A key challenge faced by small and medium-sized business entities is securely managing software updates and changes. Specifically, with rapidly evolving cybersecurity threats, changes/updates/patches to software systems are necessary to stay ahead of emerging threats and are often mandated by regulators or statutory authorities to counter these. However, security patches/updates require stress testing before they can be released in the production system. Stress testing in production environments is risky and poses security threats. Large businesses usually have a non-production environment where such changes can be made and tested before being released into production. Smaller businesses do not have such facilities. In this work, we show how "digital twins", especially for a mix of IT and IoT environments, can be created on the cloud. These digital twins act as a non-production environment where changes can be applied, and the system can be securely tested before patch release. Additionally, the non-production digital twin can be used to collect system data and run stress tests on the environment, both manually and automatically. In this paper, we show how using a small sample of real data/interactions, Generative Artificial Intelligence (AI) models can be used to generate testing scenarios to check for points of failure.

Data Science is a modern Data Intelligence practice, which is the core of many businesses and helps businesses build smart strategies around to deal with businesses challenges more efficiently. Data Science practice also helps in automating business processes using the algorithm, and it has several other benefits, which also deliver in a non-profitable framework. In regards to data science, three key components primarily influence the effective outcome of a data science project. Those are 1.Availability of Data 2.Algorithm 3.Processing power or infrastructure

I've published a couple of articles lately about the need for organizations to have an artificial intelligence (AI) strategy and how AI can help organizations with employee development. It's possible that with all the conversation about AI technologies in today's news, organizations are talking about what AI could mean for their operation and how to get started. So, I wanted to bring in another technology expert to talk specifically about the things that organizations need to consider when looking at AI tools. Matthew Geohring, MS, is a technology solutions consultant for global insurance brokerage Hub International's HUB People & Technology Consulting Practice. Prior to joining HUB, Matthew spent time as both a human resources generalist and an in-house senior HRIS analyst. I'm excited to be sharing his thoughts with you today.

Digital transformation is the megatrend driving billions in investment across the corporate world to reinvent the way they do business. In the enterprise digital transformation guide, we will address the following topics to help you master the art and science of digitalization. "When digital transformation is done right, it's like a caterpillar turning into a butterfly, but when done wrong, all you have is a really fast caterpillar." Digital transformation is a customer-centric reimagination of the future of an enterprise and subsequently rethinking the business model. Reshaping the product/service portfolio, restructuring the processes, re-platforming technologies, reskilling the workforce, and instilling a new culture to get to the end goal. That definition of what is digital transformation packs a lot of punch.

Identified as the key for further enhancing competitiveness and workforce reinforcement, AI has the potential to be included in various operation processes. One such field, AI in visual inspection – with computer vision and machine learning on the rise – has been becoming more popular thanks to the engagement of top players across industries. In the recent online sharing, experts from FPT Corporation, FPT Software, Landing AI, and Schaeffler discussed their visions for the future of'AI in Real-time Quality Inspection'. All parties emphasised on the use of data-centric approach to shorten AI training duration in machine learning and addressed critical issues faced by brownfields factories. Until just recently, factory owners were equipped with rule-based vision inspection, which required IT experts to write pages of rules for the algorithm to detect product defects.

In 2021, Spiceworks reported survey results that revealed, "Almost one-third (31%) of the professionals surveyed said their organizations are now using artificial intelligence (AI), and 43% are exploring the technology. About 34% reported their companies had not deployed any AI projects." This and other surveys show that most companies are in early stages of AI adoption -- and they most likely have not yet thought about change management for their AI systems, and what it's going to take to keep their AI systems up, running and relevant. In 2016, Microsoft developed a chatbot called Tay. Tay was designed to learn from human interactions on social media.

Implementing AI on the road to Fourth Industrial Revolution (4IR)-readiness offers unprecedented opportunities for manufacturers. Manufacturing lighthouses are the trailblazing businesses adopting 4IR technologies at scale in their plants. These industries are already sustainably capitalizing on AI's ability to enable manufacturing lighthouses to make predictions and decisions, realizing many competitive, financial and operational advantages and efficiencies. Predictive maintenance, for example, already makes possible increases in asset productivity of up to 20%. With AI offering so much scope for growth in manufacturing, what is holding businesses back from adopting the Industrial Internet of Things (IIoT)?

Implementing AI on the road to Fourth Industrial Revolution (4IR)-readiness offers unprecedented opportunities for manufacturers. Manufacturing lighthouses are the trailblazing businesses adopting 4IR technologies at scale in their plants. These industries are already sustainably capitalizing on AI's ability to enable manufacturing lighthouses to make predictions and decisions, realizing many competitive, financial and operational advantages and efficiencies. Predictive maintenance, for example, already makes possible increases in asset productivity of up to 20%. With AI offering so much scope for growth in manufacturing, what is holding businesses back from adopting the Industrial Internet of Things (IIoT)?

We are well past the hype of AI, and it is becoming clear that the technology's greatest problems revolve around making profits instead of how to make it useable. AI can provide immense value to many companies thanks to the increasing number of AI specialists and machine learning services. Companies often fail to cover initial investment when deploying AI. This seems contradictory, doesn't it? According to a recent IBM study, only 21% are capable of integrating AI into their business operations.

According to a recent study by 451 Research, part of S&P Global Market Intelligence, "more than 90% of organizations that have adopted AI began development on their first AI project within the past five years." Though nascent, AI-enabled solutions are on the rise all around us. However, many of these initiatives still aren't meeting expectations--if they even make it to deployment. To succeed, leaders should select and manage AI projects with a thoughtful strategy driven by clear expectations, alignment to business goals, and iteration. Let's look at common hurdles organizations face when designing successful end-to-end AI projects, and how to overcome them.