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

There was a time when people truly believed that humans only used 10% of their brains, so much so that it fueled Hollywood Movies and self-help personas promising untapped genius. The truth? Neuroscientists have long debunked this myth, proving that nearly all parts of our brain are active, even when we’re at rest. Now, imagine AI doing the same, providing information that is untrue, except unlike us, it doesn’t have a moment of self-doubt. That’s the bizarre and sometimes dangerous world of AI hallucinations.

AI hallucinations aren’t just funny errors; they’re a real and growing issue in AI-generated misinformation. So why do they happen, and how do we build reliable AI systems that don’t confidently mislead us? Let’s dive in.

Why Do AI Hallucinations Happen?

AI hallucinations happen when models generate errors due to incomplete, biased, or conflicting data. Other reasons include:

Human oversight: AI mirrors human biases and errors in training data, leading to AI’s false information
Lack of reasoning: Unlike humans, AI doesn’t “think” critically—it generates predictions based on patterns.

But beyond these, what if AI is too creative for its own good?

‘Creativity Gone Rogue’: When AI’s Imagination Runs Wild

AI doesn’t dream, but sometimes it gets ‘too creative’—spinning plausible-sounding stories that are basically AI-generated fake data with zero factual basis. Take the case of Meta’s Galactica, an AI model designed to generate scientific papers. It confidently fabricated entire studies with fake references, leading Meta to shut it down in three days.

This raises the question: Should AI be designed to be ‘less creative’ when AI trustworthiness matters?

The Overconfidence Problem

Ever heard the phrase, “Be confident, but not overconfident”? AI definitely hasn’t.

AI hallucinations happen because AI lacks self-doubt. When it doesn’t know something, it doesn’t hesitate—it just generates the most statistically probable answer. In one bizarre case, ChatGPT falsely accused a law professor of sexual harassment and even cited fake legal documents as proof.

Take the now-infamous case of Google’s Bard, which confidently claimed that the James Webb Space Telescope took the first-ever image of an exoplanet, a factually incorrect statement that went viral before Google had to step in and correct it.

There are more such multiple instances where AI hallucinations have led to Human hallucinations. Here are a few instances we faced.

When we tried the prompt of “Padmavaat according to the description of Malik Muhammad Jayasi-the writer ”

When we tried the prompt of “monkey to man evolution”

Now, if this is making you question your AI’s ability to get things right, then you should probably start looking have a checklist to check if your AI is reliable.

Before diving into solutions. Question your AI. If it can do these, maybe these will solve a bit of issues:

Can AI recognize its own mistakes?
What would “self-awareness” look like in AI without consciousness?
Are there techniques to make AI second-guess itself?
Can AI “consult an expert” before answering?

That might be just a checklist, but here are the strategies that make AI more reliable:

Strategies for Building Reliable AI

1. Neurosymbolic AI

It is a hybrid approach combining symbolic reasoning (logical rules) with deep learning to improve factual accuracy. IBM is pioneering this approach to build trustworthy AI systems that reason more like humans. For example, RAAPID’s solutions utilize this approach to transform clinical data into compliant, profitable risk adjustment, improving contextual understanding and reducing misdiagnoses.

2. Human-in-the-Loop Verification

Instead of random checks, AI can be trained to request human validation in critical areas. Companies like OpenAI and Google DeepMind are implementing real-time feedback loops where AI flags uncertain responses for review. A notable AI hallucination prevention use case is in medical AI, where human radiologists verify AI-detected anomalies in scans, improving diagnostic accuracy.

3. Truth Scoring Mechanism

IBM’s FactSheets AI assigns credibility scores to AI-generated content, ensuring more fact-based responses. This approach is already being used in financial risk assessment models, where AI outputs are ranked by reliability before human analysts review them.

4. AI ‘Memory’ for Context Awareness

Retrieval-Augmented Generation (RAG) allows AI to access verified sources before responding. This method is already being used by platforms like Bing AI, which cites sources instead of generating standalone answers. In legal tech, RAG-based models ensure AI-generated contracts reference actual legal precedents, reducing AI accuracy problems.

5. Red Teaming & Adversarial Testing

Companies like OpenAI and Google regularly use “red teaming”—pitting AI against expert testers who try to break its logic and expose weaknesses. This helps fine-tune AI models before public release. A practical AI reliability example is cybersecurity AI, where red teams simulate hacking attempts to uncover vulnerabilities before systems go live

The Future: AI That Knows When to Say, “I Don’t Know”

One of the most important steps toward reliable AI is training models to recognize uncertainty. Instead of making up answers, AI should be able to respond with “I’m unsure” or direct users to validated sources. Google DeepMind’s Socratic AI model is experimenting with ways to embed self-doubt into AI.

Conclusion:

AI hallucinations aren’t just quirky mistakes—they’re a major roadblock in creating trustworthy AI systems. By blending techniques like neurosymbolic AI, human-in-the-loop verification, and retrieval-augmented generation, we can push AI toward greater accuracy and reliability.

But here’s the big question: Should AI always strive to be 100% factual, or does some level of ‘creative hallucination’ have its place? After all, some of the best innovations come from thinking outside the box—even if that box is built from AI-generated data and machine learning algorithms.

At Mantra Labs, we specialize in data-driven AI solutions designed to minimize hallucinations and maximize trust. Whether you’re developing AI-powered products or enhancing decision-making with machine learning, our expertise ensures your models provide accurate information, making life easier for humans

5 Ways HR Chatbots are Simplifying Recruitment and Employee Engagement