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?

Do you want to build or buy?

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.

Perform everyday tasks with an ‘as-is’ model.

Your employee support may need the information to answer common questions across industry use cases.

• Ask questions about IT issues. It is usual to face workplace problems with common IT issues such as desktop sound not working, laptops having blurring screens, headphones’ mic not working correctly, etc. The common trait of browser search is to surface top answers from various resources, leaving users to navigate through these articles until they find satisfactory answers.

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.

• Create your knowledge base resources. ITSM platforms always need to provide up-to-date information to their users for self-service capabilities. Creating knowledge articles for IT or other employee support is challenging, requiring authors to have expertise. With the as-is model, IT support managers can reduce the workload by delegating the tasks to someone who can use prompts, generate support resources, and pass them to managers for review.

• Generate texts to provide answers. When an IT ticket escalates to the service desk agents, they communicate with users by exchanging messages through chat. Crafting responses to issues and providing a solution requires a lot of effort. The generative AI as-is model can reduce the time spent crafting messages using prompts and generate responses faster.

Get a better edge with API-led GPT 3.5 integrations.

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.

• Handle password resets. Most businesses experience productivity disruption due to password resets. Okta cited in its blog that 30% to 50% of IT help desk calls involve password resets despite the fact that users can fix some of their password problems using self-service. Generative AI-powered interfaces or self-service can help design automated workflows with extended automation levels to facilitate password reset and overcome the existing challenge.

• Apply for leaves. Fine-tuned or recalibrated Generative AI models can easily provide fundamental leave management. Leave management falls under HR's responsibilities, making it hard for users to find information at their comfort. Generative AI workflows can help you manage leave applications, let your users know the leave balances, and make requests for expense reimbursement.

• Onboard employees automatically. You can implement workflows to automate employee onboarding by fine-tuning the LLM models. Generative AI can automate a few onboarding processes—app provisions, software installations, documentation management, etc.

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.

• Create workflows for ESM. The generative AI era turns your ITSM into Enterprise Service Management or ESM by decentralizing information to help diverse areas of business functions through automation. Besides IT support, a built model can easily facilitate industry-wide workflows to manage HR, Finance, IT, Legal, and Operations tasks.

• Maintain end-to-end communications and coordination for cross-functional tasks. Regardless of your business functions, built models can help you leverage automated workflows for the intricate management of processes. For example, you can automate document verification, supplier management, customer onboarding, payment enablement, etc, for finance processes.

• Manage the unique needs of your business cases. Let’s say your IT operations must enable uptime for IT assets so your employees can work without disruption. However, downtime is unexpected and can be detrimental to your productivity. Using predictive analytics workflows, you can build visibility into systems and notify users ahead of time about upcoming threats through automated notification. This helps IT teams take appropriate actions and prevent the impact of a specific tool.

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.

• Ask questions about company-wide IT assets. An AI copilot can sit inside your MS Teams and allow users to raise questions whenever they need to solve a problem. For example, a user may need a particular tool, such as Figma licenses, for UX/UI activities. Your copilot can fetch information specific to this app in real-time. Once confirmed, user provision can take place.

• Provide specific answers and resolutions with synthesized information. AI copilot can understand queries with fewer words. Designed with company-specific data, systems, use cases, and customer interaction history, you can build your AI copilot to understand contexts, clarify ambiguities, and provide relevant answers.

• Get across intricate information on 401 (K) and tax. These topics can be complicated as they involve diverse information depending on modules, regions, pay scale, etc. With all the related training data your AI copilot is trained with, your users can easily solve their queries and remain complacent.

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.

• Ticket creation: Users can create tickets within Teams.

• Self-service: MS Teams or Slack serves as a self-service to resolve common IT issues autonomously.

• Ticket status: Users or agents can gain end-to-end visibility into ticket status, such as open tickets, tickets in queue, closed tickets, etc.

• Agent efficiency: MS Teams or Slack can provide a single screen for agents to manage support tasks. Workativ provides a shared live inbox within its GenAI-powered AI copilot for MS Teams or Slack to boost agent efficiency.

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.

Productivity

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.

• Fine-tuned LLM models contain limited company-specific data for a few numbers of use cases. The combination of the world of knowledge with the threshold of a particular timeframe and limited data bandwidth makes finding information faster for a handful of workflows only. So, you can achieve desired objectives for one or two use cases but not for the entire business function.

• Built models give you amazing flexibility to create customized workflows for all your business functions if you can gather massive datasets or large corpora of resources. Precision is always higher with a built solution, making information search faster for any repetitive task and streamlining operations across all business functions.

• No-code AI copilots exhibit similar qualities to ‘build’ models. Gathering datasets for as many business cases as you need allows your users to find information within collaboration channels, making it easy to speed up problem resolutions and reduce MTTR.

Personalization

How a Generative AI solution meets user needs is key to determining a model's personalization capability and developing the likelihood of ownership.

• Fine-tuned Generative AI solutions can improve user experience by correctly responding to some queries. However, buy models can try to give answers from its common databases for diversified queries, which can be improper. This can impact CSAT and NPS.

• Built or custom models contain massive volumes of data. They efficiently retrieve answers using RAGs from company-wide databases and answer every question. The system is also trained to assist agents with ticket triage, summary creation, response delivery, etc., to improve CSAT and NPS and help boost customer retention.

• A no-code or AI copilot can examine company-specific data to provide users with answers. Making it seamless for users to fetch answers from collaboration channels effortlessly boosts users' CSAT and NPS and helps them adopt new technology quickly.

Efficiency

Generative AI model efficiency depends on how effectively they can answer NLP queries.

• Fine-tuned or recalibrated models allow retraining some parts of the architecture, meaning models can still use their underlying database to retrieve answers. For example, if a user asks a question with no LLM match, the model can return repeated answers or hallucinate.

• Custom models accommodate datasets of billions of parameters, which helps them become efficient in answering custom questions. Custom models undergo training sessions under ML engineers or AI scientists, who ensure data validation. This capability makes it highly capable of delivering accurate responses and avoiding hallucinations. However, a lack of human oversight can aid hallucinations.

• No-code platforms or AI copilots can be fully trained with knowledge articles for user queries. The efficiency level increases in a similar manner to custom models. These models can improve information delivery and also avoid hallucinations.

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.

Built models

If you could assume, custom models need major investments from all corners. This suits only large enterprises like Meta, Google, Cohere, or OpenAI.

Major investments required for the following activities:

• Cloud platforms: Model training needs cloud platforms like GCP, Azure, or AWS. Deploying models on these architectures requires a significant investment.

• Domain expertise: It is critical to possess domain expertise if you want to invest significantly in custom model development. To be honest, organizations with the expertise to build and deploy their AI models can efficiently achieve success, as they can gain better control over each step. However, there are many instances in which AI projects fail due to a lack of AI expertise.

• Data storage: Custom model development involves storing data for cleaning, deduplicating, preprocessing, and tokenizing. This is another investment blow for fully trained models.

• Developer costs: There are many steps to customize your Generative AI model, which need developer expertise. Also, a model can scale only when it follows the changes in the technology domain. There is a continuous and recurring need to implement those changes. Mind that ML engineers or AI developers are not an easy investment.

• Time to market: Developing a model from scratch may involve indefinite time to roll out the final product. Multiple iterations, MVP, prototyping, development, testing and validation, deployment, and maintenance exist. By the time you come up with your custom product, others can control the market.

Fine-tuned models

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.

• Developer costs: If you have no AI expertise in fine-tuning, achieving the desired results from fine-tuned models is hard. It involves a few critical steps to preparing the data environment, which urges data engineering skills, ML capabilities, and prompt engineering.

• Long-grained AI expertise: Fine-tuning generative AI models can appear overwhelming within a year if you lack deep expertise in ML or haven’t worked with AI applications. This is the opinion of Eric Lamarre, the senior partner leading McKinsey Digital in North America.

• Data preparation costs: Although you want to finetune AI models for a specific use case, you must prepare a data pipeline with a modest investment in data storage systems.

• Inquiry/response-generating costs: Even if you retrain some part of a GPT 3 model, it involves costs to enable inquiry and response generation. Based on input and output, you need to pay token costs. This is an ongoing burden for every user.

• Turnaround time: Finetuned models generally take less time to train and deploy. Still, some critical processes exist, such as user experience and user acceptance testing requirements. You can launch your model with specific use cases in weeks if not months.

No-code SaaS platforms

As is the essence of no-code SaaS Generative AI platforms, they are flexible and convenient for users and organizations.

• Developer costs: A no-code platform requires zero coding expertise. It is convenient to train with knowledge articles and gain better control over data to prevent hallucinations.

• Cloud platforms: You work with a SaaS platform, eliminating the need to invest in cloud platforms and taking care of the hassles of ongoing maintenance and developer costs. Cloud-based LLM providers take care of this part.

• Data preparation costs: No-code platforms that make it easy to upload articles to the platform directly from your intranet, business systems, CMS, and websites help you save on data storage license fees.

• Turnaround time: All it takes is gather knowledge articles to feed the LLM platform and create workflows for the use cases. With a few people, you can deploy your solutions post-user testing.

• AI expertise: The best thing about no-code and SaaS-based LLM platforms is that you can effortlessly work with a plug-and-play interface only if you understand English. The language understanding helps you design flows, implement conditions, connect systems, and deploy your solution.

After considering these factors in determining ROI for Build vs. Buy, you must decide which solution provides better value for your money.

• Determine if building LLM solutions for specific use cases and small teams is okay. A built model can be a massive burden on your bottom line if you only need automated solutions for one or two use cases. Extending pre-trained models or co-pilots can be effective.

• Built models are fine if you need multiple large language models for numerous use cases. However, not every business can afford to build a custom solution because it requires money, data expertise, and time.

• With the need to complement enterprise needs for numerous business functions and even SMB needs, a no-code LLM platform can prove efficient. You can get more than what fine-tuned models provide for your use cases, yet save money and time as you can leverage the potential similar to custom LLM models.

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.

Schedule a demo today.