5 Ways HR Chatbots are Simplifying Hiring & Employee Engagement

3 minutes, 49 seconds read

Published on Jun 21, 2020

Updated on Jul 24, 2020

So far, there were three most talked about recruitment metrics — time-to-hire, cost-per-hire, and retention rate. Due to the Covid-19 outbreak, the HR industry is facing another challenge of managing and interacting with the remote workforce.

The impact of Covid-19 will be felt beyond 6 months. Organizations are, therefore, keen on revising their HR processes. Apart from hiring and retaining talents, productivity remains a crucial concern for most employers.

Over 70% of organizations are opting for virtual recruitment methods and technologies like Artificial Intelligence, Robotic Process Automation and Machine Learning are leading this change. HR Chatbots are a well-known implementation of AI technology in recruitment.

5 Important AI-powered HR Chatbots Use Cases

AI-powered HR bots can streamline and personalize recruitment and engagement processes across contract, full-time, and remote workforce.

1. Screening Candidates

Almost 50% of talent acquisition professionals consider screening candidates as their biggest challenge. Absence of standardized assessment process, lack of appropriate feedback metrics, overdependence on employment portals, and ignoring the pool of interested candidates are some of the factors that create bottlenecks in the recruitment process.

Finding the best fit for the organization is in itself a challenge. On top of that, the time lost in screening the ‘ideal candidate’ leads to losing the candidate altogether. Nearly 60% of recruiters say that they regularly lose candidates before even scheduling an interview.

AI can help in making the screening process more efficient. From collecting resumes to scanning candidates’ social & professional profiles, recent activities, and their interest in the industry/organization, AI can connect the dots and shortlist ‘best candidates’ from the talent pool. The journey begins with an HR bot that collects resumes and initiates basic conversations with the candidates.

HR operations chatbot – View Demo

2. Scheduling Interviews

The biggest challenge with scheduling interviews is finding a time that works for everyone.

According to a recent HR survey by Yello, it takes between 30 minutes and 2 hours to schedule a single interview. Nearly 33% of recruiters find scheduling interviews a barrier to improving time-to-hire.

The barriers to scheduling interviews involve time zones, prior appointments, location, and commute. AI-powered chatbots can piece it together for both — candidates and interviewers and propose an ideal time in seconds. Moreover, today’s HR bots can handle reimbursements, feedback, notifications, and post-interview sentiments of the candidates.

Appointment scheduling chatbot – View Demo

3. Applicants Tracking

Many organizations have been using Applicants Tracking Systems (ATS) — a software for handling recruitment and hiring needs. ATS provides a central location and database of resume boards (employment sites).

How ATS Applicants Tracking System Works — *(Image)*

HR chatbots with NLP capabilities can be integrated into ATS to facilitate intelligent guided semantic search capabilities.

4. Employee Engagement

Even after the orientation, employees (especially new joiners) face hurdles in keeping up with the organization’s procedures. Reaching out to HRs is the solution, but they’re also bound by time. In most of the situations, peer-support is a way through for activities like using time-sheets, leaves, holidays, reimbursements, etc.

Chatbots have always been great self-service portals. HR departments can leverage bots to answer FAQs on the company’s policies, employee training, benefits enrollment, self-assessment/reviews, votes, and company-wide polls.

HR bots with NLP capabilities can converse with employees, understand their sentiments, and offer resolutions. 89% of HR professionals believe that ongoing peer feedback and check-ins are key for successful outcomes. Especially in large enterprises, HR chatbots can engage with employees at scale. Moreover, chatbot conversations provide actual data for future analysis. This will also help the upper management with an unbiased understanding of the sentiments at the bottom of the pyramid.

5. Transparency across Teams

Recruiting data is often siloed and confined with the recruiters themselves. Leadership only has a high-level understanding of recruitment at ground levels. Often, this data is not available to other members of the HR department as well. Less than 25% of companies make recruiting data available to the entire HR team.

One of the reasons for lack of information transparency is the use of legacy systems like emails, spreadsheets, etc. for generating reports and sharing updates.

HR chatbots - how are recruitment metrics shared — *(Image)*

With AI-powered systems, controlled sharing of data, dynamic dashboards, real-time analytics, and task delegation with detailed information can be simplified. AI-chatbots, integrated within HRMs can make inter/intra departmental conversations and information requests simpler.

Final Thoughts

Today, recruiters prefer technology-based solutions to make their hiring process more efficient, increase productivity and candidate’s experiences. Tools like conversational chatbots are becoming increasingly popular because of the intuitive experiences they deliver. Chatbots can simplify HR operations to a greater extent and at the same time provide better employee engagement rates than humans.

Multilingual AI-powered HR Chatbot with Video – Hitee.chat

In 1997, the world watched in awe as IBM’s Deep Blue, a machine designed to play chess, defeated world champion Garry Kasparov. This moment wasn’t just a milestone for technology; it was a profound demonstration of data’s potential. Deep Blue analyzed millions of structured moves to anticipate outcomes. But imagine if it had access to unstructured data—Kasparov’s interviews, emotions, and instinctive reactions. Would the game have unfolded differently?

This historic clash mirrors today’s challenge in data architectures: leveraging structured, unstructured, and hybrid data systems to stay ahead. Let’s explore the nuances between Data Warehouses, Data Lakes, and Data Lakehouses—and uncover how they empower organizations to make game-changing decisions.

Deep Blue’s triumph was rooted in its ability to process structured data—moves on the chessboard, sequences of play, and pre-defined rules. Similarly, in the business world, structured data forms the backbone of decision-making. Customer transaction histories, financial ledgers, and inventory records are the “chess moves” of enterprises, neatly organized into rows and columns, ready for analysis. But as businesses grew, so did their need for a system that could not only store this structured data but also transform it into actionable insights efficiently. This need birthed the data warehouse.

Why was Data Warehouse the Best Move on the Board?

Data warehouses act as the strategic command centers for enterprises. By employing a schema-on-write approach, they ensure data is cleaned, validated, and formatted before storage. This guarantees high accuracy and consistency, making them indispensable for industries like finance and healthcare. For instance, global banks rely on data warehouses to calculate real-time risk assessments or detect fraud—a necessity when billions of transactions are processed daily, tools like Amazon Redshift, Snowflake Data Warehouse, and Azure Data Warehouse are vital. Similarly, hospitals use them to streamline patient care by integrating records, billing, and treatment plans into unified dashboards.

The impact is evident: according to a report by Global Market Insights, the global data warehouse market is projected to reach $30.4 billion by 2025, driven by the growing demand for business intelligence and real-time analytics. Yet, much like Deep Blue’s limitations in analyzing Kasparov’s emotional state, data warehouses face challenges when encountering data that doesn’t fit neatly into predefined schemas.

The question remains—what happens when businesses need to explore data outside these structured confines? The next evolution takes us to the flexible and expansive realm of data lakes, designed to embrace unstructured chaos.

The True Depth of Data Lakes

While structured data lays the foundation for traditional analytics, the modern business environment is far more complex, organizations today recognize the untapped potential in unstructured and semi-structured data. Social media conversations, customer reviews, IoT sensor feeds, audio recordings, and video content—these are the modern equivalents of Kasparov’s instinctive reactions and emotional expressions. They hold valuable insights but exist in forms that defy the rigid schemas of data warehouses.

Data lake is the system designed to embrace this chaos. Unlike warehouses, which demand structure upfront, data lakes operate on a schema-on-read approach, storing raw data in its native format until it’s needed for analysis. This flexibility makes data lakes ideal for capturing unstructured and semi-structured information. For example, Netflix uses data lakes to ingest billions of daily streaming logs, combining semi-structured metadata with unstructured viewing behaviors to deliver hyper-personalized recommendations. Similarly, Tesla stores vast amounts of raw sensor data from its autonomous vehicles in data lakes to train machine learning models.

However, this openness comes with challenges. Without proper governance, data lakes risk devolving into “data swamps,” where valuable insights are buried under poorly cataloged, duplicated, or irrelevant information. Forrester analysts estimate that 60%-73% of enterprise data goes unused for analytics, highlighting the governance gap in traditional lake implementations.

Is the Data Lakehouse the Best of Both Worlds?

This gap gave rise to the data lakehouse, a hybrid approach that marries the flexibility of data lakes with the structure and governance of warehouses. The lakehouse supports both structured and unstructured data, enabling real-time querying for business intelligence (BI) while also accommodating AI/ML workloads. Tools like Databricks Lakehouse and Snowflake Lakehouse integrate features like ACID transactions and unified metadata layers, ensuring data remains clean, compliant, and accessible.

Retailers, for instance, use lakehouses to analyze customer behavior in real time while simultaneously training AI models for predictive recommendations. Streaming services like Disney+ integrate structured subscriber data with unstructured viewing habits, enhancing personalization and engagement. In manufacturing, lakehouses process vast IoT sensor data alongside operational records, predicting maintenance needs and reducing downtime. According to a report by Databricks, organizations implementing lakehouse architectures have achieved up to 40% cost reductions and accelerated insights, proving their value as a future-ready data solution.

As businesses navigate this evolving data ecosystem, the choice between these architectures depends on their unique needs. Below is a comparison table highlighting the key attributes of data warehouses, data lakes, and data lakehouses:

Feature	Data Warehouse	Data Lake	Data Lakehouse
Data Type	Structured	Structured, Semi-Structured, Unstructured	Both
Schema Approach	Schema-on-Write	Schema-on-Read	Both
Query Performance	Optimized for BI	Slower; requires specialized tools	High performance for both BI and AI
Accessibility	Easy for analysts with SQL tools	Requires technical expertise	Accessible to both analysts and data scientists
Cost Efficiency	High	Low	Moderate
Scalability	Limited	High	High
Governance	Strong	Weak	Strong
Use Cases	BI, Compliance	AI/ML, Data Exploration	Real-Time Analytics, Unified Workloads
Best Fit For	Finance, Healthcare	Media, IoT, Research	Retail, E-commerce, Multi-Industry

Conclusion

The interplay between data warehouses, data lakes, and data lakehouses is a tale of adaptation and convergence. Just as IBM’s Deep Blue showcased the power of structured data but left questions about unstructured insights, businesses today must decide how to harness the vast potential of their data. From tools like Azure Data Lake, Amazon Redshift, and Snowflake Data Warehouse to advanced platforms like Databricks Lakehouse, the possibilities are limitless.

Ultimately, the path forward depends on an organization’s specific goals—whether optimizing BI, exploring AI/ML, or achieving unified analytics. The synergy of data engineering, data analytics, and database activity monitoring ensures that insights are not just generated but are actionable. To accelerate AI transformation journeys for evolving organizations, leveraging cutting-edge platforms like Snowflake combined with deep expertise is crucial.

At Mantra Labs, we specialize in crafting tailored data science and engineering solutions that empower businesses to achieve their analytics goals. Our experience with platforms like Snowflake and our deep domain expertise makes us the ideal partner for driving data-driven innovation and unlocking the next wave of growth for your enterprise.

5 Ways HR Chatbots are Simplifying Recruitment and Employee Engagement