How can AI reduce your service desk workload?

Generative AI is making chatbot technology much more human-like in it's responses, moving from decision-tree like responses to being much more adaptive and being able to help with more complex issues.

These are chat bots, if set up correctly by technologist can provide an amazing L1 style of brake fix Support. in truth, the L1 position really is there to gather base customer information the issue at hand, and if the low hanging fruit is quick actionable to take care of it via advice the minute you need to do a remote desktop configuration. Typically it’s kicked up to a level 1.5 or level two.. My implementing a L1 base trap and depending how it hooked up on the backend the minute a client or customer reaches out to the chat bot for assistance all core base information is auto captured, and if the issue was quickly resolved escalation. Regardless of the fix a ticket is auto generated as well. This is perfect for KPI tracking..

AI can help reduce the amount of work for service desk agents by automating routine tasks and handling simple inquiries through chatbots. This saves time and resources, allowing agents to focus on more complex issues that require human expertise. AI can also analyze data to identify patterns and suggest solutions, which further reduces the need for human intervention.

AI can help automate this process by using natural language processing (NLP) and machine learning (ML) to analyze the content and context of each ticket, and assign it to the most appropriate category, priority, and agent. This can reduce the manual effort, improve the accuracy, and speed up the resolution time.

In the digital age we are in and with more than 5 billion people in the world accessing the internet, Chatboot with AI is on the market to help and reduce time and processes, we use it today in everything, changing passwords, banking services, hotel chains and In every product and service base, a matter of improvement is always adjusted in NLP (Neural Language Processing) without effective.

Reduce helpdesk tickets with AI and a knowledge base by creating a comprehensive knowledge base, using a smart chatbot with natural language processing, automating ticket categorization, continuously improving with user feedback, integrating across channels, providing personalized experiences, analyzing data for insights, promoting self-service, and fostering collaboration between AI and human agents.

Building a knowledge base and knowledge graph for the organisation can help service desks deal with much more than simple HR or technical issues, but can extend to finding people, documents and systems to help people complete their work. A knowledge graph is a critical first step in enterprise-wide generative AI

A integração da IA na gestão do conhecimento é inovadora para aliviar a carga de trabalho em centrais de serviços. Esse processo, envolvendo criação, armazenamento e compartilhamento de informações, é otimizado pela IA ao extrair, categorizar e indexar dados automaticamente. Isso facilita a organização eficiente e o acesso ao conhecimento. Utilizando a IA, aprimoramos habilidades, evitamos duplicações e aceleramos a resolução de problemas. A IA não só busca informações relevantes, mas também sugere novos conteúdos, promovendo a evolução contínua do conhecimento. Esta interação destaca como a IA na gestão do conhecimento facilita a tomada de decisões ao oferecer insights valiosos de maneira estruturada.

Another plan might be a full on the first day of each month, a differential on the 7th, 14th, 21st and 28th day of each month with an incremental backup on all other day

This is what helpdesks were designed to do already. But most helpdesks do not use previously closed tickets to generate solutions, as the tickets are generally worded differently when they are logged by different level one support technicians.

Predictive analytics, taken to the next logical step would also allow for automated resolution of issues before they are customer impacting, this could include provisioning more hardware, software or cloud services or increasing licences for services. Also, it can be used for cost management by shutting down services and releasing licences which are not required based on predictive analytics

Ciertamente este punto es muy importante porque se requiere crear una segura y confiable base de datos, que pudiera ir acompañada de un CRM y que todo se automatizado, ya que con ello se pueden realizar métricas y llevar un control de fallas en reportes de servicios que como se comenta en este punto siendo una falla constante se pueda predecir y ganar tiempo con la predicción además saber que usuario genera muchos tickets por la misma y constante falla.

Implementation Strategy: Integrate AI-driven analytics tools to identify patterns and trends in service desk requests. Implement predictive models to foresee potential issues before they escalate. Benefits: Proactive Issue Resolution: Predictive analytics helps in identifying and addressing potential problems, preventing disruptions before they occur. Resource Optimization: Efficient allocation of resources based on predictive insights ensures optimal service desk performance.

Self service portals that integrate with the necessary backend systems are essential. Conversational prompting that is trained on issue triaging will also be helpful, especially if the software or device information are already available.

Today I come across that more and more CRM, ERP, portals, news, are integrated with AI, allowing for data analysis, capturing your niche of customers or\ and people interested in your product is essential both for the customer to know that what that your company is looking for is available and the company can integrate portal systems, with a pre-established survey with self-service, with chat responses or even a website search.

Implementing self service portal AI-drive solutions can enhance efficiency, reduce response times, and alleviate the service desk’s overall workload.

Mantener una buena comunicación con lo usuarios a través de los chatboot hace que por medio de la automatización y usando de ejemplo un CRM quede entonces un registro en nuestra base de datos para seguirla alimentando de información sobre las soluciones que pudiera resolver el usuario en primer nivel y así se pueda ir educando y aparte de eso disminuir las incidencias.

Implementation Strategy: Develop user-friendly self-service portals powered by AI for issue resolution. Implement natural language processing (NLP) capabilities for an intuitive user experience. Benefits: Empowering Users: AI-driven self-service portals empower users to find solutions independently, reducing the volume of service desk tickets. Time and Cost Savings: By promoting self-help options, the service desk can focus on critical issues, saving time and costs.

Robotic process automation (RPA): Automate repetitive tasks like data entry, form filling, and report generation, freeing up agents for more complex work.

Use Generative AI to summarise your service desk history, consider the value waiting to be unlocked in the data you hold about issues, tickets, incidents, your customers, knowledge base, feedback and what learnings could be applied from that moving forwards. The data you already hold tells a really compelling story you just need to access it.

I see that we also have to be careful with the use of AI by malicious people in creating fake news, in the face-to-face race in Brazil, Argentina and Chile (certainly in other countries there were also), AI robots were programmed, of course by a person with political intent, to denigrate the other candidate, and this is what I see that will happen more and more, we must be careful about these demonstrations about ''finishing'' and denigrating their image.

Automatización mas sofisticada y mejorada en SEO para la creación de paginas web que me ayude a mejorar en la optimización y posicionamiento en los motores de busqueda de Google y así hacer crecer mas rápido a mi empresa ahorrando tiempo

User Training: Ensure that end-users are familiar with AI-powered tools and encourage them to leverage self-service options. Data Security: Implement robust security measures to safeguard sensitive information processed by AI systems. Continuous Monitoring: Regularly monitor AI performance and gather user feedback for continuous improvement.