Top Advantages and Disadvantages of AI You Should Know in 2025

Did you know that AI creates approximately 34 million images daily, or 1.4 million per hour? This rapid output showcases AI's ability to boost productivity and streamline creative processes. Understanding the advantages and disadvantages of AI is essential for making informed decisions as AI continues to shape industries in 2025.

AI helps improve decision-making and increase efficiency across industries, from customer support to healthcare and finance. It speeds up processes, reduces human error, and allows businesses to scale faster than ever.

However, besides these benefits, AI also brings challenges. It can lead to job displacement, raise privacy concerns, and sometimes produce biased or inaccurate results.

Knowing AI's top advantages, disadvantages, and challenges helps you make smarter decisions when working with AI, whether choosing a tool or building a product.

This blog covers the advantages and disadvantages of AI, shows how it's being used in real scenarios, and highlights the practical challenges you need to consider.

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Advantages and Disadvantages of AI: A Clear Comparison

AI offers significant benefits that can transform how you work, make decisions, and deliver services. From improving efficiency to automating repetitive tasks, it helps you get more done in less time with better accuracy. These strengths make AI valuable in healthcare, finance, retail, and more.

At the same time, AI comes with challenges you must be aware of. Concerns around job displacement, data privacy, and biased decision-making continue to grow as AI adoption increases. If not managed carefully, these risks can impact both individuals and businesses.

If you want to gain expertise in AI and build intelligent systems that reduce manual effort, the following upGrad courses can help you succeed.

• Generative AI Foundations Certificate Program
• Advanced Generative AI Certification Course
• Executive Diploma in Machine Learning and AI with IIIT-B

Here are some advantages and disadvantages of AI that can help you make more balanced decisions when implementing AI into your work or organization.

Top 12 Advantages of AI You Should Know!

AI technologies have become popular because they can automate tasks, improve accuracy, and reduce workload in technical and non-technical fields. The practical impact is massive, from AI-powered chatbots that handle thousands of customer queries daily to healthcare systems that detect diseases earlier and more accurately.

It's important to understand AI's core advantages. Knowing what AI can do helps you use it more effectively, make smarter tech choices, and stay ahead in your field. Here are the top 12 advantages of AI you need to know.

Also Read: How to Build Your Own AI System: Step-by-Step Guide

1. Automation of Repetitive Tasks 

AI automates routine and repetitive tasks that would otherwise consume significant time and resources. Industries such as manufacturing, retail, and administration benefit from using AI to handle data entry, scheduling, and basic customer service without manual involvement. 

Effect: By implementing AI automation, organizations can achieve measurable improvements. For example, McKinsey reports that AI-driven automation can reduce labor costs by up to 20% and increase productivity by 20–30% in specific sectors. 

Example: A major retailer such as Walmart includes AI for inventory management, enabling the automatic monitoring of stock levels and real-time reordering of supplies. This system helped Walmart reduce out-of-stock occurrences by 16% and save crores of rupees annually in lost sales and excess inventory costs. By minimizing delays and preventing inventory issues, the company improved efficiency and enhanced the overall customer experience.

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2. Enhanced Decision-Making with Data Analysis

AI excels at processing vast amounts of data quickly and accurately, making it a powerful tool for decision-making. By analyzing real-time patterns, trends, and correlations, AI can provide valuable insights that would take humans longer to uncover.

Effect: AI's advantage in decision-making is that it helps you act on data-driven insights, reducing guesswork and bias. With AI, decisions are based on a comprehensive analysis of large datasets, improving the overall accuracy. AI allows you to adjust real-time strategies, ensuring quicker and more informed actions.

Example: Companies like DHL implement AI for route optimization and demand forecasting. DHL has reduced delivery times and operational costs by analyzing real-time traffic and shipment data, resulting in improved customer satisfaction and increased profitability.

Also read: 5 Significant Benefits of Artificial Intelligence [Deep Analysis]

3. Personalization of Customer Experiences

AI can significantly enhance the user experience by analyzing your preferences, behaviors, and past interactions. By leveraging machine learning algorithms, AI creates tailored experiences for you, making your journey more relevant and engaging. This technology is used across retail, entertainment, and hospitality industries to deliver customized content, offers, and recommendations specifically for you.

Effect: Personalization powered by AI leads to higher customer satisfaction, increased loyalty, and improved conversion rates. By providing individualized experiences, businesses can promote stronger customer relationships, resulting in repeat business and higher revenue.

Example: In healthcare, AI-driven personalization is transforming patient care. Platforms like IBM Watson Health use AI to analyze patient data, including medical history, genetics, and lifestyle factors, to recommend tailored treatment plans and preventive care strategies. This approach improves patient outcomes and enhances engagement by providing health advice and reminders specific to each individual’s needs.

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4. Improved Healthcare Diagnostics

AI algorithms enhance the healthcare industry by processing massive medical data, such as images, genetic information, and clinical records. These systems can detect diseases and conditions much earlier and more accurately than traditional methods.

Effect: Faster and more accurate diagnoses can lead to earlier intervention, better treatment outcomes, and, in some cases, even lifesaving. AI’s ability to analyze patterns in data that are often invisible to the human eye allows healthcare providers to make more informed decisions quickly. This is especially critical in emergencies or complex diseases where every moment counts.

Example: A leading diagnostic chain in Mumbai recently adopted Scispot, reporting a 40% reduction in workflow errors and enhanced patient satisfaction through instant report access. The implementation transformed the lab's operations by automating sample management, reducing manual tasks, and improving accuracy through features like barcode-based tracking and direct integration with analyzers.

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Also read: Top 20 Types of AI in 2025 Explained

5.  Enhanced Cybersecurity Measures

AI can analyze network traffic in real time, detecting unusual patterns and identifying potential security threats much faster than manual methods. By continuously monitoring systems, AI helps you stay ahead of cyber threats by proactively identifying vulnerabilities.

Effect: The advantage of using AI for cybersecurity is that it enables you to take immediate action before a potential breach occurs. AI systems can detect abnormal behavior, flagging suspicious activity like unauthorized access or data manipulation, allowing you to prevent attacks in real time. This reduces the risk of significant damage or loss.

Example: Imagine you’re working in the banking sector, and your company uses an AI-driven fraud detection system. The system quickly scans transactions for signs of fraud, such as unusual account access patterns or uncharacteristic transactions. The system flags a potentially fraudulent transaction within seconds and alerts your team, preventing financial loss. Without AI, this type of threat detection would rely on human oversight, which is slower and more prone to error.

Also read: 5 Significant Benefits of Artificial Intelligence [Deep Analysis]

6. Cost Savings and Resource Optimization

AI helps you optimize resources, improve supply chain management efficiency, and streamline energy usage. By analyzing large amounts of data, AI identifies patterns and inefficiencies that might not be immediately obvious. Whether predicting demand to avoid overproduction or adjusting energy consumption in real time, AI ensures you use your resources as efficiently as possible.

Effect: The real advantage of AI in cost savings is its ability to reduce waste, lower operating costs, and improve overall resource allocation. Whether minimizing energy costs in a factory or improving logistics to reduce fuel consumption, AI can significantly lower your company’s overheads and drive profitability. By pinpointing production or supply chain inefficiencies, AI can help you make smarter decisions that save time and money.

Example: In manufacturing, you use AI to optimize production lines. The AI system analyzes machine performance and predicts when maintenance is needed, preventing unexpected downtime. This reduces wasted materials and ensures that production runs smoothly without unnecessary delays, ultimately saving your company money and increasing productivity.

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7. Increased Speed and Accuracy

AI can process tasks at a speed and accuracy far exceeding human capabilities, allowing for faster decision-making and better-quality outputs. Whether analyzing data, generating reports, or performing complex calculations, AI handles tasks remarkably efficiently.

Effect: The tasks you would typically take hours or days to complete can be done in a fraction of the time, freeing you up for more critical activities. Additionally, AI reduces the likelihood of human errors, ensuring that your work is more accurate and reliable. By automating time-consuming tasks, AI allows you to focus on higher-level problem-solving and innovation.

Example: Imagine you are working in accounting and need to generate monthly financial reports. Traditionally, this task would require significant time to gather data, perform calculations, and format everything correctly. However, these reports are automatically generated with AI-driven accounting software, with minimal errors and within minutes. This drastically reduces audit time and helps you ensure accuracy and compliance.

8. Innovation and Creativity in Design

AI helps designers, artists, and content creators break through creative blocks and generate innovative ideas. AI-powered tools now help you in graphic design, music composition, and video editing, unlocking a new level of creativity. These tools analyze your existing designs, music, or videos to generate new variations or suggest enhancements, making your creative process faster and more dynamic.

Effect: One of AI's key advantages in design is that it accelerates the creative process by providing instant feedback, suggestions, and ideas based on learned patterns. This enables you to experiment with multiple iterations and directions for your work without starting from scratch each time. AI can also help you push boundaries by suggesting designs or concepts you might not have considered.

Example: Let’s say you’re working on a graphic design project and hit a creative block. AI tools like DeepArt use neural networks to transform your photos into different artistic styles, helping you generate unique visuals quickly. Whether you're a graphic designer or a music producer, AI tools like these provide new ways to explore your creativity and discover fresh ideas, helping you stay ahead in an ever-evolving creative industry.

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9. Better Supply Chain and Logistics Management

AI optimizes supply chain operations by improving inventory management, shipping routes, and warehouse organization. AI systems use predictive analytics and real-time data to streamline the entire process, making it more responsive to demand fluctuations and reducing delays.

Effect: Utilizing AI in your supply chain can minimize inefficiencies and improve resource allocation. This reduces costs and ensures faster and more accurate deliveries, contributing to overall business efficiency.

Example: Suppose you're working for a company like Amazon, which uses AI to predict product demand. The AI system analyzes past purchasing behavior, current trends, and other variables to forecast product demand. Based on these predictions, the company can adjust stock levels across warehouses, ensuring they meet customer needs while avoiding overstocking or stockouts. Without AI, managing this complex logistics network would be far more time-consuming and error-prone.

Also Read: What is Logistics Management? Understanding Its Types, Functions, Processes, and More

10. Enhanced Natural Language Processing (NLP)

AI has improved how computers understand, interpret, and generate human language. From language translation to sentiment analysis and text generation, NLP models help you interact with customers and analyze vast amounts of data in multiple languages.

Effect: AI-driven NLP allows you to communicate more effectively with a global audience. Whether breaking down language barriers or analyzing customer feedback to improve products, NLP ensures you can respond quickly and accurately, enhancing customer experience and operational efficiency.

Example: If you’re running a global e-commerce platform, tools like Google Translate can instantly translate your website content into over 100 languages, allowing you to serve customers worldwide without language barriers. Furthermore, AI can analyze customer reviews and social media posts to gauge sentiment and identify trends, helping you make more informed decisions about your products or services.

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Also Read: Generative AI in Practice: Real-World Use Cases and Success Stories

11. Scalable Solutions for Business Growth

AI enables you to scale your operations effectively, without dramatically increasing your workforce or physical resources. With AI’s ability to process massive data and automate tasks, you can continue growing your business quickly, even as demands increase.

Effect: As your business expands, AI systems can handle the higher volumes of work without sacrificing quality or speed. These systems provide the flexibility needed to scale, whether managing more customer interactions, processing more transactions, or handling more complex data analyses. With AI, you can achieve business growth without facing the logistical challenges typically associated with scaling operations.

Example: Imagine you're using a cloud-based AI system like Salesforce's Einstein. As your business grows, your CRM platform can automatically adjust to handle increased customer data. It can analyze more customer interactions, recommend personalized marketing strategies, and provide real-time insights without additional human resources or physical infrastructure. This allows your team to focus on strategic tasks rather than getting bogged down by the increased workload.

Also Read: The Pros and Cons of GenerativeAI

12. Empowering Remote Work and Virtual Assistance

AI-driven virtual assistants and collaborative tools have become essential for employees working remotely. These AI-powered systems help you stay organized by managing tasks such as scheduling meetings, setting reminders, and organizing documents, all without constant human intervention.
Effect: The advantage of using AI for remote work is that it boosts productivity by automating mundane tasks, allowing you to focus on more complex or creative work. It also helps reduce the cognitive load of multiple tasks simultaneously, making your workday more efficient.
 Example: Imagine you're using Microsoft Teams or Zoom for daily team meetings. These platforms leverage AI to automatically schedule meetings, transcribe real-time discussions, and suggest action items from your conversations. This reduces the time spent on administrative tasks and makes collaboration more seamless, allowing you to work smarter, not harder.

Also Read: Artificial Intelligence Jobs in 2025: Skills and Opportunities

While the advantages of AI are compelling and continue to reshape our world in remarkable ways, it's equally important to recognize the disadvantages of AI in daily life. To provide a balanced perspective, here are the top 12 disadvantages of AI you should be aware of.

Top 12 Disadvantages of AI in Daily Life!

While AI offers numerous advantages, it has significant challenges and drawbacks that can impact industries, businesses, and individuals. From the risk of job displacement to ethical concerns and biases in AI algorithms, understanding these disadvantages is key to ensuring AI's responsible use and integration.

Knowing the risks helps you make informed decisions about using AI technologies effectively while mitigating potential pitfalls, such as Privacy and Security Issues and High Implementation Costs. Here are the top 12 disadvantages of AI you need to know.

1. Job Displacement and Automation

AI's ability to automate tasks, especially repetitive ones, raises concerns about job displacement. As AI systems take over functions traditionally performed by humans, sectors like manufacturing, customer service, and retail are seeing significant workforce reductions.
Effect: Job loss and unemployment rates could increase as AI technologies replace low-skilled and routine jobs. If workers cannot transition to new roles or industries, this can lead to social and economic instability.
Example: AI and automation are expected to eliminate millions of jobs worldwide by 2030. McKinsey Global Institute projections suggest automation could displace between 400 and 800 million jobs globally by 2030 (depending on adoption speed), forcing up to 375 million workers (14% of the global workforce) to change occupations (AI Job Displacements: UBI to the Rescue? - Seven Pillars Institute). 

Also read: 17 AI Challenges in 2025: How to Overcome Artificial Intelligence Concerns?

2.  Bias and Discrimination

AI systems often reflect biases in the data on which they are trained. These biases can inadvertently lead to unfair, discriminatory outcomes, especially in high-stakes applications like hiring, lending, and criminal justice. For instance, if an AI system is trained on biased historical data, it may replicate or exacerbate those biases.

Effect: You must carefully monitor AI systems to ensure fairness. Ensuring your AI models are trained on diverse and representative data can help reduce the risk of biased decision-making. For example, IBM’s AI Fairness 360 is an open-source toolkit that allows developers to detect and mitigate bias in machine learning models.

Example: In hiring processes, AI systems trained on past employee data might favor male candidates if the historical hiring trends were skewed toward men, perpetuating gender bias and excluding qualified women from opportunities.

Also Read: How AI is Transforming Digital Marketing?

3. Over-Reliance on Automation

While AI can improve efficiency by automating tasks, over-reliance on automation can be problematic. AI excels at handling routine and repetitive tasks. However, it often struggles in complex or nuanced situations. In these cases, it may miss critical context or make errors that a human would not. Human insight remains essential where subtle judgment is required.

Effect: Without human oversight, AI systems could make critical errors that go unnoticed until significant damage is done. To avoid this pitfall, businesses should implement hybrid models where AI supports humans but doesn't completely replace them.

Example: A notable case occurred at Amazon, where an AI-powered hiring tool was found to be biased against female candidates. The system, trained on historical hiring data, began to downgrade resumes that included the word "women’s," such as in "women’s chess club captain." This bias went unnoticed until significant damage had been done to the recruitment process, highlighting the dangers of unchecked automation.

Also Read: Top 10 Artificial Intelligence Tools & Frameworks

4.  Privacy and Security Concerns

AI systems often require vast amounts of personal data to function effectively. This data can be vulnerable to hacking, theft, or misuse. With AI’s increasing ability to analyze and interpret sensitive information, privacy breaches become a significant concern.

Effect: Integrating robust security protocols and data encryption methods is essential to protecting user data from potential breaches. As AI systems become more integrated into sensitive industries, the threat of cyberattacks may increase.

Example: A significant security breach linked to an AI vulnerability occurred with OpenAI's ChatGPT in March 2023. ChatGPT’s open-source library bug caused the chatbot to leak customers' personal data, including names, email addresses, payment addresses, and partial credit card information. Some users could see data from other active users for a brief period due to this vulnerability.

5. High Implementation Costs

To build and deploy AI solutions, you invest significantly in high-performance hardware, advanced software, and skilled professionals such as data scientists and machine learning engineers. If you run a small or medium-sized business, these expenses can be challenging to manage. Understanding the financial commitment involved helps you plan more effectively and allocate your resources wisely for long-term success with AI.

Cost Breakdown (Indicative Estimates for India):

• AI Software Licenses: INR 5–15 lakhs per year, depending on the complexity and vendor.
• Hardware (GPUs/Servers): INR 10–25 lakhs for initial setup, especially if on-premises infrastructure is required.
• Cloud Services: INR 2–8 lakhs per month for scalable compute and storage (varies by usage).
• Personnel (Annual Salary):
  • Data Scientist: INR 10–25 lakhs
  • Machine Learning Engineer: INR 12–30 lakhs
  • AI Project Manager: INR 15–35 lakhs
• Training and Upskilling: INR 1–3 lakhs per employee for specialized AI training programs.

For a medium-sized enterprise, initial implementation costs can easily exceed INR 50 lakhs, with ongoing annual maintenance, cloud services, and personnel costs.

Effect: AI adoption has a substantial setup and ongoing maintenance price tag. Companies need to evaluate whether the long-term benefits justify the initial investment. Before diving into AI deployment, it’s essential to assess the total cost of ownership and operational costs.

Example: A company looking to implement a machine learning model for predictive analytics may need to purchase expensive hardware, pay for cloud storage, and hire AI specialists, which could strain its budget.

6. Lack of Transparency

Many AI systems, including deep learning models, operate as "black boxes," meaning their decision-making processes are not easily interpretable. This lack of transparency raises concerns about accountability and trust, particularly when AI is used in critical sectors like healthcare or finance.

Effect: You must carefully consider the trade-off between model complexity and transparency. Explainable AI (XAI) models are an emerging field focusing on making AI decisions more understandable, but widespread adoption is still limited.

Example: A bank using AI to approve loans might be unable to explain why a specific applicant was rejected. If customers feel they were unfairly treated, this can lead to customer frustration and even legal challenges.

7. Ethical Dilemmas

AI systems make decisions that can affect human lives, which raises serious ethical concerns. For example, if you are using autonomous vehicles, the AI might have to choose how to respond in an unavoidable accident. It may need to decide who to protect or what action to take. In healthcare, AI might suggest treatment plans that impact a patient’s survival or quality of life. These decisions involve moral judgment, which is difficult for AI to handle as humans do.

Effect: To mitigate ethical dilemmas, businesses must establish clear ethical guidelines for AI usage, ensuring that AI technologies are developed and deployed with ethical considerations at the forefront.

Example: An autonomous vehicle may be forced to choose between swerving to avoid a pedestrian and risking the passenger’s life. These ethical choices require input from human policymakers to ensure that AI decisions align with societal values.

8. Environmental Impact

Deep learning models and AI technologies require massive computational power and data processing, leading to significant energy consumption. The environmental cost of training AI models can substantially contribute to carbon emissions.

Effect: To address the environmental concerns of AI, businesses need to implement energy-efficient AI models and consider using renewable energy sources for data centers. You must also look at optimizing models to reduce their carbon footprint.

Example: Training large-scale AI models, such as GPT-3, requires powerful hardware like GPUs and TPUs, which consume vast amounts of electricity. If not managed responsibly, this can contribute to environmental degradation.

Also Read: GPT-4 vs ChatGPT: What’s the Difference?

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9. Scalability Challenges

While AI systems have the potential to scale, the complexity of deployment and the need for continuous monitoring can make scaling difficult. As your AI applications grow, they may require additional resources and more advanced algorithms to maintain performance.

Effect: Scalability challenges mean that businesses may need to invest in additional infrastructure, maintenance, and monitoring as they expand their use of AI. The potential costs associated with scaling should be carefully considered before adopting AI solutions at a large scale.

Example: A company using AI to manage inventory may find that as it expands into new regions or markets, the AI system requires more sophisticated models and additional processing power to handle the increased complexity of data.

10. Dependency on Data Quality

AI’s effectiveness heavily depends on the quality of the data it is trained on. The AI model will produce unreliable or incorrect results if the data is incomplete, inaccurate, or outdated. This can lead to poor decision-making and costly mistakes.

Effect: Ensuring high-quality data is essential for accurate and reliable AI outputs. Businesses should prioritize data governance and implement robust data collection, cleaning, and validation processes to ensure the quality of the datasets used to train AI systems.

Example: If an AI system used for fraud detection is trained on inaccurate historical data, it could miss potential fraud cases or generate false positives, disrupting the business and damaging customer trust.

11. Security Vulnerabilities

AI systems themselves can become targets for cyberattacks. If malicious actors manage to tamper with an AI model or its training data, they could alter its behavior, leading to disastrous consequences.

Effect: It's essential to adopt security measures that protect the AI models and the data they rely on. Regularly auditing AI systems for vulnerabilities and ensuring that only trusted individuals have access to sensitive information is crucial for maintaining AI system integrity.

Example: An AI-driven autonomous vehicle could be hacked, altering its decision-making algorithms and causing it to malfunction, potentially putting lives at risk.

Also Read: 30 Best Cyber Security Projects To Work On in 2025

12. Lack of Standardization and Regulation

As AI technology rapidly evolves, standardized guidelines and regulations that govern its use are lacking. AI implementations can lead to inconsistent results, unintended consequences, and ethical challenges without proper regulatory frameworks.

Effect: The absence of clear regulations on AI can lead to misuse, ethical violations, and inconsistent practices across different industries. Governments and organizations must establish clearer standards and frameworks for safe and responsible AI development.

Example: In the case of facial recognition, the lack of standard regulations means that different organizations might use the technology in an intrusive or unethical ways, leading to privacy violations and public backlash.

Now that you know the advantages and disadvantages of AI in daily life, it’s time to examine how AI is used in practical scenarios and implementation challenges.

H2: How AI Is Being Used? Practical Examples 

AI is changing industries' operations, offering practical solutions across healthcare, customer service, and retail sectors. However, along with these real-world successes, some challenges and limitations affect how effectively AI can be used in complex environments.

Practical Applications Across Industries

• Customer Support and Chatbots: AI-powered chatbots are commonly used in customer service to handle basic queries, guide users, and provide instant support across platforms. These systems reduce wait times and improve customer satisfaction.
  • Example: A telecom company uses an AI chatbot to assist customers with billing questions and service issues. The bot handles thousands of queries daily, resolving most without human intervention.
• Medical Diagnostics: AI tools enhance diagnostic accuracy by analyzing large datasets, such as medical imaging and patient records. These tools assist healthcare providers in identifying diseases early and suggesting treatment options.
  • Example: A hospital uses an AI model to detect early signs of lung cancer in radiology scans. This allows doctors to begin treatment sooner, potentially improving patient outcomes.
• Retail and Personalization: AI systems analyze your behavior and preferences to provide personalized product recommendations and marketing offers. This boosts customer engagement and drives sales.
  • Example: An e-commerce platform uses AI to recommend products based on your browsing and purchase history. This results in higher conversion rates and better shopping experiences.

Limitations and Implementation Challenges

• Dependence on Quality Data: AI systems rely on high-quality, structured data. Poor or incomplete data can lead to inaccurate predictions or biased outcomes.
  • Example: A financial institution uses AI for credit scoring, but inconsistent customer data leads to flawed assessments, impacting loan approvals.
• Public Trust and Adoption Barriers: Users may hesitate to trust AI decisions, especially in sensitive areas like healthcare or finance. Lack of transparency and past AI failures contribute to skepticism.
• Example: Patients may feel uncomfortable with AI-generated medical diagnoses unless a human doctor confirms the results, slowing adoption despite the technology's potential.

Also Read: How to Make a Chatbot in Python Step by Step [With Source Code] in 2025

Learn AI with upGrad: Build Skills for the Future!

The main advantages of AI in 2025 are increased productivity, faster decision-making, and automation of repetitive tasks. However, the disadvantages of AI in daily life include job displacement, data privacy issues, and ethical concerns. Knowing both the benefits and risks of AI helps you make strategic decisions about how to use, work with, or adapt to AI technologies.

If you're looking to stay ahead of the game and level up your AI skills, check out upGrad's courses that’ll help you get future-ready. Here are some extra courses to boost your AI journey! 

• Master the Future of Data with Microsoft 365 Copilot
• Generative AI Mastery Certificate for Data Analysis
• ChatGPT for Developers
• LL.M. in AI and Emerging Technologies (Blended Learning Program)
• Executive Program in Generative AI for Leaders
• Artificial Intelligence in the Real World

Curious which courses can help you gain expertise in AI and ML in 2025? Contact upGrad for personalized counseling and valuable insights. For more details, you can also visit your nearest upGrad offline center. 

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