Generative AI brings so much promise to transform the way service desks work.

Built on massive datasets, unlike any other AI models or tools, Generative AI applies the inherent capabilities of NLU, thus making service desks independent of manual dependencies and bringing optimal performance with data-driven actionable insights.

Every service desk leader understands the value of automation and data-driven analytics, potentially bringing operational excellence and more granular visibility for optimizing future service delivery.

Generative AI fulfills this ambition at a large scale while making sure to predict threats and prevent issues, driving toward ultimate operational resilience.

GenAI can handle massive datasets unimaginable for traditional IT support.

The fundamental nature of Generative AI or LLMs is to interact with vast billions of data and capture significant insights to help generate deeper analytics and make service desks future-ready.

Generative AI is a game changer in making data analytics an easy undertaking for service desk leaders, unlocking huge possibilities to succeed with employee support initiatives in a constantly evolving environment.

AI models are used to automate tasks within traditional IT support leverages small amounts of data. The workflows are also smaller because of the continuous need for manual efforts. Self-service seems rudimentary at some point, creating friction for employee support.

The overall impact of traditional IT support is that it curbs the ability to harness data and carry out analyses that can help with comprehensive reporting and analytics.

The problem persists year after year, and the consequences damage IT leaders, stakeholders, partners, and customers.

The lack of visibility into service desk performance and insights into how everything works can challenge resilience, uptime, operational efficiency, and user experience.

The ongoing service desk problems encompass the following—

• Smaller data insights lead to reactive service desk behavior.

• Repetitive problems persist, such as password resets, VPN settings, and software install.

• Self-service encounters repetitive issues and is unable to demonstrate extended automation.

• Manual triage and analysis prolong Mean Time to Respond (MTTR).

• Downtime lasts long for manual intervention.

• Lack of visibility and understanding intervene with agents’ efficiency.

• Real-time employee support is delayed and compromised.

• The increase in service desk tasks results in attrition issues.

These are very common challenges that exist at traditional service desks.

However, the convergence of data analytics within service desks or augmenting the existing data analytics capability with Generative AI can open up massive opportunities for you.

They can help you use rich insights and improve service operational excellence.

Generative AI trains on massive datasets from the world of knowledge or the internet.

Besides, it continues to learn from platform interaction to develop a comprehensive understanding of data visualization and analysis from incomplete or unstructured datasets.

Unlike traditional data models, Generative AI has the transformative capability to change the analytics landscape.

Here is how GenAI can unlock massive opportunities for data analytics for your service desks.

• Rapid data processing

With the ability to process data rapidly, Generative AI delivers faster responses to customer support or service desks for employee support.

Generative AI models can exhibit this ability as they are trained on massive datasets. This gives them the unique ability to identify underlying patterns within training data and later use them to create new data.

For example, if training data examples contain instructions to create blogs, Generative AI can apply inherent NLP generation and can create new content for blogs, social media posts, etc.

Also, they can be more creative and adaptable in learning new patterns to evolving or changing circumstances.

Depending on data-driven learning, Generative AI does not require explicit rules to learn new things and generate data. This helps service desk managers generate deep insights through rapid data processing.

On the contrary, traditional AI models ingest fewer amounts of data. They are trained on known scenarios, which makes them less capable of generating data for new scenarios and handling more tasks. That’s why the traditional models are less likely to process data rapidly and give opportunities to capture new insights.

• Contextual analysis of complex and unstructured data

The multimodal large language models enable Generative AI to analyze data faster. The massive training datasets give GenAI exceptional observability to learn patterns in historical data and create new data.

There is another part of creating contextual analysis of complex and unstructured data using Generative AI.

By feeding structured, semi-structured, or unstructured data feed into the Generative AI platform, query languages can be created.

Then, by running these query languages against the database, you can run data automatically and extract graphs seamlessly. This is a massive opportunity for service desk managers to generate data contextually from PDF files, CSV files, XML data, etc., and remove hallucinations.

Traditionally, it has been difficult for AI models to adapt to new circumstances beyond thresholds and to natural language and visualizations in real-time.

As you can see, Generative AI is a game changer for generating new data from raw or immature data and can also ensure its integrity.

Hence, for any interaction across service desks, Generative AI can easily help fetch information, harness key insights from unstructured data in any format, clarify doubts, etc.

This gives service desk managers a better way to build comprehensive data dashboards to create insights and drive toward efficiency and productivity gains for employee support and user experience.

IT support or service relies on improved data analytics as they offer enhanced visibility into service desk interactions.

Generative AI analytics can help a service desk improve its service delivery and user experience in many ways.

• Better interpretation of data

It is common for service desks to receive requests in image formats, PDFs, or XLS files. This information can be vague. However, Generative AI can efficiently interpret it in the best-understanding formats and provide clarity.

This is also a better way for service desk managers to harness data and use them to create exhaustive visualizations that help them improve service delivery.

• Anomaly detection

Generative AI uses deep learning models or neural network frameworks to learn the distribution of data in their training datasets. As these models learn continuously, they can easily identify and flag a mismatch in the data instantly. Regardless of what data formats are shared with the service desk platforms, Generative AI can easily examine them and identify anomalies if exist.

Anomaly detection using Generative AI models can help build dynamic real-time monitoring and improve service delivery.

• Report generation

It is no longer a hard iteration for service desk managers to create reports manually. The embedded GenAI capability can help create reports with a click of a button. It can track end-to-end service desk interactions and create comprehensive reporting for your company.

• Predictive analytics

Depending on Generative AI’s enhanced data analytics capabilities, GenAI models can learn from historical data and actions taken and adjust to new circumstances to provide new predictive models. This gives enhanced risk management capability without excessive manual effort that goes into triaging tickets and allocating the right person to improve risk communications and mitigation.

Along with helping with IT support analytics, GenAI can help with ongoing predictive maintenance and offer improved service desk management capabilities.

It is undeniably tough for service desk leaders to build GenAI models for data analytics from scratch built on top of traditional infrastructure.

IT leaders allocate the extra budget required to manage developer costs, database maintenance and license costs, cloud platforms, and many other iteration processes.

Workativ has a simple yet powerful technique to help you build your Generative AI analytics so that you can run service desk operations and support efficiently.

Workativ’s Knowledge AI bot has a super-rich bot analytics dashboard to let you leverage AI-driven data and analytics. This powerful analytics feature can help you ramp up your service desk’s performance and user experience

• Easy-to-use dashboard for chatbot analytics and performance

Get an intuitive dashboard that combines the power of GenAI to simplify the tasks of data harnessing and implementing steps to carefully remove bottlenecks from your service desk’s support.

With a single pane of view into overall service desk performances, you can gain enhanced visibility into every aspect of IT support operations and manage them efficiently.

• Bot session

With Workativ’s unified and intuitive analytics dashboard, you can get a comprehensive view of chatbot usage by employees. The bot session can look into chatbot conversations, failed conversations, tickets resolved, unaddressed tickets, self-service resolution, average session duration, etc.

• Agent handover

Find out through our comprehensive GenAI dashboard how many requests were being handed over to agents. What has been the most number of issues employees seek agent assistance for. What was the rate of success of agent assistance? And so on.

• User metrics

This is a feature inside our GenAI bot analytics platform where you to track bot usage. You can track frequent users and regular users over time and study their problems for employee support.

• ROI

With our GenAI analytics, you can also have the flexibility of fetching information involving ROI. Discover the number of issues resolved by the bot, agent time being saved, cost savings, etc.

By leveraging GenAI analytics for your service desk or IT support, you can ensure the effectiveness of ITSM. This means you can succeed with your ITSM objectives, ensure user experience, and build employee trust.

Generative AI can help you comply with ITIL norms and ultimately maintain ITSM in a number of ways.

Here’s what you can do with Workativ GenAI analytics.

Change management

Let’s say your self-service has repetitive requests that escalate to the agents most often. GenAI analytics can help derive which problems are most repetitive so that you can make changes to your knowledge articles and set some rules for self-service for autonomous resolutions.

With Workativ, self-service adjustment is easy, which helps you bring change effectively and adjust to change successfully.

Problem management

Workativ Generative AI analytics can be useful in deriving incident or support data to find recurring problems. Using these data, you can also identify and detect problems and propose solutions to prevent future problems.

Personalized employee support

With GenAI data analytics across your service desks, you can learn about employee dissatisfaction about employee support. It is easier for you to make changes to the existing support and make personalized offerings for each category of employee support. For example, if a particular employee needs help with preparing a summary of IT support issues, service desk managers can help provide information through an agent screen.

Data-driven analytics is significant in transforming service desk performance and user experiences. Unfortunately, conventional AI models used to automate traditional AI can offer very little to transform your service desks. GenAI holds huge promise to transform your service desk with advanced service desk analytics by changing the current state of automation and analytics landscape.

Workativ has an embedded GenAI analytics dashboard to give comprehensive views of service desk interactions and ultimately brings success for IT leaders.

GenAI analytics is the ultimate game changer for service desk leaders for service desk transformation. If you are keen to bring comprehensive analytics and make a change, Workativ can help you.

Let’s connect today.

1. How does GenAI change the data analytics landscape for service desks?

Generative AI uses massive datasets or large language models to understand patterns in their training data and learn to adjust to new and changing data circumstances to create new data. This gives Generative AI the outstanding ability to create advanced data analytics capability, which helps service desks improve their performances.

2. How does GenAI assist data analytics in your IT support operations?

LLM capabilities help with rapid data processing for unstructured and semi-structured data to ensure data context. If your service desks generate such data formats, GenAI can rapidly process these datasets, enabling you to fetch meaningful data and turn them into actionable insights.

3. In what way does GenAI help with ITIL or ITSM compliance?

GenAI-driven automation and data-driven analytics are a huge treasure trove that unravels bottlenecks with the service desks and helps create meaningful insights to combat existing service desk challenges. This further helps service desks eliminate workflow challenges, enhance automation for self-service, and accelerate coordination and problem-solving.