IT support can be challenging. The key to building an effective support system is making information available and helping users find answers to their queries.
Over the years, support teams have had growing challenges, such as reduced headcounts and increased volumes of queries.
51% of respondents in a study pointed to skill gaps, whereas 53% blamed COVID-19 for the increase in support queries.
With Generative AI unlocking amazing NLP-related capabilities to deliver satisfactory responses, support systems can gain better benefits for driving user experience.
IT or customer support can also use Generative AI to overcome the growing challenge of support ecosystems. They can use advanced automation capabilities that significantly help increase users' efficiency for problem resolution.
However, how do you consider using Generative AI to maximize the productivity and efficiency of a large language model?
As you look to integrate built or bought solutions into your workflows, they can unlock functions to the best of their capabilities and help you tackle IT support issues. However, there are some limitations to build vs. buy for IT support use cases.
Learn to explore what can best work for your needs.
You can consume Generative AI for your IT support use cases through API integrations with off-the-shelf systems like ChatGPT or GPT 3.5.
The other straightforward way to apply Generative AI for IT support is through direct access to the platform.
With an ‘as is’ platform, you have limited choices regarding how to leverage LLM knowledge to help users. Yet, the information provided by the Generative AI interface can be helpful.
API-led integration within your IT support systems can give you a slightly better edge over the as-is model.
You can control a portion of the LLM model, recalibrate it with data, create a few workflows, and overcome the limitations of information discovery.
Your employee support may need the information to answer common questions across industry use cases.
However, Generative AI applications like Gemini, ChatGPT, or Claude make finding information easy with consolidated versions. Even if issues are related to common industry cases, users can fetch the right information, apply suggestions to their IT issues, and solve problems.
More often than not, one-size-fits-all can work for processes only with shared objectives and not for diverse and expanded needs.
Fine-tuning the model is an effective way to customize your support needs to some extent.
It can be helpful in a few cases. Shaping an existing model requires gathering data for workflows you want to implement, retraining some parts of the model, and facilitating workflow automation.
Finetuning can yield relevant results with limited automation capabilities. So, off-the-shelf tools can be limiting when it comes to offering competitive differentiation.
The build model is definitely for someone who can gather massive data resources and put huge investments into building a customized application or interface on top of an LLM architecture.
However, built-LLM solutions are best for competitive differentiation for your objectives.
A custom solution you build from scratch can help you train the model with business-specific data for unique use cases. So, your employees can turn to your interface, solve their problems with accurate business-specific information, and love the work they do.
AI copilot can resemble the full potential of a built model but with a more effortless approach. We can assume IT supports AI copilot as an integration tool for your communication channels like MS Teams or Slack yet unleashes custom workflows to manage various business processes across ITSM.
AI copilot or Generative AI assistant can be trained with your business-specific data and leverage automated workflows to eliminate repetitive processes and reduce MTTR.
AI copilot is a seamless Generative AI IT support assistant to guide your users with every piece of workplace query that boosts productivity and efficiency.
The best part of the no-code AI copilot is MS Teamsor Slack integrations.
Using the AI copilot for MS Teams makes it easy to manage end-to-end ITSM tasks.
Workativ helps businesses build customized workflows with the power of Generative AI and create AI copilots for IT support tasks. With its no-code platform, users can leverage a large language model to train it with company-specific data and implement custom workflows through MS Teams or Slack integrations.
Workativ provides one integrated employee support platform that allows you to leverage conversational AI, Knowledge AI, shared live inbox, and app workflow automation.
Put no extra coding effort, yet get started with Workativ in a few days as soon as you get your training articles.
Build vs. buy isn’t an easy projection. Generative AI delivers numerous benefits companies want to leverage to gain a competitive advantage.
The belief is that if companies consider GenAI an unnecessary enterprise need, they will experience customer churn from customers who prefer GenAI-infested value.
Here’s how you can measure the value of each model option—a fine-tuned model, custom model, and no-code AI copilot.
Pay heed to faster data processing for accurate transcriptions, summaries, content generation, response generations, etc. These capabilities can increase the productivity of your support teams and users.
How a Generative AI solution meets user needs is key to determining a model's personalization capability and developing the likelihood of ownership.
Generative AI model efficiency depends on how effectively they can answer NLP queries.
So far, we can assume that built models are the most effective for satisfying users' custom needs. Fine-tuned models meet some of these needs, whereas no-code SaaS-based platforms or AI copilots can fully fulfill them like custom solutions.
To determine the ROI of these models, let’s consider some key points.
If you could assume, custom models need major investments from all corners. This suits only large enterprises like Meta, Google, Cohere, or OpenAI.
Fine-tuning can be feasible for most organizations that want to start soon and create Generative AI experiences for their users. However, there are also glitches.
As is the essence of no-code SaaS Generative AI platforms, they are flexible and convenient for users and organizations.
After considering these factors in determining ROI for Build vs. Buy, you must decide which solution provides better value for your money.
Generative AI readiness is critical to driving growth for your business. If you act dormant and postpone AI initiatives, you ultimately deprive your team of their productivity and efficiency.
The best thing is to pay attention to the market demand and gain that competitive differentiation.
Depending on the ROI of Build vs. Buy, you can consider what will best suit your current and evolving needs.
The effectiveness of finetuned models lies in what specific use case you have, whereas custom solutions can satiate the diverse needs of your business with continuous investments from your side.
Given the no-code platform provides more than what finetuned and custom models can do for you, they are convenient, cost-effective, and user-friendly for everyone.
However, not to mention, security compliance is the biggest priority—no matter which model you choose. No-code LLM providers take major responsibility for privacy and data security while you unleash efforts to facilitate knowledge article veracity.
Whatever your preferred model is, ensure they can effectively meet your needs and help derive value.
If you want to get started with a no-code LLM platform to improve your employee support experience with Generative AI properties, Workativ can help you.
Deepa Majumder is a writer who nails the art of crafting bespoke thought leadership articles to help business leaders tap into rich insights in their journey of organization-wide digital transformation. Over the years, she has dedicatedly engaged herself in the process of continuous learning and development across business continuity management and organizational resilience.
Her pieces intricately highlight the best ways to transform employee and customer experience. When not writing, she spends time on leisure activities.