From AI Data to AI Agent

When someone asks me what is crucial for a successful AI project, I answer:

Good AI data, a well-designed AI agent and a well-defined transformation process for project implementation!

An AI agent automates your processes, communication and tasks or whatever your pain point is.

But let’s start from the beginning and look at the AI data.

Data is at the heart of every AI project. It forms the basis on which the AI is trained to recognise patterns and make predictions and intelligent decisions. High-quality, relevant and well-structured data is crucial for achieving precise and reliable results. Without a solid database, even the most advanced AI cannot realise its potential. It is therefore essential to invest time and resources in the preparation of data to ensure the success of your AI project.

Missing data is a significant challenge for AI projects. If not all data is available or insufficient, this affects the accuracy and reliability of the AI project. The best way to prepare your data for your AI project and complete any missing data is to build a centralised knowledge base, such as knowledge graphs. Knowledge graphs have standardised semantics and link your structured and unstructured data in context. While large language models (LLMs) are developing rapidly, the knowledge graph remains the stable anchor of your software landscape. It structures your data pipelines, is easily expandable and enables you to provide your generative AI (GenAI) with the best available data. This allows you to develop more efficient and intelligent automation applications and ensure that your AI agent is based on a solid foundation.

Whether in customer service, human resources or knowledge management – an AI agent based on knowledge graphs optimises your company through automation based on secure and verified data.

Automation via an AI agent not only relieves your employees, saves resources and reduces costs, but also increases your efficiency and improves your business processes. With knowledge graphs as a knowledge base, AI agents contribute to the long-term strengthening of your company and create real added value for your customers, employees and partners.

With generative AI and knowledge graphs in combination with a workflow tool, you can create your AI agent that seamlessly integrates with the existing enterprise software and orchestrates the processes and automation according to your transformation process. This allows you to successfully apply AI to help your company.

In customer service, you want to reduce the workload on your employees and increase efficiency at the same time. One way to achieve this is to have customer enquiries answered automatically by an AI agent. This technology makes it possible to process standard enquiries quickly and accurately, giving your employees more time to deal with complex or individual customer concerns. The AI agent can answer a huge volume of customer enquiries around the clock and immediately. And via the most relevant customer channels – chatbot, voicebot, messenger services or email. This not only improves the quality of customer service, but also shortens response times, which leads to better customer satisfaction overall.

An AI agent can take over standardised tasks in Human Resources automatically. The AI agent enables fast and precise processing of employee and applicant requests. This gives your HR employees more time to evaluate complex applications and conduct personal interviews. Employees and potential future employees have 24/7 access to information relevant to them. The information can be retrieved quickly and easily via a chatbot, voicebot, email or messenger services. The fact that the AI agent is based on knowledge graphs ensures that the most up-to-date and accurate information is always available to employees and applicants.

By using an AI agent in your knowledge management, documents, data and information are made available to your employees quickly and via various channels – chatbot, voicebot, email or messenger services. As the AI Agent is based on knowledge graphs, the information provided is up-to-date and secure. The AI Agent enables your employees to find relevant company information accurately and quickly, giving them more time for strategic tasks and allowing them to focus on applying knowledge. This promotes internal collaboration and innovation, as all employees have access to up-to-date information at all times.

Follow these steps to successfully implement your AI agent:

Knowledge graphs have clearly defined semantics, allow the structure to be easily extended and objects to be linked. In terms of data, this means that you obtain a central knowledge base from your structured and unstructured data by using metadata or extracted data with clearly defined semantics and meaning. The underlying organising system is the domain specifications that define your data based on standards or customer-specific requirements. Google has been doing this for years, forcing us to structure our websites in schema.org in order to achieve better Google search results for our website content. This also gives Google a great opportunity to build its knowledge graph. Knowledge graphs form a central knowledge base for your organisation, allowing you to feed generative AI with well-structured data to create your AI models and your AI agent. The better the input data for generative AI, the better the results of any RAG (Retrieval Augmented Generation) system or trained model.