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Business Continuity for Call-Center Operations: Case Study

3 minutes, 39 seconds read

Coronavirus outbreak has led to prolonged lockdown in several countries, which has crippled many back-office operations. The Government of India imposed a nationwide lockdown (currently 40 days). Because of travel restrictions and health concerns, customer queries are increasing exponentially. Companies dependent on call-centres are struggling to deploy work from home solutions and their business continuity plans. 

Most companies are not prepared for work-from-home arrangements, but there are exceptions. Those with the right strategy and timely action are able to keep their business operationally afloat. Before we delve into the case study, let’s take a quick look at the current situation of call centres.

COVID-19 is testing call-centre businesses. How?

Voice-services are facing a tough time transitioning to a work-from-home model. Companies are not willing to allow access to private and sensitive data outside of the protected office premises. 

Teleperformance, a specialized omnichannel customer experience management company, was able to allow mobility to only 50% of its employees by the first week of April (2nd week of lockdown). The company aims at managing 66% of operations remotely by mid-April. 

This raises a question — why not 100%?

Most of the companies lack the digital infrastructure and a rigid business continuity plan. For instance, the airline business relies heavily on call-centres. After coronavirus outbreaks and resulting lockdowns, most of the call-centres failed to respond to increasing customer queries. To continue communication with customers and support them in whatever ways possible, many airports turned to social media. Delhi’s Indira Gandhi International Airport had over 3.5 million social media engagements during the period.

But, what’s the major limiting factor for adopting virtual call-centre models?

Virtual call-centre adoption challenges

Theoretically, technology can simplify call-centre operations with mobility solutions. But mobility requires an uninterrupted internet connection and developing countries like India struggles for it. Telcos are surely rushing to fill the gaps in customer communications; the fact is— only 2-3% of Indians use wired broadband and the majority of users rely on mobile data. 

The Telcos infrastructure here is designed and built to operate on 75% network capacity utilization. But, due to lockdown, many cities are witnessing 90-100% load capacity and circles like Karnataka are stretching beyond 100% capacity. The country’s inadequate telcos facility is also a limiting factor for setting up virtual call-centres.

Migrating from traditional to virtual operations (ensuring workplace mobility) will require moving the core systems to the cloud. During times like this, the frailed supply-chain defies the thought of procuring devices to achieve 100% mobility.

Despite the aforementioned challenges, some voice-service extensive organizations managed to seamlessly implement mobility at work. 

[Related: Enterprises investing in Workplace Mobility Can Survive Pandemics]

Ensuring call-centre business continuity during a lockdown: a case study

India’s Leading Health Insurer— Religare demonstrated its preparedness against the COVID-19 situation. A major part of the insurer’s customer servicing relies on call-centre based communication, which would have become operationally impossible amidst the ongoing lockdown. To respond to this critical situation and remain operationally afloat, Mantra implemented a call-centre mobility solution with quick turn-around time.

In a typical call centre, the team leader manages and supports callers to handle customer queries. Now that the workforce is operating remotely, the critical question before the company was how to make information available to the callers.

A new virtual call-centre (computer telephony/dialer) system was implemented in the organization’s Lead Management System, which manages the complete customer journey. Through this cloud-based solution, the necessary information is always available to the caller, also eliminating dependencies. 

Companies are sceptical to allow access to private data outside of on-premise systems. To ensure information security and privacy, the new call centre application allowed only required caller IPs, service APIs and Dialer APIs for remote access to the platform.

[Related: The impact of COVID-19 on the global economy and insurance]

Merits of the case

40% of businesses do not reopen after a disaster. Of those who do, 25% reopen and fail. The main reason is firms are unprepared to withstand the short and long term effects of severe business disruptions. 

That’s why leaders emphasize on business continuity plans (BCP).
The benefits of BCP abstracts to –

  1. Protecting the safety of employees.
  2. Maintaining customer service by minimizing interruptions of business operations.
  3. Protecting assets and brand.
  4. Preventing environmental contamination.
  5. Protecting the investment and leveraging the chance to survive and thrive post-disaster.

To secure operational continuity, organizations need to proactively invest in digital workplaces that remain virtually uninterrupted even during Pandemics. 

Do you need help in ensuring business continuity? We’re listening to you. Write to us at hello@mantralabsglobal.com.

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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

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