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How to implement AI-driven Operations Forecasting in your business

Unlocking multiple sources of value in operations is a constant pursuit for businesses. And in today’s fast-paced and ever-changing world, harnessing the power of artificial intelligence (AI) can be a game-changer. Enter AI-driven forecasting – a revolutionary approach that combines advanced analytics with machine learning to predict outcomes and optimize operations like never before.

 

Imagine being able to automate half of your workforce planning and performance management processes, while simultaneously reducing costs by 10-15% through increased efficiency. Sounds appealing, right? Well, that’s exactly what AI-based predictive models can offer. Not only do they streamline operations, but they also improve workforce resilience through targeted performance management strategies.

 

In our last blog post, we’ve explored how AI-driven forecast models hold the key to unlocking these remarkable benefits. In this blog post we’ll delve into their inner workings and discuss how you can implement them in your own business.

 

How do AI-driven forecast models work?

 

AI-driven forecast models utilize advanced algorithms and machine learning techniques to analyze vast amounts of data and generate accurate predictions. These models are designed to identify patterns, trends, and correlations within the data, allowing businesses to make informed decisions based on reliable forecasts.

 

At the core of AI-driven forecasting is the concept of training the model. Initially, historical data is fed into the system to enable it to learn from past patterns and behaviors. As more data becomes available over time, the model continuously updates itself, refining its predictions with each iteration.

 

The beauty of AI-driven forecasting lies in its ability to handle complex datasets with ease. Whether it’s analyzing sales figures, customer behavior patterns, or market trends, these models can process large volumes of information quickly and efficiently. This not only saves valuable time but also eliminates human error inherent in manual analysis.

 

Furthermore, AI-driven forecast models have a remarkable adaptability factor. They can adjust their predictions as new variables come into play or unexpected events occur that may impact business operations. This flexibility allows organizations to respond swiftly and effectively in dynamic environments.

 

To leverage these benefits for your business, you need access to quality data sources and a robust infrastructure capable of handling high computational loads. Implementing an AI-driven forecast model requires collaboration between domain experts who understand the specific nuances of your industry and skilled data scientists who can develop sophisticated algorithms tailored to your needs.

 

AI-powered forecast models offer unparalleled accuracy by leveraging machine learning algorithms trained on extensive historical data sets. Their ability to process complex information quickly provides businesses with reliable insights for making strategic decisions while adapting seamlessly to rapidly changing markets or circumstances.

How to implement AI-driven forecasting in your business?

 

Implementing AI-driven forecasting in your business can seem like a daunting task, but with the right approach and guidance, it can be a valuable addition to your operations. Here are some steps you can take to successfully implement AI-driven forecasting:

 

  • Define your objectives: Start by clearly defining what goals you want to achieve through AI-driven forecasting. Whether it’s improving operational efficiency, reducing costs, or enhancing customer satisfaction, having clear objectives will guide your implementation strategy.

 

  • Gather quality data: Data is the fuel that powers AI algorithms. Ensure that you have access to accurate and relevant data from various sources within your organization. This may include historical sales data, customer behavior patterns, market trends, and more.

 

  • Choose the right tools: There are numerous AI-powered forecast models available in the market today. Research and evaluate different tools based on their features, scalability, ease of integration with existing systems, and cost-effectiveness.

 

  • Build a skilled team: Implementing AI-driven forecasting requires expertise in both data analytics and domain knowledge specific to your industry. Build a team of skilled professionals who can effectively leverage the technology for maximum impact.

 

  • Integrate with existing systems: To ensure seamless integration into your business operations, work closely with IT teams to integrate the chosen forecast model with existing systems such as CRM software or inventory management tools.

 

  • Test and refine: Before fully deploying an AI-driven forecast model across all aspects of your business operations,

 

  • Evaluate performance regularly: Regularly assess how well the implemented forecast models are performing against predefined metrics

 

  • Continuous learning process: Keep up-to-date with advancements in AI technology

 

 

Case Studies

 

Several case studies have demonstrated the effectiveness of AI-driven forecasting in different industries. Companies ranging from retail giants to healthcare providers have witnessed significant cost savings, increased operational efficiencies, and improved customer satisfaction by integrating these advanced technologies into their operations. Below are some real-life examples of how AI-driven forecast models have unlocked multiple sources of value in operations. These case studies highlight the tangible benefits that businesses can achieve by implementing AI-based predictive models.

 

One case study involves a manufacturing company that struggled with workforce planning and performance management. By adopting an AI-driven forecasting solution, they were able to automate half of their workforce planning process. This not only saved time but also improved accuracy and efficiency, leading to cost savings of 10-15%.

 

Another case study focuses on a retail business that utilized AI-driven forecast models to improve its inventory management. By analyzing historical sales data and external factors such as weather patterns, holidays, and promotions, they were able to optimize their inventory levels and reduce holding costs while ensuring the availability of popular products.

 

In yet another example, a healthcare organization implemented AI-based predictive models for patient demand forecasting. By accurately predicting patient volumes based on various factors like demographics, seasonality, and disease trends, they were able to optimize resource allocation and staffing levels. This resulted in improved patient care outcomes while reducing operational costs.

 

These case studies demonstrate the versatility and effectiveness of AI-driven forecast models across different industries. Whether it is improving workforce planning or optimizing supply chain operations, businesses can derive significant value from leveraging these advanced technologies.

 

By harnessing the power of artificial intelligence in forecasting processes, organizations can gain valuable insights into future trends and make data-driven decisions for better outcomes. The possibilities are endless when it comes to unlocking multiple sources of value through AI-driven forecast models!

 

Conclusion

 

AI-driven forecasting has the potential to revolutionize operations across various industries. By harnessing the power of AI, businesses can unlock multiple sources of value and drive efficiency in workforce planning and performance management.

 

Adopting AI-based predictive models can automate half of workforce planning and performance management while reducing costs by 10-15% through increased efficiency. This transformative technology not only drives financial benefits but also enhances workforce resilience through targeted performance management strategies. The future belongs to those who are willing to embrace innovation – so why wait? At Xorlogics, we advise you on all questions regarding the introduction, update or optimization, maintenance, and further development of your IT systems according to your needs and are at your side as a competent partner. We are happy to assist you in all technical areas. Thanks to our many years of experience, we know what is important, and which hardware and software make sense for your work processes. Just contact us and we will be happy to advise you.

Emerging technologies that are reshaping the Digital World

The digital world is constantly evolving, and with each passing day, new technologies emerge that have the potential to reshape the way we live, work, and interact. These emerging technologies, driven by innovation and research, are revolutionizing various aspects of our lives. Below are some of the key technologies that are currently reshaping the digital world and the incredible possibilities they offer.

Artificial Intelligence (AI)

Artificial Intelligence, or AI, is a branch of computer science that focuses on the development of intelligent machines capable of performing tasks that typically require human intelligence. AI systems can learn, reason, and adapt, enabling them to process vast amounts of data and make intelligent decisions.

  • Applications in the Digital World: AI has found numerous applications in the digital world, transforming various industries. In e-commerce, AI-powered recommendation systems analyze customer preferences and behavior to provide personalized product suggestions. AI also plays a significant role in healthcare, where it aids in diagnosis, drug discovery, and personalized medicine. Additionally, AI is revolutionizing customer service, data analysis, and cybersecurity, among many other fields.

AInternet of Things (IoT)

The Internet of Things, or IoT, refers to the network of physical devices embedded with sensors, software, and connectivity that enables them to collect and exchange data. These interconnected devices can communicate with each other and perform tasks without human intervention.

  • Impact on the Digital World: The IoT has brought about a new era of connectivity, where everyday objects, from household appliances to industrial machinery, are interconnected. This technology has immense potential in areas such as smart homes, smart cities, and industrial automation. With IoT devices, individuals can remotely control and monitor their homes, while businesses can optimize operations, increase efficiency, and gather valuable data for analysis.

ABlockchain Technology

Blockchain technology is a decentralized, distributed ledger system that securely records and verifies transactions. It enables participants to make peer-to-peer transactions without the need for intermediaries, providing transparency, immutability, and security.

  • Applications in the Digital World: Blockchain technology has gained significant attention due to its potential to revolutionize various industries. In finance, blockchain enables secure and transparent transactions, reducing the need for intermediaries like banks. It also finds applications in supply chain management, healthcare, voting systems, and intellectual property protection. By eliminating intermediaries and ensuring data integrity, blockchain technology is reshaping the digital world’s trust and security landscape.

Augmented Reality (AR) and Virtual Reality (VR)

Augmented Reality (AR) is a technology that overlays digital information, such as images and sounds, onto the real world. Virtual Reality (VR), on the other hand, immerses users in a simulated environment through the use of headsets and controllers.

  • Influence on the Digital World: AR and VR technologies are transforming various industries, including gaming, entertainment, education, and healthcare. AR enhances user experiences by providing additional information and interactivity in real-time. VR, on the other hand, transports users to virtual worlds, enabling them to explore and interact with digital environments. These technologies offer new possibilities for training simulations, virtual travel experiences, immersive storytelling, and collaborative work environments.

5G Technology

5G technology is the fifth generation of wireless communication technology, offering significantly faster data transfer speeds, lower latency, and increased network capacity compared to its predecessors.

  • Transforming the Digital World: 5G technology is a game-changer in the digital world, enabling faster and more reliable connections. It paves the way for innovations such as autonomous vehicles, smart cities, and the Internet of Things. With its low latency and high bandwidth, 5G technology has the potential to revolutionize industries like healthcare, transportation, and manufacturing.

Conclusion

The digital world is experiencing a rapid transformation driven by emerging technologies. Artificial Intelligence, the Internet of Things, Blockchain, Augmented Reality, Virtual Reality, and 5G are just a few examples of technologies reshaping our lives. These advancements offer unprecedented possibilities, revolutionizing industries, improving connectivity, and enhancing user experiences.  As these technologies continue to evolve, they hold the potential to reshape our digital future. The adoption of these technologies often requires human expertise for implementation, management, and maintenance. At Xorlogics we have a strong understanding of business needs and technology.  Contact us for your next digital project !

 

The biggest challenges of BigData in 2023

The use of big data is on the rise, with organizations investing heavily in big data analytics and technology to gain insights and improve business performance. With the rapid growth of the internet, social media, and the IoT, the amount of data being generated is increasing exponentially. As a result, there is a need for better tools and techniques to collect, store, analyze, and extract insights from this data.

 

Additionally, the growth of the global datasphere and the projected increase in the size of the big data market suggest that big data will continue to be a critical driver of innovation and growth across various industries. In a study by Accenture, 79% of executives reported that companies that do not embrace big data will lose their competitive position and could face extinction.

 

Advancements in big data technologies such as machine learning, artificial intelligence, and natural language processing are also foreseen. These technologies have the goal to enable businesses and organizations to make better decisions, gain a competitive advantage, and improve customer experiences.

Xorlogics participating Cebit 2016

Here are a few examples of how big data is being effectively used in various industries:

 

  • Healthcare: Big data is being used to improve patient care, disease diagnosis, and treatment outcomes. For instance, healthcare providers can analyze electronic health records to identify patterns and trends that may help diagnose diseases earlier and predict patient outcomes. Additionally, big data analytics can help hospitals and healthcare organizations optimize their operations, such as reducing wait times and improving patient flow.
  • Finance: Big data is being used to identify and prevent fraud, assess risk, and personalize financial products and services. For instance, financial institutions can use big data to analyze customer behavior and preferences, in order to develop personalized marketing campaigns and offers. Additionally, big data analytics can help banks and other financial organizations to detect fraudulent activity and reduce the risk of financial crime.
  • Retail: Big data is being used to personalize the shopping experience, optimize inventory management, and improve customer loyalty. For instance, retailers can use big data to analyze customer behavior and preferences, in order to develop targeted marketing campaigns and personalized recommendations. Additionally, big data analytics can help retailers to optimize their inventory levels, reduce waste, and improve supply chain efficiency.
  • Manufacturing: Big data is being used to optimize production processes, reduce downtime, and improve quality control. For instance, manufacturers can use big data to monitor equipment performance and predict maintenance needs, in order to reduce downtime and optimize production schedules. Additionally, big data analytics can help manufacturers to identify quality issues early, reducing waste and improving product quality.
  • Transportation: Big data is being used to optimize transportation networks, reduce congestion, and improve safety. For instance, transportation companies can use big data to analyze traffic patterns and optimize routes, reducing travel time and congestion. Additionally, big data analytics can help transportation companies to monitor vehicle performance and identify potential safety issues, reducing accidents and improving overall safety.

 

Generally, big data is being effectively used across a range of industries to drive innovation and create value, improve operational efficiency, reduce costs, and improve customer satisfaction. Along with the benefits of Bigdata, it’s challenges cannot be ignored. Here below are few potential challenges that bigdata may face in the future:

 

  • Data Privacy and Security: As the amount of data collected and stored increases, so does the risk of data breaches and cyber-attacks. Protecting sensitive information will be critical, particularly as more businesses move towards storing their data in the cloud.
  • Data Quality: As the volume of data grows, so does the risk of inaccuracies and inconsistencies in the data. Ensuring data quality and accuracy will become increasingly challenging, particularly as the data comes from a wide range of sources.
  • Data Management: Managing large amounts of data can be complex and costly. Businesses will need to invest in tools and technologies to help manage and process the data effectively.
  • Talent Shortage: The demand for skilled data professionals is growing rapidly, and there may be a shortage of qualified individuals with the necessary skills to analyze and interpret big data.
  • Data Integration: With data coming from various sources, integrating, and combining the data can be a challenging process. This could lead to delays in data processing and analysis.
  • Ethical Use of Data: As the amount of data collected grows, it becomes increasingly important to ensure that it is used ethically and responsibly. This includes addressing issues related to bias, fairness, and transparency.
  • Scalability: As the volume of data continues to grow, businesses will need to ensure that their infrastructure and systems can scale to accommodate the increased data load.

 

Overall, these challenges could impact the effective use of big data in various industries, including healthcare, finance, retail, and others. Addressing these challenges will require ongoing investment in technologies and skills, as well as a commitment to ethical and responsible use of data.

 

If you are looking for a partner who can give you both strategic and technical advice on everything to do with the cloud, than contact us so we can talk about your cloud project and evaluate the most suitable solution for your business.

How to measure Resilience and success in Machine Learning and Artificial Intelligence models?

ML and AI are powerful tool that can be used to solve complex problems with minimal effort. With the rapid advances in technology, there still exists many challenges when it comes to making sure these models are resilient and reliable.Resilience is the ability of a system to resist and recover from unexpected and adverse events. In the context of AI and ML systems, resilience can be defined as the ability of a system to continue functioning even when it encounters unexpected inputs, errors, or other forms of disruptions.

 

Measuring resilience in AI/ML systems is a complex task that can be approached from various perspectives. Fortunately, there are some steps you can take to ensure your ML models are built with robustness. There is absolutely no one-size-fits-all answer to measuring resilience in AI and ML systems. However, there are a number of factors that can be considered when designing a resilience metric for these systems.

 

  • It is important to consider the types of failures that can occur in AI and ML systems. These failures can be classified into three categories: data corruption, algorithm failure, and system failure. Data corruption refers to errors in the training data that can lead to incorrect results. Algorithm failure occurs when the learning algorithm fails to connect a correct solution. System failure happens when the hardware or software components of the system fail. In other terms it’s also called robustness testing. This type of testing involves subjecting the AI/ML system to various types of unexpected inputs, errors, and perturbations to evaluate how well it can handle these challenges. Thus the system’s resilience can be measured by how well it continues to perform its tasks despite encountering these challenges. A resilient system is one that is able to recover from failures and continue operating correctly.

 

  • It is necessary to identify what creates a resilient AI or ML system. It is also important for a resilient system to be able to detect errors and correct them before they cause significant damage. Usually, the fault injection method makes easier to evaluate how the system response to intentionally introduced faults and if it’s able to detect & recover. With this method, the resilience of the system can be measured by how quickly and effectively it can recover from these faults. It is also mandatory to develop a metric that can be used to measure resilience in AI and ML systems. This metric takes into account the type of failures that can occur, as well as the ability of the system to recover from those failures.

 

  • The performance monitoring of the AI/ML systems cannot be considered insignificant as this monitors the performance of the AI/ML system over time, including its accuracy, response time, and other metrics. The resilience of the system can be measured by how well it maintains its performance despite changes in its operating environment.

Overall, measuring resilience in AI/ML systems requires a combination of methods and metrics that are tailored to the specific application and context of the system. Along with that, we also need to ensure that the data which is use to train ML models is representative of the real-world data. This means using a diverse set of training data that includes all the different types of inputs our model is likely to see. For example, if our model is going to be used by people from all over the world, we need to make sure it is trained on data from a variety of geographical locations.

 

Last but not the least, ML systems need regular training “refreshers” to keep them accurate and up-to-date. Otherwise, the system will eventually become outdated and less effective. There are a few ways to provide these training refreshers. AI/ML systems are typically trained using large amounts of data to learn patterns and relationships, which they then use to make predictions or decisions. However, the data that the system is trained on may not be representative of all possible scenarios or may become outdated over time. One way is to simply retrain the system on new data periodically. In addition, the system may encounter new types of data or situations that it was not trained on, which can lead to decreased performance or errors.

 

To address these issues, AI/ML systems often require periodic retraining or updates to their algorithms and models. This can involve collecting new data to train the system on, adjusting the model parameters or architecture, or incorporating new features or data sources.This can be done on a schedule (e.g., monthly or quarterly) or in response to changes in the data (e.g., when a new batch of data is received).

 

Another way to provide training refreshers is to use transfer learning. With transfer learning, a model that has been trained on one task can be reused and adapted to another related task. This can be helpful when there is limited training data for the new task. For example, if you want to build a machine learning model for image recognition but only have a small dataset, you could use a model that has been trained on a large dataset of images (such as ImageNet).

 

Measuring the resilience of AI/Ml systems requires extended range of tools and expertise. We at Xorlogics make sure to produce the best model with the highest standard of resilience & accuracy. Tell us about your business needs and our experts will help you find the best solution.

Benefits & Challenges of Software Development with Machine Learning

Software development with machine learning involves using ML algorithms and techniques to build software applications. These applications can range from simple data analysis and prediction tools to more complex systems such as image recognition, natural language processing, and autonomous systems. The process of developing such software typically involves the collection and cleaning of data, selecting and training models, evaluating performance, and deploying the final product.

 

Several benefits are associated with software development, such as:

 

Automation and improved efficiency: ML models can automate tasks that would be time-consuming or difficult for humans to perform, such as image recognition or natural language processing. This can lead to improved efficiency and cost savings.

Increased accuracy: ml models can achieve higher levels of accuracy than traditional software in tasks such as prediction and classification.

Handling big data: ML models can handle and process large amounts of data, making it possible to extract insights and identify patterns that would be difficult or impossible to detect manually.

Personalization: ML models can be trained on individual user data, making it possible to personalize recommendations and experiences.

Real-time decision-making: With the development of edge computing, ML models can make decisions in real time, enabling the development of applications such as autonomous vehicles, robots, and IoT devices.

Innovation: Using ML models and techniques opens doors for new possibilities, which can lead to the development of new products, services, and business models.

Overall, software development with ML offers the potential for significant advancements in automation, accuracy, and efficiency in a wide range of industries and applications.

 

But, while there are multiple benefits of software development with ML, there are also some challenges that may arise:

Data availability and quality: ML models require a large amount of high-quality data to train and test on. If data is not available or is of poor quality, this can make it difficult to develop accurate models.

Model selection and tuning: There are many different ML algorithms and models to choose from, and selecting the right one for a given task can be challenging. Additionally, fine-tuning the parameters of a model to achieve optimal performance is a time-consuming process.

Overfitting: Overfitting occurs when a model is trained too well on the training data and does not perform well on new, unseen data. This can be a common problem and can be addressed using techniques such as cross-validation and regularization.

Explainability: Some ML models, such as deep neural networks, can be difficult to interpret and understand. This can make it challenging to explain how a model is making its predictions and to identify any potential biases in the data.

Deployment and maintenance: Deploying ML models in production environments can be complex and requires specialized knowledge. Additionally, these models need to be continuously updated and maintained as the data and requirements change over time.

Ethical concerns: There are many ethical concerns that arise when using ML such as bias, transparency, and accountability. It’s important to consider these concerns when developing and deploying such models.

 

ML is becoming increasingly popular in many industries and is expected to have a significant impact on the economy in the near future. In general, ML and AI are considered to be one of the most promising fields in technology and key driver of digital transformation and innovation. As companies are investing in this technology to improve their products and services, automate tasks, and gain a competitive edge. And this is across different industries such as healthcare, finance, retail, logistics, and manufacturing.

 

According to a report by the Belgian government, the AI market in Belgium is expected to grow rapidly in the next few years, with the government investing heavily in research and development in this field. VLAIC, aka AI research center Vlaamse AI-coalition, is an initiative from the Flemish Government to support the development and use of Artificial Intelligence (AI) in Flanders, Belgium. The goal is to make Flanders a leading region in AI by 2025.

AI: The next step in Software Development

AI has been revolutionizing businesses worldwide, from healthcare to banking, from automobiles to logistics. It’s innovations are developing very quickly and growing significantly on a global scale. AI refers to technologies that make it possible to equip computer systems based on algorithms with human abilities such as thinking, learning, problem-solving, etc., to make them intelligent and thereby help people to carry out different tasks.

 

With advances in machine learning, natural language processing, and data analysis, also in the world of software development, technology is changing rapidly and AI is leading the way. The global AI market reached USD 93.5 billion in 2021 and will expand at a growth rate of 38.1% annually by 2030. Innovations such as Edge AI, computer vision, decision intelligence (DI) and machine learning (ML) are shaping the market in the years to come. Additionally, robots are increasingly penetrating our everyday lives. And the current research suggests that this trend will continue in the coming years when robots and drones can take on more and more tasks in a meaningful way. These advances are related to the popularity and widespread use of AI & its promises of impressive growth opportunities.

 

The aim of AI is to create machines that can work and react like humans. However, AI is not just about creating human-like machines; it is also about making machines that can work better than humans. For example, a machine is able to process data much faster than a human can & also remember more information than a human can.

 

Since AI offers great potential for different areas, it is no wonder that its use cases are becoming more diverse with each passing year. AI solutions are already helping with:

  • Business process automation
  • Automated document creation
  • Management of production processes
  • Predictive Maintenance
  • Customer Analytics
  • Risk management
  • Supply chain management
  • Personalized service delivery
  • Software development

 

Since its inception, AI has made significant progress in software development. Early successes included creating programs that could play checkers and chess, as well as solve simple mathematical problems. In recent years, AI has been used to develop more complex applications such as autonomous vehicles, facial recognition systems, and machine translation. Looking to the future, AI will continue to play an important role in software development. With the rapid advancements being made in machine learning and natural language processing, there is no limit to what AI can achieve. As we move forward into the next era of computing, it is exciting to think about all the new possibilities that AI will enable us to realize. Let’s explore how AI is impacting software development and how it will continue to revolutionize the industry in the years ahead.

AI Software Development

Software development aka application development consists of winding together instructions for one or more programs that carry out required tasks or actions. The development team carries the task of translating problem-solving processes & algorithms into program code. Basically, we’ve known the classic methods of software development such as the agile and waterfall methodology. However, AI development works differently than classic software development. In AI development, data plays a central role – it is the center. In AI software development the behavior of AI depends on the self-training with the data. In the classic approach, the programmer had to set the rules himself, something that isn’t possible with AI development.

 

With AI, developers can create smarter and faster algorithms that can more accurately comprehend our intentions and behaviors within their applications. AI platforms promise faster development, more accurate prediction of user needs and behaviors, and continuously improving algorithms for data processing. This helps developers automate various tasks, from code quality analysis to bug fixing & save time on repetitive tasks. For example, if a developer needs to fix a bug that occurs often, they can train an AI system to automatically detect and fix that bug. This frees up the developer’s time so they can focus on more important tasks.

 

AI can also improve the quality of code. By using AI-powered static code analysis tools, developers can identify potential errors and bugs before they even write any code. This not only saves time and money by preventing buggy code from being deployed, but it also helps improve the overall quality of the software.

 

AI can help developers create more user-friendly applications. By using machine learning algorithms, developers can automatically generate user interface (UI) designs that are optimized for conversion and usability. This means that users are more likely to have a positive experience with the application, which could lead to increased customer retention and loyalty.

 

The work & results in AI development are characterized by the acquisition, analysis, and preparation of data and by training the models. We can say that an AI solution is gradually approached through smaller experiments and experience is gained in the process. For this reason, exact, conscientious, and transparent documentation of every single step & every attempt are essential.

 

To achieve quicker results, however, several work streams can run in parallel, all of which are dedicated to solving the same topic. This requires a high degree of flexibility.  It is evident that the rise of AI will revolutionize software development and open up a world of new possibilities. With its ability to process information faster than ever before, AI technology can help streamline projects and shorten production times. As more companies continue to invest in this form of technology, we can expect even greater advancements from artificial intelligence in the future. There’s no doubt that there are both positive and negative implications associated with embracing this kind of technology but for now, we must use what advantages it offers us to move forward into the digital age.

Data as a Service: Enterprise search benefits from Artificial Intelligence and Machine Learning

According to IDC, data as a service, voice-controlled apps, and AI-based services are the most important IT trends of the coming years. Data marketing and data analysis are among the most important challenges for companies. IDC expects that the amount of data created in 2023 will reach over 100ZB, one trillion gigabytes), most organizations also need to turn to external data in order to execute a variety of business processes. This translates into more than 90% of large companies generating their revenue from data as a service.

Digital Era ARTIFICIAL INTELLIGENCE MACHINE LEARNING

This is the provision of information and distribution of data for customers. AI-based services and machine learning methods help to interpret data and are therefore becoming increasingly important. In the future, digital services or apps will hardly be able to keep up with the market without the support of artificial intelligence. Artificial intelligence (AI) is a driving force behind digital transformation, encompassing innovations such as machine learning, natural language processing (NLP), data labeling platforms, and predictive analytics. According to IBM 35% of companies report using AI in their business, and an additional 42% of respondents say they are exploring AI.

 

The latest report by Research and Markets also shows that AI is expected to achieve a compound annual growth rate of 52% by 2025, indicating its rapid adoption by global businesses. The global AI spending reached over $55 billion in 2021 (IDC) and the AI market is expected to grow to $1.81 trillion by 2030 (GrandViewResearch). By 2023, 60% of all digitization projects will be supported by artificial intelligence functions and almost half of all apps will be voice controlled. One survey also found that 87% of global organizations believe that AI technologies will give them a competitive edge.

 

The majority of companies are using machine learning processes in marketing & sales, healthcare, banking, manufacturing, and retail sectors. The machine processing of language is becoming increasingly important. Self-learning systems that process complex amounts of data in real time are also gaining importance. AI-based systems are expected to take on more and more tasks and relieve entire departments.

 

The benefits from artificial intelligence and machine learning are numerous:

  • 42% of companies stated that the profitability of their ML and AI initiatives exceeded their expectations, while only 1% said it didn’t meet expectations. (Accenture)
  • By 2025, 50% of companies will have devised AI orchestration platforms to operationalize AI, up from fewer than 10% in 2020.Gartner 
  • Global machine learning as a service (MLaaS) is currently used mostly in healthcare and life sciences, but it is expected to grow in industries including manufacturing, retail, telecom, finance, energy and utilities, education, and the government.
  • 92.1% of companies state they are achieving returns on their data and AI investments. (NewVantage Partners)
  • AI-powered enterprises will be able to respond 50% faster to customers, competitors, regulators, and partners than their peers. (Oracle)
  • AI and machine learning will contribute to the labor productivity increase by up to 37% by 2025.  Industry, Research and Energy (ITRE),
  • 27% of the respondents of the latest McKinsey Global Survey on AI report at least 5% of earnings before interest and taxes attributable to machine learning-based AI.
  • PwC research shows that the global GDP could be up to 14% higher in 2030 (up to $15.7 trillion1 to the global economy) as a result of the ML and AI accelerated development and take-up. (PwC)
  • According to PwC research, 45% of total economic gains by 2030 will be the result of AI-driven product enhancement, stimulating consumer demand.
  • IBM’s new cognitive phishing detection capability uses machine learning to help businesses detect a phishing site up to 250% faster than traditional methods.

#Data : An Important Piece To “The #InternetOfThings” Puzzle

Every day, connected objects generate billions of information that must be processed and analyzed to make them usable. Thanks to the development of connectivity on multiple devices, the arrival of inexpensive sensors, and the data inflation they transmit, IoT have taken an irreplaceable place in our daily lives. IoT Analytics forecasts the IoT market size to grow at a CAGR of 22.0% to $525 billion from 2022 until 2027. The number of connected IoT devices growing 9% to 12.3 billion globally, and cellular IoT now surpassing 2 billion.

 

These very serious estimations do not, however, take into account the full extent of this digital revolution. If the design of connected objects is the showcase of the IoT and its vast possibilities, it still requires strong skills in the processing of the exploited data collected from sensors terminals, machines, and platforms to interpret it in order to boost productivity and increase performance.

 

Just as in the jewel market, the big winners are gold/diamond dealers. In the IoT domain, this role is played by companies able to manage the mountains of data generated by these connected devices because the collected data is profoundly changing the way businesses used to operate. Almost every day, new applications are imagined, with consequences at all levels of organizations because the real added value of connected objects only comes from the uses and the ability of companies to create new services.

 

Several studies demonstrate that companies are still facing a gap between the collection of new data and the presentation of the analyzed information so that it can be understood and explored in great detail, whether it is for a connected house, connected car, or a portable terminal or an industrial solution.

 

Here below is the list of tips companies must consider before every IOT project implementation:

 

  • Sort valuable information among a big volume of data:
    Exploiting IoT means generating a huge amount of data. The challenge for companies is to filter the stray information and find the ones that are really important. This is why many companies integrate a flow analysis and a process analysis. The first provides real-time information from data streams such as navigation paths, logs, and measurement data, and the second is to take machine data captures.

 

  • Set and manage priorities:
    The IoT implies different levels of necessity in terms of urgency and latency. It’s important to take this into account because one expects to interact with the “real world” in real-time. For example, sensors in mines must trigger an alert as soon as they detect the presence of toxic gases. Similarly, other IoT information may not be needed “just in time”, such as regularly collected data to further refine and improve the predictive model itself. This data can potentially be collected and processed several times a day, for example.

 

  • Design considerations for IoT technologies:
    Information security, privacy, and data protection should systematically be worked at the design stage. Unfortunately, in many cases, they are added on later once the intended functionality is in place. This not only limits the effectiveness of the added-on information security and privacy measures but also is less efficient in terms of the cost to implement them. Although industries are actively working to address this, it stays a major IoT problem.

 

  • Cross the Data:
    In the case of preventive operations, for example, companies want to collect data from objects (such as smart meters) and cross them with relevant relational data, such as maintenance agreements, warranty information, and life cycle components. It is therefore essential that companies can rely on the data from which they make important decisions.

 

  • Tracing the data:
    The increased collection of data may raise issues of authentication and trust in the objects. In addition, it should also be noted that by using information collected about and from multiple objects related to a single person, that person may become more easily identifiable and better known. So in order to fully exploit the potential of IoT, tools must be much more flexible and allow users to shape and adapt data in different ways, depending on their needs or those of their organization.

 

Collaboration between the IT team and business experts is more critical than ever before in analyzing IoT data. In addition to those who understand the data, it takes experts to analyze gathered data from specific devices or sensors. While any analyst can understand the data in the context of a company’s performance indicators, only a data specialist would be able to explain what kind of hidden data contains a wealth of information, and how with the right tools, companies can unleash that potential.

Digitization: Why should companies invest in Artificial Intelligence training

Technology is a key helper on the way to the digital future. Artificial intelligence is considered a crucial future technology in the worldwide economy and more and more companies see an opportunity for their own business in artificial intelligence (AI). Whether predictive maintenance, process optimization, system control, or individualized products – everyone is talking about the fact that everything will be AI-supported in the future if not even function autonomously. AI can also improve processes in companies from production to sales or serve as the basis for new products and services.

 

Artificial intelligence also gives enormous competitive advantages. A survey conducted by McKinsey highlighted that a majority of survey respondents say their organizations have adopted AI capabilities, as AI’s impact on both the bottom line and cost-saving. Regarding the employees, however, there is an urgent need for action as they are poorly prepared for the use of artificial intelligence in this suddenly changing environment. Employees must perform skilled jobs that require more education and training compared to their normal routine jobs.

 

Another survey conducted by the market research company Statista on behalf of the TÜV Association among 1,000 people aged sixteen and over, including 568 employed people has revealed that 78 percent of employers agree that companies need to invest more in training their employees when it comes to AI. Many companies must invest significantly more in further training in artificial intelligence to make their workforce fit for the digital world. This involves both in-depth knowledge for the use of the technology, but also user knowledge since many tools already work with AI today. According to the results of the survey, a start has been made, 28 percent of the employees surveyed have taken part in further training on AI content in the past two years. And 34 percent of those in employment planned to do so within a year.

 

With basic AI knowledge, TÜV association expert Fliege observes considerable deficits in the companies. “Many employees only have a vague idea of ​​what AI is and where they encounter it.” AI is already in use in many cases, sometimes even unnoticed. “Algorithms work quietly in numerous systems – for example in IT security, where they recognize and resist cyber-attacks,” says Fliehe. AI is perceived more strongly in factories, for example, where it supports production control. The use of AI promises more efficiency and greater process automation in production. That doesn’t have to have a negative impact on employment, says Fliege: “Interesting new fields of work can arise for employees because they are relieved of routine activities.” The development is still in its infancy, and a lot is in flux. “A whole new door is opening for companies and employees.” According to Fliehe, the use of AI for small and medium-sized enterprises (SMEs) is particularly promising. “They usually have to make do with scarcer resources and are committed to high efficiency.”

 

“Knowledge about artificial intelligence is improving as the technology spreads,” said Stenkamp. At the same time, the attitude of the citizens is also improving. Fifty-one percent of respondents feel something positive when they think of AI, compared to the previous study by the TÜV Association in 2019, this is an increase of five percentage points. On the other hand, only 14 percent feel something negative, two years ago this value was twice as high at 28 percent. Thirty-five percent are neutral (up 14 points).

 

However, one thing is certain: the responsibility of the employees will increase, because they will remain the final decision-making authority. “Users in companies must know that AI decisions are not optimal in every situation,” says Fliehe. It may therefore be necessary to check whether an algorithm has captured all the valuable information. “Human expertise and experience will not become less important through the use of AI, but even more important,” emphasizes Fliehe. Employees would have to be able to guide the algorithm and classify the results. “Employees must become designers and also recognize the limits of AI.” In this way, the employees also contributed to the security of AI systems. “AI applications must not endanger or disadvantage people,” says Fliege. Corresponding legal regulations for the use of AI in security-critical areas are currently being developed in the EU as part of the planned “AI Acts”. The “TÜV AI Lab”, founded last year, supports politicians in developing standardized testing tools for artificial intelligence.

 

To prepare workers for more automated workplaces, professional training must be considered as an individual right. The transition to modern technologies and onwards will be a continuous process. Thus, the training and re-training of employees must not be ignored.

COVID19: Digitization Recovery Plan for 2021

 

To help repair the economic and social damage caused by the coronavirus pandemic, digitization will continue to boost in 2021. All those lock-down measures in response to the coronavirus outbreak have frozen the economic activity in certain sectors and harshly disrupted others, which resulted in worldwide unemployment and bankruptcies. In order to overcome all restrictions in the contactless world, citizens and businesses are relying on the internet and connectivity as social and economic activities became more digital.

 

What is now very clear, is that COVID-19 has accelerated digitalization. IT decision-makers are under pressure with using new technologies to modernize legacy processes and recognize and implement new business model innovation opportunities, more than ever before. All digital business transformations must begin and end with a crisis recovery plan and must reinforce companies’ foundations for a modern and more sustainable future. Additionally, as customers are getting more used to doing things digitally. This opens up opportunities for businesses to introduce new products and to reduce costs. The Belgian government has also agreed on an investment package of 4,3 billion EUR in digitalization, sustainability, education, health care, R&D, 5G, mobility.

 

Here below are the top digital transformation trends that will shape 2021.

 

  • Businesses’ journey to the cloud will accelerate in order to digitize quickly and effectively in the response of COVID-19. Across industries, this acceleration will result in their digital business transformation for long-term growth and profitability. This migration of infrastructure and applications to the cloud will enable leaders to innovate quickly, by significantly reducing development and solution delivery cycles, empowering operational efficiency through cost optimization, and benefiting from real-time accessibility of data.

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  • Companies will invest in 5G in order to enhance the experience of workforce mobility (remote work), videoconferencing, and digital collaboration. This adoption is motivated by the need for more bandwidth, faster speeds, reliable and resilient connectivity as businesses cannot afford to be disconnected. 5G will boost the company’s existing network by providing diverse connectivity, to ensure scalability for growth and business continuity with a faster response time, delivering a quality user experience subsequently increasing productivity and customer service. The IEEE 2020 Global Survey of CIOs and CTOs surveyed 350 CIOs or CTOs in the US, China, UK, India, and Brazil from Sept. 21-Oct. 9, 2020, revealed that 52% have accelerated 5G adoption. Doubtlessly, with all these earlier mentioned benefits, the value and adoption of 5G will become increasingly mainstream in 2021.  Additionally, by 2030, 5G modules are expected to account for almost 30 percent of total B2B 5G IoT module revenues.
  • Worldwide industries will turn their attention to robot resilience in 2021. Organizations will accelerate automation projects more critically. With intelligent automation projects, ROI is realized instantaneously, offsetting the upfront investment. Automation (RPA) allows organizations to automate certain types of work processes to reduce the time spent on costly manual tasks and reallocate resources elsewhere. Software robots will automate the work of most people by taking the unpredictable, dreary, and monotonous tasks, faster and with fewer mistakes, while human capital resources will be assigned to higher-value tasks or to fill critical gaps.
  • COVID-19 has forced companies all over the world to adapt to and embrace remote work. With the safety concerns that continue to grow, companies are negotiating remote work policies as their business strategy for the long term. Even though many companies succeeded in the rapid transitions to remote work, they are realizing that remote work is here to stay. Companies and employees are realizing the significant benefits of remote work, such as increased flexibility, autonomy and productivity, better work and life balance, lowered business expenses, etc. according to a Gartner study, 74% of CFOs and Finance Leaders say that they will move at least 5% of their employees to remote working permanently post-pandemic, another 25% of the participants say they will move 10% of their workforce to remote working permanently.
  • Countries around the world are working on more comprehensive and accessible electronic health records. The access of the patient to his medical record by electronic means is part of the eHealth Action Plan validated by all the Belgian ministers of health. By simply asking your healthcare provider to activate the option, you can access to the contents of your documents. The online medical record allows the patient to have their health record at any time. Additionally, the AI healthcare market is expected to exceed $34 billion by 2025 as artificial intelligence is already playing a huge role in the digital transformation of healthcare. In fact, AI is being used currently for AI-assisted robotic surgery, assist pathologists in making more accurate diagnoses and treat illness, detect cancer in its earliest stages and subsequently develop new treatments, and doctors are using AI-enhanced microscopes to scan for harmful bacteria’s in blood samples at a faster rate than is possible using manual scanning.

 

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