IBM reports that globally businesses spend over $1.3 trillion/year to handle roughly 265 billion customer calls. Chatbots spring up to minimize the expenditure on handling customer queries, especially the most redundant ones.

It’s quite common for businesses to assess the return on investment before adopting new technology.

However, ROI from chatbots may vary according to the purpose it serves. For example, an insurance chatbot ROI differs from that of an HR chatbot. Here are certain parameters to consider for calculating the return on investment from chatbots.

#1 Average Human Live-chat Cost

The total number of tickets raised per month and the number of agents involved gives an idea of the average price per contact.

According to Help Desk Institute, the average cost/minute for a live chat is $1.05, while the average cost per chat session is $16.80. Assuming an organization handles 10,000 chats in a month, the cost incurred sums up to $168,000/month.

Depending on the number of people involved and their compensation, you can calculate the amount you’re spending on your organization’s customer support. Here’s a salary reference, which can be used in further calculations.

sample customer support operational cost

The salaries mentioned are referred from Job Futuromat 2019 wrt 12 months, 18 working days, 8 hours.

The actual operational cost also depends on material resources invested like office space, conveyance, communications, gadgets, etc. You can consider these aspects on your chatbot ROI calculator.

#2 Bot Installation Cost

The phases of bot installation cost involves brainstorming sessions, integration, and training both bots and agents.

During kick-off sessions, stakeholders discuss the scope of the bot, define goals and responsibilities, and make a project plan. After this, programmers and managers integrate the bot on the organization’s website and other platforms. Customizing the bot according to the client’s support cases covers the bot training phase. Testing the bot and training agents to use it are also factored into the ‘bot’ installation costs.

According to Ometrics, the average development charge for a chatbot may range from $1,000 to $5,000. But, this is a one-time charge, and after that the bot-developer may bill for maintenance charges.

chatbot roi calculator: installation cost

If the chatbot requires a higher level of customization, then the bot-developer may also claim additional charges. Also, the number of days spent for bot installation varies according to industries and organizations.

#3 Gains through Bots

Here we’re assuming all the customer queries are routed through the bot and it is accurate 50% of the time. Out of the 50% queries handled by a bot, if half of them are self-served and the remaining required human intervention, then monthly gains from the bot can be-

chatbot roi calculator: gains from chatbot

You can find the exact cases and accuracy from your bot’s analytics dashboard.

#4 Monthly Maintenance Cost

Like humans, bots also require human assistance for its successful operation. Its monthly maintenance cost is a summation of the organization’s human resources it needs and developer’s charges. Here, let’s assume a chatbot maintenance fee, which ranges from $100 to $1,000 a month. Similar to the bot development charges, maintenance fees vary according to bot capabilities.

chatbot roi calculator: montly maintenance cost

#5 Chatbots Return on Investment Calculation

The return on investment is a ratio of benefit from the investment to the cost of investment. It evaluates the efficiency of an investment. Mathematically, ROI = (Current Value of Investment – Cost of Investment) / Cost of Investment.

Since chatbots incur a one-time development cost and recurring monthly maintenance cost, here’s the chatbot ROI calculation from both perspectives.

Chatbot ROI during the first month: This includes the bot installation charges.

For the above case,

ROI = (Gains through bot – Installation charge – maintenance charge)/(installation charge + maintenance charge)

ROI = ($63,000 – $9,292 – $3,647)/($9,292 – $3,647)

ROI = 3.9 or 390%

Chatbot ROI after the first month: This excludes the bot installation charges.

For the above case,

ROI = (Gains through bot – maintenance charge)/(maintenance charge)

ROI = ($63,000 – $3,647)/($3,647)

ROI = 16.3 or 1630%

Using this method, you can build your own chatbot ROI calculator considering your own business parameters.

NLP and AI-powered chatbots can yield a better return on investment. For instance, Religare has incorporated a service chatbot on its Web portal and WhatsApp integration to handle customer queries. It has resulted in 10 times more customer interaction and 5 times more sales conversion.

Conclusion

For the above case, where bots are able to handle 50% of customer queries, there’s a direct 50% capital gain to the organization. The human-time saved can be utilized for more productive tasks, which can eventually accelerate the organization’s productivity.

Powerful bots result in better success rates for customer facing operations. For example, Diageo’s iDia chatbot has led to a 55% drop in help desk tickets.

Here are more enterprise chatbot use cases.

Researchers predict that by 2025, chatbots will accomplish more than 90% of the B2C interactions. Also, chatbots can cut operational costs by more than $8 billion per year in the next three years.

AI in Insurance will value at $36B by 2026. Chatbots will occupy 40% of overall deployment, predominantly within customer service roles.
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We specialize in developing industry-specific AI-powered chatbots. Drop us a word at hello@mantralabsglobal.com to learn more.

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

By : Sampada S M

Published on Apr 1, 2025

AI AI Hallucinations Artificial Intelligence Reliable AI 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

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