Is There a Competitive Advantage for Companies Using Blockchain?

Published on May 9, 2018

Updated on Oct 16, 2019

Do you mostly buy from your ‘favourite’ local store? What if all the outlets of your favourite store knew your shopping habits and preferences? Provided your data is kept secure along the transaction chain, are the promise of hyper-personalization and ultra-high convenience worth the trade-off?

Thankfully, this isn’t hypothetical. Companies using blockchain or distributed ledger technology are able to track records easily on a global scale. Not only in retail, but almost every industry is applying blockchain to simplify its processes and offer personalized solutions to its customers.

In this article, we’ll discuss what makes blockchain a compelling technology and its continuing adoption across industries.

Why are Companies Using Blockchain?

Blockchains are encrypted, growing lists of records. It records every single transaction with a time-stamp. No one, including the owner, can modify the ledgers (or records) in a blockchain.

The following features make the blockchain technology a perfect fit for transactional record-keeping in different industries.

Distributed: Blockchain is a decentralized technology, i.e. there’s no authority looking after the framework and operations. The data is accessible to all participants in the network.
Immutable: One of the key advantages of blockchain over any other technology is unchangeability. Post-transaction, no one including the creator can modify the records.
Robust: Traditional communication channels involve many indirections. For example, a bank executes transactions in its centralized database. Then the bank sends the corresponding email/SMS to the user about the transaction. Blockchain is a decentralized technology i.e. users have direct access to the transaction settlements. Companies using blockchain are more robust towards internal and client services.
Encrypted: Encryption is core to the security in blockchain technology. It means only the authorized users and participants can access the information. It also secures the identity of the participants. Ciphertexts (encrypted data, which is meaningless to external users) protects the information from intruders.
Consensus: The consensus algorithms are core to the blockchain architecture. The consensus is a decision-making process for a group of active nodes (participants). Participants agree to the decision made by the algorithm.
Tracking: It is easier to track transactions in a blockchain. The technology records every transaction with a time-stamp thus preventing corruption.

The finance industry was an early adopter of blockchain technology. In fact, the credit for the popularity of this technology goes to ‘bitcoins’, which are completely digital financial transactions.

Blockchain Adoption Across Industries

Here’s an overview of how industries using blockchain are enhancing the operations.

Use of Blockchain in Supply Chain and Logistics

E-commerce is certainly giving a boost to the supply chain and logistics industry. But, are traditional record-keeping compatible with the growing demands? Because, today, to deliver a product from point A to point B might include multiple geographies and involve multiple entities, invoices, payments, and extend over months. However, tracking shipments and business transparency is one of the key challenges that the supply chain industry struggles with. Companies using blockchain in the supply chain domain can benefit in the following ways-

Payments and fund transfers are fast and simple for stakeholders at the international level.
It’s possible to keep a track record for the product from its source of origin to end-users. For example, Walmart uses blockchain to track pork it sources from China. It records where each piece of meat came from, processed, stored, its sell-by-date, and the buyer.
Since every participant can collaborate and share records, blockchain ensures transparency in information sharing.

Blockchain in Financial Services

Statista expects that the global blockchain technology market will reach $23.3 bn by 2023. It also suggests that the financial sector will cover more than 60% of investments in this technology.

Financial services can harness blockchain for robust cross-border payments and processing, P2P payments, micropayments, and currency exchange. Investors, day traders, and market makers can also deploy blockchain for clearing and settlement in almost real-time.

Blockchain in Travel

Travel is one of the fastest-growing aspects of the global economy. Both customers and travel & tourism service providers can harness blockchain applications. Customers need not hassle with forex and can access in-depth travel-related information of the destination.

Travel businesses can bring transparency in flight and hotel bookings. For instance, for flight and hotel for a customer, a travel agency needs to share information to the customer and different firms. Blockchain can reduce manual dependencies by sharing relevant information to different stakeholders instantly.

In the list of travel companies using blockchain, Winding Tree is a leading name. It is a decentralized travel ecosystem startup that connects travellers to service providers like airlines, hotels, and tour guides directly. By eliminating the third-party fees associated, it reduces travel overheads. Blockchain’s LIF tokens, Smart Contracts, and ERC827 protocol are at the core of Winding Tree’s travel technology.

Blockchain in Insurance

The insurance industry often struggles with double-booking, counterfeiting, and premium diversions through unlicensed brokers. Distributed ledger technology in insurance can help to minimize the instances of fraudulent activities.

Smart contracts, insurance claims automation, UAVs (unmanned aerial vehicles) for underwriting, and shared databases to simplify insurance can bring transparency in the insurance industry.

Blockchain Benefits in Healthcare

The traditional healthcare record-keeping is cumbersome and the surgeon might lose important remarks, allergies, etc. while going through manual files and folders. Blockchain can track one’s medical history since birth. Also, every minute detail of diagnosis would be available to the medical professionals, even if the patient loses the prescriptions and reports.

WHO reports that developing nations produce about 10%-30% of the counterfeit drugs. Moreover, the counterfeit drug market hit $200 billion worth in 2018. Blockchain can track the drug right from sourcing the raw materials to manufacturing and distribution, reducing the instances of this critical challenge of counterfeiting.

Concluding Remarks

The International Data Corporation (IDC) predicts- investment in blockchain solutions will reach $11.7 billion in 2022 from $552 million during 2018. The blockchain trends that different industries will witness include-

Blockchain as a Service (BaaS)
Favourable regulations around the world towards blockchain
Consumer-centric digital assets
Additional security layers
Use of blockchain technology for better user experiences (UX).

Building blockchain systems are transforming the transaction value chain across industries. Talk to our experts to learn how blockchain is shaping the future of digital enterprises. Drop us a word at hello@mantralabsglobal.com

Contributing Authors: Nidhi Agrawal (Content Writer @Mantra Labs)

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

How blockchain is disrupting businesses?

Why are Companies Using Blockchain?

Blockchain Adoption Across Industries