What Advantages Does a Mid-sized Enterprise Gain by Migrating to the Cloud?

Published on Jan 31, 2024

In today’s fast-paced business world, staying ahead of the competition is crucial for the success of any mid-sized enterprise. One way to gain a competitive edge is through cloud migration.

But what exactly is cloud migration, and what are the advantages for a mid-sized enterprise? This article will explore the answers to these questions and more.

What is Cloud Migration?

“Cloud migration” refers to transferring an organization’s data, applications, and other business elements from on-premises servers to a cloud-based infrastructure. This allows businesses to access their data and applications from anywhere with an internet connection instead of being restricted to a physical location.

Competitive Edge

Cloud migration can give mid-sized enterprises a competitive edge by providing access to the latest technology, tools, and scalability. This allows businesses to quickly adjust their resources and services to meet changing market conditions and customer demands.

Cost Savings

Another significant benefit of cloud migration is cost savings. By moving to the cloud, businesses can eliminate the need for expensive on-premises hardware and maintenance costs.

Cloud-based solutions also offer a pay-as-you-go model, meaning businesses only pay for the resources and services they use.

Mantra Labs recently helped India’s largest private insurer to migrate its customer portal to AWS. It enabled the company to improve its performance and discoverability metrics by 75%. Additionally, it helped them reduce their time to market for new feature launches by 43%.

Also, GlobalSCAPE, a data security company, moved its entire infrastructure to a hybrid cloud model with Microsoft Azure. This resulted in a 30% reduction in infrastructure costs, improved disaster recovery capabilities, and enhanced data security compliance.

Increased Efficiency and Productivity

Cloud migration can also lead to increased efficiency and productivity for mid-sized enterprises. With data and applications accessible from anywhere, employees can work remotely, improving flexibility and reducing the need for physical office space.

Zapier saw a 100% increase in developer productivity after migrating to Amazon Web Services (AWS). Improved collaboration features and access to real-time data also fueled their innovation pipeline.

Enhanced Security

Security is a top concern for any business, and cloud migration can help mid-sized enterprises improve their security measures. Cloud service providers have dedicated teams and resources to ensure the security of their clients’ data and applications. This means businesses can benefit from advanced security measures without investing in expensive security systems.

They also offer data backup and disaster recovery options, ensuring businesses can quickly recover from any potential data loss or system failures. This added layer of security can give mid-sized enterprises peace of mind and protect them from possible financial losses.

Flexibility and Scalability

As mentioned earlier, cloud-based solutions offer scalability, allowing businesses to adjust their resources and services easily. This flexibility is especially beneficial for mid-sized enterprises, as they can quickly adapt to changing market conditions and customer demands without investing in expensive hardware and software.

Cloud-based solutions also offer the flexibility to choose the services and resources that best suit the needs of the business. This means that mid-sized enterprises can customize their cloud infrastructure to fit their specific requirements rather than being limited to a one-size-fits-all solution.

Improved Customer Experience

In today’s digital age, customers expect businesses to be available and accessible at all times. Cloud migration can help mid-sized enterprises meet these expectations by providing 24/7 access to data and applications. This can lead to improved customer experience and satisfaction, ultimately resulting in increased customer loyalty and retention.

Cloud-based solutions also offer the ability to integrate with other systems and applications, making it easier for businesses to provide a seamless customer experience. This can lead to improved customer engagement and increased sales and revenue.

Real-World Examples of Cloud Migration Benefits

Let’s look at some real-world examples of mid-sized enterprises that have benefited from cloud migration.

Netflix

Netflix is a prime example of a mid-sized enterprise successfully migrating to the cloud. By moving its entire infrastructure to the cloud, Netflix was able to reduce its costs and improve its scalability. This allowed them to quickly adapt to the growing service demand and expand their customer base.

Airbnb

Airbnb is another mid-sized enterprise that has benefited from cloud migration. By moving to the cloud, Airbnb improved its customer experience by providing 24/7 access to its services. This has increased customer satisfaction and loyalty, ultimately resulting in the company’s success.

How to Migrate to the Cloud

Now that we’ve explored the benefits of cloud migration, you may be wondering how to get started. Here are some steps to follow when migrating to the cloud:

Assess Your Current Infrastructure

The first step in cloud migration is to assess your current infrastructure. This will help you determine which applications and data must be migrated and which can remain on-premises.

Choose a Cloud Service Provider

Next, you’ll need to choose a cloud service provider. Researching and comparing different providers is essential to find the one that best fits your business’s needs and budget.

Plan Your Migration Strategy

Once you’ve chosen a cloud service provider, it’s time to plan your migration strategy. This involves determining which applications and data will be migrated first and creating a timeline for the migration process.

Migrate Your Data and Applications

The next step is to migrate your data and applications to the cloud. This can be done manually or with the help of a migration tool provided by your cloud service provider.

Test and Optimize

After the migration, testing and optimizing your new cloud infrastructure is essential. This will ensure that everything functions correctly and that your business can benefit from all the advantages of cloud migration.

At Mantra Labs, we’ve helped over 30 clients in their cloud-first journey. Connect with us for a discussion today.

Conclusion

In today’s digital age, cloud migration is becoming increasingly crucial for mid-sized enterprises looking to gain a competitive edge. By moving to the cloud, businesses can benefit from cost savings, increased efficiency and productivity, enhanced security, and improved customer experience. With careful planning and the right cloud service provider, mid-sized enterprises can successfully migrate to the cloud and reap its benefits.

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