September 16, 2024

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. 

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

In knowledge retrieval apps, the way prompts work has to do directly with accuracy, efficiency, and user experience. An unclear prompt might lead to inaccurate and irrelevant results, negatively impacting the user experience. This article covers some best practices to ensure your AI responds precisely to the information you are seeking. As and when you step into a huge library, stacked with millions of books, each holding a repository of knowledge on diverse subjects, you begin searching for something specific – let’s say, the latest advancements in solar energy technology. However, without knowing how to effectively ask the librarian or use the cataloging system, you could end up with books on basic solar concepts, historial solar studies, or even unrelated subjects like lunar astronomy. This narrative vividly illustrates the crux of prompt engineering in the sophisticated digital arena of knowledge retrieval.  Prompt engineering in artificial intelligence (AI) is akin to asking a librarian a well-formulated question. It involves the adept creation of queries and instructions, guiding AI systems—our contemporary digital librarians—to navigate through extensive information repositories and extract the most pertinent and precise answers. Let’s learn the subject in more detail. Introduction to Prompt Engineering for Knowledge Retrieval Applications Prompt engineering, at its most fundamental, involves the design and optimization of queries or instructions to guide AI systems in effectively parsing and retrieving the right information from expansive data sets. It is a nuanced subject that combines elements of language, psychology, and data science to interact with AI in a way that yields the most accurate and relevant results. In knowledge retrieval apps, prompt engineering is not just about asking questions. It’s about asking the right questions in the right way. Whether it’s a business analyst seeking specific market trends or a student exploring a complex scientific concept, how they frame their query significantly impacts the quality of information retrieved. Importance in Knowledge Retrieval Applications The importance of prompt engineering in knowledge retrieval applications is multi-faceted: Core Principles of Prompt Engineering Understanding User Intent Fundamental to effective prompt engineering is grasping the user’s underlying intent. This involves interpreting not just the words used, but the context and purpose behind a query. For instance, when a user asks about “the impact of climate change on agriculture,” they could be seeking economic, environmental, or social perspectives. Recognizing these nuances is critical in shaping accurate prompts. Clarity and Precision in Prompt Design The effectiveness of a prompt is often tied to its clarity and specificity. Vague or overly broad prompts can lead AI systems down a rabbit hole of irrelevant information. Precision in prompt design helps in narrowing down the focus, leading to more relevant and concise answers. Contextualization of Queries Embedding context within prompts is a skill that significantly enhances the relevance of the information retrieved. It involves adding necessary background details that guide the AI system. For instance, specifying the time frame or geographic focus in a prompt can drastically change the nature of the information retrieved. Types of Prompts in Knowledge Retrieval Open-ended vs. Targeted Prompts Open-ended prompts are designed to explore a wide range of responses, ideal for brainstorming or exploratory research. In contrast, targeted prompts are specific, seeking particular pieces of information, suitable for precise, fact-based queries. Iterative Prompts These prompts involve a series of questions that build on each other, allowing users to delve deeper into a topic. Iterative prompts are particularly useful in complex research areas where understanding evolves step by step. Exploratory vs. Confirmatory Prompts Exploratory prompts are used to gather broad information on a new or unfamiliar topic. Confirmatory prompts, on the other hand, aim to validate or refute specific hypotheses or beliefs. Best Practices for Prompt Engineering for Knowledge Retrieval Applications Balancing Specificity and Flexibility Crafting prompts that strike the right balance between being too broad and overly narrow is crucial. For instance, if a researcher is looking into the “effects of meditation on stress,” a prompt that’s too broad like “tell me about meditation” might bring up a vast array of unrelated information. Conversely, a prompt that’s overly narrow, such as “how does meditation reduce cortisol levels in women aged 30-40?” might miss relevant studies outside this demographic. An optimally balanced prompt might be “summarize recent research on meditation’s impact on stress management.” Incorporating Context and Background Information Including relevant context can significantly refine the information retrieved. Consider a business analyst seeking information on “emerging market trends.” Without context, this prompt could return a generic overview. However, by adding context, such as “emerging market trends in the electric vehicle industry in Europe in 2023,” the prompt becomes far more targeted, likely yielding specific and useful insights. Use of Natural Language and User-Friendly Terminology Prompts should be phrased in a way that’s both natural and easy to understand. For example, a medical student might seek information on a complex topic like “myocardial infarction.” Instead of using technical terms, a more effective prompt could be “explain heart attacks and their causes in simple terms.” This approach makes the interaction more intuitive, especially for users not well-versed in medical jargon. Iterative Refinement of Prompts The process of developing an effective prompt is often iterative. Start with a general prompt and refine it based on the responses received. For instance, an initial query about “renewable energy sources” might lead to various subtopics. Based on interest, subsequent prompts can be more specific, like “compare solar and wind energy efficiency,” gradually honing in on the precise information needed. Leveraging Keywords and Phrases Identifying and using the right keywords or phrases can dramatically enhance the precision of information retrieval. For a student researching “Shakespeare’s influence on modern literature,” including keywords like “Shakespearean themes,” “contemporary adaptations,” or “modern Shakespeare interpretations” in the prompt can direct the AI to focus on specific aspects, ensuring more relevant results. Anticipating Misinterpretations and Ambiguities Being aware of how an AI might misinterpret a prompt is important. For instance, a query about “Apple’s latest developments” could be interpreted as concerning the fruit or the tech company.