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AI for ESG (Environmental, Social, and Governance) is quickly becoming a critical tool for organizations seeking to become more sustainable. Sustainable investments were predicted to be worth $30 trillion in 2018, a 34% increase over 2016. Investors (and the general public) are increasingly interested in determining if and how enterprises are ecologically and socially responsible. Simultaneously, boards and management have realized that ESG is critical to their firms’ long-term sustainability. It’s no surprise, therefore, that over 90% of S&P 500 companies are already publishing ESG reports in some form. As firms face unprecedented ESG concerns, artificial intelligence (AI) can help establish more responsible business practices. However, organizations must employ AI responsibly since the computational power required to gather, analyze, and act on huge volumes of data is huge. How AI Addresses Challenges Data collection and standardization Collecting and standardizing data on ESG performance can be difficult and time-consuming. Many organizations struggle to gather the necessary data, particularly for social and governance metrics. Additionally, without widely accepted standards for ESG data collection and reporting, comparing performance across organizations is challenging. AI can help automate the data collection process, reducing the time and resources for gathering and processing ESG data. It may also standardize data by recognizing patterns and trends, making it simpler to compare performance across organizations. Materiality It can be challenging to determine which ESG issues are most material to an organization and its stakeholders. Materiality is often context-specific, and different stakeholders could have different priorities. Organizations must identify ESG issues that matter to them and their stakeholders and report on those issues meaningfully. AI makes this possible by analyzing large amounts of data, including social media and other online content, helping identify patterns and trends. Organizations can thus identify the ESG issues that are most important to them and their stakeholders. Assurance Assuring the accuracy and integrity of Environmental, Social, and Governance (ESG) data is crucial for making informed investment decisions and promoting sustainable business practices. However, due to the reliance on self-reported data, it can be challenging. Self-reported data could be biased and manipulated, leading to inaccuracies and unreliable information. AI plays a significant role in addressing these challenges by validating and verifying self-reported data. The algorithms can analyze large amounts of data and identify patterns and anomalies that indicate inaccurate or unreliable information. Additionally, AI can assist in data gathering and collection, ensuring that information is collected in a consistent and unbiased manner. Furthermore, AI can create a more efficient and effective assurance process by automating data cleaning, analysis, and report generation tasks. This can help reduce the risk of human error and improve the overall accuracy and integrity of ESG data. Integration Integrating ESG information into financial reporting and decision-making can be challenging. Many organizations still view ESG information as separate from financial information and may not fully integrate it into their decision-making processes. AI can assist in integrating ESG data into financial reporting and decision-making by providing organizations with an automated and streamlined data collection, analysis, and reporting process. It can help organizations to understand the potential risks and opportunities associated with their operations and make more informed decisions. By providing insights not immediately apparent when looking at financial data alone, AI enables organizations to make better-informed decisions considering long-term sustainability. Sustainability Organizations may find it challenging to balance short-term financial goals with long-term sustainability objectives. This may cause organizations to prioritize short-term goals over long-term sustainability initiatives. AI can help by providing insights into the trade-offs between different ESG initiatives and the potential financial and reputational risks. Additionally, AI can monitor the progress of ESG initiatives and identify areas of improvement. As a result, organizations can stay on track to achieve their sustainability objectives. Furthermore, AI can analyze data from various sources and provide early warning signals of potential reputational risks and financial impacts. Limited understanding Some organizations may have a limited understanding of ESG issues and the impact of their operations on the environment and society. This makes it difficult for them to identify and report on the most material ESG issues. AI can help organizations better understand the environmental and social impacts of their operations by providing them with insights into data that they may not have been aware of or able to gather previously. As ESG reporting becomes more important for organizations, it is important that they address these challenges to ensure that they provide accurate, reliable, and meaningful information to stakeholders. Benefits of Using AI for ESG Environmental By integrating data from sensors and other sources to assist with decision-making, AI has the ability to make greener judgments and mitigate environmental hazards caused by climate change.  A research paper from Elsevier shows that over 20% of energy savings can be achieved by forecasting and adjusting the building’s real-time energy needs based on sensor data. Other applications include: Social AI can assist in studying social networks, identifying patterns, and addressing social concerns more quickly and correctly. Research published in an Elsevier journal indicates that AI can estimate the demand for healthcare services and improve the deployment of healthcare staff and resources, particularly in disadvantaged regions. According to the study, this method can result in more effective resource allocation and better healthcare results. Other use cases include: Governance Having a more efficient way to function is only one example of how AI can promote the “G” of ESG. It can, for example, be used to study public spending and service delivery. It can help firms make better-informed, data-driven decisions that include environmental, social, and governance aspects. A study published in the journal IEEEAccess showed how government forms and applications were rapidly and accurately processed through AI-led automation. This reduced the burden on individuals and organizations while increasing the speed and accuracy of decision-making. Other use cases include: The Future of AI-led ESG Initiatives Data is the common thread in AI’s ESG applications. Over the past decade, data has grown from 6.5 zettabytes in 2012 to 97 zettabytes in 2022, enabling current AI

Low code no code platforms have revolutionized the software development industry recently. Gartner predicts that by 2024, 65% of app activity will result from no-code development. Further, the market for these platforms will grow to $26.9 billion by 2023, $3.8 billion up from 2017. The demand for low code, no code (LCNC) platforms is driven by the need for organizations to deliver software faster, with increased agility, and at lower costs. This is especially important in the current digital landscape, where businesses face increased competition and pressure to innovate. This blog post will explore what LCNC platforms mean, their capabilities, key features, everyday use cases, and ROI. What Is Low-Code, No-Code Platform Development? LCNC platforms are software development tools that allow users to create, deploy, and manage applications without extensive programming knowledge. These platforms typically provide drag-and-drop interfaces and pre-built templates, making it easy for non-technical users to create basic software applications. Some everyday use cases include building workflow automation tools, developing web and mobile applications, and creating simple databases and dashboards. The main idea behind the LCNC platform is to empower business users and other non-technical stakeholders to become more self-sufficient and take a more active role in the software development process. By providing drag-and-drop interfaces and pre-built templates, LCNC platforms allow organizations to automate business processes, build web and mobile apps, and create custom software solutions, all while reducing development time and costs.  Choosing Between No Code and Low Code Low-code and no-code are similar concepts in that both refer to software development tools that do not require extensive programming knowledge. However, there is a subtle difference between the two. Low-code refers to platforms that provide a visual, drag-and-drop interface for creating software applications and also include the option for manual coding. As a result, users can take advantage of pre-built templates and other visual tools to quickly create basic applications. At the same time, they will have the flexibility to add custom functionality using code if necessary. No-code, however, refers to platforms that do not require manual coding. Instead, users can create software applications using pre-built templates and visual tools. While being more manageable and accessible to non-technical users, they also have more limitations, as there is no flexibility to write code manually. Thus, the main differences are the level of technical understanding required to use the platform and the customizability. Why Opt For An LCNC Platform? Essential Use Cases Of LCNC Platforms LCNC platforms are suitable for a wide range of use cases, but some of the most common include: While these are only a few examples, LCNC platforms have several other use cases. For example, some LCNC platforms are generic, while others cater to specific industries, like healthcare or retail. The ROI Of Low Code No Code Platforms Various statistics demonstrate how LCNC platforms increase return on investment (ROI). Some studies show that LCNC platforms can help organizations significantly reduce the time and cost of developing and deploying new applications. For example, a study by Forrester Research found that organizations with lesser coding can develop and deploy an application in just a fraction of the time and cost compared to traditional development methods, as much as 10x faster and at 60% lower prices Another study by Gartner Research found that by 2024, 65% of application development will be via platforms with minimal coding. An LCNC approach to developing enterprise apps requires fewer developer resources and is more appealing to small businesses. When coding is less, average companies can avoid hiring two software developers and save more than $4.4 million in three years. Further, an IDC report found that customers achieved a five-year ROI of 59% with low-code and intelligent process automation. These statistics demonstrate that LCNC platforms can help organizations reduce the time and cost of developing and deploying new applications, which can lead to significant savings in terms of both time and money, thus increasing ROI. However, it is essential to note that the specifics of the return on investment will depend on the specific use case and should be carefully considered and evaluated. 

Low code no code AI platforms function much like other LCNC platforms, allowing non-technical users to leverage AI.  It employs a drag-and-drop interface that enables customers to personalize AI solutions with pre-built models and data connectors. No code platforms go a step further— users can create and deploy artificial intelligence models and applications without any code. Its immense potential lies in the fact that even organizations with limited programming knowledge can unleash the power of AI. Market Outlook for No Code Low Code AI Low-code development technologies will reach $26.9 billion in 2023, up 19.6% from 2022, according to Gartner. By 2025, 70% of new workplace apps will use LCNC technology, a big jump from less than 25% in 2020. The spurt results from companies using complex legacy technologies and techniques having to formulate resilient responses to changing market requirements. This has increased the demand for quick and cost-effective applications, fueling the advent of low-code and no-code (LCNC) development platforms. The growing popularity of LCNCs also reflects the growing requirement to develop applications and digitize processes more quickly. Despite the AI programmer scarcity, LCNC AI helps firms innovate and go to market more quickly. Use Cases of Low Code No Code AI Industries with High Usage of LCNC AI Applications  Currently, many industries and organizations use low-code and no-code AI, including: As AI technologies evolve, many industries and business areas will adopt low-code and no-code platforms. The Future of Low Code, No Code AI We can expect low-code and no-code AI platforms to become increasingly automated and self-service oriented. As a result, users can design and deploy AI models and applications with minimal support. As LCNC AI platforms combine with cloud, IoT, and edge computing, they can provide more powerful and adaptable AI solutions. Moreover, with greater democratization of AI, organizations can leverage AI for strategic decision-making rather than for executive tasks. For instance, LCNC systems will focus more on providing transparent and interpretable models that consumers can trust. Natural language processing (NLP) will enable LCNC systems to handle complex tasks, including language translation, sentiment analysis, and text summarization. Wrapping Up As companies embrace AI, low-code and no-code AI markets are projected to increase. On the other hand, a major disadvantage could be an increasing dependence on a single provider, leading to compromised security. However, the benefits far outweigh the risks.  The frameworks are based on coding languages such as PHP, Python, and Java. Users work in graphically rich simulation environments where they can drag and drop program components, link them, and observe what happens. LCNC platforms are also minimal in maintenance and extremely scalable, allowing operations to achieve enterprise-grade performance, have great readability, expedite debugging and code updates, and allow faster iteration.  As a result, the LCNC wave could be a major driving force toward becoming what Gartner has termed a “Composable Enterprise.”  

In today’s global economy, monitoring the complete supply chain is critical to gaining a competitive edge. Demand and output alter dynamically, making it essential for supply chains to enhance their operations and adapt to evolving client demand. In addition, effective supply chains should provide products and services in a timely, reliable, and cost-effective manner. Leaders and analysts realize that Supply Chain Disruptions are significant hazards that must be understood. Non-integrated production and distribution systems can hinder progress. Many experts need help understanding these disturbances and what tactics and policies can help cope with them. And this is precisely where Supply Chain Simulation can help. By analyzing performance before execution, you can understand how a new plan will influence the whole supply chain before implementing it. Why Should the Supply Chain be Simulated? A faulty plan can cause a ripple effect across the supply chain, resulting in excess inventory, severe backlogs, poor product predictions, imbalanced capacity, poor customer service, unclear production plans, high backlog charges, or even lost revenues. Although ERP and SCM solutions provide several benefits, their ability to undertake what-if scenarios and predictive analyses is restricted. A simulation helps one better understand how connections and operations change over time due to evolving policies and parameters. The following are some examples of popular models used across industries: Increasingly, academicians propose that the discrete event simulation (DES) system may be the most appropriate for supply chains. DES replicates real-world supply chain systems, divided into logically independent operations that continue autonomously across time. Each event occurs within a specific process, and a timestamp is attributed to it. The behavior of intricate systems is codified as an organized sequence of well-defined events happening at one particular instant in time and marking a state change in the system. Discrete event simulation provides dynamic details and opportunities for greater insight by expanding the design, analysis, and optimization toolset for supply chain managers. Eventually, the simulation model used would depend on the purpose and requirements of the firm. The most significant advantage of supply chain simulation is that it allows you to evaluate the system’s performance before implementing an action. It also enables you to conduct better what-if analyses to improve planning and evaluate different operational options without interrupting the real-world process. The results? Seamless flow of goods, on-shelf availability resulting, and improved efficiency thanks to eliminating nonvalue added work. How Does Supply Chain Simulation Work? A supply chain simulation depicts the behavior of a logistics network over time using logical principles. For example, it is possible to begin manufacturing only when inventory dips below a certain level. You can also integrate various rules and study and test their interactions during disruptive events like strikes and natural catastrophes. When Should Supply Chain Simulation Be Used? Modern supply chains produce a large amount of data and are vulnerable to various threats. Both these aspects enhance analytical complexity and encourage dynamic simulation modeling. Simulation is especially beneficial when the underlying system is too complex to be explored using mathematical-analytical approaches. Simulation can also achieve the following: Use Cases of Supply Chain Simulation A supply chain simulation has multiple elements, including analyzing the product mix, evaluating different scenarios, and answering what-if questions related to strategy. Here are some examples of when to use supply chain simulation. Product Mix Analysis Choosing the correct product mix is one of the most critical tasks facing a supply chain company. Some goods may be aligned with the organization’s long-term plan, while others may need to be more profitable. In most cases, these decisions are complicated and determining how they impact the organization’s bottom line is difficult. Simulations give helpful solutions to product mix concerns, allowing planners to analyze potential consequences before executing a strategy, resulting in higher profit margins. Scenario Analysis When procuring items, supply chain planners often have many possibilities. Many decisions about supplier location, pricing, delivery cost, shipping, risk, tariffs, supply continuity, and quality come into play. Using a scenario analysis based on predictive analytics, planners can examine these and other aspects to decide which of many alternatives presents the lowest risk and highest profit while best supporting the company. What-if Analysis What-if analysis allows supply chain planners to anticipate what might happen if X, Y, or Z happened. It identifies threats not merely profitability threats but reputational and other risks to the whole supply chain. With advanced scenario modeling and constraint-based planning, it prepares planners for shifts in market demand. Planners create and test what-if scenarios to evaluate how one event affects projections across the supply chain. Conclusion Supply chain simulation is an evolving, practical technique critical for succeeding in today’s volatile environment. According to a McKinsey study, 73 % of companies encountered problems in their supplier base, while 75% faced production and distribution problems. Though getting everyone on board with a supply chain optimization initiative might be difficult, there is significant value in gathering suppliers, consumers, and management in the same room to examine the whole process using an interactive simulation that animates each link in the chain. Even when teams first utilize simulation as a silo tool to drive in-house process improvement initiatives, they rapidly learn that sharing their simulations with customers can help them interact with stakeholders and strengthen the bidding process. In addition, organizations that experience supply chain difficulties require the reliability of supply chain simulations.

Toilet paper is perhaps the one phrase that sums up the pandemonium that was unleashed across several parts of the world when the COVID-19 pandemic first began to sweep across borders.  Depending on where they were in the world, customers may associate another word with the supply chain disruption that characterized the pandemic in its early days. Many items disappeared from the shelves for weeks. Several others that were being imported from other countries remained off the shelves, as the importer permanently closed the business due to the unforeseen crisis. As companies today vie for competitive advantage, their ability to proactively find and deal with supply chain risks will be a critical differentiator. Although supply chain planners have been recording textual data about risks in business systems, this information is rarely used strategically. Moreover, evaluating hundreds of textual bits manually is difficult, time-consuming, and error-prone. For instance, a single product could amass over 400,000 comments in the course of a year! This is where emerging technologies such as NLP hold immense potential by automating and providing predictive insights. Natural Language Processing (NLP) is a combination of computer science, artificial intelligence, and linguistics that uses models to understand and find patterns in natural language. Typically, humans communicate with machines through the use of specialized programming languages or predefined answers. However, NLP transcends these limits and enables users to connect with computer systems using natural speech and writing patterns. Natural language processing can be applied in several ways to supply chains and logistics. For instance, through training, the model begins recognizing different language patterns; analyzing this data can lead to information, insights, and even automated actions. These capabilities allow businesses to resolve supply chain risks proactively rather than reactively. Benefits Of NLP Capture information Using natural language processing and unstructured data querying, it is possible to look at published material that is available to the public. This content may appear in blogs, videos, social media, news, and other formats. By keeping an eye on social media and scraping websites, businesses can be proactive and evolve their strategy. For instance, social media listening via Twitter and Facebook can be effectively tracked. The software monitors important terms or phrases that can potentially affect the supply chain, and an algorithm analyzes and derives business intelligence from the gathered data. Thus, NLP can efficiently detect issues with certain suppliers, keep track of major environmental changes, provide insights on repercussions, verify sourcing, and monitor competition. It can also help improve supply chain governance, ethical practices, policy and procedure changes, reputation management, and other predictable trends. Generate supply chain maps Supply chain mapping (SCM) is the process of documenting information shared by companies, suppliers, and individuals involved in a company’s supply chain to create a global map of the entire network. For instance, the precise origin of all goods and shipments can be mapped. Initial findings indicate that supply chain mapping solutions generated through Natural Language Processing (NLP) can help businesses: Leverage adaptive chatbots Natural language processing solutions for the supply chain improve consumer-facing applications such as customer service chatbots that can engage with clients, suppliers, manufacturers, and distributors. This would reduce the amount of effort required to carry out orders and eliminate potential inaccuracies. Chatbots can also expedite the procurement process by communicating with supply chain professionals, collecting requirements by chatting in natural language from any location, and simplifying the process for people with low technical expertise. Monitor data changes NLP algorithms can monitor internal data changes in real time, which can aid in maintaining accurate master data. Web scraping collects benchmark industry data for finding transportation rates, fuel, and expenses. This information assists firms in benchmarking their performance against industry standards and identifying potential cost savings. Similarly, NLP can be used to scrape data from logistics carriers’ and shipping ports’ websites. It can analyze the impact of a crisis and suggest measures like increasing the safety stock inventory levels, adopting other modes of transportation and locating new routes to ship products. Web scraping and social media listening can also provide useful supplier data for labor relations, regional political constraints, local news about strikes or riots, and weather events that can disrupt supplier operations and result in supply risk. This data can be further evaluated to generate early warning indicators. Improve customer service down the chain Because supply chains generate a lot of data, finding the best way to use this data to optimize the supply chain is important. NLP allows users to ask complicated questions and guides them through the data to help them find answers. Companies can respond well to stakeholders further down the supply chain if they automate their customer service. Using natural language processing makes automating customer service much easier. When stakeholders ask questions, NLP gives them the correct answers or points them in the right direction. Because of this, the administrative costs in customer service centers go down and customer satisfaction goes up all along the supply chain. Capture information across multiple languages Most businesses operate on a worldwide scale, and language constraints can impede process efficiency. Language limitations are a major concern in global supply chains and logistical performance (e.g., pickup directions and instructions for truck drivers). NLP helps resolve this issue by allowing local stakeholders to communicate in their own language. It can also analyze, organize, and translate data so that it is accessible to all users, as well as translate papers from one language to another to decrease regional language barriers. Track compliance among suppliers Web scraping can be used to track crucial external information about major suppliers. Monitoring a supplier’s stock market performance and reviewing its financial records, for example, can give information about the supplier’s financial stability. Furthermore, online scraping and social media listening can provide additional information about labor relations, regional political constraints, local news about strikes or riots, and weather events, all of which can affect supplier operations and increase supply risk. Using this data, early warning indicators can be generated, and supply chain

Demand planning is the practice of forecasting the demand for a product or service so it can better serve customers. Having the right amount of inventory without incurring shortages or spending money on surplus inventory is at the heart of demand planning. While it may seem unrealistic to expect businesses to consistently match their supply to demand without losing revenue or clients, several customers have realized this vision through AI-based demand planning. So, what exactly is AI-based demand planning? As the name suggests, AI-based demand planning leverages the power of artificial intelligence and machine learning to analyze sales and consumer trends, historical sales, and seasonality data through a combination of sales forecasting, supply chain management, and inventory management. By optimizing your ability to forecast demand efficiently, demand planning can become an established, continuous process that informs your sales and operations strategy. Why Choose AI-based Demand Planning? Utilizing ML and AI demand forecasting has numerous advantages. Adopting AI methodologies can facilitate accurate forecasting at all organizational levels. According to McKinsey, demand management in the supply chain using AI-based methods can reduce supply chain network errors by 30–50%. Applications based on AI and ML utilize data to make predictions. The dimension reduction, cross-validation, and grid search mechanisms enable algorithms to optimize the model and minimize errors by adjusting various features and parameters. Forrester estimates that over the next two years, 55% of organizations will invest in artificial intelligence.  If you have been considering investing in AI-based demand planning, here are five good reasons to make the transition. Top Five Benefits of AI-based Demand Planning   1  Brings accuracy to demand forecasting A superior AI ensures that data can be organized and analyzed in minutes. Since AI transcends historical data and is not reliant solely on rules, it requires businesses to establish fewer rules initially. With the AI handling time-consuming data preparation processes, your precision will improve. Moreover, since data is frequently updated, the results are more accurate. Thus, adopting ML and AI can help retailers harness the power of their data while retaining complete control of it. It is also possible to deliver a unique forecast model for each product.  AI can recognize similarities, make connections, and anticipate patterns that were not initially programmed, providing supply chain professionals with a bird’s-eye view of their inventory and its underlying relationships.  Considering the importance of forecasting from a financial standpoint, retailers’ losses from missed sales or excess inventory add up quickly. With innovative solutions, it is possible to avoid overstock, understock, and out-of-stock situations. 2 Timeliness in demand forecasting  AI systems are often superior to humans when it comes to data-intensive, monotonous tasks. They can assist logistics companies and retailers in analyzing vast amounts of data, identifying inefficiencies and detecting opportunities for improvement. AI systems can analyze multiple data sets simultaneously, combing through information manually and making connections that usually take a lot of time. Therefore, they can provide results in a fraction of the time. Amazon, the leading e-commerce giant, uses AI at nearly every level of its supply chain, particularly in its warehouses. The timely AI predictions help the company fulfil orders faster than most competitors. AI can also help optimize route planning and shipment updates, allowing companies to predict demand and supply. For instance, many companies use technologies like RFID tags to track products through the supply chain. However, sometimes items ship without these tags or events, causing them to be unreadable. Using AI, businesses can analyze things like average shipment times and weather patterns to offer accurate results. 3 Immense cost savings with the right demand planning strategy Besides enhancing accuracy and timeliness, AI can save time, money, and productivity by taking on various administrative and data-intensive tasks. The AI handles administrative tasks, allowing human employees to focus on other projects. For instance, manual tasks such as document filing can cost businesses 6,500 hours per year–a significant amount of time. Imagine what a business could accomplish if it had over 6,000 additional hours to work! This productivity benefit is far too advantageous for logistics companies to ignore. A logistics leader saved 100 million miles and 10 million gallons of fuel annually with AI-optimized route planning. 4 Improved customer service with demand estimation and forecasting Chatbots, typically used in customer service roles, are one of the most common applications of AI, enabling supply chains to provide 24-hour customer service. By delegating customer engagement to artificial intelligence, logistics companies can free up human employees’ time. Not only does this improve efficiency, but it also enhances customer service by increasing the customer’s access to information. Customers can check their order status with an application like Alexa to provide prompt and accurate responses. In addition to being a quick, hands-free information gateway, smart speakers are already common in many homes. People who are used to using these technologies will appreciate the ability to use them to learn about their orders. 5 Strategic decision-making with demand analysis and forecasting Using AI to find outliers in demand planning makes it easier for a business to make good decisions by spotting changes early on and putting adequate measures in place at the right time. A superior AI goes even further by recommending clear courses of action that consider internal constraints and parameters. The risk of interventions that don’t work to meet demand is reduced, as are inefficiencies in the supply chain. Supply chains must make the most of their shipping to deliver products on time. Often, this means planning the fastest, safest way to get from point A to point B. AI is ideal at making these predictions by looking at traffic and weather patterns to determine the best course of action. Since factors like these are dynamic, optimal routes may change daily. As a result, supply chains need tools like AI to analyze data and plan routes quickly. Wrapping Up AI demand forecasting alters how businesses manage their supply chains and make essential business decisions. Rather than relying on manual processes, AI-based demand forecasting collects,

A data silo is a store of data maintained by one division or team and isolated from the rest of the company. The phrase has agricultural origins and refers to the ideal circumstance in which grain and grass in a field “silo” are shielded from the weather. Within a business unit, however, siloed data is far from desirable. It is frequently incompatible with other data sets, making it difficult for users in other parts of the organization to access it for insights. For instance, finance, administration, human resources, marketing teams, and other departments require precise information to execute their jobs. These departments typically store their data in distinct locations known as data or information silos. As the quantity and diversity of data assets increase, so do data silos. Technical, organizational, and cultural factors can all contribute to data silos. They are common in large corporations because various business units can function independently and have their own goals, priorities, and IT budgets. However, without a well-planned data management strategy, any firm can end up with data silos. The Problem with Data Silos Data is healthy only when it is freely accessible and understood within your organization. If information is difficult to obtain and use promptly, or if it cannot be trusted, it does not offer value. A company that digitizes but does not break down data silos will not reap the full benefits of digital transformation.  Organizations must provide decision-makers with a 360-degree perspective of data relevant to their analyses to become genuinely data-driven. Data silos can lead to a host of challenges: Leads to flawed decision-making  Incomplete and inconsistent data sets can result in poor decision-making. Since data silos prevent users from accessing data, corporate plans and decisions are not based on all accessible information. Data warehouses and lakes that integrate diverse data types for business intelligence (BI) and analytics applications might be derailed by silos. In addition, data from one silo could be inconsistent with other data sets. For instance, a marketing department may arrange consumer data differently than others. Such inconsistencies can produce data quality, accuracy, and integrity challenges that affect both operational and analytic applications’ end users. Gets in the way of productivity Data silos drive up an organization’s IT costs by acquiring more servers and storage devices. In many cases, these additional procurements are also implemented and managed by departments rather than the organization’s data management team, leading to increased costs and inefficient use of IT resources. Moreover, isolated data sets limit opportunities for data exchange and collaboration among users from multiple departments. Achieving business objectives or a shared vision is impossible without teamwork and open communication. Teams working on a self-contained project may overlook critical data streams or mix up comparable data sets that should be kept separate. They may even be working with an outdated version of the same data. When teams only have access to a portion of the data, they may operate with inadequate information, which can limit efficiency and lead to duplication of effort since some of the information requested by one department may already exist in another.  Causes data security and compliance issues Businesses with siloed data find it challenging to establish a complete and effective data governance structure capable of protecting them from data breaches and cyber threats. Data silos also impede an organization’s capacity to detect occurrences that could potentially result in data privacy violations. Such businesses are highly vulnerable to noncompliance with data privacy requirements.  Silos also make it more challenging to comply with data privacy and protection requirements. Individual users, for instance, may keep certain data in Excel spreadsheets or online business platforms like Google Drive. This data can create a security challenge, especially if accessed via mobile devices. Provides a lackluster customer experience 47% of marketers believe that data silos are the key reason they don’t have a complete picture of the customer journey. By decreasing productivity and making it harder for enterprises to access relevant information, client satisfaction and customer experience suffer. Throughout their buying journey, customers have multiple interactions with a particular company, including marketing and sales communications, website and social media visits, and support and billing discussions. When all this information is kept in separate silos, it can pose considerable hurdles and hinder marketing efforts. How Valiance Can Help  At Valiance, we have a simple approach to breaking down data silos. Integrating data The most obvious strategy to break down data silos is integrating them with other systems. The most common type of data integration is via extract, transform, and load (ETL), which involves extracting data from source systems, consolidating it, and loading it into a target system or application. Real-time integration, data virtualization, and extract, load, and transform (ETL) are other data integration approaches that may be employed against silos. Centralizing data repositories These repositories could be data warehouses or data lakes and contain massive amounts of data from many systems in the form of structured, unstructured, and semistructured data, which are utilized in data science applications. Structured transaction data is stored in data warehouses for BI, analytics, and reporting applications. These centralized repositories, when combined, provide a logical solution to silos. Enterprise data management and governance A good data governance program may directly minimize the number of data silos in an organization and promote shared data standards and norms. An enterprise data strategy better connects the data management process with business activities. This method will not only remove current data silos but also prevent the formation of new ones. A comprehensive data architecture design helps document data assets, maps data flows, and offers a roadmap for data platform deployments. The right interface Finding the right interface to allow employees to examine the organization’s data is also important. A change management project to transform an organization’s culture may also be required. Low-code, cloud-native technologies can also help since they can merge various data silos quickly and effectively via intelligent connectivity and automation services. Adding artificial intelligence (AI) and machine learning

In the early years of retail, assortment management was limited to a handful of variables. Mostly, the populace was relatively localized, with most people having similar roots and ideas and spending their whole lives in the same town. Local business owners, therefore, had a good idea of what their customers desired. Today, retailers run national and international chains. A one-size-fits-all strategy would lead to “stock outs” on popular goods and markdowns on others, which would lose the store’s hard-earned revenues. When customers cannot locate the items they want to purchase, customer satisfaction and loyalty are negatively affected. To keep pace with evolving needs, retailers must develop more complex methods of matching consumer needs. A failure to provide a varied assortment that meets the demands of a broader customer base will lead to higher billings, while a failure to do so would leave the door open for competitors. AI makes assortment management and optimization more timely, aligned, and lucrative by precisely forecasting how many variations to provide, how many of a given item are needed to minimize stockouts and markdowns, the storage and display capacity required, etc. How Does AI Help? The right stock at the right store AI models can look at factors like past sales, retail display space, local trends, internet activity, weather forecasts, and more to determine which products would be best for a specific retail location. This AI-based optimization ensures that items are displayed where they can be sold at full price, thus helping to cut down on markdowns. Real-time data analysis also allows retailers to respond instantly to changes in demand, reducing stockouts by moving more items to where they are most likely to be sold. Also, AI models can move goods from one store to another so that businesses can take advantage of local trends. By generating shopper-focused, trend-appropriate assortments, the company can meet customers’ short-term and long-term needs across every category and even predict them. Predictive capabilities AI-enabled technology and systems intelligently mimic human behavior to improve outcomes. Using machine learning, automatic triggers detect periodic trends, peaks, and dips in data and notify merchants and suppliers. Retailers can predict future market behavior by researching past buying patterns, resulting in more precise forecasting and inventory alignment.  By better understanding client preferences, intentions, and behaviors, AI enables shops to collect shopper information in an automated and predictive manner. It can also prevent under or overstocking that affects the bottom line and, in the case of perishable commodities, causes spoilage. Furthermore, the complicated mathematics inherent in AI allows it to provide credible recommendations for upselling and cross-selling more effectively. Better curation Curation helps clients find their needs without overwhelming them with options, brands, or packing sizes. It also increases shelf space. Traditional curation (micro merchandising) is primarily concerned with margins, volume, shop size, location, and what shoppers within a specific zip code purchase. While these are necessary prerequisites, they lack the analytical capacity of artificial intelligence to cross-reference a massive array of data points across various consumer indices. Using AI will allow merchants to understand, for example, if shoppers prefer the brand over price or whether they prioritize pricing over packaging size. This data demonstrates to a retailer what alternatives a client will accept within a specific category or price range. It can help eliminate “dead inventory” and discover items that perform poorly but are frequently purchased by a chain’s highest-volume customers. On the other hand, traditional curation is concerned with gross profit or volume and often overlooks opportunities to retain valuable clients. Respond before competition Because of its extensive supply networks, massive product assortments, and poor profits, retail is regarded as one of the most competitive industries. Traditional offline stores, as well as e-commerce, face stiff competition. AI can help retailers stay relevant by constantly refining product assortments and improving business operations. Product selection is one of the most critical factors that merchants can control to differentiate themselves from the competition. With sophisticated analytics and artificial intelligence, retailers can make better decisions about which things to stock in their stores and adjust their product assortments to local client preferences and store sizes. By leveraging AI’s ability to foresee upcoming trends and identify shortcomings before negatively impacting the market, retailers can create a more profitable and competitive private-label strategy.   Needs-driven assortment optimization  AI employs data mining to examine data samples in real-time and make recommendations based on what works. There is no need to wait until the following calendar review to understand whether an item has experienced a temporary dip or a massive drop. This allows retailers to choose products with proven results. In terms of what customers genuinely want, AI enables merchants to differentiate between perception and reality. For instance, customers may desire to purchase a specific product, but pricing or other factors may discourage the purchase. More likely, probabilities can be generated using advanced modeling and forecasting approaches.  AI can also accelerate product success by projecting SKU-level customer preferences and affinity using demand patterns and buyer propensity modeling. Over time, this approach would lead to increased sales and margins and improved retailer and supplier collaboration. As a result, there is a more level playing field with products that provide tangible benefits and are in line with actual client demands. Conclusion According to research from IDC, 65% of retailers say AI is essential for merchandise analytics, and 54% cite improving ecosystem collaboration with suppliers as a top priority. As more and more retailers get on board with AI, it will not be a differentiator but a necessity. Making suitable investments early on helps you get an early start and leverage your first-mover advantage. Talk to Valiance to understand how our AI solutions can help you optimize the assortment at your retail outlet or e-commerce site.

What is driving the marketplace today? According to Gartner, CX drives over two-thirds of customer loyalty, which represents more than brand and price combined. 86% of consumers say they will leave a brand after as few as two poor experiences, while 65% of consumers find a positive experience with a brand to be more influential than great advertising. Customer experience monitoring is no longer a luxury; it’s vital to your business’s very survival. While providing superlative service is the ultimate goal of every business, it is important to strike a balance between operational efficiency and profitability as well. Speech analytics is an integral component that helps you truly understand your customer so that you can remain highly profitable and in sync with their needs.  Speech analytics is a software technology that transcribes and extracts profound insights, trends, and metrics from every voice call with AI services that encompass transcription, speech technologies, and natural language processing. By comprehending, analyzing, and extracting insights from voice conversations, you can assess agent performance, the customer’s experience, and enterprise-wide strengths and weaknesses. Why Speech Analytics? According to McKinsey, in 2019, only 37% of contact centers believe they are creating value with advanced analytics. With contact centers receiving and making thousands of phone calls each day, ranging from sales and billing inquiries to reviews and complaints from customers, manually listening to and scoring every call received can be arduous. As a result, on an average, only about 5% of calls are scored, causing a lot of information to slip through the cracks. Here is where speech analytics comes into play. When a phone conversation concludes, a speech recognition platform converts the conversation into text for near-immediate analysis. Speech analytics reveals precisely how sentences are uttered and thus reveals their underlying meanings, enabling you to evaluate the types of calls you receive, how your agents handle them, and how the customer felt throughout each conversation. Often, the data is not synthesized into meaningful information for various reasons, including unclean or incomplete data, spread of data across multiple platforms, and a lack of understanding of which metrics are the most important. Combining advanced analytics such as speech and text will provide a clearer picture of the decisions that can improve your contact center’s performance. Maximizing Conversations with Speech Analytics Speech and text analytics allow businesses to comprehend the entire customer journey by discovering information they may not yet possess. It can identify trends and patterns that lead to vital business insights by analyzing millions of transactions across all channels, including traditional phone call recordings, call transcriptions, and even emails and text messages. Here are a few tangible benefits of incorporating speech analytics into your contact center. Surface actionable data Speech and sentiment analysis give you a deeper understanding of your customers’ pain points and help you identify business or product areas that require further research or improvement. You can quickly identify changing customer trends and adapt proactively to meet customers’ desires and needs. Boost agent retention Feedback is a crucial component for an agent to succeed. However, contact center managers cannot monitor and analyze every call. Speech analytics empowers you to extract the sentiment of both the customer and the agent under broad themes. For instance, an agent who is scored as demonstrating a “lack of knowledge” or “lack of compliance” will need to be mentored. Agents who demonstrate concepts of courtesy and ownership effectively can be recognized and compensated for their exemplary performance, leading to a boost in morale.   Enhance the client experience By combining speech and text analysis, it is possible to derive meaning from interactions across all channels and transform data into structured, usable, and even graphical information that is clearly understood by the entire organization. Understanding the hiccups within your multi-channel ecosystem, how customers perceive your organization, and where representatives miss the mark are all useful tools for making better, more data-driven decisions. Real-World Benefits and Use Cases McKinsey reported in 2019 that contact centers that utilized advanced analytics such as speech and text were able to decrease average response time by up to 40%, increase self-service containment rates by 5 to 20%, reduce employee costs by up to $5 million, and increase service-to-sales call conversion rates by nearly 50%— all while meeting customer expectations and engaging employees.  While deployment also includes streamlining your data and internal processes, when implemented right, you can use this information to drive improved results without the need for highly specialised data professionals. Speech to text analytics can: How Valiance Uses Speech to Text Analytics Valiance is a global AI & data analytics consulting firm helping clients of different sizes create decisioning software products using AI & Cloud Computing technologies. We use Amazon Transcribe, an Automatic Speech Recognition (ASR) tool, to generate transcripts out of audio/video files. We help customers monitor the conversation between the customer care executive and the customer through a speech to text algorithm, which checks if the right keywords are being used and flags inappropriate words. When this algorithm is run, the agents are scored and conversation insights such as call sentiment are extracted. These insights form the basis for training the agents further. It can also lead to product revisions and policy changes. Without this information, the real feedback from the customer may never reach management.  We help customers: Wrapping Up It’s time to gain a competitive advantage by leveraging the wealth of information at your fingertips and transforming it into actionable insights. Speech and text analytics enable you to get the most out of every conversation by delving beneath the surface of what customers are saying and getting to the core of their questions in real time. Moreover, AI-driven analytics provides predictive insights thanks to the continually learning, automated technology, so you can proactively address any gaps in your customers’ needs or your agents’ actions.

A PwC poll revealed that over half of the respondents said their buying habits had become “more digital” as a result of the pandemic. In 2020, online shopping accounted for 19.6% of retail sales, up from 15.8% in 2019. As customers realized how quickly and easily they could buy what they wanted from their homes, many became lifelong converts.  However, even as e-commerce continues to grow, it’s not all smooth sailing. Retail brands and marketplaces with thousands of products in their inventory face two common problems: One: An increasing number of product views that do not lead to conversions Two: Replicating the personal touch of in-store shopping AI can help address both these challenges effectively through laser-sharp personalization. Personalization in the E-Commerce Era A sad reality for many e-commerce retailers is that product views do not translate into better revenues. Customers may flip through scores of options, but are they really making purchases? One study revealed that 70% of consumers do not look past the first three pages, making the subsequent pages irrelevant inventory.  Many businesses have attempted to bridge the gap by offering some form of personalization. However, to most online retailers, this is little more than a revolving carousel of recommendations that works on segmentation. It simply lumps customers into large demographic groups based on historical data and stereotyped categories such as gender and age. Though this form of segmentation can suggest a few relevant items, it does not really count as personalization as every client in the specified category sees the same product. Many other sites use machine learning technologies that examine product correlations, identifying which goods are often bought together. This insight is limited in scope; it would be far more useful to examine the interaction between the specific customer and the type of goods he/she engages with. But where neither segmentation nor machine learning have delivered the customized experiences customers desire, AI can make a real difference because of its innate personalization capabilities. Ecommerce personalization refers to a set of practices in which an ecommerce marketplace showcases dynamic content based on consumer data, such as demographic factors, purchase intent, interests, browsing habits, purchasing patterns, and device type. It’s an important consideration as studies show that personalized product recommendations drive revenue. Here are a few recent statistics that underscore the importance of personalization: Clearly, when brands can provide convenience, customer understanding, and emotional engagement, customers are likely to be more loyal, resulting in higher profitability. How AI Can Help Personalize With AI, e-commerce businesses can provide the personalization shoppers crave, transforming every visit into a highly personal experience. New, AI-powered solutions take a one-product-to-one-shopper approach, building unique behavioral profiles based on over 300 data points per customer and predicting the next steps with a fairly high degree of certainty. This kind of e-commerce shopping experience is highly adaptable, reacting and changing in real-time based on the customer’s unique demands and needs. If done right, no two customers will ever have the same experience. Here’s how AI-powered personalization can help online retailers. Meet Customers at Their Point of Need There is no better way to create enhanced consumer experiences than by anticipating desires and providing customers with their favorite items at the right time. AI employs sophisticated machine learning algorithms to monitor browser history, page clicks, social activities (likes, shares), prior purchases, dwell times, geography, and so on, to determine a customer’s interests and preferences. By analyzing patterns of frequently purchased items and adapting web pages and features to meet the demands of individual customers, AI can meet customers at their point of need. As a result, businesses can better manage their inventory while customers benefit from an enjoyable shopping experience.  Enhance the In-Store/Offline Experience AI is also capable of enhancing the in-store experience. For example, AI-enabled kiosks and robots can assist customers in locating products within the store through voice commands or touch screen interfaces. AI can also assist in developing product suggestions based on a customer’s profile, shopping history, search queries, etc. AI-driven virtual assistants can respond to customer questions and make data-driven recommendations whenever customers require them. Augmented Reality interfaces, powered by artificial intelligence, will help customers test things without physically trying them on, thus aiding the purchase decision and enhancing the customer experience. Improve Customer Support In customer service, artificial intelligence significantly saves service time and helps minimize operational expenses. Using AI-powered chatbots and messaging agents to address customer issues in real-time will make customers feel cherished and enhance their overall experience. Programs powered by artificial intelligence can also deliver automated communications to customers of a scheduled service, a replacement part, or a recurring order. It can also facilitate creating automated service requests and provide support for order status, basic order changes, returns, and refunds. Promote Omnichannel Buying Eventually, the online and offline experiences should work together to boost sales. For instance, one retail giant has come up with the concept of offering various 4-star rated products at a  physical store by using its product recommendation engine to identify trending products and customers’ favorites. This ensures that the offline experience is profitable for both the company and the customer; the company saves retail real estate by stocking products with a high likelihood of conversions, while customers can shop with confidence, knowing they are buying products that are peer-approved. Others use AI-gathered data for personalized targeting, allowing customers to try best selling products at the physical stores, thus simplifying the shopping experience and saving the company the hassle of having to deal with product returns or investing in full-fledged retail stores. Reach Customers With More Targeted Campaigns AI can help retailers reach prospects with on-point offers. A smart use of AI-powered customization collects a shopper’s preferences at the time of purchase and pairs them with relevant product data to give suggestions across numerous touchpoints. AI-driven emails can recommend products based on shoppers’ style profiles, preferences, and browsing history. It can encourage repeat purchases. If a consumer recently bought jeans, offer them matching shirts, shoes, and