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New Year, New Resolutions - How AI employee helpdesks are evolving in 2024 (part 1)

Thu Dec 28 2023

3 mins read
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New Year, New Resolutions - How AI employee helpdesks are evolving in 2024 (part 1)
As we are wrapping up 2023, the year that Generative AI rose and gained its major momentum, it’s time to acknowledge that whether we like it or not, AI is transforming the way we live and work. Most aspects of our work life are fundamentally changing, and the best way to adapt is to learn and prepare for the next AI trends to come. \ \ As an IT and helpdesk manager, you have definitely witnessed how Artificial Intelligence, Automation and Virtual Agents are taking over the employee helpdesk. AI is here to stay, so the more you understand the latest AI advancements and how they impact your work, the more future-ready you will be. The beginning of the new year is a great opportunity to start thinking about incorporating new technologies into your helpdesk. That’s why we decided to delve into some of the newest AI developments that we believe could shape the AI helpdesk’s future in 2024. \ \ So, without further ado, let’s jump into what these are, and how they will impact the AI employee helpdesk. ## The RAG revolution: transforming AI employee helpdesks with precision and adaptability Retrieval Augmented Generation (RAG) is an advanced artificial intelligence technique that combines information retrieval with text generation, allowing AI models to retrieve relevant information from a knowledge source and incorporate it into generated text. \ \ Retrieval-based models focus on selecting pre-existing responses from a database or knowledge base. Generative models, on the other hand, are capable of creating responses from scratch. These models use sophisticated language generation techniques, often based on deep learning architectures, to produce contextually relevant and coherent responses. \ \ The integration of retrieval and generation mechanisms in RAG models aims to leverage the strengths of both approaches. In a nutshell, the retrieval model acts as a specialized 'librarian,' pulling in relevant information from a database or a corpus of documents. This information is then fed to the generative model, which acts as a 'writer,' crafting coherent and informative text based on the retrieved data. \ \ RAG models are very beneficial for companies and their AI helpdesks: instead of relying solely on knowledge derived from the training data, a RAG workflow pulls relevant information and connects static LLMs with real-time data retrieval. With the RAG architecture, companies can deploy any LLM model and augment it to return company-related results by giving it a small amount of their data without the costs and time of fine-tuning or pre-training the model. \ \ RAG models have a transformative, positive impact on the efficiency and effectiveness of the AI employee helpdesk. To begin with, RAG serves as a solution for producing text that not only flows smoothly but is also both factually precise and information-rich. Retrieval models contribute the factual content ("what"), while generative models handle the art of crafting these facts into coherent and meaningful language ("how"), leading to higher-quality responses that better address user queries and issues. \ \ Secondly, the dual nature of RAG offers an inherent advantage in tasks specific external knowledge or contextual understanding. For example, in question-answering systems, traditional generative models may struggle to provide accurate responses. In contrast, RAG excels by accessing real-time information through its retrieval component, offering more precise and up-to-date answers. \ \ Lastly, RAG truly excels in scenarios that demand multi-step reasoning or the synthesis of information from diverse sources. When it comes to complex employee queries, RAG's ability to search, select, and synthesize information sets it apart. \ \ At Gaspar AI for instance, we use [RAG to eliminate the limitation of the initial knowledge the model was trained on](https://www.gaspar.ai/blog/gaspar-ai-mastery-for-it-automation). As a result, we ensure that our customers offer precise and accurate answers in natural language to their employees’ queries, based on their own, company-specific information and knowledge. ## The Multimodal AI advantage for employee helpdesk platforms Multimodal AI refers to artificial intelligence systems that can process and understand information from multiple modalities or sources. Modalities in this context refer to different types of data, such as text, speech, images, videos, and other forms of sensory input. Unlike traditional AI models that might specialize in one type of data, multimodal AI aims to integrate and comprehend information from various sources simultaneously. \ \ The key strength of multimodal AI lies in its ability to capture a more comprehensive understanding of complex data by combining different modalities. For instance, a multimodal system could analyze both the text and images in a document to gain a richer understanding of its content. This approach allows AI models to better mimic human-like perception and interaction, as humans naturally process information from multiple senses and forms of communication. \ \ Multimodal AI represents a transformative leap for AI-powered employee helpdesk platforms, revolutionizing the way users interact with and receive assistance from these systems. The flexibility to communicate via speech, image or text allows users to choose the most convenient and natural means of expressing their issues, making interactions more intuitive and user-friendly. The incorporation of multiple modalities allows users to provide detailed information about their problems. For example, users can attach screenshots or images of error messages. By analyzing both text and visual elements, these platforms can better comprehend the context and extract a more nuanced understanding of user needs. This leads to more accurate and empathetic responses, significantly improving user satisfaction. Overall, multimodal capabilities enhance the platform's ability to understand and address complex problems, streamline the troubleshooting process and fosters a more user-centric experience. ## Composite AI: a game-changer for enterprise success in Artificial Intelligence The latest report by [Dynatrace](https://ir.dynatrace.com/news-events/press-releases/detail/319/global-report-reveals-composite-ai-will-be-central-to), the leader in unified observability and security, and [Gartner’s Top 10 Strategic Technology Trends for 2024](https://www.gartner.com/en/articles/gartner-top-10-strategic-technology-trends-for-2024) reveal that Composite AI will be central to the successful adoption of Artificial Intelligence in the enterprise. Composite AI refers to the combination of multiple AI technologies and models, such as generative, predictive, and causal, to create a more powerful and versatile system that can address complex challenges. This approach recognizes that no single AI model can provide a complete solution to every problem, and thus, a combination of AI technologies is leveraged to create intelligent systems that can accomplish jobs that individual AI components cannot. \ \ One of the most significant challenges companies face with Generative AI is achieving meaningful responses that users can trust to solve specific use cases and problems. Thus, there is a need for a combined or "composite" approach to AI, especially for tasks that involve automation and depend on specific data – hence, employee helpdesk tasks. \ \ Whether it's automating software services, fixing security issues, predicting when maintenance is needed, or analyzing business data, a mix of AI methods in the employee helpdesk is crucial. This approach delivers the precision of "causal AI" (figuring out why things happen) and "predictive AI" (guessing what might happen based on past data). These combined AI types provide important context for the responses crafted by Generative AI, making sure they're accurate and not just probable. If companies use this strategy wisely, combining different AI types with good monitoring, security measures, and comprehensive business data can really boost their teams’ productivity and bring long-lasting business benefits. ## A new horizon for AI employee helpdesks in 2024 As we step into 2024, the message is clear: AI is not just evolving; it's revolutionizing employee helpdesks. Embrace these trends, stay informed, and position your helpdesk for success in the era of intelligent and user-centric support systems. The new year brings not just resolutions but opportunities to shape the future of AI in the workplace.