional pipelines. Additionally, companies should consider Lakehouse solutions that include
generative AI to help data engineers and non-technical users easily discover, expand, and enrich data using natural language. Data lakes improve the efficiency of AI implementation and data pipeline generation.

AI-powered knowledge management systems store sensitive data, including HR email automation, marketing video translation, and call center transcript analysis. Access to secure data is becoming increasingly important for thissensitive information. Customers need a data lake that provides integrated centralized management and automated local policy enforcement, supported by data cataloging, access control, security and data lineage visibility.


With this database created by Data Lakehouse, data scientists can safely use managed data to securely build, train, optimize, and deploy AI models.

Provision of accountable, transparent and understandable knowledge management systems As mentioned above, chatbots are a popular form of AI-based generative knowledge management systems used for customer service.This use can create added value for the company, but also involvesrisks.

For example, a chatbot for a healthcare company could reduce caregiver workload and improve customer service by answering treatment questions based on known details from previous interactions. However, if the data quality is poor or an error was introduced into the model during quick or fine tuning, the model is likely unreliable. As a result, the chatbot may respond to a patient with inappropriate language or reveal another patient’s personal information.
To prevent this, companies must proactively identify and mitigate errors and deviations when implementing AI models.The ability to automatically filter content for PAH and PII leaks would reduce the burden on model validators who have to manually validate models to ensure toxic content isavoided.

Realize possibilities with Watsonx As previously mentioned, knowledge management strategy refers to the collection, creation and sharing of knowledge within an organization. They are often implemented in a knowledge exchange system that can be shared with stakeholders to learn and leverage existing collective knowledge and ideas of the organization. For example, an AI RAG task can help identify the skills required for a role based on internal HR documents or help a customer service chatbot search internal documents to respond to a customer query and generate personalized results.

To implement generative AI models, companies need to work with a trusted partner that has built quality models or derived them from high-quality data- a model that can be adapted to data and business goals.


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