Category: Emerging technologies

Of all the emerging technologies today, it is blockchain that is discussed most often, yet implemented only by niche entities. One of the reasons why blockchain has not caught up as quickly as we might have assumed is, it feels intimidating even to larger enterprises. In reality, blockchain is accessible, easily implementable, and extremely useful for a business to enhance security, do background checks, maintain records, comply with regulations, and meet various governance and business requirements. As a decentralized public ledger of all transactions, blockchain is proving to be a revolutionary way to maintain a record of all transactions and to ensure that every transaction takes place smoothly without ambiguities. However, it may seem overwhelming in the beginning. To achieve operational efficiency through blockchain, it helps to choose the right blockchain model and start incrementally. Understanding and demystifying blockchain A blockchain is a distributed database, which acts as a tamper-proof ledger of all transactions and events that a group of entities (or even a single entity) might want to record. As the information is stored on multiple computers, making changes to recorded data (or events) on a single computer will not result in changes across the network. On the other hand, whoever tries to make those changes to recorded data (or events) can easily be tracked. In other words, blockchain ensures that all digital transactions are stored permanently, and any revisions made to it, whether authorized or unauthorized, is always recorded, along with previous versions of the data. Everything stored in blockchain is protected by advanced cryptography keys, adding an impenetrable layer of security and privacy. Today, such secure and tamper-proof public ledgers of digital transactions can be used to store and maintain assets, validate and manage identities, contracts, policies, and more. Blockchain is revolutionary because : It is transparent and remains in a constant state of consensus Functions as a distributed network via large number of computers, eliminating security threats Data is always tamper-proof and stored permanently. Changes made to data need to be authorized, and unauthorized changes are impossible across a distributed network Identical blocks of information are stored across a network of hundreds to millions of computers, making data stored on blockchain incorruptible Transactions are immutable. Once agreed and implemented across a network, it can’t be undone What kind of blockchains are available for businesses to use? It is natural to wonder if there are different kinds of blockchains for various needs, or if there is a one-size-fits-all model. Blockchain technology can be customized and implemented for every business need, and there are three major kinds of blockchain networks that can be deployed today. Public blockchain In a public blockchain, every authorized person can read, send and validate transactions without explicit prior permission. This is particularly useful when decentralization is needed in peer-to-peer situations. You might want to understand a public blockchain as being similar to Wikipedia, where any editor can make changes to a document, though previous versions are always available, and no change can go unnoticed. Public blockchains are great because : Every authorized user can make transactions on a common platform, and remain anonymous too Circumvent third-party vendors, as intermediaries are not required to facilitate transactions between users Affordable to implement Safe and secure Changes need not be permanent and can be discarded if they are not in the interest of the entire system Application developers do not have overarching powers beyond developing apps However, a public blockchain isn’t without disadvantages. They are : Resource heavy, and in the long-term, prove to be expensive as computational requirements increase with time and frequency of use May not be suitable for fast-paced transactions like trading, as transaction verification process can be delayed by a couple of hours Privacy may not be sufficient for regulatory transactions at enterprise level Decentralization may lead to possibility of collusion, and unintended consequences Private blockchain A private blockchain comes with more privacy and a greater degree of control. Only a certain organization or a group of individuals have write permissions and only they can create new transactions within the network. Authorized individuals and groups may have read permissions. As there are restrictions imposed on read, write and validate permissions, private blockchains are not distributed or decentralized to the level of a public blockchain. Private blockchains are perfect for intra-business usage where only company executives have access to the network. It is also the perfect blockchain solution where outside-interference or activity is not required. Private blockchains have a number of advantages such as : Suitable for traditional business and governance models Transaction costs are low, as computational power required is lower as well Cyber attacks are eliminated because blockchains are immutable, and in a small and closed network, the identity of the attacker can easily be established Transactions go through quickly as only a few devices need to verify them With restricted access comes better privacy A few drawbacks of private blockchains are : Decentralization can’t be compared with public networks Easier for those with write permissions to unilaterally make changes in spite of disagreements, if any Lacks the openness of public blockchains Is there an alternative to public and private blockchains? Well, there is. As both public and private blockchains come with their own drawbacks, there is a hybrid version of the two, also known as permissioned or consortium blockchain. In a permissioned blockchain, write permissions are not assigned to a single organization or a few individuals. Instead, a few pre-determined entities control the process of validating transactions consensually. They also have the permission to assign read permissions. Permissioned blockchains come with the immutability and efficiency of public blockchains, with a degree of privacy seen in private blockchains. A consortium blockchain (permissioned blockchain) is easily scalable, private, and consensual. Transaction costs are low, and you can start implementing it right away. Making a choice It is always difficult to make decisions related to technology, but one must make them when the time is ripe. Every blockchain model comes with its

Who could possibly blame the IT manager or the CEO of a company, who has watched Spike Jonze’s 2013 film Her and concluded that artificial intelligence and machine learning signal something ominous? While most news headlines either glamorize or sensationalize artificial intelligence, the reality is much more nuanced. Artificial intelligence has already begun to revolutionize businesses all over the world, and it is only a matter of time before everyone else will have to play catch-up. Delaying the adoption of artificial intelligence and machine learning comes at the cost of being left behind, and eventually having to hurriedly implement AI and ML. The time is ripe now to adopt artificial intelligence and machine learning in small and incremental phases, using agile methodology. Let us take a look at why and how every business in any industry vertical should implement AI and ML, without affecting one’s core business. Understanding machine learning and artificial intelligence in simple terms Artificial intelligence, in layman’s terms, is a machine’s (computer’s) ability to mimic human cognition, such as learning and problem-solving. Its applications and scope range from building autonomous cars, advanced medical treatments, Internet of Things (IoT), advanced statistics and computational intelligence, etc. Machine learning is the ability of computers to learn without explicit programming. In other words, machines can be programmed to learn independently, without human supervision or intervention. While machine learning has many applications, the most popular application is in the form of deep learning, which is about building mathematical algorithms that process large amounts of data. These algorithms are often called neural networks, as they mimic human information processing. Artificial intelligence and machine learning will be used by almost every business to detect anomalies, recommend products, improve services, or predict trends in the very near future. More than 90% of the 100 early-stage startups we met in the last six months plan to use machine learning to improve customer experience. Currently, businesses that have already implemented AI and ML can be classified into two groups: those that use AI/ML in their applications and services, and those that build and develop AI and ML middleware for others. Get Started with Artificial Intelligence and Machine Learning Now If you feel intimidated by the quick advent of AI and ML, allow us to put your worries to rest. Instead, make the best use of AI and ML by keeping aside technicalities, and by identifying simple and non-business critical areas. Look at AI and ML as an innovation project that can be started small, and built upon gradually as the technology evolves. It is not necessary to bring dramatic and disruptive changes to your business. Practically, you can enhance every area of your business in small and non-critical ways using AI and ML. Let us look at possible scenarios in each department. Here are some of the areas where you can introduce AI/ML without affecting your core business: Business intelligence Predictive analysis helps you access trends, predict outcomes, and provide better solutions. Use old databases for rich insights, or merge AI with your BI tools to access insights and predict outcomes. Customer service Choose from improving search results, boost sales, retargeting potential customers, personalizing content, engaging visitors with a chatbot that answers queries related to products, etc. The sky is the limit. Enterprise asset management Use AI to manage contracts and assets, predictive behavior analysis and alarms, automated intelligent processing and standardization of data. Etc. Customer Experience Provide AI chatbot assistants to your customers, or use deep insights to enhance customer experience. Use machine learning to improve search results based on user behavior. Human resource Surprise your employees with accurate appraisals, incentives, and payouts. AI does not understand human concepts of prejudice and bias. Sales and marketing Design and implement sales & marketing campaigns based on deep insights derived from machine learning and AI. Predictive analysis can help you develop marketing campaigns that are prophetic in nature. Manage your social media without having to be online 24/7. Product development and production Machine learning is most often used in product development and service enhancements. You can use machine learning in the most innocuous manner to improve your product development and production. Procurement and inventory management Track your raw materials, predict when you might need to replenish, and intelligently procure the best at the lowest rates. Distribution Monitor fleets, track packages, and provide shipping and delivery services that are on the dot. There’s a storm approaching? Predict it before it happens, and have a contingency plan. Strategize AI/ML implementation Now that you know AI and ML can be implemented incrementally in almost all business areas, it is time to strategize the entire implementation process. In 6 easy steps, you can implement and use artificial intelligence and/or machine learning as part of your innovation project, without interfering with your core business. Identify safe targets to innovate Think about the problems you currently have in your business, and which one is the least critical to everyday operations, if changes were brought. Choose from the list above, and identify targets that could be safely innovated with AI or ML. Look around for inspiration If you are unable to decide what might be a safe target, look at what your competitors are doing. If none of your competitors are doing anything related to AI or ML, you might even want to find inspiration in adjacent industries. Assess your budget for innovation It is advisable to start small, as that helps you remain confident throughout your innovation project. Assess how much you are willing to spend on bringing innovation to your chosen area. Choosing open source technologies is another tried and tested method to innovate, as it costs lesser. Look for agile vendors Once you identify the area you wish to innovate, look for vendors with similar agile mindsets who believe in adopting and implementing technology incrementally. Implement AI innovation slowly but steadily As discussed earlier, it is important to adopt artificial intelligence slowly, as it gives you time to adjust to the