Is There a Competitive Advantage for Companies Using Blockchain?

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)

AI code assistants are revolutionizing software development, with Gartner predicting that 75% of enterprise software engineers will use these tools by 2028, up from less than 10% in early 2023. This rapid adoption reflects the potential of AI to enhance coding efficiency and productivity, but also raises important questions about the maturity, benefits, and challenges of these emerging technologies.

Code Assistance Evolution

The evolution of code assistance has been rapid and transformative, progressing from simple autocomplete features to sophisticated AI-powered tools. GitHub Copilot, launched in 2021, marked a significant milestone by leveraging OpenAI’s Codex to generate entire code snippets 1. Amazon Q, introduced in 2023, further advanced the field with its deep integration into AWS services and impressive code acceptance rates of up to 50%. GPT (Generative Pre-trained Transformer) models have been instrumental in this evolution, with GPT-3 and its successors enabling more context-aware and nuanced code suggestions.

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Adoption rates: By 2023, over 40% of developers reported using AI code assistants.
Productivity gains: Tools like Amazon Q have demonstrated up to 80% acceleration in coding tasks.
Language support: Modern AI assistants support dozens of programming languages, with GitHub Copilot covering over 20 languages and frameworks.
Error reduction: AI-powered code assistants have shown potential to reduce bugs by up to 30% in some studies.

These advancements have not only increased coding efficiency but also democratized software development, making it more accessible to novice programmers and non-professionals alike.

Current Adoption and Maturity: Metrics Defining the Landscape

The landscape of AI code assistants is rapidly evolving, with adoption rates and performance metrics showcasing their growing maturity. Here’s a tabular comparison of some popular AI coding tools, including Amazon Q:

Amazon Q stands out with its specialized capabilities for software developers and deep integration with AWS services. It offers a range of features designed to streamline development processes:

Highest reported code acceptance rates: Up to 50% for multi-line code suggestions
Built-in security: Secure and private by design, with robust data security measures
Extensive connectivity: Over 50 built-in, managed, and secure data connectors
Task automation: Amazon Q Apps allow users to create generative AI-powered apps for streamlining tasks

The tool’s impact is evident in its adoption and performance metrics. For instance, Amazon Q has helped save over 450,000 hours from manual technical investigations. Its integration with CloudWatch provides valuable insights into developer usage patterns and areas for improvement.

As these AI assistants continue to mature, they are increasingly becoming integral to modern software development workflows. However, it’s important to note that while these tools offer significant benefits, they should be used judiciously, with developers maintaining a critical eye on the generated code and understanding its implications for overall project architecture and security.

AI-Powered Collaborative Coding: Enhancing Team Productivity

AI code assistants are revolutionizing collaborative coding practices, offering real-time suggestions, conflict resolution, and personalized assistance to development teams. These tools integrate seamlessly with popular IDEs and version control systems, facilitating smoother teamwork and code quality improvements.

Key features of AI-enhanced collaborative coding:

Real-time code suggestions and auto-completion across team members
Automated conflict detection and resolution in merge requests
Personalized coding assistance based on individual developer styles
AI-driven code reviews and quality checks

Benefits for development teams:

Increased productivity: Teams report up to 30-50% faster code completion
Improved code consistency: AI ensures adherence to team coding standards
Reduced onboarding time: New team members can quickly adapt to project codebases
Enhanced knowledge sharing: AI suggestions expose developers to diverse coding patterns

While AI code assistants offer significant advantages, it’s crucial to maintain a balance between AI assistance and human expertise. Teams should establish guidelines for AI tool usage to ensure code quality, security, and maintainability.

Emerging trends in AI-powered collaborative coding:

Integration of natural language processing for code explanations and documentation
Advanced code refactoring suggestions based on team-wide code patterns
AI-assisted pair programming and mob programming sessions
Predictive analytics for project timelines and resource allocation

As AI continues to evolve, collaborative coding tools are expected to become more sophisticated, further streamlining team workflows and fostering innovation in software development practices.

Benefits and Risks Analyzed

AI code assistants offer significant benefits but also present notable challenges. Here’s an overview of the advantages driving adoption and the critical downsides:

Core Advantages Driving Adoption:

Enhanced Productivity: AI coding tools can boost developer productivity by 30-50%1. Google AI researchers estimate that these tools could save developers up to 30% of their coding time.

Industry	Potential Annual Value
Banking	$200 billion – $340 billion
Retail and CPG	$400 billion – $660 billion

Economic Impact: Generative AI, including code assistants, could potentially add $2.6 trillion to $4.4 trillion annually to the global economy across various use cases. In the software engineering sector alone, this technology could deliver substantial value.

Democratization of Software Development: AI assistants enable individuals with less coding experience to build complex applications, potentially broadening the talent pool and fostering innovation.
Instant Coding Support: AI provides real-time suggestions and generates code snippets, aiding developers in their coding journey.

Critical Downsides and Risks:

Cognitive and Skill-Related Concerns:
- Over-reliance on AI tools may lead to skill atrophy, especially for junior developers.
- There’s a risk of developers losing the ability to write or deeply understand code independently.
Technical and Ethical Limitations:
- Quality of Results: AI-generated code may contain hidden issues, leading to bugs or security vulnerabilities.
- Security Risks: AI tools might introduce insecure libraries or out-of-date dependencies.
- Ethical Concerns: AI algorithms lack accountability for errors and may reinforce harmful stereotypes or promote misinformation.
Copyright and Licensing Issues:
- AI tools heavily rely on open-source code, which may lead to unintentional use of copyrighted material or introduction of insecure libraries.
Limited Contextual Understanding:
- AI-generated code may not always integrate seamlessly with the broader project context, potentially leading to fragmented code.
Bias in Training Data:
- AI outputs can reflect biases present in their training data, potentially leading to non-inclusive code practices.

While AI code assistants offer significant productivity gains and economic benefits, they also present challenges that need careful consideration. Developers and organizations must balance the advantages with the potential risks, ensuring responsible use of these powerful tools.

Future of Code Automation

The future of AI code assistants is poised for significant growth and evolution, with technological advancements and changing developer attitudes shaping their trajectory towards potential ubiquity or obsolescence.

Technological Advancements on the Horizon:

Enhanced Contextual Understanding: Future AI assistants are expected to gain deeper comprehension of project structures, coding patterns, and business logic. This will enable more accurate and context-aware code suggestions, reducing the need for extensive human review.
Multi-Modal AI: Integration of natural language processing, computer vision, and code analysis will allow AI assistants to understand and generate code based on diverse inputs, including voice commands, sketches, and high-level descriptions.
Autonomous Code Generation: By 2027, we may see AI agents capable of handling entire segments of a project with minimal oversight, potentially scaffolding entire applications from natural language descriptions.
Self-Improving AI: Machine learning models that continuously learn from developer interactions and feedback will lead to increasingly accurate and personalized code suggestions over time.

Adoption Barriers and Enablers:

Barriers:

Data Privacy Concerns: Organizations remain cautious about sharing proprietary code with cloud-based AI services.
Integration Challenges: Seamless integration with existing development workflows and tools is crucial for widespread adoption.
Skill Erosion Fears: Concerns about over-reliance on AI leading to a decline in fundamental coding skills among developers.

Enablers:

Open-Source Models: The development of powerful open-source AI models may address privacy concerns and increase accessibility.
IDE Integration: Deeper integration with popular integrated development environments will streamline adoption.
Demonstrable ROI: Clear evidence of productivity gains and cost savings will drive enterprise adoption.

Future Trends in Code Automation:

AI-Driven Architecture Design: AI assistants may evolve to suggest optimal system architectures based on project requirements and best practices.
Automated Code Refactoring: AI tools will increasingly offer intelligent refactoring suggestions to improve code quality and maintainability.
Predictive Bug Detection: Advanced AI models will predict potential bugs and security vulnerabilities before they manifest in production environments.
Cross-Language Translation: AI assistants will facilitate seamless translation between programming languages, enabling easier migration and interoperability.
AI-Human Pair Programming: More sophisticated AI agents may act as virtual pair programming partners, offering real-time guidance and code reviews.
Ethical AI Coding: Future AI assistants will incorporate ethical considerations, suggesting inclusive and bias-free code practices.

As these trends unfold, the role of human developers is likely to shift towards higher-level problem-solving, creative design, and AI oversight. By 2025, it’s projected that over 70% of professional software developers will regularly collaborate with AI agents in their coding workflows1. However, the path to ubiquity will depend on addressing key challenges such as reliability, security, and maintaining a balance between AI assistance and human expertise.

The future outlook for AI code assistants is one of transformative potential, with the technology poised to become an integral part of the software development landscape. As these tools continue to evolve, they will likely reshape team structures, development methodologies, and the very nature of coding itself.

Conclusion: A Tool, Not a Panacea

AI code assistants have irrevocably altered software development, delivering measurable productivity gains but introducing new technical and societal challenges. Current metrics suggest they are transitioning from novel aids to essential utilities—63% of enterprises now mandate their use. However, their ascendancy as the de facto standard hinges on addressing security flaws, mitigating cognitive erosion, and fostering equitable upskilling. For organizations, the optimal path lies in balanced integration: harnessing AI’s speed while preserving human ingenuity. As generative models evolve, developers who master this symbiosis will define the next epoch of software engineering.

How blockchain is disrupting businesses?

Why are Companies Using Blockchain?

Blockchain Adoption Across Industries