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Advances in natural language processes through the manifestation of Generative AI or its underlying technologies, deep-neural network-based large language models have massive economic potential on a global scale.
Riding the new wave of Generative AI innovation, businesses and society can leverage massive productivity and economic gains by automating routine tasks, streamlining workflows, and creating new business applications.
According to Goldman Sachs Research, this incredible Generative AI potential could help businesses drive a 7% increase in global GDP or create 7 trillion worth of economic value over a 10-year period. This similar report also suggests productivity growth by 1.5 points.
Perhaps this is relieving news for CFOs in every industry looking to reduce the balance sheet burden during the toughest economic times when Eurozone and the US have already slipped into recession.
The higher inflation costs have them rethinking their IT allocation budget strategies 一 putting automation on top of their priority list to optimize operational efficiency and save costs. Generative AI promises to recession-proof your business and drive toward cost efficiency.
In fact, the seamless human-machine interactivity with accessibility to LLM capability also provides significantbusiness benefits for ITSM operations.
Alike many CFOs or CXO, if you also aim to ramp up cost efficiency and improve ITSM operational efficiency, here’s our article. Keep reading to reveal how Generative AI helps you save service desk management costs.
Password resets lead to a growing employee challenge, coupled with other mundane IT support tasks. According to Forrester, 66% of employees prefer solving their password reset problems using a self-service mechanism.
Unfortunately, over a third of an organization’s service desk cannot resolve this problem effectively.
Even with self-service support, most organizations use chatbots with less advanced AI technology and limited capacity. When queries go beyond pre-defined FAQs or conversation templates, self-service isn’t able to draw users’ intent and provide relevant information for their queries.
What happens, then? The service request moves through several tiers of support. With that increases the cumulative price of a ticket, which also combines salaries of agents, salaries of third-party service, office supplies, rental of facilities, and a wide range of IT tools.
For example, if a ticket costs up to $10 at self-service or 0-tier level, it can cost up to $47 when it reaches Level 1 [the cost of 0-tier ($10) and the cost of tier-1 ($37)]. The higher the tier-level support, the higher the ticket cost due to an investment of time, effort, expertise, and IT resources.
By improving the intelligence of self-service, Generative AI efficiently delivers user experience by resolving many common service requests and fostering a shift-left strategy.
With Generative AI being attached to LLM or the entire internet, it can fetch enormous datasets or massively diverse knowledge sets to provide a unified self-service interface.
The unique capabilities of Generative AI on the service management side help organizations -
As these unique capabilities offer a fast and accurate response, Generative AI prevents reliance on the next-level- tier support and nurtures a shift-left strategy to effectively manage downstream activities so that service requests get resolved at the lower-cost delivery channels.
As a result, Generative AI aims to resolve issues at the zero-level tier and reduces the total cost of ownership, with an increase in agent productivity that can be dedicated to high-level tasks.
A study also supports that Generative AI-based conversational assistant improves user productivity to perform 14% more user requests in an hour.
Some more cost savings for you!
When building a chatbot, it is obvious to continuously train the language model to conform to the present business scenario or use cases so as to allow users to find information to perform a task or solve a problem.
With the ability to self-learn from inputs of user interaction, the Gen AI chatbot can keep ingesting data and make changes to its behavior.
On the other hand, in an enterprise setting, a Generative AI chatbot needs to have ample data for training to simplify domain-specific use cases. In scenarios where data is scarce, a large language model can be used to generate synthetic data and train the models to support self-service functionality.
By 2025, Generative AI techniques would augment creative work by 30% — and one of the many industry use cases would be synthetic data generation, claims Brian Burke, Research VP for Technology Innovation at Gartner.
The benefit of using synthetic data is it saves costs on subject matter experts for data generation or buying data services from a third-party service provider to a great extent.
The added bonus is when data is limited, a large language model helps generate NLP training data and pushes for fast time to market for chatbot implementation.
As a whole, the need for fine-tuning will go down, improving cost efficiency.
Optimizing large language models provides maximum logical reasoning capabilities that improve self-service for enterprise use cases. But each prompt also brings high costs for token consumption during prompt inputs and query generation. For example, OpenAI’s DaVinci model uses 1,000 tokens per query, which costs about $0.12.
If the number of prompt tokens can be reduced, the cost of using large language models for each conversation will also decrease. You can use prompt engineering to modify the prompt while requesting a query and take control of the token budget.
For example, you want to know about ‘how to apply for sick leaves’? You can specify the number of tokens or paragraphs to retrieve your answer for this specific HR request.
Ask a Generative AI interface to surface the information within 100 tokens or 3 sentences, it will certainly do it while maintaining the truthfulness of the information.
Stanford researchers also claimed that reducing the number of prompts helps reduce the computational costs of using large language models while improving the output quality. They further claimed even with the reduction of 100 tokens, one could expect huge savings on prompt tokens when used multiple times.
Backed by large language models, a Generative AI self-service is more efficient and effective in resolving end-user queries.
Interestingly, the human-machine interaction feels more human and useful in cutting down additional steps to look for human assistance.
With the flexibility of generating knowledge articles relevant to the thousands of IT or HR support use cases available online or particular enterprise domain-specific, the Generative AI can power conversational AI and surface any kind of information to help your people solve common and repetitive tasks immediately.
For example, if a user has IT issues with his desktop, which shuts down frequently when running an application like ERP, a more specific user guide or knowledge article can instantly help the user fix the issue and get back to work.
This, in return, reduces the reliance on third-party support resources, regardless of the fact that your organization will grow and scale because Generative AI utilizes the power of self-learning and improves the ability to detect NLP queries and their intent.
In the ITSM spectrum, complying with user experience by delivering the right IT support is significant. The instances of change management must be properly communicated to the internal employees so they can be well informed of the situation and prepared to take their work ahead.
Using extraordinary capabilities to automate incident detection through root cause analysis by synthesizing its large language model, Generative AI can predict upcoming incidents to the IT assets, thereby alerting the right person to take charge and reduce the impact of the incidents on the user productivity and experience.
Similarly, there are fewer wait times for most common workplace support issues when your conversational AI self-service uses Generative AI properties.
For example, an onboarding process takes up much HR time and effort. Although it is the same old repetitive process, HR professionals must invest a lot of time in assisting a new hire with documentation processes, accessing company policies, or being invited for an introduction session with a company stakeholder.
Users can surface specific information and get going by using simple prompts in the conversational AI interface.
The instantaneous and accurate information delivery capability that improves auto-resolution effectively helps boost user satisfaction levels and make them feel valued.
The possible business outcome of Generative AI user experience includes reduced DSAT, increased CSAT, and long-term employee engagement. As a result, businesses are less likely to restart their hiring process and scale with their top talent without the fear of attrition.
Workativ is a dedicated player in the conversational AI segment whose objective is to untangle the complexities of employee support in the modern workplace.
Using its conversational AI platform that leverages the large language model properties, Workativ improves 80% of repetitive tasks with app workflow automation.
Above the cut, the Workativ Hybrid NLU, which improves user response by surfacing the most relevant response for user queries, Workativ ensures users get maximum benefits on the Hybrid chat interface no matter how complicated the NLP query turns out, considering domain-specific knowledge around enterprise needs.
With Workativ Hybrid NLU, it is easier for your employees to boost self-service for common tasks such as
All mundane tasks can be seamlessly automated using Workativ conversational AI chatbot for MS Team, Slack, or Web Widget at ease.
On top of it, the conversation on Workativ Virtual Agent feels more like a human, improving user experience and reducing reliance on human agents. As a result, it gradually reduces the human effort from L1 to L4, eliminating the high ticket handling costs and helping your organization conform to the shift-left strategy and reduce TCO.
For example, when using automation in an organization with an employee headcount of about 3000, the organization tends to reduce costs of service from $45,000 monthly to $21,000 per month. Although the cost does not include IT tools, capex, HR costs, and ITSM licensing fees, automation has a staggering amount of benefits for self-service functionality.
When using Generative AI, your ability to maximize self-service increases for employee support, which gives cost savings through avoided internal HR services, real-time resolutions of common IT issues, and, finally, cost savings for an increase in employee productivity.
The bottom line is Generative AI, combined with conversational AI, enables CFOs to optimize working capital efficiency and drive financial gains for ITSM to drive business success further.
In addition to cost savings, McKinsey estimates that Generative AI can automate 60 to 70 percent of the work time of a worker to complete a job due to its ability to understand natural language.
Furthermore, Generative AI can augment labor productivity from 0.1 to 0.6 percent through 2040. The implication of Generation AI use cases would lie in maximizing them to their fullest potential and driving business results.
Want to drive cost optimization for your ITSM? Schedule an appointment with Workativ Sales experts to learn more.