7 Top Digital Transformation Companies in the United Kingdom

Digital transformation is a continuous process of adopting new technologies and data-driven models to gain a competitive edge and increase business ROI. Here, we’ll discuss its importance and the top digital transformation companies in the UK.  Technology is essential for a business to survive in today’s world. From a startup to a multinational enterprise, every business organization needs to invest in relevant technologies to manage resources, speed up daily processes, enhance customer experience, and increase revenue.  This indicates a strong need for digital transformation. B2B and B2C organizations should revamp their outdated systems and digitalize their processes. However, this is a continuous process and involves various elements. From experts to analysts to top management, mid-level decision-makers, employees, and stakeholders, many people are a part of digital transformation. Digital transformation companies have a prominent position because of their extensive range of services.  In this blog, let’s find out more about digital transformation, its importance, and the top service providers in the UK.  What is Digital Transformation? Digital transformation is using digital technologies to revamp existing systems or create new systems, business processes, work culture, and customer experiences in an enterprise. Data is stored digitally in a centralized database (data warehouse or data lake) and is connected to different applications to allow data analytics in real-time. Digital transformation brings agility, scalability, and flexibility to a business. It provides a competitive edge and allows businesses to make proactive decisions.  What is Digital Transformation Consulting? Statistics show that 89% of large and global enterprises have invested in digital transformation and artificial intelligence. However, only 31% of these companies have achieved the expected results and revenue. It’s a clear indication that spending money on digital transformation without a comprehensive strategy and clear guidance will not be beneficial to the business.  That’s where consulting companies play a vital role. Digital transformation consulting is the process of hiring an offshore service provider or a third-party consultant to provide end-to-end services to digitally transform the business process and achieve the expected results. Digital transformation experts from consulting companies understand your business vision, mission, internal systems, end goals, etc., to create a foolproof plan for digitalizing your business at all levels.  The consulting company will also upgrade the existing systems, build new ones, and strengthen your IT infrastructure. The consultants will work with your employees to streamline data flow between different applications. Furthermore, they offer long-term maintenance and support services to ensure the systems are working without glitches.  Top Digital Solution Providers in the UK 1. DataToBiz DataToBiz is one of the best digital strategy agencies in the UK, with clients from different continents around the globe. The company offers end-to-end solutions to help SMBs, MSMEs, and large enterprises adopt the latest technology like artificial intelligence, machine learning, data science, business intelligence, etc. It provides comprehensive data engineering and management solutions to streamline data flow and use real-time data for advanced analytics. PrepAI, HirelakeAI, Virtual Try-On Platform, etc., are some products developed by the company.  A few services offered by the company are as follows:  The company promotes digital transformation to speed up decision-making and provide a competitive edge to businesses from different industries. Whether manufacturing, healthcare, supply chain, EdTech, FinTech, surveillance, or travel, the company knows how to help clients succeed. From maximizing efficiency to boosting revenue, DataToBiz assists enterprises in effectively overcoming business challenges and achieving their goals. It has a transparent and flexible pricing system and is a certified cloud partner with AWS, Microsoft Azure, and Google.  2. Cognizant Cognizant is a global digital transformation company with clients from different industries and sectors. It helps enterprises transform their business experiences by reimagining the processes and modernizing technology. The company provides services to unlock business potential and stay up-to-date in today’s fast-changing world. It develops responsive technologies without ignoring social and environmental responsibility. It ranked 194 among the Fortune 500 companies in 2022. The services offered by the company are listed below:  Cognizant also offers research services in areas like Metaverse, future-ready cities, deep green, future-ready business benchmarking, and more. It integrates data and analytical tools with various enterprise systems to provide real-time insights for effective decision-making. The company believes in engineering excellence through high-end technological solutions. Furthermore, the company’s digital strategy speeds up digital adoption and streamlines change management in clients’ businesses.  3. HCL HCL Technologies was founded in 1976 and is one of the leading IT companies in the world. It has over 2000 patents and 10,000 clients, with a presence in many countries. The company’s products and services are used by large enterprises to revamp their internal processes and stay ahead in competitive markets. The company works with establishments from various industries and promises world-class innovation. It has many top service providers in its ecosystem (AWS, RedHat, Dell, Google, Intel, Microsoft, etc.). The company has categorized its services into four areas of expertise, such as:  The company is also focused on sustainability and aims to provide long-lasting results to its clients and stakeholders. HCL Technologies has divided its ecosystem to cater separately to start-ups and large enterprises. With a tagline of Supercharging Process, the company promises to offer industry-leading capabilities to help clients transform their businesses across all verticals.  4. Boston Consulting Group Boston Consulting Group (BCG) is a global consulting firm with 100+ offices in 50+ countries worldwide. It works with many Fortune 500 companies and has an annual revenue of $11.7 billion. The company provides various services to help businesses unlock the power of technology and expand their reach in global markets. It has expertise in many fields, ranging from finance to marketing, manufacturing, strategy management, people management, etc. The company has a unique collaborative model that enables it to revamp the processes at all levels and departments in the client’s enterprise. It offers the following services:  BCG also offers services to tackle climate change and promote sustainability in an enterprise. It helps organizations build technologies and IT systems for the future. The company offers scalable services for artificial intelligence adoption in several industries, such as aerospace, education,

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Top 10 Benefits of Procurement Analytics 2023 – Why Is it Important?

Procurement analytics is much more than just spend analysis. It can help all departments in the business to correct and strengthen their strategies and increase overall efficiency. Here, we’ll discuss the benefits of procurement analytics for an enterprise. Procurement is crucial for a business. It influences the various elements of the supply chain, right from production to quality control to distribution of the goods. With such an important role, it’s no surprise that businesses need to adopt the latest technologies to stay up to date in the dynamic global market and survive the competition.  In many enterprises, the procurement teams are already handling cross-functional activities and working with teams from other departments. In fact, according to a survey by Procurious, more than 81% of procurement teams feel pressurized to be more effective when responding to supply chain challenges. While this is alarming, the good news is that close to 80% of CFOs intend to maintain or increase enterprise digital investment in 2023. Procurement analytics is the process of using data and technology to help procurement teams make better, faster, and more efficient decisions. Instead of relying on guesswork or outdated reports, the teams can use real-time analytics to choose the raw materials, vendors, procurement methods, transportation routes, and so on. As per the report by Verified Market Research, the global procurement software market will touch $9.5 billion by 2028 with a CAGR (compound annual growth rate) of 7.6%.  In this blog, we’ll read more about the benefits of procurement data analysis and how businesses can overcome challenges to fill the gaps in skills and technology.  Why is Data Analysis Important in Procurement? Procurement analytics involves data collection from multiple sources (and ERP systems) and the analysis of this data to derive actionable insights for faster decision-making. The collected data is categorized into different segments based on the business KPIs. The analytical reports are shared using business intelligence or data visualization tools.  However, procurement and spend analysis are much more than data visualization reports. It helps business enterprises make use of historical and current data to determine the best way forward in managing vendors, suppliers, and other members of the supply chain. The ultimate aim of procurement analytics is to optimize resources and increase ROI while enhancing customer satisfaction.  Types of Procurement Analytics How can Procurement Analytics Benefit Your Business? CPOs (Chief Procurement Officers) have a great responsibility on their shoulders. They need to ensure the cost of procurement is within the budget without compromising quality and also maintain good partnerships with vendors. At the same time, CPOs have to consider various business goals and align the department goals accordingly.  Fortunately, procurement analytics can provide a comprehensive solution to all these aspects and more. Let’s look at the various benefits of using purchasing analytics in the business.  1. Resource Allocation How to manage resources for the entire business? Are there areas in a business that is overspending? Can the organization reduce expenses without cutting corners? The answers to these questions can be found by relying on procurement analytics. That’s because the various activities of procurement analytics are interdependent and influence one another.  For example, by monitoring inventory, the teams can determine the usage of different materials within the businesses. It helps track which items are necessary and which can be procured in fewer quantities to prevent overspending. By optimizing resource allocation, the business can improve the spending management indicators and increase opportunities to save costs.  2. Risk Management Countless things could go wrong during the procurement process. External factors can lead to delays, damages, and other issues, resulting in losses for the business. Even if an enterprise has been following the same procurement process for years, the element of risk will always remain.  However, procurement analysis can help in managing this risk efficiently. It empowers the teams to identify bottlenecks and challenges that can and cannot be controlled by the business. Then, these issues are addressed individually to establish a risk management policy and minimize losses.  For example, an organization with vendors from other countries has to create a risk policy to handle geographical border restrictions, weather-related delays, socio-political factors like wars, etc., to ensure that the supply keeps coming despite the adversities.  3. Data-Driven Decisions Data is the key to making informed decisions in today’s world. Data analytics help combine past and present data to derive meaningful insights for identifying trends, understanding the challenges, and becoming proactive to make the most of market opportunities.   Procurement analytics will help the enterprise track the demand and supply in the market and adjust the orders accordingly. Demand forecasting reports help stay one step ahead of competitors and meet customer demands before other businesses can take advantage.  4. Supply Chain Transparency The supply chain network of a business can be lengthy and complex. Maintaining transparency throughout the process can be difficult without using the right technology. That’s where using analytics in procurement helps organizations. Data is collected from different sources and made available to teams from various departments across the business. This allows the finance, IT, marketing, and customer service teams to use the data to improve sales and customer experience.  For example, customers around the world are becoming more aware of sustainability and eco-friendly practices. Many customers want to know where and how a business procures its materials. A transparent supply chain makes it easy to share this information with customers. In fact, some brands use it as a marketing strategy to attract customers.  5. Category Management Category management is a part of spend analysis and vice versa. The spend categories are created by grouping similar types of purchases under individual heads. Spend analysis gives the procurement teams the necessary data to segment the purchases and expenses by creating a basis for category structure. It is the starting point of category management and allows businesses to have a clear picture of how much they spend on each category and ways to optimize the expenses.  Not only do procurement and spend analysis help with category management but also provide businesses with the necessary insights to link

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7 Top Artificial Intelligence Companies in UAE 

Artificial intelligence has gained prominence in Middle East countries. Many businesses in UAE are revamping their existing processes to achieve greater success through AI. Here, we’ll discuss the top AI companies in the UAE and the benefits of partnering with them.  UAE has been a hub for the oil and gas industry for decades. However, the region is aiming to become a leading center for ICT (information and communication technology) industry for international trade. Middle East countries are setting up free trade zones to promote the use of software products and services in all industries.  Artificial intelligence, cloud computing, cybersecurity, the Internet of Things (IoT), etc., are gaining popularity in the UAE market. In this blog, we’ll read more about AI and the list of artificial intelligence companies in the UAE that offer tailor-made services to various enterprises.  What is Artificial Intelligence? Artificial intelligence is an umbrella term encompassing various techniques, technologies, processes, applications, and models. It includes fields like machine learning, deep learning, computer vision, etc., which are used for an array of purposes by different businesses. Every industry needs AI in today’s world to adopt the latest technologies and survive market competition.  The global AI market is expected to reach $407 billion by 2027, according to a report by MarketsandMarkets. As per Forbes, 60% of businesses say AI will enhance customer relationships and productivity.  Most SMBs and large enterprises partner with AI consulting firms to digitally transform their business processes and adopt AI to achieve the desired results. This helps them save time, money, and resources while reducing the risk of losses. AI experts from offshore consulting companies create the necessary strategies to implement, integrate, and maintain AI applications in an organization.   Is Artificial Intelligence in Demand in UAE? Yes. The UAE government has ambitious plans to promote the adoption of artificial intelligence in the country. It has adopted the National Artificial Intelligence Strategy 2031 and established Many companies that are entering the market regularly to accelerate the use of AI in the region. Apart from these, local businesses and enterprises work with several offshore AI firms to increase their productivity, ROI, and revenue.  Top Artificial Intelligence Companies in UAE 1. DataToBiz It offers AI platform customization, pilot implementation, and full-scale digital transformation to adopt the data-driven model in a business. The company takes care of all the steps from start to finish and provides complete assistance and expert support at every stage. From strengthening the supply chain to enhancing customer experience, increasing productivity, quality, and business efficiency.  DataToBiz offers various services, such as:  The company has clients from the Middle East, North America, South America, Asia, Australia, and Africa. It is a certified partner of Microsoft, Google, and AWS (Amazon Warehouse Services) with over 97% client satisfaction rate. Businesses can use the real-time actionable insights provided by DataToBiz to make effective decisions, create innovative products, and amplify revenue.  2. Accubits Technologies Accubits is an AI company with clients from the Fortune 500 list from around the world. It works with tech startups to provide comprehensive software product development services and technological solutions. The company is known for building enterprise-grade blockchain platforms with greater transparency, security, and efficiency. It combines the world of blockchain and web3 using advanced technologies.  The company’s AI services include generative AI development and integration, automation, computer vision, and consultancy services to help various organizations benefit from using artificial intelligence for day-to-day work. Accubits also provides business intelligence solutions to drive accurate insights for decision-making.  The company offers the following services:  Accubits has been named the most promising brand in 2021 by the Economic Times. It promises ROI plus (Return of Innovation Positive), a new metric developed by the company to determine how their knowledge is helping clients achieve success. The company has an active presence in UAE and the Middle East.  3. Silwana Infotech Silwana Infotech is a software development and AI company in UAE offering transformative and technology-driven solutions to clients from several industries. It has more than two decades of experience in the IT industry and follows a client-first approach to provide scalable, flexible, and result-oriented solutions to each business.  The company primarily works with enterprises from the MENA region (Middle East and African Countries). It uses cutting-edge technologies to cater to client’s requirements and help them achieve the desired results. The company has offices in nine locations and has worked on 700+ projects in more than ten industries.  Silwana Infotech offers the below services:  The company starts by analyzing the feasibility of the project before developing and implementing the right technology and tools in a business. It also offers support and maintenance services to ensure the business doesn’t suffer from disruptions due to technical glitches. It offers onshore and offshore services, along with a hybrid model that combines both business models.  4. Citrus Consulting Services Citrus Consulting is a technology firm that offers several IT-based services to clients in the UAE. It provides AWS hyperscaling services to help businesses expand and develop their IT infrastructure on the cloud. It believes in innovation to make systems and processes more agile in an enterprise. The company uses modular solutions to provide clients with a choice to select their technology stack instead of relying on a single tool.  The company is a hybrid cloud expert and also specializes in information and cybersecurity system implementation and development. It works with businesses from the Middle East and African regions and India in assisting enterprises through their digital transformation journey.  Citrus Consulting Company provides the following services: The company has a robust ecosystem and belongs to the Redington Group, known for its presence in the IT and mobile industry for many decades. It promotes the use of new-age technologies like AI, IoT, robotics, big data analytics, and 5G to help businesses overcome challenges, automate processes, and optimize workflow to increase ROI.  5. EORA.ai EORA is a group of companies that uses machine learning, natural language processing, computer vision, and data science to provide unique solutions to businesses. With offices in Moscow and Innopolis,

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15 Common AI Mistakes to Avoid: Ensure a Smooth AI Journey

Artificial intelligence offers endless benefits to a business but can be overwhelming to implement without proper guidance and support. Here, we’ll discuss the common errors enterprises make in AI implementation along with the ways to avoid them.  Artificial intelligence has been a part of the global market for years. While it has several definitions, AI is mainly the science and engineering of making machines intelligent. It combines computer science and datasets to enable quicker problem-solving.  Many SMBs, MSMEs, and large enterprises are adopting AI technology and tools for various purposes. A research report shows 44% of companies in the private sector will adopt AI in 2023.  According to Statista, AI is mainly used for managing data as a business asset, establishing data culture, driving innovation, performing data analytics, and building a data-driven enterprise. However, several business organizations have suffered from artificial intelligence failures. In fact, various studies show that 70% of companies have minimal or no impact of AI, and a whopping 87% of data science projects don’t make it to production.  In a way, AI mishaps are one of the reasons for organizations to be wary of adopting advanced technology for digital transformation. For example, the automated hiring algorithm used by Amazon turned out to be biased against women and hired only white men (due to biased training data). A real estate tycoon in Hong Kong filed a lawsuit against a business that sold him an AI robot to manage his funds. The robot lost around $20 million every day instead of increasing the funds by making the right investment decisions.  It’s crucial to understand where things go wrong and avoid such mistakes to successfully implement AI technology in a business.  In this blog, we’ll read about the common AI mistakes every business should avoid and ways to achieve the desired results.  What are the Most Common AI Mistakes to Avoid? 1. Half-Hearted Attempts Artificial intelligence can offer a multitude of benefits to a business. However, it requires heavy investment in building a strong IT infrastructure and training employees to work with the latest tools. Making half-hearted attempts or investing in AI for the sake of it is a sure way to generate losses.  AI adoption requires planning, implementation, and continuous tracking to deliver results. Haphazard processes will only waste business resources and lead to additional complications. To prevent such issues, businesses should take the time to develop a strategy for AI adoption and follow it.  2. Lack of Data Quality Is your data large enough to make AI effective? This is one question many people ask since AI is associated with large datasets. However, businesses forget to consider data quality and its impact on artificial intelligence. The AI model is as good as the data used to train it. If businesses don’t provide high-quality data input, the model will not deliver accurate results.  Lack of data quality is another major reason for AI failure in enterprises. This can be avoided by investing in proper data systems and ETL (extract, transform, load) models to collect, clean, format, and process the data before it is fed into AI models for training. Data management is necessary to avoid skewed or incorrect models.  3. Unclear Business Goals Why should a business invest in AI? What problems does the top management wish to solve using artificial intelligence? Which business goals can be achieved through AI adoption? Every enterprise should have clear answers to such questions before investing in AI. Simply copying a competitor is likely to result in failure.  Establish definite and measurable business goals. Align these goals with the business vision and mission. Then create an AI adoption strategy that supports the business goals and objectives. Evaluate the impact the AI model would have on the establishment and calculate the expected ROI. The trick to avoiding AI failure is to spend more time strengthening the foundation instead of building AI on weak bases.  4. Not Focusing on Change Management AI adoption requires many changes across the organization. The internal processes, IT systems, employee working methodologies, and organizational culture have to be changed and revamped to align with how AI systems work. Quite a few businesses don’t pay enough attention to these changes. They don’t have a comprehensive plan to implement the changes cohesively at each level. This results in disruptions, miscommunication, delays, and unexpected losses.  Companies offering AI consulting services insist on developing a change management strategy to ensure employees, management, systems, and processes are in sync and work towards the same goals. It’s vital to address the cultural and behavioral aspects of AI adoption to overcome roadblocks and ensure a smoother transition from outdated methods to the latest ones.  5. Relying on Black Box Models AI models are complex and hard to understand. There’s no denying this statement. However, when a business relies on such black box models (ones that offer almost no explanation of how the algorithms work), it creates opacity in the enterprise. Lack of transparency and accountability can lead to many issues for the business. Who will be responsible for the wrong insights?  Avoid using black-box AI models in the organization. Work towards building transparent models and maintaining clear documentation to understand how things are done. Businesses that cannot afford to build AI models from scratch can partner with AI companies to customize existing tools and increase transparency. Put in extra effort to explain the process to employees and establish procedures that make people accountable.  6. Not Enough Expertise Introducing artificial intelligence in a business is no small task. AI models are best handled by experts with the necessary domain knowledge. Asking the existing talent pool to use AI technology without proper training is likely to cause errors and additional problems. When planning the digital transformation of a business, it’s important to identify the talent gap and find ways to fill it.  Organizations can hire an in-house team of AI experts to initiate the process or rely on offshore service providers. Building a team from scratch is cost-intensive and time-consuming. However, working with AI companies is quicker, cheaper, and more effective.

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10 Steps to Create a Data-Driven Culture

With proper planning and implementation, enterprises can effectively use data to make business decisions. However, a strong data culture is still a theory for many business organizations. Here, we’ll discuss the steps to create a data-driven culture in an enterprise.   Businesses need to work with quality data to make effective business decisions. While we cannot ignore the importance of human expertise, combining both is the best way to boost a business in a competitive market. This requires using data and data analytics to make decisions.  Statista reports that the global big data analytics market will reach $655 billion by 2029, while the predictive analytics market is estimated to touch $41.52 billion by 2028. A business organization can enjoy the benefits of data analytics and business intelligence by adopting a data-driven culture. Another report shows that 57% of leading brands are already using data to drive innovation.  So how do you create an effective data-driven culture in your enterprise? Let’s find out in this blog. But first, let’s dive into the basics. What is a Data-Driven Culture? Is data the key to a data-driven culture? Absolutely! A data-driven culture is where the workforce uses statistics, analytics, facts, insights, predictions, etc., to make everyday business decisions and optimize their tasks. Team leaders, managers, and C-level executives use insights to understand various elements of work and how these affect business performance.  Many components contribute to creating a data-driven culture in an organization. However, the major aspects are as follows:  1. Data Maturity Data maturity refers to the process of storing and retrieving data over time. It depends on your data governance policies and how well you manage and maintain the datasets with accurate tags, metadata, etc.  2. Data Leadership Data leadership defines the role of leaders and decision-makers in managing business data. These people understand the importance of quality data and help maintain a work culture where decisions are made based on data analytical reports.  3. Data Literacy Data literacy is the act of ensuring business data is structured, accessible, reliable, and accurate. It also involves training employees to understand and use datasets effectively for day-to-day work.   Investing in a data-driven culture requires expert guidance and support. SMBs and large organizations partner with a reputed data analytics company to revamp their internal processes and work culture the right way.  What are the Steps in Data Management? The data management framework has to be structured and aligned with the business process. Here’s how to implement successful data management in your enterprise and create a strong foundation for data-driven culture.  Steps to Create a Data-Driven Culture Understand that creating a data-driven culture is not limited to technological investments. The focus is equally on changing the existing work culture to help employees use new technology and tools at work.  The following steps will help you build an effective data-driven culture in your enterprise.  1. Start with the Top Management The top management and C-level executives play a vital role in influencing other employees. They need to understand the importance of data-driven solutions and establish it in the organization. When the management makes it a norm to use data and evidence for decision-making, employees will follow it over time.  For example, the team leader or manager can allocate a few minutes at the beginning of a meeting to go through the analytical reports and observe whether the proposals are backed by data. Then, they can discuss the proposals and the reports to help other employees realize why they should work with data. When the top management sets an example, it becomes easier for employees to adapt to the changes.  2. Choose Metrics Carefully How do you analyze the business performance? It can get complicated and confusing without metrics. Every enterprise has its own metrics for analytics. However, there’s no guarantee that the existing metrics are correct or suitable for accurately measuring the business.  For example, many businesses use competitor analysis because they need to keep track of what other brands in their industry are up to. An FMCG company will have to decide its pricing and marketing strategy based on customer behavior, market trends, and competitor’s offerings. Not factoring in either factor can result in skewed analytics, which invariably leads to wrong decisions.  Define metrics carefully and make sure they are always aligned with the business vision, goals, and industry standards.  3. Don’t Isolate Data Scientists One major mistake many organizations make is to keep the data scientists and business leaders in separate units. While the analytical reports are shared between the teams, the people responsible are not always collaborating and usually work in isolated teams. This can affect the quality of analytics and will soon widen the gap between reality and analysis.  Leading brands have managed to avoid this issue by eliminating the boundaries between data scientists and business leaders. The staff (team members) is rotated between different departments to keep communication flowing. Another method is to ensure that the top management has the necessary technical know-how to directly interact with data scientists and work with them. While it is not necessary to replace existing executives with AI and ML engineers, it is important to train them in the basics.  4. Provide Access to Data A common complaint from employees is that there don’t have access to data or analytics at work. It takes too much time and energy to obtain even the basic data, and this isn’t enough to make data-driven decisions. Despite democratizing the storage systems, analysts aren’t given access to information from other departments due to different constraints.  This challenge can be handled by identifying the data related to the KPIs for the project/ quarter and providing enterprise-wide access to this information. For example, if the sales analyst has to create a demand forecasting report, they should have access to information about past sales, customer feedback, inventory, etc. While data security is a concern, it can be handled through effective data governance and by setting up authorized access employees based on their project requirements.  5. Assess Uncertain Aspects To build an

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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

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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

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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

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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

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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,

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