An overview and a breakdown of a Generative AI use case for an HR Assistance Chatbot from the perspective of an AI Product Manager.
Intro
In talking with a few HR Managers across the country, in medium to large companies, I learned they spend a significant portion of their time answering repetitive employee questions about benefits, compensation, PTO, retirement plans, and company policies. They do this via email, slack channel messages, and recurring scheduled Q&A sessions.
Their current process is inefficient, which is standard across most enterprises in the US. They would also rather spend their time on more strategic work, especially with having to handle a lot more nowadays with lower budgets and shorter HR staff.
So why not combine the latest capabilities of Generative AI to create an AI-powered HR Chatbot assistant solution that can answer top employee questions based on their HR benefits and policies?
Most of the answers to typical employee questions are found in the lengthy HR policies. We all know that not many employees have the patience to read through all of them. I know I didn't. Most of the time, I just contacted the HR manager through Slack.
This Gen AI chatbot solution can provide an HR self-service using Retrieval-Augmented Generation (RAG) and Large Language Models (LLM). This is precisely the solution I created, and in this blog post, we will explore the following:
Looking at the business problem Fortune 500 companies face in HR automation from the lens of an AI Product Manager
How an AI-powered HR chatbot improves efficiency and reduces workload by 20-30%
The technical architecture and security considerations behind this solution
The ROI and implementation approach for enterprises looking to deploy this technology
The HR Challenge: Repetitive Inquiries & Wasted HR Resources
Business Problem
HR professionals in large enterprises often handle hundreds of employee inquiries per week. HR managers spend 20% of their time answering repetitive HR inquiries, reducing the time available for strategic HR work. On average, this represents 416 hours per year. With an average $90K yearly HR manager salary this represents ~$18K in salary costs per HR employee.
Employees, in turn, experience delays and inefficiencies in getting HR-related responses.
Most of these questions are found in main HR policies, such as:
Benefits Policy
Compensation Policy
Leave Policies
Retirement Plans Policy
Employee Relations Policy
The AI-Powered HR Chatbot Solution:
How It Works:
The HR manager uploads the policies into the HR Knowledgebase. The Retrieval-Augmented Generation (RAG) AI chatbot solutions enable smart search across the HR documents and provide a concise answer to each employee-specific question in a natural language via Slack or the HR web portal. It's like every employee always has an on-demand personal HR assistant.
Key Features:
AI-Powered Smart Search: Retrieves the most relevant HR policies based on employee questions
Summarized HR Responses: AI extracts key policy points, reducing long document searches.
Reference Document Source: The chatbot displays the source information at the end of each chatbot response.
Slack & Web Portal Integration: Employees access the chatbot via Slack or the HR web portal.
24/7 Availability: The HR chatbot operates round the clock, reducing HR staff workload.
Technical Architecture Overview
Architecture Diagram & Flow
Looking at the architecture diagram, let's walk through the process. Following the diagram will make more sense. I don't know about you, but I can't function without a visual.
1 - Behind the scenes, the HR manager uploads the documentation into the HR Knowledge Base
2 - The system automatically splits the documentation into chunks,
3 - Converts into embeddings
4 - Saves it in a vector database for faster data retrieval.
5 - Then, when the employee asks a question from one of the portals,
6 - That system searches against those data chunks,
7 - Combines all the retrieved information and
8 - Provides it to an LLM, which in turn crafts the concise message to
9 - Answer the question from the employee.
Below, we continue by calling out the main system components required, which is just a more technical way of looking at what we just explained.
System Components
These are the main components of a typical RAG LLM solution that we will use for our HR Chatbot use case.
HR Knowledge Base:
Data Processing Pipeline - Converts documents to embeddings
Vector Store Layer - Indexes and stores document embeddings
User Query Embedding:
Search & Retrieval – Uses semantic search for relevant document retrieval
Context Assembly Layer - Combines retrieved documents for LLM input
AI Generation Model:
AI Generation Layer - LLM for response creation, accuracy checks
Employee Access Layer:
Employee Access Layer – Slack Integration & HR Web Portal.
Privacy & Security
In terms of Privacy & Security, at a minimum, we will need two things:
For Privacy, Internally Hosted LLM: Uses an open-source, internally hosted LLM to ensure privacy and keep all data in-house.
For security, role-based access control ensures that only authorized employees can access the HR chatbot. In most enterprises, we could use the existing Enterprise single sign-on (SSO), which is most likely in use.
LLM Configuration
Before selecting an LLM, a comparison and analysis needs to be done based on each specific use case and requirements. For the purpose of this example, we used an open source LLM like Llama 3 and host it in the internal company cloud, so that our data stays private and is not shared outside the company.
The next LLM configuration to consider is the temperature property in the LLM, which controls how creative the chatbot answers our questions. The value starts at 0 and goes up to 1. Since our questions are all based on our HR documentation, we want to limit creativity in this specific scenario and optimize for a more factual and accurate response. Which is why we got it at a 0.2 value. This should keep variability at a minimum.
Top-K sampling limits the model from choosing the top 50 words most likely to be used for its response. The purpose is to remain relevant while still having some flexibility.
These settings balance accuracy and adaptability while ensuring employees receive clear and concise answers.
ROI & Metrics
Quantifiable Business Benefits
To monitor if our solution is successful, we will need to establish key metrics from the beginning that later can tell us if we are meeting all stakeholder's needs. For a start, we will capture:
Operational Key Metrics
Operational Key Metrics that we will want to capture are:
Runs: Total number of runs over the selected period.
Latency (sec): Average response time per week. Helps identify performance bottlenecks.
Failures: Number of failed runs per week. Helpful in diagnosing reliability issues.
Tokens: Total number of tokens processed. Indicates the volume of data handled.
AI Credits: Amount of AI credits consumed. Tracks cost associated with usage.
These metrics will allow us to capture more operational insights into our solution, see where we have bottlenecks, and capture the cost of our LLM. This data will allow us to iterate and improve our HR Chatbot solution.
ROI and implementation approach (MVP version)
We can consider this version of our HR Assistant Chatbot our MVP. It tackles a specific and confirmed HR problem while still providing value for both the enterprise employees and the HR manager by saving them time. The 20% time saved per HR employee can be directly tied to a financial benefit of around $18,000 annually. This is a significant ROI for the business and gives us a strong case for getting executive approval to implement our solution. This will allow us to get the product out quickly and get feedback to iterate and make improvements.
Future Expansions:
As a second phase of our solution, we could integrate our HR Assistant Chatbot with the Workday platform and give it access to live data on employee benefit information. The purpose is to provide real-time employee data retrieval.
For example, each employee will have different vacation accrued data, benefits information, or time & attendance data. This way, the chatbot can answer more specific personalized questions for each employee.
Version 2 - Workday Integration & Personalized Responses
New Capabilities: Answers employee-specific HR questions based on the Workday employee profile.
Benefits Information: Enrolled benefits & details per employee.
Vacation Accrued Data: Personalized vacation balances & eligibility.
Time & Attendance: Work schedules, time-off requests, and leave balances.
Suppose you or your teams are considering AI solutions and implementations within your organization and are uncertain about managing the delivery of these projects. In that case, you need someone to bridge the gap between the business stakeholders and your technical engineering team or vendor. As a delivery expert, I can assist your team in understanding and managing the technical aspects, all while also focusing on the business value that needs to be delivered. I'm here to help.
Contact me today to discuss collaborating on your next significant initiative.
Let's connect at info@intotheagileshop.com
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