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Ayasdi

ayasdi

has the potential to revolutionize the way we approach and address global challenges. As an experienced AI developer and a thought leader in the area with a proven track record of building and deploying AI products in both industry and academia, I have witnessed firsthand the transformative potential of AI. Artificial intelligence (AI) One area where AI can make a significant impact is in promoting environmental, social, and governance (ESG) practices. ESG factors have gained prominence in recent years, with investors, companies, and governments recognizing their importance in building a sustainable future. How can AI be used to promote ESG practices? Let's explore real-world examples to illustrate the potential of AI-driven solutions. AI For Environmental Management AI can offer innovative ways to monitor, analyze and reduce our impact on the environment. AI-driven climate models can provide more accurate predictions and help policymakers design effective strategies to mitigate the effects of climate change. DeepMind's AI system has in improving the accuracy of weather forecasts, which can contribute to better climate modeling. Climate change modeling. shown promise Satellite imagery analysis can detect illegal logging and deforestation in real-time, enabling rapid intervention. Global Forest Watch, for example, uses AI to and identify patterns of forest loss, which helps conservation organizations take timely action. Monitoring deforestation. analyze satellite images Energy consumption can be optimized in buildings and industries, reducing greenhouse gas emissions. For example, Google works with DeepMind to of its data centers by 40%, significantly lowering its carbon footprint. Energy management. reduce the energy consumption AI For Social Responsibility AI can contribute to social responsibility by promoting inclusiveness, fairness, and ethical behavior. Hiring and promotion data can be analyzed to identify and correct potential biases. Unilever has adopted to screen candidates fairly and objectively, which helps ensure a more diverse and inclusive workforce. Fighting discrimination. AI-driven recruitment tools Healthcare solutions can use AI to improve access to quality healthcare for underprivileged communities. Zipline, a drone delivery company, to remote areas in Rwanda and Ghana, significantly improving access to life-saving treatments. Health and well-being. delivers medical supplies Fintech solutions can use AI to provide access to affordable financial services for unbanked populations. Mobile lending platform Tala uses AI algorithms to , enabling it to provide microloans to people who would otherwise be excluded from traditional financial services. Financial inclusion. assess creditworthiness AI For Governance And Transparency AI can help improve governance and transparency by automating routine tasks, detecting fraudulent activities and ensuring compliance with regulations. Potential regulatory breaches can be detected by automatically monitoring and analyzing vast amounts of data. Ayasdi helps banks and ensure compliance with anti-money laundering regulations. Regulatory compliance. detect suspicious activities AI-driven solutions can streamline public sector processes and improve service delivery. The U.K.'s National Health Service (NHS) has begun , freeing up staff time for more critical responsibilities. Public sector efficiency. automating routine administrative tasks AI can analyze corporate governance data to assess a company's ESG performance and identify areas for improvement. Datamaran can help companies and opportunities, providing insights to enhance their sustainability strategies. Corporate governance. track ESG risks Challenges And Ethical Considerations While AI can drive ESG practices, it's crucial to address the challenges and ethical considerations associated with AI implementation, such as algorithmic bias, job displacement, and accountability. However, two of the most pressing challenges that we should focus on right now are data privacy and environmental impact. Data Privacy As AI relies heavily on data, it is essential to ensure that data collection, storage, and processing adhere to privacy regulations and respect individual rights. One way to address this is by implementing robust security measures within your organization to protect data from unauthorized access, tampering, or theft while concurrently providing training and resources for employees to understand the importance of data privacy. You can also use privacy-preserving AI methods such as federated learning in your projects, which allow AI models to learn from data without directly accessing sensitive information. We use such mechanisms to train AI models to reduce food waste based on consumers' data without actually accessing them. Also, don't forget to conduct periodic privacy impact assessments and security audits to identify potential risks and vulnerabilities in data-handling practices. It is also crucial to ensure that data processing within the company complies with data protection laws and regulations such as GDPR in the European Union and CCPA in the United States. Environmental Impact AI systems, particularly large-scale models, can consume significant energy, contributing to carbon emissions. To mitigate this, focus on the development and implementation of energy-efficient AI algorithms that require fewer computational resources and consume less power. You can also employ techniques such as model pruning, quantization and knowledge distillation to reduce the size and complexity of AI models. Moreover, opt for energy-efficient hardware such as specialized AI accelerators or low-power processors, which can help reduce the energy consumption of AI. Moving forward, also make sure to encourage and support research in sustainable AI, including the development of energy-efficient algorithms/hardware and privacy-preserving techniques. Collaborating with academic institutions and industry partners can help drive innovation in this area. Conclusion AI has the potential to transform the way we approach and address ESG issues, driving sustainable development and fostering a more equitable and just society. By leveraging AI-driven solutions in environmental management, social responsibility and governance, we can make significant strides toward a sustainable future. However, it is essential to recognize the challenges and ethical considerations associated with AI implementation. By addressing these concerns and adopting a responsible and ethical approach to AI deployment, we can harness the power of AI to promote ESG practices and contribute to the long-term well-being of our planet and its inhabitants. Also published here. Lead image . source

Enhancing ESG Initiatives with Artificial Intelligence

In the world of fintech, financial analysis is the cornerstone of making informed decisions about investments, risk management, and overall financial strategy. Traditional financial analysis methods have long been the go-to for analysts, but the is proving to be a game-changer. Now, let’s explore why machine learning and AI are the next big things in fintech and how they are transforming financial analysis. rise of machine learning and artificial intelligence (AI) in fintech Traditional Financial Analysis vs. Machine Learning and AI Traditional financial analysis methods rely on analyzing historical data and financial statements, which can be time-consuming and may not account for real-time market fluctuations. On the other hand, machine learning and AI are able to analyze vast amounts of data in real-time and identify patterns and trends that traditional methods may not be able to detect. This allows for more accurate predictions and faster decision-making. Applications of Machine Learning and AI in Financial Analysis Machine learning and AI have a wide range of applications in financial analysis. One of the most significant is . By analyzing customer behavior and identifying abnormal patterns, machine learning and AI can detect fraudulent activity and prevent it before it happens. This is especially important for financial institutions and e-commerce companies, who are often targeted by fraudsters. fraud detection and prevention Investment analysis and portfolio management is another area where machine learning and AI can be highly effective. By analyzing vast amounts of data, these technologies can provide investors with more accurate predictions and recommendations for their portfolios. This allows investors to make more informed decisions and potentially achieve higher returns. is also an area where machine learning and AI can have a significant impact. By analyzing risk factors and identifying potential risks in real-time, these technologies can help financial institutions and other companies mitigate risk and avoid losses. Risk management Credit scoring and lending decisions are also being revolutionized by machine learning and AI. By analyzing customer data and credit histories, these technologies can provide more accurate credit scores and lending decisions. This allows lenders to make more informed decisions and offer loans to more qualified applicants. Finally, customer service and personalized recommendations are being transformed by machine learning and AI. By analyzing customer data and behavior, these technologies can provide personalized recommendations and support to customers. This can lead to increased customer satisfaction and loyalty. Examples of Machine Learning and AI in Financial Analysis There are several examples of machine learning and AI being used in financial analysis. One of the most well-known is , which specializes in fraud detection and prevention. Feedzai uses machine learning algorithms to analyze customer data and identify fraudulent activity in real-time. This allows financial institutions and e-commerce companies to prevent fraud before it happens, saving them time and money. Feedzai Another example is , which specializes in investment analysis and portfolio management. Kensho uses machine learning and natural language processing to analyze financial data and provide investors with more accurate recommendations for their portfolios. This allows investors to make more informed decisions and potentially achieve higher returns. Kensho is another example of machine learning and AI being used in financial analysis. Ayasdi specializes in risk management and uses machine learning algorithms to analyze risk factors and identify potential risks in real-time. This allows financial institutions and other companies to mitigate risk and avoid losses. Ayasdi Challenges and Limitations of Machine Learning and AI in Financial Analysis Despite the many benefits of machine learning and AI in financial analysis, there are also several challenges and limitations. One of the biggest challenges is data quality and bias. Machine learning algorithms rely on vast amounts of data, but if the data is biased or of poor quality, it can lead to inaccurate predictions and recommendations Regulatory challenges are also a limitation for machine learning and AI in financial analysis. Financial institutions and other companies must comply with various regulations when using these technologies, and there are often concerns about data privacy and security. Transparency and interpretability are also important considerations. Machine learning and AI algorithms can be difficult to interpret and understand, which can lead to mistrust and uncertainty. Future of Machine Learning and AI in Financial Analysis Despite these challenges, the future of machine learning and AI in financial analysis looks bright. As the technology continues to evolve, we can expect to see more advanced algorithms and more accurate predictions. We can also expect to see increased integration with other fintech technologies, such as blockchain and cryptocurrency. Predictions for the future of machine learning and AI in financial analysis include increased automation and more personalized recommendations. As the technology becomes more advanced, we can expect to see more personalized financial advice and recommendations based on individual customer data. Conclusion In conclusion, machine learning and AI are the next big things in fintech, and they are transforming financial analysis in significant ways. From fraud detection and prevention to investment analysis and risk management, these technologies are providing more accurate predictions and faster decision-making. While there are challenges and limitations, the future looks bright for machine learning and AI in financial analysis. Companies that adopt these technologies are likely to see significant benefits in terms of efficiency, accuracy, and customer satisfaction.

The Future of Financial Analysis: Why Machine Learning and AI are the Next Big Things in Fintech

A brief infographic at the end of this blog. AI — Artificial Intelligence is to mimic human intelligence for better life. The invention of that learn and perform actions took place in the classic times. Alongside , there were many fictional stories and dramas depicting artificial things and their immense potentials. artificial things by Calculus Ratiocinator Llull Marry Shelly’s Frankenstein? You must watch it if you haven’t. The idea behind machines being able to imitate humans, and their cognitive process like , , and finding out is, analysing sorting patterns — which means machines can simulate any process of formal reasoning (from ). Church-Turing thesis Wiki In common terms, machines can imitate anything with connected and meaningful information (datasets). Theory that backed up the brains of creators like and . Allen Newell, Herbert Simon, John McCarthy, , Marvin Minsky Arthur Samuel PDAbM — Public Display of Attention by Machine ;) When machines can’t watch someone misspelling their creator’s name, Well, these are some of the bold stories from our past around (Artificial Intelligence). If we look into AI’s present state and what we are up to — is prevailing from that are online to we have in our AI AI (Artificial Intelligence) bots things home. The best part is, we now have of helping us and r. cavalcades AI machines stay connected, informed, only do things that matte For the later ones, and objects that barely need our attention come hand in hand. automated tools Imagine the days when speech recognition will have transformed into ‘ ’. recognizing intents from eyeballs’ dilation range Or, one that is called which stores the behavioural pattern of every particle of a subject. Which on requirement lets the subject travel through the encrypted particle patterns and produces a brand new outcome. Dr. Quantum Do you still think ? that it lacks a unit of time? Here is the . time never stops Chronon Let’s quickly jump into the that are making our day-day life darn effortless! list of 50 AI (Artificial Intelligence) tools AI “This as a hitchhiker is so smart which is driving to our desired destination having our least conscious of what’s happening” Artificial Intelligence for Vision — helps you to organise media libraries Clarifai — visual analysis for medical and transport industry Cortica — image detection for a range of uses and industries Deepomatic — brand and face recognition DeepVision — makes satellite imagery useful Descartes Labs — the first intelligent cloud video platform Flixsense — computer vision for automotive and human detection FotoNation — matches similar products and helps to suggests combinations GrokStyle — facial recognition Haystack — uses facial recognition to help you decided on job candidates HireVue Artificial Intelligence for IoT/IIoT learns about shoppers behaviour in your store Black — helps to unify application development and data science C3 IoT — sensor analytics solution for businesses KONUX — machine learning for manufacturing process optimisation Imubit — a range of workflow optimisation products for fuel and industry Maana — helps you develop, deploy, and operate industrial apps Predix — helps renewable energy companies utilise their data better Planet OS — manufacturing analytics Sight Machine — automates data engineering for data science Sentenai — add a voice assistant to your connected product Snips — Artificial Intelligence for Development develop more adaptive, trusted and programmable AI models Bonsai — helps predict project timelines Deckard.ai — adds intelligent decision making to web and mobile apps Fuzzy.ai — connecting projects with the right team Gigster — augments your coding environment with web available knowledge Kite — deep learning platform for prediction and personalisation Layer 6 AI — makes developing chatbots for your business easy Morph — make your bot smarter, by helping it self learn Ozz — rapidly web and mobile app testing RainforestQA — increase server uptime and predict downtime SignifAI — Artificial Intelligence for Data Analytics — helps with sentiment analysis, search relevance, and more CrowdFlower — data science platform for prototype, deploy, and run at scale Dataiku — enterprise data science platform for R&D and production DataScience — platform for collaborating, building and deploying Domino Data Lab — makes DS accessible to analysts with Open Source algorithms Exploratory — helps you learn, work, and play with machine learning models Kaggle — makes data science teams more productive RapidMiner — helps DS teams put machine learning models into production Seldon — a platform to build, test, and deploy AI algorithms SherlockML — research engine, capable of discovering complex patterns in data Spark Artificial Intelligence for BI (Business Intelligence) LOL! behavioural AI for IoT Arimo — a suite of intelligent applications for enterprise Ayasdi — a range of products to improve enterprise products DataRobot — discovers events and breaking information before the news Dataminr — helps you to answer questions about your business Electra by Lore — a smarter Salesforce Einstein — adds intelligent decision making to web and mobile apps Fuzzy AI — helps you index, search, visualise and analyse your data Logz.io — is a framework for temporal pattern recognition and prediction NXT AI — to transform raw data into useful information automatically Paxata — Seems like a lot? There are many other routine AI technologies that give us precise and actionable data. The better the data are used, the better our future will be. If you come across any noteworthy AI technology, add them in the comments. As I promised, here is the infographic of List Of 50 Unique AI Technologies What else! Leave some 👏👏👏 :)

List Of 50 Unique/Routine AI Technologies

The long awaited of this series has arrived. It has taken me a significant amount of time longer to put together than I had anticipated. Now the wait is over, here it is… Part 2 of 3 This list contains companies working on artificial intelligence and machine learning products primarily for business use, non-specific to any industry. Industry specific AI will be the final part of this series. Rise of artificial intelligence is rapidly taking over the world, especially countries like Anguilla; a small island in the Eastern Caribbean with the use of their .ai top level domain for companies within the AI and robotics industry. But in all seriousness, AI is without a doubt accelerating much faster than is probably known and much more established within our daily lives than most would realise. Conducting this research has demonstrated sheer scale of the landscape and progress of the artificial intelligence industry. “Already humans and AI are co-evolving and no one has paid attention to this yet…” “When we search on Google, it’s an AI deciding what we should see. When a dating site matches two people together there’s a machine learning algorithm… that is getting people together, that then have babies. And so in effect you have this machine learning algorithm that is breeding humans.”— Sam Altman, discussing AI . here In the case of the companies listed below, business and AI are co-evolving. With so many websites to go through and dissect there may be some descriptions or names that need changing. Please let me know if that’s the case or if you think there are companies I should add to the list. Shoutout to me , and I’ll get on to it! here Artificial Intelligence for businesses (enterprise intelligence): 🔉 Audio — language transcription and recognition — ? — transcribes insights from phone calls, video footage, and online — discover more customer and employee conversations** **— makes sound searchable — critical insights about your customers conversations — building blocks to add messaging, voice, and video to web+apps Capio Clover Intelligence Deepgram Gridspace MindMeld — advanced AI to power conversational interface Nexidia — turns customer interactions into valuable insights Pop Up Archive TalkIQ Twilio 📊 Business Intelligence & Analytics — optimise and autonomously IT and business operations** ** —behavioural AI for IoT — a suite of intelligent applications for enterprise — a range of products to improve enterprise products** ** — automates repetitive tasks within your business — making complex data science easy for enterprise Arago/HIRO Arimo Ayasdi DataRobot Dataminr — discovers events and breaking information before the news — helps you to answer questions about your business — a smarter Salesforce — adds intelligent decision making to web and mobile apps — helps you index, search, visualise and analyse your data — is a framework for temporal pattern recognition and prediction — to transform raw data into useful information automatically — helps you monitor server bugs Electra by Lore Einstein Fuzzy AI Logz.io NXT AI Paxata Poweredby.ai Sundown UBIX Ruths.ai — helps you do more with your data — a marketplace for analytics — captures, categorises, and extracts key information from all your data — fast data insights using search — solves big data better, faster and at less cost — find hidden value in complex human and business ecosystems — helps to optimise pricing Exchange.ai Owl.ai AnswerRocket iSeek.ai Ecosystem.AI Prix 🍎 Core AI — a common API for many algorithms, functions and models — workbench for neural networks — advanced industry specific ML for the enterprise — advanced machine learning for enterprise — advanced machine learning for enterprise — open source machine learning and deep learning platform — advanced machine learning for enterprise — deep learning as computational system — advanced machine learning for enterprise — advanced machine learning for enterprise — range of financial, ecommerce and digital marketing AI products — open-source deep learning and ETL for enterprise on the JVM — advanced machine learning for enterprise — log Analysis for enterprises Algorithmia Arya CognitiveScale Digital Reasoning Fluid AI H2O.ai Loop AI Labs Nervana Petuum Scaled Inference Sentient Skymind Vicarious Loom Systems PipelineAI — solves the problem of production ML and AI at scale — building AI using neuroscience Ogma 📋 Data Capture — marketplace to automate simple processes — automate the discovery of objects at scale — automatically prepare any data for immediate & compliant use — helps structure data from social media and blog — automatically extract web pages as structured data — extract data from almost any website — training data, image annotation and more for enterprise** ** — tools for operations team to automate business processes Amazon Mechanical Turk CrowdAI Datalogue DataSift Diffbot Import.io Playment WorkFusion 🔬 Data Science — single platform for all predictive use cases — training data, image annotation for machine learning teams — data science platform for prototype, deploy, and run at scale — enterprise data science platform for R&D and production** ** — helps you learn, work, and play with machine learning models — makes data science teams more productive — helps DS teams put machine learning models into production — a platform to build, test, and deploy AI algorithms** **— research engine, capable of discovering complex patterns in data — makes data unification of data silos possible — helps put data into useful structures for analysis — allows data scientists to deploy and update predictive models rapidly — automate the writing of reports, websites, emails, articles and more BigML CrowdFlower Dataiku DataScience Domino Data Lab — platform for collaborating, building and deploying — makes DS accessible to analysts with OpenSource algorithms Exploratory Kaggle RapidMiner Seldon SherlockML Spark Tamr Trifacta Yhat Yseop 💻 Development — detects business incidents — develop more adaptive, trusted and programmable AI models — helps predict project timelines — adds intelligent decision making to web and mobile apps — connecting projects with the right team — augments your coding environment with web available knowledge — deep learning platform for prediction and personalisation — makes developing chatbots for your business easy — make your bot smarter, by helping it self learn — rapidly web and mobile app testing — increase server uptime and predict downtime — project management and chat software that’s easy for teams AnOdot Bonsai Deckard.ai Fuzzy.ai Gigster Kite Layer 6 AI Morph Ozz RainforestQA SignifAI Turtle Improve.ai — automatically optimises app content, design, pricing etc. — gesture recognition for developers — trains deep learning algorithms to learn like human brain — abstracts away the complexity of ML libraries like TensorFlow for more effective management of AI models — helps with decision making for designers, engineers and leaders — helps analyse algorithm Gesture.ai Cognitive Toolkit Bonsai Tangle Imandra 📂 Internal Data — helps you work together, improve productivity, and data indexing** ** — helps employees with tasks and access data using micro apps — better website analytics Alation Cycorp — a range of different AI enterprise products — takes the pain of cluster management away to focus on DS — helps predict project timelines — insight into online trends and other text analytics tools — AI platform for business Databricks Deckard.ai Gavagi IBM Watson Kyndi — helps knowledge workers process vast amounts of information — SaaS AI for risk management and operations — makes your business software smarter oneFactor Probot Sapho Sofia eContext — structure for unstructured data — create intelligent interactions between people, devices, and data — deep insights into customers and internal data —automate decision-making at scale Hayley RelativeInsight Rainbird 🤖 Machine Learning —No-code end-to-end AutoML tool that takes you from data to predictions in minutes* *__ — a range of different AI enterprise products — helps you focus your digital ad on people you want to target — analyse data in the form of images, speech, text and video — now apart of the Uber AI Labs — can do menial task work, saving time for employees — platform to unite siloed data for better recommendations — improves machine learning models 100x faster PI.Exchange — develop more adaptive, trusted and programmable AI models__ Bonsai Cycorp Datacratic deepsense.io Geometric Intelligence HyperScience Nara Logics SigOpt Amazon Machine Learn — ML-as-a-service, amongst other things — import predictive models and scale infinitely to answer existential questions Providence 📡 Sensor (IoT/IIoT) — platform delivers real-time operational insights for industry — learns about shoppers behaviour in your store — helps to unify application development and data science — sensor analytics solution for businesses — machine learning for manufacturing process optimisation — a range of workflow optimisation products for fuel and industry — helps you develop, deploy, and operate industrial apps — helps renewable energy companies utilise their data better — manufacturing analytics — automates data engineering for data science — add a voice assistant to your connected product — platform to manage the development for your IoT applications — a predictive platform for major industries — smart building management for commercial buildings Alluvium Black C3 IoT KONUX Imubit Maana Predix Planet OS Sight Machine Sentenai Snips ThingWorx Uptake Verdigris 📚 Text Analysis/Generation — creates summaries from your text and information in real-time — extract meaning from your text and visuals — text analytics and mining which works without any training** **—automatic knowledge acquisition from text — build profile and understand your users better — scaleable text analytics software — capture, measure, and act on customer feedback — scalable API to automate text classification — interprets your data more useful information — helps you find errors and inaccuracies in documents — free open-source library natural of language processing in Python — automates information extraction, management and analysis — turn text into insights — helps improve how your job ads are written — automate the writing of reports, websites, emails, articles and more Agolo AYLIEN Compreno Cortical.io — advanced language processing fido.ai IntroSpect by Lore Lexalytics Luminoso MonkeyLearn Narrative Science Qeep spaCy Salient Stride Textio Yseop 😎 Vision — add instant text capture functionality to mobile apps and more! — automated drones with vision and diagnosis capabilities — analyses subtle facial expressions to identify human emotions — makes every camera in the world smart — helps cameras to detect analyse the video feed — finds defects in wind turbines — extracts and transforms data from handwritten and typed forms — helps you to organise media libraries — visual analysis for medical and transport industry — image detection for a range of uses and industries — brand and face recognition — makes satellite imagery useful — the first intelligent cloud video platform** ** — computer vision for automative and human detection — matches similar products and helps to suggests combinations — facial recognition — uses facial recognition to help you decided on job candidates — medical data analysis and interpretation — recognises different objects and things — dedicated brand recognition for social networks — satellite image analysis — deep-learning based computer vision platform — planet monitoring and analysis using satellite imagery — satellite image analysis — is an eye and emotion tracking platform — understands and describes video content — image analysis primarily for industrial purposes ABBYY Achron Affectiva Algocian __ __i Angus.a Birds.ai Captricity Clarifai Cortica Deepomatic DeepVision Descartes Labs Flixsense FotoNation GrokStyle Haystack HireVue Lunit Inc. Matroid Netra Orbital Insight Pilot AI Labs Planet Spaceknow Sticky.ai Valossa Vidi CloudSight — high quality understanding of images within seconds — detects foreign and intentionally placed objects for security — a range of intelligent computer vision techniques Irvine Sensors Pilot.ai Originally posted → liamiscool.com

A list of artificial intelligence tools you can use today — for businesses (2/3)

Unsupervised Investments (I): A Guide to AI Investors

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Going AGILE. R.I.P Mina Today almost every job description in software business has requirement of knowing agile methodologies. While it’s often good to do things the requirement itself doesn’t make much sense. It’s like requiring the applicants to know how to brush their teeth — every developer who is abreast of the times knows how and does it regularly on daily basis. Or at least they should. agile, I can still remember the days when all agile methodologies were new (at least to where I worked at that time). That was about 8 years ago today. Moving to agile at that time required huge amount of effort and lot of people were recruited just to coach people in the agile way of doing things. For some this actually improved productivity but for teams like ours, were we already worked in somewhat agile mode, it only caused more pain. That time the requirement was added to every new job description automatically even the position did not have anything to do with software development. Thing that bugs me about the requirement is that agile methodologies aren’t necessarily used in the company at all — it’s just something that a job description should include because it’s hip. In almost every organization I have worked, the values and principles are not followed or are only followed partially. This makes the requirement for new employees even more obsolete. Agile Manifesto While it, in most cases, makes no sense to do agile using Scrum or some other agile framework strictly by the book, it would still make sense to actually use agile in some magnitude if you are requiring it from your employees. Especially the Manifesto values and are many times forgotten. This is unfortunately true at least for most bigger companies. “ Working Software Over Comprehensive Documentation” “ Individuals and interactions over processes and tools” Root of the problem I have worked as Scrum master for many teams over the past years. The problem hardly ever comes from the team as most of the agile violations are forced by the project or quality management. Pressuring teams to work with cumbersome processes and doing superfluous documentation is not the right way of getting things done. It is also very frustrating for the developers and makes the Agile word to be replaced with good old Waterfall. I would recommend for you to also read this by about the same problem. article ayasin Solution To start solving the problem you should first let your developers do their job and concentrate on developing software. Going further you should pay close attention to what your developers have to say of existing or prepared processes. Listen very carefully especially if you are . a manager without engineering experience While doing these won’t necessarily solve all your problems, I can assure that it’s the first step for better tomorrow. Take the jump to the cold water and cancel all unnecessary meetings, walk your processes through once again with your development and most importantly your developers flow. don’t interrupt Task for you Next time you write a job description for open position please save yourself some typing and leave the requirement for agile methodologies knowledge out of it. And if you are lucky enough to get an interview to place with the requirement please do yourself a favor and ask exactly are the agile methodologies used in the company. how Extra task: Leave me a comment what kind of problems have you seen in your organization and how much do you follow the Manifesto? Or have you completely dropped the agile way of doing things and why? About me I am Heikki Hellgren, Software developer and technology enthusiast working at . My interests are in software construction, tools, automatic testing and all the new and cool stuff like AI and autonomous driving. You can follow me on and . Also you can check out my for more information. Elektrobit Automotive Medium Twitter website

Why is Agile the most overused word in software business?

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