Try : Insurtech, Application Development

AgriTech(1)

Augmented Reality(21)

Clean Tech(9)

Customer Journey(17)

Design(45)

Solar Industry(8)

User Experience(68)

Edtech(10)

Events(34)

HR Tech(3)

Interviews(10)

Life@mantra(11)

Logistics(5)

Manufacturing(3)

Strategy(18)

Testing(9)

Android(48)

Backend(32)

Dev Ops(11)

Enterprise Solution(33)

Technology Modernization(9)

Frontend(29)

iOS(43)

Javascript(15)

AI in Insurance(38)

Insurtech(66)

Product Innovation(58)

Solutions(22)

E-health(12)

HealthTech(24)

mHealth(5)

Telehealth Care(4)

Telemedicine(5)

Artificial Intelligence(152)

Bitcoin(8)

Blockchain(19)

Cognitive Computing(8)

Computer Vision(8)

Data Science(23)

FinTech(51)

Banking(7)

Intelligent Automation(27)

Machine Learning(48)

Natural Language Processing(14)

expand Menu Filters

Why should businesses consider chatbots?

3 minutes, 10 seconds read

Imagine you’ve recently started an online fresh vegetable business. You have a catalog for fruits and vegetables explaining price and availability. Although most of the information is clearly mentioned on the website, you get hundreds of emails and phone calls regarding deliveries, discounts, and availability of your services in a particular location. Now, you could appoint someone for customer support and reply to these queries or simply — can implement a chatbot on your app and website that instantly answers such routine questions.

[Related: Conversational Chatbots for SMEs to continue business from home]

Chatbots for business are the need of the hour. The reasons are obvious. It is efficient, reduces workload, and responds to customer requests immediately. Nearly 1 in 4 customers have interacted with a brand via chatbots in the past 12 months, according to a Salesforce study published in late 2018. 

As more and more customers are using e-commerce and digital medium for purchases, the incoming requests have also increased at the same rate. Companies need a larger workforce to handle customer support, failing which may lead to dangling customer satisfaction. Immediate query resolution also implies better customer experiences.

chatbots for business

Chatbots for business: benefits at large

1. Humanized conversations

NLP-powered chatbots have the power to initiate and handle conversations with humans based on a set of predefined rules and upgrade its dictionary based on learning. Chatbots are a game-changer in terms of overall customer satisfaction pushing the market to reach 1.34 billion by 2024. As per the reports, smart chat agents will manage 40% of mobile interactions by 2020.

2. Easy to implement

A myth surrounding chatbots was doing rounds that it is expensive and exclusive to only fortune 500 companies. But, this is no more the case as it is predicted that by 2020, 85% of the chat interactions will be automated and will not need human intervention. In recent months, several new players like the virtual banker and progressive native chat have introduced schemes that help companies to set up chatbots instantly with reasonable investments. Also, 10K+ developers are building chatbots with the Facebook messenger.

3. People prefer self-serve interactions

Today, millennials represent 27% (2 billion) of the global population. This tech-savvy generation prefers immediate resolution to their concerns and instead of talking to the support, they’re happy about settlements over chats.

Making a customer happy is what all businesses need, and chatbots serve this purpose adequately. They are capable of resolving customer queries in just a few seconds, eliminating wait times and queues. It is a win-win situation for both the consumer and the provider as the customer gets instant replies and the provider saves on operational costs. By the end of 2018 automated customer agents will be able to recognize their customers through voice and face recognition.

AI Chatbot in Insurance Report

AI in Insurance will value at $36B by 2026. Chatbots will occupy 40% of overall deployment, predominantly within customer service roles.
DOWNLOAD REPORT

4. Fact-based decision making

All the conversations accomplished through chatbots are recorded and this contributes to the database for training future NLP models for more humanized conversations. Also, the data collected can help identify business bottlenecks and customer preferences towards specific products or services. All these, sum up to providing fact-based analytics for effective decision making.

5. Continuous innovations

Chatbots are here to stay. The innovations around chatbots are still in progress and time is not far when one will witness intelligent bots capable of resolving complicated issues on its own. We’ve already seen voice and vernacular chatbots in the market. 

Big Techs are working on AI and machine learning to make smart chatbots that can offer much more than simple answers. If you haven’t thought about chatbots yet, then certainly you are missing on a significant business opportunity.


The significance of chatbots is already depicted in banking and marketing, and with time its influence will subsequently increase. Customers also expect chatbots and automated assistants from their business providers. They like to engage in live-chat as it helps them to get answers to their queries instantly. As of now, chatbots are only used for simple conversation. But, in the coming future, it will handle complex decision-making tasks. Any business that wants to evolve should consider chatbots and make it an integral part of their business.

We’re the makers of the world’s first insurance-specific chatbots. For further queries, please feel free to reach out to us at hello@mantralabsglobal.com.

Related Reads:

Cancel

Knowledge thats worth delivered in your inbox

Machines That Make Up Facts? Stopping AI Hallucinations with Reliable Systems

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

Cancel

Knowledge thats worth delivered in your inbox

Loading More Posts ...
Go Top
ml floating chatbot