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5 Reasons why Customer Service Chatbots are the Need of the Hour

3 minutes, 59 seconds read

The rapidly advancing world suddenly came to a halt with the outbreak of the COVID-19 pandemic. If anything positive that has come out of this crisis is that it has made people more comfortable with technology. Even people from non-tech-savvy older generations are readily adopting technological advancements. Especially the customer service verticals (helpdesk and support portals) if businesses are seeking automation the most. 

Chatbots have come a long way since they were first introduced. In 2016, Facebook allowed chatbots into its Messenger platform to let businesses deliver automated customer support, e-commerce guidance, content, and interactive experiences through chatbots. From answering simple queries to scheduling appointments, chatbots have evolved into AI-driven Virtual Assistants. Given the variety of purposes they solve, chatbots are here to stay. The chatbot market is projected to reach $1.25 billion by 2025

Let’s look at some of the most pressing points which make Customer Service Chatbots so relevant in the current period.

1. The Need to Save Time, Money and Resources

The prolonged lock-downs have left a deep impact on the business cash-flows. To manage the business with limited resources and constraints on budget, this is the right time to integrate chatbots which can take up routine tasks and save bandwidth of human resources for more complex ones. These days, chatbots are available at affordable prices and even on monthly subscription models.

2. Elevate Digital Customer Experience

During the initial stage of the COVID outbreak, people struggled to get essentials. The volume of customer grievances and queries were very high. Businesses struggled to address them. AI-driven chatbots in such situations prove to be a great asset in acknowledging the problems and providing relevant solutions. 

Voice-enabled customer service chatbots give a human-like experience to customers which helps add that personal touch in a digital environment. Unlike command-based chatbots, AI-based or Machine Learning chatbots can answer ambiguous questions. Based on the responses, chatbots are learning and can provide better answers over time. NLP chatbots will take the digital CX to another level which is a crucial differentiator for businesses in these times.

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.
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3. Build Customer Engagement and Brand Loyalty

One of the biggest pain-points of the lock-downs and social distancing is keeping the existing customers and clients engaged and building trust amongst them. Retaining brand loyalty has been challenging since customers during these times will watch out for businesses that provide the best services. Big names with a huge customer base may fail if they continue with legacy systems and traditional models even in these crucial times. 

To keep the business running, organizations will have to engage with customers. Bots can derive data through it’s AI capabilities which can be used to re-engage with customers. Especially in the e-commerce sector, bots can remind customers of the unbought items from their wish-list, suggest items to pair with the selected ones, take feedback, and so on. 

Customers remember brands that provide good services during difficult times. 

4. Dealing with the Issues of Modern Workforce

Due to lockdown, organizations faced a pressing challenge to ensure the smooth functioning of business with a remote workforce. A part of this challenge was also to ensure healthy and transparent communication with the internal workforce i.e. employees.

Especially the larger organizations and MNCs faced communication challenges with their employees across the globe. For instance, the HR department might not be able to reach all its employees. This calls for a need for chatbots that can address some of the basic queries. As Gartner predicts — by 2022, 70% of white-collar workers will interact with conversational platforms daily. The current pandemic is just fueling the adoption of helpdesk automation. 

5. Lead Generation

The business development and sales departments have a difficult road ahead. Given the economic slowdown, how to generate leads? Considering the current situation, many businesses are going digital as sales representatives cannot meet clients in-person. 

In the B2C space, cold calling and email marketing will soon become redundant. The situation requires an interaction with people through which leads can be found. Bots can provide data on the back-end while interacting with prospects and help businesses reach out to them. Thus, enabling more sales conversions. 

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

What Does the Future Look Like for Customer Service Chatbots?

Modern customers include Millennials and Gen Z who represent 2 billion (27%) and 1.8 billion (24%) of the population respectively. They have a high affinity for self-service portals and look out for their query resolution instantly. Chatbots with integrated workflows can drive historical consumer data and accordingly suggest resolution. 

Companies like Uber and Amazon are already deploying self-service customer support, which not only releases the load from call-centers but also satisfies the growing preference for convenience. According to a recent Salesforce survey, 60% of businesses are ready to adopt self-service portals and chatbots are a crucial part of facilitating this. 

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Will AI Be the Future’s Definition of Sustainable Manufacturing?

Governments worldwide are implementing strict energy and emission policies to drive sustainability and efficiency in industries:

  • China’s Dual Control Policy (since 2016) enforces strict limits on energy intensity and usage to regulate industrial consumption.
  • The EU’s Fit for 55 Package mandates industries to adopt circular economy practices and cut emissions by at least 55% by 2030.
  • Japan’s Green Growth Strategy incentivizes manufacturers to implement energy-efficient technologies through targeted tax benefits.
  • India’s Perform, Achieve, and Trade (PAT) Scheme encourages energy-intensive industries to improve efficiency, rewarding those who exceed targets with tradable energy-saving certificates.

These policies reflect a global push toward sustainability, urging industries to innovate, reduce carbon footprints, and embrace energy efficiency.

What’s driving the world to impose these mandates in manufacturing?

This is because the manufacturing industry is at a crossroads. With environmental concerns mounting, the sector faces some stark realities. Annually, it generates 9.2 billion tonnes of industrial waste—enough to fill 3.7 million Olympic-sized swimming pools or cover the entire city of Manhattan in a 340-foot layer of waste. Manufacturing also consumes 54% of the world’s energy resources, roughly equal to the total energy usage of India, Japan, and Germany combined. And with the sector contributing around 25% of global greenhouse gas emissions, it outpaces emissions from all passenger vehicles worldwide.

These regulations are ambitious and necessary. But here’s the question: Can industries meet these demands without sacrificing profitability?

Yes, sustainability initiatives are not a recent phenomenon. They have traditionally been driven by the emergence of smart technologies like the Internet of Things (IoT), which laid the groundwork for more efficient and responsible manufacturing practices.

Today, most enterprises are turning to AI in manufacturing to further drive efficiencies, lower costs while staying compliant with regulations. Here’s how AI-driven manufacturing is enhancing energy efficiency, waste reduction, and sustainable supply chain practices across the manufacturing landscape.

How Does AI Help in Building a Sustainable Future for Manufacturing?

1. Energy Efficiency

Energy consumption is a major contributor to manufacturing emissions. AI-powered systems help optimize energy usage by analyzing production data, monitoring equipment performance, and identifying inefficiencies.

  • Siemens has implemented AI in its manufacturing facilities to optimize energy usage in real-time. By analyzing historical data and predicting energy demand, Siemens reduced energy consumption by 10% across its plants. 
  • In China, manufacturers are leveraging AI-driven energy management platforms to comply with the Dual Control Policy. These systems forecast energy consumption patterns and recommend adjustments to stay within mandated limits.

Impact: AI-driven energy management systems not only reduce costs but also ensure compliance with stringent energy caps, proving that sustainability and profitability can go hand in hand.

2. Waste Reduction

Manufacturing waste is a double-edged sword—it pollutes the environment and represents inefficiencies in production. AI helps manufacturers minimize waste by enhancing production accuracy and enabling circular practices like recycling and reuse.

  • Procter & Gamble (P&G) uses AI-powered vision systems to detect defects in manufacturing lines, reducing waste caused by faulty products. This not only ensures higher quality but also significantly reduces raw material usage.
  • The European Union‘s circular economy mandates have inspired manufacturers in the steel and cement industries to adopt AI-driven waste recovery systems. For example, AI algorithms are used to identify recyclable materials from production waste streams, enabling closed-loop systems. 

Impact: AI helps companies cut down on waste while complying with mandates like the EU’s Fit for 55 package, making sustainability an operational advantage.

3. Sustainable Supply Chains

Supply chains in manufacturing are vast and complex, often contributing significantly to carbon footprints. AI-powered analytics enable manufacturers to monitor and optimize supply chain operations, from sourcing raw materials to final delivery.

  • Unilever uses AI to track and reduce the carbon emissions of its suppliers. By analyzing data across the supply chain, the company ensures that partners comply with sustainability standards, reducing overall emissions.
  • In Japan, automotive manufacturers are leveraging AI for supply chain optimization. AI algorithms optimize delivery routes and load capacities, cutting fuel usage and emissions while benefiting from tax incentives under Japan’s Green Growth Strategy.

Impact: By making supply chains more efficient, AI not only reduces emissions but also builds resilience, helping manufacturers adapt to global disruptions while staying sustainable.

4. Predictive Maintenance

Industrial machinery is a significant source of emissions and waste when it operates inefficiently or breaks down. AI-driven predictive maintenance ensures that equipment is operating at peak performance, reducing energy consumption and downtime.

  • General Electric (GE) uses AI-powered sensors to monitor the health of manufacturing equipment. These systems predict failures before they happen, allowing timely maintenance and reducing energy waste.
  • AI-enabled predictive tools are also being adopted under India’s PAT scheme, where energy-intensive industries leverage real-time equipment monitoring to enhance efficiency. (Source)

Impact: Predictive maintenance not only extends the lifespan of machinery but also ensures that energy-intensive equipment operates within sustainable parameters.

The Road Ahead

AI is no longer just a tool—it’s a critical partner in achieving sustainability. By addressing challenges in energy usage, waste management, and supply chain optimization, AI helps manufacturers not just comply with global mandates but thrive in a world increasingly focused on sustainability.

As countries continue to tighten regulations and push for decarbonization, manufacturers that embrace AI stand to gain a competitive edge while contributing to a cleaner, greener future.

Mantra Labs helps manufacturers achieve sustainable outcomes—driving efficiencies across the shop floor to operational excellence, lowering costs, and enabling them to hit ESG targets. By integrating AI-driven solutions, manufacturers can turn sustainability challenges into opportunities for innovation and growth, building a more resilient and responsible industry for the future.

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