Empowering IT Teams: Using Generative AI to Augment, Not Replace, Human Expertise

The fear looms for how the world of Generative AI work holds promise for humans do they replace them completely or have companies looking for specialized roles?

According to McKinsey & Co., Generative AI can add about $4 trillion to the global economy annually. Bloomberg also predicts that the GenAI market will reach $1.3 trillion over the next ten years.

The reason is the public aims for AI-enhanced experiences and transactions.

That’s why businesses want to apply Generative AI to varied use cases to win customers’ trust and build long-term relationships.

However, it isn’t at the cost of human replacement. The recent Klarna action may throw a tantrum about the AI fear of snatching human jobs.

But, under the hood, it is not AI replacing humans, but rather human with AI replacing human with no knowledge of handling AI.

According to Karim Lakhani, a Harvard Business School professor, AI will lower the cost of cognition.

Further, AI empowers humans to be more strategic problem solvers and fast solution providers.

Similarly, AI cannot be superior without human intelligence, whereas humans can lack specific capabilities in certain scenarios.

In the context of IT support, combining AI and humans can help service desks navigate the unique challenges of employee support and offer a more balanced ecosystem that allows both AI and humans to thrive. Also, MS Teams integration with Generative AI workflows is super easy to handle and unleash commands that help solve a problem.

IT service desks receive a wide variety of requests daily.

Some queries can have chatbots solving the problems, whereas others don’t. Similarly, requests escalating to human agents can have mixed responses.

In many scenarios, a service desk can suffer from a lack of human-machine intelligence. In the absence of human-machine synergy, service desks struggle to meet users’ needs.

Challenges for IT Teams

Your IT teams are overwhelmed with repetitive tasks, including cases with similar IT requests.

The effort that goes into handling mundane tasks for millions of employee inquiries is susceptible to driving human mistakes. It is fundamental human nature to become overwhelmed by the pile of similar tasks, offer required attention, and be as good towards a job as needed.

At a certain point, the ability to handle routine tasks can decrease, stripping your human agents of their basic cognition. This would frustrate users of the service desks and reduce adequate productivity.

Additionally, skill gaps can elevate service desk challenges. Agents often fail to provide appropriate recommendations at the early stage.

Let’s say a user asks for a high-level graphics monitor. If an agent can’t access service desk information about inventory stocks, he cannot offer instant help. Simultaneously, if he suggests accommodating the need with another monitor, it can be unsatisfactory for the user. In this scenario, a skilled agent who can ultimately offer help must intervene.

Having proper skills to handle specific cases would help offer user experience.

Chatbot Challenges for Service Desks

A legacy service desk can have rudimentary chatbots that lack perception-related capabilities.

Self-service often fails to meet users' expectations.

They fetch answers from predefined templates, so when specific queries find no matches in the predefined FAQs or knowledge articles, users get irrelevant or repetitive answers, which is not useful for solving a problem autonomously.

As a result, your employees need to connect with the service desk agents. However, the pain point is that messages are often unclear, forcing users to repeat the story to convey the message.

On the other hand, agents struggle to capture the necessary information to get context. This affects the shift-left strategy and escalates the tickets to the higher tier, raising the ticket cost for proper resolution.

Another challenge with FAQ-based self-service is that knowledge keeps outdated information and impacts user experience.

Be it a human or chatbot-side challenge, a company finds it difficult to adjust to change management and promote an efficient work culture.

To put an end to the debate that AI replaces human expertise, let’s paint a vivid picture of AI and human collaboration across the IT help desks that would ultimately augment human expertise and facilitate service desk management efficiently.

Predictive models to improve mitigation decisions

In the ITSM environment, there are many instances where users provide vague information about a request or an issue.

It is probably hard for the service desk to comprehend the behind-the-scene picture, analyze the priority, and assign it to the right person for real-time mitigation.

With Generative AI predictive models, the service desk can empower human agents to overcome vagueness in information.

Also, AI predictive models allow room for corrections or reviews instead of letting agents rely solely on predictions that may otherwise emerge as wrong decisions.

This is a fantastic use case of human-machine synergy that empowers human decisions and model effectiveness to unleash a better version of the service desk in reducing MTTR.

Let’s see how it works. A user sends one unclear message to the service desk. A GenAI-powered predictive model can derive insights into historical tickets, newly created tickets, and agent recommendations.

A predictive model can provide an action plan based on these inputs. Agents can also look for anomalies in AI predictions, correct them, and send them for approval by admins. Again, AI models can learn from the corrections and create a better predictive model for real-time request mitigation.

With the combined capabilities of human and AI models, agents can remove ticket triage challenges, get the right person to handle an issue, and mitigate problems efficiently.

New knowledge to tackle industry-specific cases

Generative AI unlocks text-generation capabilities against prompts by fetching information from the underlying large language models. It can also learn from ongoing interactions between users and agents, conversational AI interfaces, and actions provided to deliver responses to new questions about industry-specific cases.

If a user wants to know ‘what the fastest way to fix the blue screen is,’ GenAI can quickly surface information from the world of knowledge in its LLM and help him escape trouble.

On the other hand, if a query like ‘MS 365 apps are not in sync with the cloud and desktop and how to fix the issue’ has no related answers from the predefined templates, the request can be escalated to the IT team.

They can apply critical thinking to GenAI interfaces and produce resources that can help them fetch answers relevant to unique user cases. By creating new knowledge for unique requests, IT teams can reduce the time to tackle issues and avoid service desk failure.

This unique capability of Generative AI also helps IT teams continuously learn new techniques for service desk cases and offer solutions without hiccups.

Automated workflows for service desk cases

Let’s tie this use case back to predictive models.

As GenAI predictive AI models learn from interactions, new tickets, historical data, or corrections made by agents, they can easily develop a deep knowledge of patterns in the behavior of requests or incidents.

A model can suggest an action plan whenever a related case pattern is noticed. This is greatly helpful in reducing the time to create and maintain static rules for workflows.

Saving time on creating workflows gives IT teams enough spare time to indulge in strategic problem resolutions that require critical thinking.

AI can help augment the productivity of IT teams, whereas IT teams can handle problems more efficiently.

Rapid response generation

One-to-one interaction requires critical thinking abilities to respond to user queries and help them solve problems effectively.

Can we instantiate critical thinking into machines? The fact is machines are sentient; they cannot feel it; hence, critical thinking cannot be instantiated in machines.

At a time when machines fail in critical thinking, human can extend their capability to provide unique suggestions and accelerate the pace of response delivery using AI suggestions.

IT teams can enjoy the flexibility of AI-generated automated responses.

Not only do they save time for typing texts, but they also allow for reviewing them and adding appropriate texts to make them look more meaningful and valuable for users.

If AI misses anything in its response, IT teams can add empathy to create personalized conversations and improve user experience.

Apart from generating agent responses, Generative AI helps IT teams communicate with users proficient in their native languages. By leveraging translation capability, service desk agents can easily translate languages and offer help in the same language they are comfortable with.

While this effectiveness of response generation speeds up the resolution and mitigation of impacts, it can quickly improve the shift-left strategy.

Again, the human-machine synergy calls for inclusion and not exclusion. In the absence of any pairs, service desks can lack the necessary capabilities and prevent service desk workflows from being effective in their natural course.

Quick summary for stakeholders

You can aim for a proactive service desk only when you communicate with your stakeholders after resolving service desk requests. This helps keep stakeholders in the loop and plan necessary action to prepare an effective strategy to build foolproof service desk help for users.

What is annoying is that crafting an incident or request summary requires excellent acumen to include all elements of a user request, including the action provided, challenges, time to mitigate, and user experience rate.

This is most often the job of senior IT teams with a flair for writing. Due to their time spent on critical problems, senior IT teams need more time to craft a summary and delay stakeholder insights.

However, anyone from the IT teams can take control of preparing a summary by prompting Generative AI to create a summarized version of the solution provided.

No matter how many cases your service desk handles, your team can use Generative AI to create as many summaries as they need.

Workativ turns Generative AI into a human companion and not a threat to the support system.

By leveraging its all-in-one support automation platform, users can take advantage of Generative AI properties through its conversational AI, Knowledge AI, shared live inbox, and app workflow automation.

These tools are designed to empower service desk agents in critical situations and efficiently solve them, offering a superior employee experience.

Knowledge AI helps you turn your use cases into efficient AI copilots for your agents so that they can provide accurate action plans on time and mitigate their impacts.

With conversational AI, agents can take advantage of NLP capabilities to parse any query and suggest effective help.

A shared live inbox consolidates all user requests in a single screen for agents, allowing them to address issues priority-wise with constant input from fellow mates and Generative AI knowledge.

Whether your employees are looking for HR support or IT support, Workativ Assistant can easily integrate with any app, allow you to create app workflow automation, and streamline tasks for common and complicated user queries.

You can reimagine your service desk with Generative AI workflows and innovative tools for agents. It helps your agents and allows them to deliver user support twice as fast as possible.

If you are eager to learn how Generative can transform your service desk and empower your agents, contact Workativ for a demo.