Page 22 – DataToBiz

Kavika Roy
August 15, 2023
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Artificial Intelligence vs. Machine Learning: Key Differences

Often, individuals in the technology world use the terms Artificial Intelligence and Machine Learning interchangeably. However, while AI and ML are closely related, they’re different in almost every aspect. Want to know the difference between AI and Machine Learning or how they’re different? Read along. The internet is full of definitions that fail to define AI and ML properly, which is why the confusions arise. However, in this blog, you’ll find a detailed differentiation of AI vs. Machine Learning. So much so that even a non-tech-savvy person will be able to differentiate these terms. In addition, you’ll also find the advantages or use cases/applications of AI and ML in different business domains, which will further help deepen your understanding. So, read in full. AI Vs. Machine Learning What is AI or Artificial Intelligence? Some individuals think of AI as creating a conscious sentient being via programming, which is often termed artificial general Intelligence. On the other hand, data scientists use AI at a practical scale for solving real-world problems such as customer service, data analysis, manufacturing, etc. So, we can conclude that Artificial Intelligence is an umbrella term for technology that helps individuals autonomously solve problems by simulating human Intelligence. And this is done using algorithms that analyze input data and produce outputs. Take a chatbot, for example. Whenever a customer types in his/her query, the bot comes up with a suitable answer (output) based on the question (input). What’s more amazing is that AI algorithms can react to input data in different forms. Take virtual assistants or personal assistants like Google or Siri, for example. Whether you say, “What’s the weather like” or “What’s the temperature” or maybe “How’s the weather” the assistant is going to notice the keyword and provide you with the correct answer. Note. AI has numerous subsets such as Machine Learning, deep learning (common application: virtual assistants), neural networks (common application: facial recognition), computer vision (common application: image recognition or object recognition), and natural language processing. What is Machine Learning? Machine Learning, as stated above, is one of the subsets of Artificial Intelligence. However, unlike AI, Machine Learning as a term doesn’t mean creating smart machines or computer programs that can simulate human behavior to solve problems. It means creating computer systems that learn and improve on their own, using experiences (huge volumes of data) without human intervention. We can cite the example of voice assistants to understand Machine Learning. Earlier, the assistants used only to recognize specific statements like “What is the weather outside” and give an output. If anything else was said, the assistant wasn’t able to produce the desired output. However, because of Machine Learning, assistants can now recognize and respond to a variety of different statements having the same meaning. Another example of ML is predictive analysis. Several companies, such as Netflix and Amazon, use predictive analysis to predict user behavior and deliver a better user experience. What’s more, banks use predictive analysis to predict fraudulent patterns and stop illicit transactions from happening. In a nutshell, if AI is the grand vision of creating intelligent computer programs or machines, ML consists of the models, technology, and processes that are being used by experts to reach there. How are Machine Learning and Artificial Intelligence Different? Machine Learning and Artificial Intelligence can be differentiated based on several factors such as their scope, primary goal, application, the data type they deal with, etc. Here’s a table that can help you differentiate Machine Learning and Artificial Intelligence better: Criteria Artificial Intelligence Machine Learning Primary Goal To create computer systems that simulate human intelligence for solving problems. To enable machines to learn automatically from data and improve the accuracy of outputs Hierarchy AI sits at the top with Machine Learning, deep learning, computer vision, neural networks, and natural language processing being its subsets. ML is a subset of AI and deep learning is the subset of Machine Learning that involves deep learning algorithms, vigorous training data, and multilayered neural networks. Scope AI has a pretty broad scope. Limited scope, as the focus is on making machines more accurate. Applications Chatbots, voice assistants, humanoid robots, etc. Google search algorithms, recommendation engines, etc. Types Weak AI, Strong AI, and General AI. Supervised learning, Unsupervised learning, and Reinforcement learning Use Cases of AI And Machine Learning? 1. Fraud Detection (Anomaly Detection) AI and ML are revolutionizing the finance industry because of predictive analysis. ML can help finance companies, especially banks, with fraud pattern recognition and raise the alarm whenever there’s something fishy, thus preventing fraud from happening. Please Note. While there’s already a rule-based system that’s used by different banks, it’s accused of causing false positives. And this costs the banks a lot of time as the employees have to mitigate the issue only to find it was a false alarm. However, fortunately, as an ML system learns from its experiences, the chances of false positives reduce which helps save time and resources. 2. Automation With AI in place, companies can easily automate redundant tasks and improve efficiency. And this is true for every domain across the globe. Here are some examples: Education Sector Teachers have to manually process attendance and create questions or exam papers redundantly, which requires a lot of time and effort. However, with AI, teachers can use attendance tools or biometric systems to mark attendance and use tools such as PrepAI for generating intuitive question papers within minutes. Manufacturing Manufacturing is highly labor intensive and has a lot of moving parts which increase the likelihood of making mistakes. However, AI makes it easy. Using automation tools or robots at a manufacturing facility, companies are drastically improving their delivery potential and reducing the error margin while ensuring worker safety and innovation. 3. Customer Support Machine Learning algorithms work behind the voice assistants such as Google’s Google Assistant, Apple’s Siri, or Microsoft’s Cortana for speech recognition using NLP or Natural Language Processing to provide you with an answer to your query. And several companies integrate voice assistants or chatbots on their

Yashica Chopra
August 13, 2023
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7 Top Procurement Analytics Companies to Improve Your Supply Chain

Procurement analytics is the collection and analysis of manufacturing data in an enterprise. It helps manufacturers make data-driven decisions in real time. Here, we’ll discuss the top procurement analytical companies in detail. Data and artificial intelligence help many manufacturers make informed decisions based on actionable insights. Enterprises spend heavy amounts on production and supply chain management to deliver the end product to customers. However, many external and internal factors can lead to delays, shortages, greater expenditures, and losses. Such adverse situations can be avoided by adopting the data-driven model. Procurement and spend analysis provides decision-makers with the necessary information to minimize risk and maximize returns. It helps supervisors and managers make smarter decisions based on insights derived by processing manufacturing data. Analytics for procurement is not a new concept. Several enterprises are already using business intelligence consultancy services to get analytical and data visualization reports in real time. In this blog, we’ll understand what procurement analytics is and the top data analytical companies that help manufacturers improve their supply chain. What is Procurement Data Analytics? Procurement analytics is the process of collecting, cleaning, and analyzing manufacturing data to derive actionable insights. The insights help make decisions about vendors, purchases, invoice payments, credit, overhead expenses, equipment maintenance, workplace safety, production cycle, quality control, supply chain management, and much more. According to Research and Markets, the global procurement analytics market is valued at $4.88 billion in 2023 and is expected to touch $10.26 billion at a CAGR (Compound Annual Growth Rate) of 20.4%. Many MSMEs and large enterprises from the manufacturing industry are investing in procurement data analysis to gain a competitive edge in the market by enhancing customer experience. Why is data analysis important in procurement? Analytics is the most vital component that helps manufacturers leverage data to streamline operations, production, maintenance, supply chain, and all aspects of the businesses to save money and resources and increase ROI. A few reasons to use analytics in procurement are as follows: Top Procurement Analytics Companies 1. DataToBiz DataToBiz is an award-winning data analytics company that works with MSMEs, SMBs, and large enterprises from different continents. The company helps enterprises unlock the true potential of data to facilitate business expansion and growth in competitive markets. It offers end-to-end services for: DataToBiz has earned a reputation for being a reliable Power BI consultancy provider in various industries like manufacturing, transportation, healthcare, FinTech, EdTech, etc. The company is a certified partner of AWS, Google, and Microsoft and has worked with 300+ firms. It has a satisfaction rate of more than 97% for offering tailor-made solutions to each client and helping them generate higher profits. 2. Everstream Analytics Everstream Analytics offers risk management and predictive analytics services to help improve global supply chain standards. The company converts uni-dimensional data into multi-dimensional to provide complete information and sharper analytics to clients. The company helps enterprises from many industries like manufacturing, automotive, heavy equipment, chemical, food and beverages, energy, etc. It offers the following services: Everstream Analytics has helped many enterprises reduce the time to identify and assess disruptions by around 50% to 70%. The company works with leading supply chain providers to strengthen their services and save costs. Data, procurement, logistics, supply chain, and weather are the primary focus areas of the company. It also helps manufacturers create sustainable and lean supply chains. 3. Sievo Sievo is a procurement analytics firm that helps data-driven enterprises to make transparent sourcing decisions. The company deals with extracting, classifying, and enriching data from various ERP systems and external data sources. It aims to save time for enterprises by implementing advanced technologies to derive analytics. The company offers services such as: Sievo assists several manufacturers to find the right suppliers and meet compliance standards with ease. From material forecasting to savings lifecycle management and reducing scope 3 CO2 emissions, the company provides the necessary software and analytics for business to achieve their goals. With a head office in Finland, Sievo has clients from around the globe and works mainly with large enterprises. 4. AnyDATA AnyData offers ready-to-go data-driven software products to help businesses use advanced technology to digitally transform internal processes. The company has developed a Modern Data Platform with all the vital components required to collect and analyze large datasets. It helps clients spend less time on data analytics and more time on expanding the business. The company’s services include: AnyData provides analytical solutions to enterprises and consultants. The company calls itself a one-stop shop for advanced analytics, dashboard customizations, integrated security, artificial intelligence, contract management, and much more. The products developed by the company can be run on the cloud, on-premises servers, desktops, and mobile devices. From supplier management to procurement analytics, it delivers complete services to enterprises from various countries. 5. Robobai Robobai is a global FinTech leveraging AI company that helps businesses transform their supply chains. The company emphasizes creating ethical supply chains through 360° visibility. It focuses on providing accurate reports about sanctions, indigenous suppliers, ESG initiatives, etc., to help clients make better decisions. The company’s services are as follows: Robobai empowers treasury and procurement teams to use data wisely, make better decisions in choosing suppliers, and make the supply chain more transparent. The company provides AI tools and analytics to automate processes and save time and resources for enterprises. Businesses from the manufacturing industry and other niches can use RobobAI to automatically improve data quality and derive insights. 6. Simfoni Simfoni is a digital transformation company, revolutionizing how businesses spend money. The company eliminates the hurdles and hassles from procurement to control and save business spend. It also helps automate procurement and supply chain processes to increase sustainability and achieve business objectives. The company offers an array of services, such as: Simfoni helps enterprises understand the benefits of procurement and purchase analytics by providing modular, purpose-driven solutions customized for each business. It uses AI technology to enable deep automation and streamline procurement to reduce unwanted expenses. The company supports manufacturers to benchmark performance, enhance spend visibility, and track the KPIs

Kavika Roy
August 11, 2023
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Everything You Need to Know About Large Language Models

Large language models use NLP and deep learning technologies to deliver personalized and contextually relevant output for the given input. LLMs are powerful, robust, and useful to an enterprise. Here, we’ll discuss the benefits, challenges, applications, and examples of LLMs. Artificial intelligence is growing leaps and bounds in recent times. Generative AI brought a revolution and disrupted the global industry. The top brands are following suit by investing heavily in AI and large language models to develop customized applications like ChatGPT. According to a Verta, Inc. survey, 63% of business organizations plan to continue or increase their budgets for AI adoption. Based on a report by Juniper Research, ML spending has increased by 230% between 2019 and 2023. Large Language models are being extensively researched and developed by universities and leading multinational brands in the international world. While there’s no denying the heavy expenses required to implement LLMs in an enterprise, it cannot be ignored either. LLMs are proving to be beneficial on many fronts. From R&D to customer service, large language models can be used for a variety of tasks. In this blog, we’ll find out everything we know to know about AI large language models. What is Meant by Large Language Models? A large language model is typically an AI algorithm that uses artificial neural networks to process and understand inputs provided in the human language or text. The algorithms use self-supervised learning techniques by analyzing massive data in various formats and understanding the patterns, context, etc., to provide a relevant output as the answer. LLMs can perform tasks like text generation, image generation (from text), audio-visual media generation, translating text, summarizing input, identifying errors in code, etc., depending on how and why it has been developed. The models can converse with humans and provide human-like answers. Large language models essentially use deep learning and natural language processing technologies to understand complex entity relationships and generate output that is semantically and contextually correct. However, developing an LLM from scratch is cost-intensive and time-consuming. Large Language Model consulting companies work with open-source LLMs and train them with the client’s proprietary data to fine-tune the algorithm as per the business requirements. Enterprises can adopt LLM applications in a quick time and gain a competitive advantage. What are the Parts of a Large Language Model? An LLM has a highly complicated architecture with various components, technologies, and connections. However, the following parts are important in building a large language model for a transformer-based architecture: 1. Input Embeddings The input text is transformed into individual words and sub-words in a process called tokenization. These tokens are embedded in a continuous vector representation. The semantic and syntactic information of the input is captured here. 2. Positional Encoding This part deals with providing the position of each token based on the input. This ensures that the model understands the input in sequential order to retain its meaning and intent. 3. Encoder Encoder analysis is based on the neural network technique. An LLM will have multiple encoder layers. These are the core of the transformer architecture and have two stages – self-attention mechanism (identifying the importance of tokens based on attention scores) and feed-forward neural network (capturing interactions between the tokens). 4. Decoder Layers Not all LLMs have a decoder layer. However, the decoder enables autoregressive generation for the model to generate the output based on the tokens. 5. Multi-Head Attention Multi-head attention is where several self-attention mechanisms are run simultaneously to understand all possible relationships between the tokens. This allows the model to interpret the input text in multiple ways (useful when the text is vague). 6. Layer Normalization Applied to each layer in the LLM, this part stabilizes the learning process of the algorithm and makes the model more effective in generating a more generalized output for various inputs. 7. Output Layers The output layers change from one LLM to another as they depend on the type of application you want to build. Benefits of Using Large Language Models Now that you know how large language models work, let’s look at the advantages of implementing LLMs in an enterprise. 1. Adaptability Large language models can be used in different departments for varied tasks. You can fine-tune the model with different datasets and change the output layers to deliver the expected results. LLMs can be used for numerous use cases. Businesses can develop more LLMs based on the core model and add more layers to expand it and use the applications across the enterprise. Over time, the LLMs can be adopted throughout the organization and integrated with the existing systems. 2. Flexibility Even though LLMs are yet to reach their full potential, they are already flexible and versatile. You can use an LLM application for content generation, media generation (image, audio, video, etc.), classification, recognition, innovation, and many other tasks. Furthermore, the models can process input of any size (from a single line to hundreds of pages of text). You can deploy the models in each department and assign different tasks to save time for your employees. 3. Scalability Large language models can be expanded as the business grows. You don’t have to limit the role of LLMs in your enterprise as the business volume increases. The applications can be scaled to accommodate the changing requirements. They also can be upgraded with the latest technologies and datasets to continue providing accurate and relevant results. LLMs are easy to train as they can read and process unstructured and unlabeled data. There’s no need to spend additional resources on labeling data. However, low data quality can lead to inaccurate output and inefficient applications. 4. Performance LLMs are robust, powerful, and highly efficient. They can generate responses in near-real-time and have low latency. Using an LLM application saves time for employees. It allows them to use the results right away and complete their tasks. For example, an employee doesn’t have to read dozens of pages to understand the content. They can use LLMs to summarize the information and read only the important points that

Yashica Chopra
August 09, 2023
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16 Common Mistakes to Avoid in Digital Transformation

Digital transformation streamlines business processes and promotes data-driven decision-making through digitalization. Here, we’ll discuss the common mistakes to avoid when digitally transforming your enterprise and how to achieve success. Digital transformation is the use of technology to radicalize an enterprise and turn it into a digital-first data-driven model. This enhances business processes, increases customer experience, and improves revenue. Statistics show that digital transformation with grow at a CAGR (compound annual growth rate) of 19.48% to reach $767 billion by 2026. Another survey report shows that 70% of businesses have a digital transformation plan or are developing one. Furthermore, around 80% to 93% of employees think that increasing the use of technology will improve business performance. However, 41% of organizations are investing in digital transformation without researching customer requirements. Not all enterprises know how to initiate and continue the digital transformation, thus making numerous mistakes and generating losses. In this blog, we’ll read more about the mistakes and the reasons for DX failure in organizations, the challenges, barriers, and ways to avoid them. Why does Digital Transformation Fail? Research shows that a majority of digital transformation projects fail. In fact, according to a study by Boston Consulting Group, the failure rate is 70%. 825 organizations from around the world were included in the study. But why do 70% of digital transformations fail despite access to technology and talent? Experts say the reasons are straightforward, though many factors result in failure instead of success. In most instances, digital transformations fail not due to the lack of technology but due to mismanagement or lack of experience in implementing transformation in the organization. These issues can be effectively handled by partnering with digital transformation service providers. Offshore companies not only provide access to the necessary technology but also help leaders and employees proactively tackle the changes in their business. What are the 4 Key Challenges of Digital Transformation? What are the 5 Barriers to Effective Digital Transformation? Mistakes to Avoid in Digital Transformation Do you know that 87% of businesses know digital transformation will disrupt the industry, but only 44% are prepared to handle the disruptions? It’s no surprise that the digital transformation failure rate is high, with several enterprises not taking the necessary steps to avoid even the most common mistakes of digital transformation. Take a look at the mistakes to avoid when digitally transforming your organization. 1. Having an Incorrect Idea of Digital Transformation This is the biggest mistake to avoid in digital transformation adoption. The business must understand what it means to digitally transform the enterprise and its impact on the internal process, employee, customer relationships, finances, and market. Just because every industry expert says you need digital transformation doesn’t mean you dive right into it without understanding the core concepts. Spend time researching all aspects of digital transformation and how it can help your business. Find a digital transformation consulting company to help you get the answers to your questions. Don’t initiate any process or change without gaining clarity on the basics. 2. Blindly Following Trends Without Goals and Objectives Digital transformation is not a trend. It’s not something you do because your competitors are working on it. Following the ‘trends’ of digital transformation can cause more harm than good to your organization. Connectivity and integrations are the core of DX, and this can lead to many risks, such as data security lapses, violating data privacy regulations, etc. Instead, start with risk analysis to understand how digital transformation can affect your enterprise. Then, define the goals, objectives, and outcomes for the process. Align these with your business goals to have something concrete to work on. Avoid random digitalization of processes. 3. Not Considering All Phases of Digital Transformation Business digital transformation is not a single step or phase. It’s not a one-thing solution that will fix all problems in the organization. DX is implemented in phases and is a continuous process. It is a cycle where changes in one department impact the other and create a cohesive internal process across the enterprise. However, many businesses don’t consider the post-implementation phases of digital transformation. This creates uncertainty and leads to process errors as employees don’t know what to do when the new technology goes live. A simple way to avoid this mistake is by creating awareness and educating every involved about DX. 4. Not Having a Definite Outcome for the Process Why do you want to digitally transform the enterprise? What outcome do you wish to achieve? Digital transformation is likely to fail when you don’t have clear answers to these questions. The top management, decision-makers, and employees should know the implications of DX. Start by defining the outcomes and work backward to create a feasible and comprehensive digital transformation strategy. For example, the outcomes desired by a healthcare center will be different from that of an insurance company. You can make the right chances when you know what you want to achieve. 5. Underestimating the Cost of Digital Transformation This is another common mistake many enterprises make. Digital transformation is expensive. It requires continuous investment in revamping the IT infrastructure, training employees, and redefining the work culture. Many hidden costs affect your budget. CFOs need to be aware of the expenses before they approve the budget for digital transformation. That’s why CFOs, CEOs, COOs, and other decision-makers have to understand DX and be fully involved from the early days. You also have to choose between modernizing legacy systems and replacing them with completely new tools. Digital transformation experts can help make the right decisions and plan the costs based on your business requirements. 6. Excluding a Majority of the Stakeholders A recurring mistake several businesses make is to leave the frontline workers in the dark. If employees who will use the new technology and processes don’t know what’s happening in the background, they won’t be in a position to jump on board and accept the changes. For example, if you want to develop a model to assist customer care representatives, the AI experts need direct input from the representatives. This will enable them to build a model

Yashica Chopra
August 05, 2023
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Leveraging AI in Media and Entertainment

The presence of AI in the media and entertainment industry has been growing with an increase in customer demand for more flexibility, choice, personalization, and access to unlimited content. Here, we’ll discuss the role of artificial intelligence in media and entertainment. The media and entertainment industry is flourishing in today’s world. Whether it is social media networking, news portals, or OTT platforms, the industry has lots to offer to the common public. Statistics show that the M&E industry will be $2.9 trillion by 2026. It’s no surprise, given that the media and entertainment houses are adopting AI to cater to the changing market demands and customer requirements. Artificial intelligence is now a part of the industry and has varied roles to play. From personalizing content for users to auto-generating news and predicting future events, AI in media and entertainment is redefining how people consume content. In this blog, we’ll look at how artificial intelligence is transforming the media industry and the future of entertainment. What is the Market Size of AI in Media and Entertainment? According to a report by Business Wire, the M&E industry will be spending $1860.9 million on artificial intelligence by 2025, while the overall AI market is expected to touch $118.6 billion. Another report shows that AI in social media alone will touch $12 billion by 2031. There’s no denying that artificial intelligence will continue to be a prominent part of the M&E industry. What is the Role of AI in Media? Many millennials and Gen Z users consume media on digital platforms and expect personalized customer experiences and content curated exclusively for them. This led to a greater digital revolution and the adoption of artificial intelligence by M&E businesses. However, the media houses had to overcome quite a few challenges to ensure they provide quality content to consumers and take advantage of the benefits offered by artificial intelligence. AI is not limited to a single role or department in the media industry. For example, How is AI Impacting the Entertainment Industry? Many media houses and entertainment companies are investing in AI to retain their competitive edge and stay relevant in the market. Here’s how AI is impacting the entertainment industry. 1. Content Development Creating unique content takes time and effort. While it is still a human-dominated field, generative AI solutions are disrupting established practices in many ways. While online writing assistants and grammar checkers are fine-tuning human-written content, platforms like ChatGPT are churning out pieces on their own. Narrato and Copy.ai are two examples of AI-based writing assistants and content-creation tools used by various companies and professionals. No doubt, generating written content is faster with AI. Nevertheless, there’s no denying that artificial intelligence enhances the quality of content writing and will continue to assist human writers fine-tune their works. 2. Image Generation DALL-E is not the only AI-based image generator in the market. Many free and paid generative AI tools can create realistic HD-quality images based on a text prompt or an input image. These images are being used in blogs, news articles, social media posts, etc., to increase viewership. Using AI for image generation is a cost-effective move and allows entertainment companies to deliver the final product quickly to the market. However, establishments still have to deal with the challenges of plagiarism (it’s not clear how AI generates the images) and ethical AI. However, artists can use AI image generators to enhance their existing work and make it more appealing to audiences. 3. Marketing A survey report shows that 73% of marketing executives are already using generative AI for B2B and B2C marketing. Businesses from different industries use AI for marketing. The entertainment and media industry can hugely benefit from partnering with an artificial intelligence consulting firm to streamline its sales and marketing strategies. From identifying the right target markets to understanding customer behavioral patterns and using feedback to create compelling content, AI-powered marketing can increase the visibility and reach of an entertainment company in competitive market conditions. AI also makes it easy to manage omnichannel marketing and enhance the customer journey with the business. 4. Film Production AI tools can now write the script and make a film on behalf of humans. It takes less time, effort, and money to use AI for film production. For example, 28 Squared Studios collaborated with Moon Ventures to create a short film titled The Safe Zone. The entire film is written and produced by ChatGPT, down to the facial expressions required, character script, outfits, camera positioning, etc. This film was made to showcase how generative AI can help the entertainment industry. While writers and content creators are still on strike against the use of AI to replace human creativity, there’s no denying the role of artificial intelligence in storytelling. The high-end graphics and visual enhancements in movies is the best example of how useful AI is in film production. 5. Monetization With consumers shifting from traditional platforms to digital service providers and OTTs, ensuring content monetization can be tough the production houses and streaming companies. However, using AI in entertainment helps with personalizing content, hyper-targeting audiences through marketing, and mitigating churn to keep the income flowing. AI analytics and advertisement strategies can increase subscriptions and ensure consumers are loyal to the platform. Personalization plays a major role in monetizing content for a diverse group of users from around the globe. OTT platforms like Netflix and Amazon Prime use powerful AI engines to customize the interface and content for each user. 6. Content Optimization Entertainment companies produce and share content to generate money. However, managing the content distribution and licensing terms can hugely affect its profits. AI helps in making the right decisions based on market demands and trends. Predictive analytics is used to understand what type of content the target audiences will enjoy. The marketing teams rely on these insights to determine the right channel and distributor to share the content with the public. Similarly, adding optimized metatags, labels, comments, alt text, etc., will take the content to a wider group and increase views. In today’s world,

Yashica Chopra
August 03, 2023
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7 Top Digital Transformation Consultants in Australia

Digital transformation is a continuous and ongoing process that helps businesses create and implement a digital strategy to enhance enterprise value and customer satisfaction. Here, we’ll discuss the top business transformation consultants in Australia and their importance in the global market. Digital transformation is the process of using the latest technologies and solutions to revamp existing business procedures or create new ones to increase productivity and cater to changing market demands without compromising quality or increasing costs. According to Zippa, the global digital transformation spend is expected to reach $6.8 trillion by 2023 year-end. Around 70% of businesses are already working on their digital transformation strategies. However, the Australian market is yet to fully invest in advanced technologies. According to the BCG-MIT report, Australian companies and governments are showing more interest in adopting AI for digital transformation. Microsoft Azure, AWS, and Google have a strong presence in the Australian market. Many local and offshore companies are also providing tailor-made services to promote digital transformation across enterprises from all industries and niches. Though a study shows that up to 70% of Australian companies are yet to achieve the expected results, the companies are surely taking strong steps to adopt data-driven models and AI tools to become on par with global enterprises. In this blog, we’ll look at the top digital transformation consulting companies assisting Australian businesses to become successful. What Makes a Successful Transformation? The digital transformation of a business is a continuous and scalable process but consists of three main steps – digitization, digitalization, and digital transformation. The process of converting analog data to digital data is called digitization. It is the first step where all information is available in digital format and can be edited, shared, updated, and processed easily. Digitalization is the process of simplifying the work using various relevant tools or applications. For example, the basic purpose of customer service hasn’t changed, but the procedure has been upgraded to deliver quicker and more efficient results. Digital transformation deals with questions like, how can we streamline the backend work to reduce pressure on employees but enhance customer experience? Successful transformation is when a business optimizes the use of resources to amplify ROI and profits while ensuring greater customer experience. It increases overall productivity, quality, and brand value. Top Digital Transformation Consultants in Australia 1. DataToBiz DataToBiz is a reputed Digital transformation consultant with a presence in different regions around the globe. The company offers tailor-made digital transformation services to SMBs, MSMEs, and large enterprises from Australia, Asia, North America, Africa, and the Middle East. It helps businesses from various industries and niches unlock the potential of data by creating powerful strategies and implementing them in various verticals. The company aims to make the organization future-proof through services such as: DataToBiz follows a systematic process to initiate digital transformation in an enterprise. The company has expert digital strategy consultants who take care of every single aspect and guarantee higher ROI through data-driven decisions. 2. Boston Consulting Group Popularly known as BCG, the multinational company offers business strategy and digital transformation services to clients from various parts of the world. The company follows a collaborative model to assist enterprises in overcoming business challenges and achieving their long-term goals. The company also has specialty teams in different industries to ensure customized solutions based on business requirements. Founded in 1963, BCG believes that the clients come first. It offers services such as: Given the extensive services offered by the company, BCG is best suited for large enterprises with greater budgets to adopt digital transformation at multiple levels simultaneously. The company offers total transformation to help the organization grow and drive bottom-line impact. 3. PwC PwC is a global digital transformation agency promising end-to-end services to help businesses solve complex problems and realize strategic business goals. PwC Australia calls itself a human-led, technology-powered community of solvers. The New Equation is the latest mega project by the company, where it aims to assist several Australian businesses adopt digital transformation and find the best solutions to their organizational problems. The company is among the top 50 brands worldwide and works with multinational organizations. It offers the following services: PwC focuses on how technology can enable a business to become more efficient, productive, and successful. It helps organizations make the right investments and choose emerging technologies to empower employees and unlock the business’s digital potential. 4. Synergy Group Synergy Group offers a full range of consultancy services to help businesses and government agencies address the various challenges in the ever-changing global markets. It deals with creating, designing, and integrating the necessary solutions to help enterprises drive their future strategies and achieve the expected results. The company handles everything from strategy to implementation to ensure the long-lasting and sustainable performance of the business. It works with businesses from several industries to provide customized solutions and make them react proactively and tackle market competition. It provides services such as the following: Synergy Group understands that people resist change and aims to systematically implement transformation. This allows people to understand the benefits of digitalization and adapt to the changes without excess stress or pressure. 5. Nous Group Nous Group is an international management consultancy that works with establishments from the public, private, and community sectors. It helps solve complex strategic challenges and uses the expertise of in-house digital transformation consultants to bring a compelling change for the clients. The company has clients from various industries like agriculture, environment, education, government, finance, healthcare, transportation, human services, etc. The company works with a primary aim to help businesses create a positive impact on people’s lives through different technologies and services. Nous Group has a presence in five countries and provides tailor-made organizational development strategies to improve overall employee experience and performance. The company also works with Victorian small businesses to streamline processes and enhance customer engagement. It also focuses on helping a business grow without compromising public safety. 6. FTI Consulting FTI Consulting offers extensive services to help businesses handle the most complex and challenging events in the industry. From managing change to mitigating

Yashica Chopra
July 31, 2023
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AI in Paints and Coatings Industry

Paints and coatings companies use AI to can gain competitive advantages, save costs, reduce production time, and improve product quality. It helps with color matching, formulation optimization, quality control, and predictive maintenance. Here, we’ll discuss the role of artificial intelligence in the paints and coatings industry. The paints and coatings industry is a dynamic market and includes various sectors like buildings and construction, automobile finishes, furniture (wood and non-wood), medical products, and other equipment. From antimicrobial coatings to self-cleaning paints and solar panel paintings, the products are used in a plethora of industries. The Covid-19 pandemic affected the industrial coatings sector in 2020, resulting in a 4.2% decline in the global market size. Fortunately, the industry is regaining its position and returning to pre-pandemic levels. According to Statista, the global paints and coatings industry is expected to touch $235.06 billion by 2029. Digitalization and sustainability are two key factors driving the paints and coatings industry in today’s world. Manufacturers and retailers are adopting artificial intelligence and machine learning tools to streamline production, quality control, distribution, and sales. AI can forecast coating performance, increase production efficiency, optimize formulas, improve customer satisfaction, and save time, effort, and cost for the paints and coatings industry. In this blog, we’ll read about the role of AI in the paints and coatings industry and the need for partnering with an artificial intelligence consulting company to increase ROI and profits. Importance of AI in the paints and coatings industry Artificial intelligence has varied roles in the paints and coatings industry. From developing corrosion and wear-resistant paints to increasing their adhesiveness and enhancing the self-lubricating nature of paints, AI can help in conducting the necessary research into material properties and creating new chemical compositions. AI and ML algorithms are being used for data modeling about the properties of different types of paints (based on their chemical composition). Factors like the toxicity of the paint and coating, price fluctuations based on market demand, environmental impact, alternative materials, etc., can also be determined using AI and ML technologies. In fact, computer vision and AI are used to analyze images in bulk to develop smart coatings. By understanding the functioning of the microstructures in the materials, a manufacturer can not only create more durable and better-quality paints but can also optimize production to reduce input costs. An article by American Coatings Association shows how AI has been instrumental in using advanced algorithms to provide recommendations around resin formulations and recipes. Furthermore, Eric Sapper, the assistant professor in the Department of Chemistry & Biochemistry and the Western Coatings Technology Center said “Centuries of scientific learning and discovery do not need to be tossed out the window when you’re building an AI workflow. Instead, new AI workflows can be built around the institutional and domain-specific knowledge that already exists within the organization.” This means if you’re looking to implement AI in your product development and coatings experimentation, you don’t need to start everything from scratch. And the transition from manual to AI workflows and innovation around paintings & coatings would be smooth and friction-free. Now, let’s understand how AI will help save time, effort, and money in the broad parameters of the paintings and coatings industry. How can machine learning and artificial intelligence help paints and coatings companies? The role of AI and ML in the paints and coatings industry is not limited to research and development. It can also be used to match colors and create new shades, help customers choose the right colors based on their requirements, develop the right marketing strategy for the target audiences, streamline the supply chain, and enhance customer experience. Color Matching KCC Corporation, a South Korean paint company, developed an AI-based color matching and design system called K-Smart that can complete color mixing from any combination design in just five minutes. The company used fifty years’ worth of data to develop this AI model. Similarly, there are other AI apps available, such as Sherwin-Williams ColorSnap Match, X-Rite Color-Eye, Datacolor MATCH TEXTILE, Nix Pro 2, etc. that help choose the best colors and combinations to paint the walls (interiors and exteriors). Using artificial intelligence for matching and mixing colors delivers the following advantages: 1. Increased Efficiency AI-based color-matching tools can match colors within minutes, which is significantly faster than traditional color-matching methods that can take hours or even days. Traditionally, paint companies rely on an expert technician or a spectrophotometer (or both). However, investing in an AI model results in greater efficiency. 2. Improved Accuracy These tools also eliminate the potential for human error and subjectivity, resulting in more accurate and consistent color matches. This leads to fewer color mismatches and less rework. Color mismatches can be due to changes in the paint formula, weather conditions, paint quality and age, and the texture and type of surface on which it will be applied. The changes in the pigmentation levels also lead to color mismatch. 3. Enhanced Sustainability By enabling manufacturers to use only the optimum quantity of paint, these tools reduce waste and improve sustainability. 4. Better Customer Experience AI-based color-matching tools enhance the overall customer experience by satisfying their requirements. This leads to greater customer satisfaction and loyalty. 5. Increased Innovation AI companies that provide these color-matching tools help manufacturers develop new and innovative color options that were previously difficult to achieve with traditional methods. As a result, painting companies are able to stand out in the crowded market. Formulation Optimization The significance of formulation optimization in the paints and coatings industry lies in its ability to enhance the quality and effectiveness, of the final product while reducing its cost of production. While using the traditional methods, the companies had to go through a lot of trial and error. Due to this, formulations were often suboptimal, time-consuming, and expensive. With AI, optimization has become much more efficient and accurate in recent years. AI algorithms can analyze vast amounts of data on raw materials, formulations, and product properties to identify the most effective combinations and optimize

Yashica Chopra
July 29, 2023
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Benefits of Using AI in Solar Panel Detection From Satellite Images

AI offers a powerful solution for detecting solar panels from satellite images. In this blog, you’ll learn about the benefits, challenges, and real-world applications of AI in solar panel detection and AI’s potential impact in accelerating renewable energy adoption. The global shift towards clean and renewable energy sources has led to the growing popularity of solar energy. Solar panels have become an increasingly popular way of generating electricity for residential and commercial use. However, as the solar energy market continues to expand, there is a growing need for detailed information about distributed rooftop solar panel installations. This information is crucial for solar power providers, customers, urban planners, grid system operators, and energy policymakers. Currently, there is a lack of granular data on solar panel installations, and solar panel installers often do not share information about their installations. This has created a demand for an effective solar panel detection algorithm that can be used to create detailed datasets of installations and their power capacities. Unfortunately, there has been limited progress in developing such algorithms for detecting solar panels from aerial or satellite images. But a few players have already taken the leap and are ready to provide the valued benefits. In this blog, we will discuss in detail the benefits of using artificial intelligence (AI) to detect solar panels from satellite images, the challenges and limitations of this technology, and real-world applications of AI in the solar panel detection. How can AI detect solar panels? AI can detect solar panels by analyzing satellite or aerial images using advanced image recognition algorithms. Image recognition involves several steps, including image preprocessing, feature extraction, and classification. During image preprocessing, the image is enhanced to improve its quality and reduce noise. Feature extraction involves identifying relevant features, such as the shape and colour of the solar panels within the image. Finally, the classification step involves using neural network architectures, such as convolutional neural networks (CNNs), to make use of extracted features & identify solar panels within the image. While executing the whole process, several types of AI algorithms are used for solar panel detection, including core deep learning algorithms, classical machine learning algorithms, and CNNs. AI can also integrate with LiDAR and GIS (Geographic Information Systems) data to detect solar panels by analyzing the 3D map created from laser beams bouncing off objects. It enhances solar panel detection by combining AI algorithms with geospatial data, such as satellite imagery and digital elevation models, to improve accuracy and identify optimal installation locations. Benefits of using AI in solar panel detection AI offers a powerful solution for monitoring solar panel installations, enabling solar energy providers to improve the efficiency and performance of their installations enhancing their performance efficiency while improving accuracy and reducing costs. AI offers a powerful solution for monitoring solar panel installations, while reducing costs and improving accuracy. By leveraging the power of AI, the solar energy industry can continue to grow and expand, providing clean, renewable energy to communities worldwide. Here are a few benefits of using AI in solar panel detection from satellite images. 1.Increased accuracy AI algorithms can identify solar panels with high accuracy, even in complex and large-scale installations. This reduces the risk of missing critical information and enables solar energy providers to make more informed decisions about their installations. 2. Time and cost savings Manual monitoring of solar panel installations can be time-consuming and expensive. By contrast, AI algorithms can process large amounts of data quickly and accurately, saving both time and money. 3. Detection of solar panels in remote locations Traditional monitoring methods can be challenging in remote or hard-to-reach locations. With satellite images, AI algorithms can detect solar panels anywhere in the world, enabling the monitoring of installations in areas that would otherwise be difficult to access. 4. Enhanced monitoring capabilities for installations Solar farms and utility-scale installations can cover vast areas, making manual monitoring challenging. AI algorithms can analyze satellite images to monitor these installations in real time, providing detailed insights into their performance and enabling early detection of potential issues. 5. Improved safety Traditional monitoring methods can require personnel to climb onto rooftops or other elevated areas to inspect solar panels, which can be dangerous. With AI and satellite imagery, inspections can be performed remotely, reducing the risk of accidents and injuries. 6. Enhanced maintenance and repair AI algorithms can monitor solar panel installations for signs of wear and tear or damage, allowing for proactive maintenance and repair. This can help prevent downtime and extend the lifespan of solar panel installations. 7. Better energy yield estimation Accurately estimating the energy yield of a solar panel installation is crucial for energy providers. AI algorithms can analyze satellite imagery to estimate the energy yield of an installation, providing valuable data for energy planning and forecasting. 8. Improved regulatory compliance In some jurisdictions, solar energy providers must report the size and location of their installations to regulatory bodies. AI algorithms can help ensure compliance by accurately detecting and identifying all solar panel installations. Real-World Applications Several companies and organizations are already using AI for solar panel detection. For example, SunPower, a leading provider of solar power solutions, has partnered with Google to use AI and machine learning algorithms to improve solar power forecasting. The partnership uses Google’s TensorFlow platform to analyze satellite imagery and other data to predict how much solar power will be generated by a given installation. Another company, Planet, uses a network of satellites to capture high-resolution images of the Earth’s surface. The company’s AI algorithms can detect solar panels in these images, allowing customers to monitor solar panel installations and optimize energy production. Impact of AI on the solar energy industry The impact of AI on the solar energy industry is significant. By improving the efficiency and accuracy of solar panel detection, AI can help energy providers make more informed decisions about where to install new solar panel arrays and how to optimize existing installations. AI can also help reduce costs and improve the

Yashica Chopra
July 26, 2023
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6 Top Large Language Model Consulting Companies in the USA

Generative AI opens up the field to offer unlimited possibilities for business enterprises. LLMs are the foundation of generative AI tools. Here, we’ll discuss LLMs and the best consulting companies offering large language model consulting services in the USA. According to PwC’s Global Artificial Intelligence Study, AI will contribute $15.7 trillion to the global economy by 2030 while boosting the GDP of local economies by up to 26%. There have been many new developments in artificial intelligence in recent times. ChatGPT brought a revolution, followed by AI art makers and other online tools that generate appealing content in just a few minutes. The study says artificial intelligence enhances employee productivity by 72%, increases customer engagement by 66%, and helps in research & development by 53%. However, ai is much more than what end users see and interact with. Complex technologies and programming happen in the backend to provide AI models for consumer use. The large language model is one such backend technology that powers many of the latest AI tools available in the market. Businesses can use LLM to develop advanced AI tools to increase their productivity and ROI. In this blog, we’ll understand what large language models are and how these work. We will also find out more about the best LLM consulting firms in the US. What is a Large Language Model? The large language model is a huge database of text that helps generate human-like responses to user input or queries. In simple terms, it is called a next-word prediction engine. LLMs are the backbone of generative AI tools like ChatGPT. They process natural language inputs to predict the next word and the words afterward until the answer is complete. LLMs have multiple sources of text, such as literature works, online content, social media, news, and current affairs. Then the models are trained using the text to generate human-like responses when interacting with end users. What are Some Examples of Large Language Models? A few examples of AI large language models are as follows: How do Large Language Models Work? Large language models use deep learning to analyze data, identify patterns, and learn the intricacies of human language. It involves knowing the grammar and syntax rules as well as the context of the text. In fact, understanding the context is a crucial part of LLMs’ work. Then, LLMs use the natural language generation (NLG) technique where the input text is analyzed and compared to existing patterns and data to generate an accurate response based on the context. Large language models can also customize the answers based on the emotional tone of the input text. However, there are limitations to LLMs. This includes implicit bias in training data (which leads to incorrect responses), the ambiguity of input query or text (which made it hard to determine exactly what the user wants), and a lack of the ability to develop common sense (which is an inherent human quality gained through life-long experiences). Nevertheless, the advancements in artificial intelligence increase the chances of getting accurate and contextually correct responses from LLMs. Top Large Language Model Consulting Companies in the USA What is Large Language Model (LLM) consulting? Simply put, it is the process of partnering with offshore or third-party AI service providers to adopt the latest technology in a business. The LLM consulting company will customize a large language model to develop a generative AI tool for the client. The service provider will take care of the entire process from start to finish and offer long-term maintenance solutions to ensure the LLM is error-free and working correctly at all times. But why should a business consider LLM consulting or implement LLM in its process? There are many reasons to embrace large language models in an enterprise. LLMs can- Large language models are vital for an enterprise to become successful in the current scenario and in the future. LLMs are powerful and highly useful for a business in any industry. Here are the top large language model consulting firms to partner with in the US: 1. DataToBiz DataToBiz is among the top-growing Large Language Model consulting companies in the US. The company empowers businesses to unlock the true potential of their proprietary data. It uses technologies like data science, artificial intelligence, and natural language processing to help clients achieve their business goals and generate greater revenue in addition to scalability. The company specializes in working with AI models and has created products such as PrepAI, HirelakeAI, SensiblyAI, etc., with powerful NLP algorithms. DataToBiz provides extensive services listed below: The company has clients from the US, UK, the Middle East, Africa, Australia, and Asia. They work with open-source large language models to build custom solutions for clients. The LLMs can be seamlessly integrated with existing and new business processes and applications. DataToBiz is a partner of AWS, Google Cloud, and Microsoft Azure (Gold). It has successfully worked with 300+ businesses from manufacturing, FinTech, EdTech, supply chain, travel, healthcare, mining, oil & gas, media & entertainment, hospitality, etc. 2. Arcee.ai Arcee.ai is an LLM and AI consulting provider that helps clients unlock the power of proprietary data to empower innovation. The company trains and deploys specialized AI models to simplify complex business processes and enhance customer experience. The generative AI models developed by the company are scalable and seamlessly use the in-domain data of the enterprise. It covers all parts of the process, ranging from raw data processing to model deployment and continuous monitoring. Acree offers end-to-end LLM services such as: The company builds LLM stacks and deploys them in the business to help clients overcome challenges and gain a competitive advantage in the markets. The Acree products contain three main components- Acree Enlighten (training LLMs), Acree Align (build proprietary datasets and deploy LLMs), and In-Environment (Acree.ai SaaS exclusively for the business). The company uses open-source large language models and fine-tunes them as per the client’s requirements. It has a transparent pricing system with flexible payment options (monthly or annual subscriptions). 3. Winder.AI Winder.AI is one of the best LLM consulting firms that provide

Manpreet Kaur
July 25, 2023
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What is a Neobank? How to Implement AI in Neobanking Market

In this article, we will learn about neobanking, explore the technologies employed by neobanks, understand the role of AI in neobanking, and the future of AI in shaping the banking sector. The world of banking is undergoing a profound transformation with the rise of Neobanks, a new genre of financial institutions that are redefining the way we interact with money. Neobanks have emerged as disruptive players in the financial industry, offering innovative and customer-centric banking experiences. With the advent of digitalization and advancements in technology, neobanks are capitalizing on the power of artificial intelligence (AI), machine learning, big data analytics, and cloud computing to provide a range of financial solutions. AI, in particular, plays a pivotal role in enhancing the efficiency, accuracy, and security of neobanking operations. From AI-powered chatbots providing 24/7 customer support to advanced fraud detection systems, AI is revolutionizing the neobanking landscape. In this blog, we will delve into how AI can enhance the customer experience of neobanking. We will also answer questions like: ● How does a neobank work? ● What are the benefits of implementing AI in neobanking? ● How is the future of AI & neobanking aligned? But first, let’s understand, What is a neo bank? What is a neobank? A neobank, also known as a digital bank or challenger bank, is a financial institution that operates exclusively online without any physical branches. Neobanks leverage technology to offer a range of banking services to customers in a convenient, user-friendly, and digital-first manner. These innovative financial institutions aim to disrupt the traditional banking industry by providing personalized and customer-centric experiences. How does a neobank work? As previously mentioned neobank is a digital-only bank, so it operates through online platforms and mobile apps. Neobanks have a distinct business model compared to traditional banks and also generate revenue through the margin between deposits and loans, similar to their conventional counterparts. It allows customers to manage their accounts, make transactions, and get support using digital tools. Neobanks focus on convenience, affordability, and personalized services, using technology to streamline operations and offer a seamless banking experience. The rise of neobanks is driven by their ability to offer cost-effective and highly personalized services through the utilization of advanced technologies such as artificial intelligence (AI), automation, and cloud computing. A survey conducted by McKinsey and Company in 2021 reveals a significant surge in the adoption of digital banking among customers in developing countries across the Asia-Pacific region. The survey findings indicate a substantial increase from 54% in 2017 to 88% in 2021, showcasing the rapid expansion of customers actively engaging with digital banking services. This growing demand for digitalization in the banking sector is poised to act as a catalyst for the further growth and development of the neobanking market. How can AI be implemented in neobanking? AI plays a crucial role in neobanking, enabling a seamless and personalized banking experience. Neobanks harness AI technologies to analyze customer data and deliver customized recommendations for financial products and services. This personalized approach helps meet individual customers’ needs and preferences, enhancing their overall banking experience. AI-driven risk management is another area where neobanks benefit. By leveraging AI algorithms, neobanks can detect and mitigate fraud and other financial crimes effectively. Real-time transaction monitoring and AI-powered analysis enable the identification of suspicious activities, ensuring the security of customers’ assets and combating financial fraud. Improving customer experience is a key focus for neobanks, and AI plays a significant role in achieving this goal. AI-powered chatbots and virtual assistants enable neobanks to provide round-the-clock customer support and quick responses to queries. This enhances customer satisfaction by providing immediate assistance and reducing the reliance on human customer service representatives. AI & neobanking: Around the world Neobanks are disrupting the global financial services industry on the back of strong trends, but their long-term success hinges on embedding data and AI capabilities extensively across all aspects of their operations. In 2022, neobank investors shifted focus from growth at all costs to profitability. Most of the world’s 291 neobanks were not able to turn a profit, and investors are losing patience. With the funding route unlikely to abate, 2023 is shaping up to be a do-or-die year. Neobanks provide highly personalized services at lower prices by utilizing technology such as artificial intelligence (AI), automation, and machine learning. For Instance, Revolut is a UK-based neobank that uses AI to analyze customer data and provide tailored recommendations for financial products and services. In addition to Revolut, NatWest has introduced a personal finance app called Mimo. This app takes advantage of open-banking APIs, AI, and data analytics to create a unique social feed that assists customers in effectively managing their finances. By utilizing these advanced technologies, Mimo helps users gain insights into their spending habits, make informed financial decisions, and stay connected with their financial goals. Industry experts, such as Gartner, predict Banking-as-a-Service (BaaS) will hit mainstream adoption within two years. Gartner also mentions that 30% of banks with greater than $1 billion in assets will launch BaaS for new revenue by the end of 2024, but half will Benefits of AI in neobanking The integration of artificial intelligence (AI) in neobanking brings numerous benefits to both customers and financial institutions. Here are some key advantages of AI in neobanking: 1. Enhanced Customer Experience: AI-powered chatbots and virtual assistants offer 24/7 customer support, providing instant and accurate responses to inquiries and improving overall customer experience. These virtual assistants can handle a wide range of queries, from account balance inquiries to transaction history and account management, ensuring prompt and efficient customer service. 2. Personalization: AI algorithms analyze customer data, financial behaviour, and preferences to deliver highly personalized financial recommendations. This level of personalization allows neobanks to offer tailored products and services, such as investment portfolios, insurance plans, and loan options, catering to individual customer needs and increasing customer satisfaction. 3. Efficient Risk Management: AI algorithms enable neobanks to assess creditworthiness and risk profiles accurately. By analyzing customer data