A significant 64% of businesses, according to a Forbes Advisor survey, are confident that artificial intelligence can enhance their overall productivity. This sentiment reflects the expanding trust in AI's ability to revolutionize business processes.
AI tools have already become the ultimate problem-solvers in customer service. They provide maximum efficiency, set higher expectations, and increase customer satisfaction. However, simply offering prompt responses via Conversational AI chatbots no longer suffices in delivering a stellar customer experience. Recent HubSpot data indicates that 42% of customers today expect companies to address their issues and take their requests into account as fast.
In this article, we'll delve into how AI for tech support is used. We’ll check how AI-driven tools aid tech teams, trimming the time required for issue classification, resolution, and validation of different requests.
The Role of Technical Support in Product Development
First-line support typically addresses routine client inquiries that don't necessitate product modifications.
In contrast, technical support shoulders the critical tasks: resolving critical issues, cataloging feature requests, and even prioritizing issues based on user demand. Much of this unfolds behind the curtain, yet the magnitude of this work is commendable. For larger teams, technical support demands even greater coordination to ensure the product flourishes and remains responsive to external feedback and functions without hitches.
GitHub has established itself as a gold standard in version control systems, serving as a pivotal tool to enhance collaboration between development and tech support teams. Both established tech powerhouses and emerging startups favor the platform. Prominent contributors include industry leaders like Microsoft, Amazon (which boasts over 1,200 projects on GitHub), and Google.
Technical support is essential to support extensive projects on GitHub and ensure they work flawlessly.
The well-known 15-10-5 rule in Scrum methodology dictates that 15% of a team's capacity is typically allocated to technical debt and refactoring, 10% is set aside for addressing bugs, and 5% is dedicated to research. For extensive projects, if not managed efficiently, technical support could consume 25% of the team’s capacity.
Examining the direct relationship between technical support quality and customer satisfaction, it becomes evident that trimming the time allocated to technical support, addressing bugs, and processing feature requests could lead to diminished user satisfaction.
So, can an AI-powered tech support tool become the solution, helping teams cut down on time and expenses in tech support?
Current AI Solutions for Tech Support in the Market
Currently, many AI solutions assist in providing quality support. You can always find them listed on the web, so there’s no point in going through them all. Instead, we'll focus on highlighting the most feature-rich and promising options.
The AI-powered tools can be divided into 2 categories:
| Conversational solutions | NLP-driven solutions |
|---|---|
WatsonXAssistant by IBM Offers advanced conversational capabilities to understand and respond to user queries. | Uses natural language processing to understand and categorize support tickets, aiding in faster resolution. |
Uses machine learning to automatically respond to customer queries by suggesting relevant knowledge base articles. | Helps in analyzing and understanding user queries in support tickets. |
While these solutions have revolutionized interaction with users, they may not address all the time-consuming tasks support teams face the way you need to see real business impact.
The Challenges Faced by Technical Support Teams
Besides resolving issues and fixing bugs, teams often handle routine tasks that AI for tech support could potentially automate.
Sorting and managing similar or related issues
While tech support teams often encounter issues previously addressed by the development team, retracing the solutions within vast databases or documentation becomes a daunting task. Even if specific solutions have been referenced in past tickets, identifying related issues, confirming their resolution status, pinpointing the exact solution provided, and associating them with particular tasks in the backlog consume significant time. Although many tech support teams attempt to streamline this process by establishing internal databases, crafting comprehensive documentation, or adopting unique issue-labeling conventions, the intricate nature of expansive projects often disrupts synchronization efforts.
Issues classification
Though issue classification might seem an easy task, it occupies a considerable portion of a tech support team's bandwidth. The team must classify each issue, ascertain its urgency, and differentiate genuine issues from feature requests when users anticipate functionalities beyond the product's intended scope.
Syntax troubles
A common challenge for many support specialists lies in identifying syntax errors in user-provided code. There are instances where considerable time is invested trying to replicate an issue, only to find that the root cause is a syntax or versioning error.
Many teams using GitHub follow standardized workflows for issues and feature requests but often overlook the full automation potential AI provides for these processes.
How to Use AI for Tech Support
AI support assistants are crafted to enhance the efficiency of technical support specialists when interacting with users. By training on a response database, they deliver automatic and precise answers, elevating the user experience.
But that's only part of what AI brings to the table for tech support.
Some AI-powered tools can be pivotal in classifying issues and pinpointing similar requests and solutions. These tools can also effectively aid tech support professionals with routine, allowing them to focus on more critical and unique challenges.
How the AI Tech Support Assistant Tool Works
Like many other teams, Akvelon uses GitHub in our projects. We prioritize the quality of our support solutions and understand the importance of optimizing our tech support team's processes. Recognizing the potential of AI to address these challenges, we've used it for:
- Searching for duplicate issues within chosen repositories
- Creating a priority estimate and searching for errors in the code syntax of issues
- Generating answers to the most common issues
We utilized OpenAI's GPT-3.5 for data preparation and generating predictions in our LLM backend. This was complemented by traditional machine learning and statistical methods for analysis.
Let’s learn about how our tech support assistant handles these tasks.
Duplicate search
We've developed a duplicate search method using LLM encoders to transform parsed issue data into machine-readable embeddings. This method then merges vector similarity search with TF-IDF encodings to derive a unified analysis metric.
TF-IDF stands for Term Frequency-Inverse Document Frequency. It is a numerical statistic used in information retrieval and text mining to measure the importance of a word in a document relative to a collection of documents (corpus).
Term Frequency (TF) measures how frequently a term (word) appears in a document. The intuition behind TF is that the more often a word appears in a document, the more important it is to that document.
Inverse Document Frequency (IDF) measures how important a term is across a collection of documents. Terms that occur rarely but in many documents contribute more to the overall score.
TF-IDF Score for a term in a document is obtained by multiplying its TF and IDF scores. The resulting TF-IDF score reflects how important a word is to a document in a collection or corpus. High TF-IDF scores are attained by terms that appear often in a particular document but rarely in the entire corpus, indicating that those terms are unique to that document and are likely to be meaningful and relevant in the context of that document.
After identifying potential duplicate issues, we employ GPT to confirm the similarity scores fall within our designated parameters and determine that the tickets address the same issue or request.
Duplicate search step-by-step process
Now let’s see an AI-powered support tool in action. As the support team receives a new issue (in our case, it’s issue #108 in the “setup-python” repository), instead of looking for a similar solution manually, the tech specialist can specify it and use the tool to look for related or similar issues.
Based on the similarity score, the tech support specialist can identify the issues that match the set criteria. In our case, AI-powered tech support tool has highlighted issue #678 as the one with the highest similarity score.
Priority estimate and code snippet analysis
Our AI-driven tech support tool excels at determining issue priority and analyzing code snippets for potential syntax errors. We've developed a system that extracts code snippets from GitHub issues, primarily focusing on syntax and import errors. Typically, this code includes build logs, runner-image configuration in YAML, or node/python code. Since users occasionally submit configurations with errors, we deploy LLM to pinpoint and correct these inaccuracies.
The model readily detects blatant syntax errors, though there's a limited number of such errors in the target repositories.
It's also important to note that intricate errors, dependent on information not directly available in the issue text, are harder to identify. This encompasses details from user repositories using GitHub actions, local deployments, code installations on user systems, and other data outside the ticket's content.
Code snippet analysis example
AI-powered tech support assistant is capable of detecting issues in the code snippets provided within the issues, saving time that would otherwise be spent on troubleshooting an error.
First-line answer generation
One of the standout features of our AI-powered tech support assistant is its ability to respond to common GitHub issues. We've grounded our method on training GPT-3.5 using comments from resolved tickets.
Initially, we simply parsed the first response from closed tickets, thinking these answers would bolster the model's capability to forecast clear-cut issue responses. However, this approach didn't meet our accuracy standards, leading us to adopt manual answer labeling.
First-line answer generation example
After the support specialist defines the issue, the LLM model generates answer prediction.
AI-Powered Tech Support Assistant Implementation Results
Our team implemented AI for tech support internally to assess its effectiveness. Based on our findings, this tool conserves around 20 hours monthly for a team of 5-6 members, solely through its Duplicate Search feature. In general, using AI tool for technical support can save your team up to 40 hours monthly.
Such time savings not only boost team efficiency but also offer potential cost reductions in product support. Notably, the tool upholds quality while simultaneously enhancing user experience. This innovation holds promise for numerous businesses that use GitHub repositories and are committed to their support.
Addressing Common Concerns AI-Powered Support Usage Might Arise
As often happens with emerging technology implementation, certain concerns might prevent teams from adopting it — especially when considering the automation of user interactions.
Ease of use
Introducing new technology always comes with concerns about steep learning curves or potential ineffectiveness. Fortunately, with the AI-powered tech support assistant, these worries are alleviated. The tool integrates smoothly with Slack, ensuring that tech support specialists work within a familiar interface, making adoption straightforward and efficient.
Security and privacy risks
Security and privacy are paramount, especially when business processes with LLM-based tools handle data. At Akvelon, we prioritize these concerns by employing our security testing framework for LLMs, ensuring their safe use. We encourage everyone to review it for assurance.
AI-Powered Technical Support Tool: Is It Worth the Investment?
AI for tech support is not just a fleeting trend. AI-based tools can drive businesses towards more streamlined operations and heightened customer satisfaction. They aren't simply about automating tasks — using them, you can elevate the entire support ecosystem.
In an era where competition is fierce and customer experience is paramount, it's no longer a question of 'if' but 'when' businesses should integrate these AI innovations into their support workflows.
By leveraging AI for tech support, you can gain a distinctive competitive edge, ensuring not just efficient operations but also delivering an unparalleled support experience to your product users.
Dmitriy Drogovoz
Machine Learning Engineer