Cloud adoption is accelerating fast in enterprises surging towards modernity. But are there better ways of utilizing the full potential of cloud computing? Leaving behind the constraints of a single cloud computing platform, you will find various other arrangements like hybrid and multi-cloud computing. The annual RightScale State of the Cloud Report suggests, 90% of respondents believe that multi-cloud is already the most common pattern with businesses and enterprises. So, let's delve into understanding more about multi-cloud for modern enterprises.

Artificial Intelligence (AI) has arguably become a household term in modern enterprises. By now, most companies have embraced some type of business initiative that includes AI in their digital transformation. Artificial Intelligence is a broad term, but much current research and development focuses on machine learning (ML), a subdiscipline whereby machines learn from data as opposed to being explicitly programmed. With AI and ML targeting a broad spectrum of enterprise users, IT professionals must develop new skills to succeed in this emerging space. An understanding of the business and its most pressing problems is a transcendent competency for any IT professional.

Cybersecurity has become an important strategic imperative and enterprises today need to monitor and defend their IT assets from the ever-changing cyber threat landscape. All modern enterprises need a robust and comprehensive cybersecurity program to prevent, detect, assess, and respond to cybersecurity threats and breaches. In many ways, cybersecurity is unique – much of detection and monitoring is all about correlation and prediction--and can benefit from the infusion of artificial intelligence and machine learning solutions for assessment, analytics, and automation. In a hyper-connected digital world, organizations need to process humongous quantities of data originating from disparate systems to detect anomalies, locate vulnerabilities, and pre-empt threats. Unlike most manual tracking methods, AI and ML-based systems can monitor millions of events on a daily basis and facilitate timely threat detection as well as appropriate and quick response.

To stay competitive and relevant in their industries, enterprises increasingly need to become AI-driven. AI is a new key to improving business processes, making better decisions, monetizing data, increasing security and more. The growing importance of AI in the enterprise is a point that industry observers now emphasize. Just consider this view from the global consulting firm Deloitte: "As AI technologies standardize across industries, becoming an AI-fueled organization will likely be table stakes for survival. And that means rethinking the way humans and machines interact within working environments."1

The increased sophistication of artificial neural networks (ANNs) coupled with the availability of AI-powered chips have driven am unparalleled enterprise interest in computer vision (CV). This exciting new technology will find myriad applications in several industries, and according to GlobalData forecasts, it would reach a market size of $28bn by 2030. The increasing adoption of AI-powered computer vision solutions, consumer drones; and the rising Industry 4.0 adoption will drive this phenomenal change. Deep learning has bought a new change in the role of machine vision used for smart manufacturing and industrial automation. The integration of deep learning propels machine vision systems to adapt itself to manufacturing variations.

We live in a digital era where every business aspect revolves around technology, making IT systems the core of modern-day operations. Primarily, the role that IT systems play begins and ends with data, which is the constant input and output of computerized devices. It is therefore not surprising that the current trend for businesses is to seek solutions for big data adoption and usage. This is no longer a reserve for companies that specialize in technology-based solutions but a need that cuts across the board. Data has consequently become the most valuable asset that necessitating substantial investments in the current business environment.

In a recent survey from the AIOps Exchange, 91% of respondents said they are looking at machine learning-powered tools to help IT operations teams be more productive. With all the disparate data sets and tools operators have to review, and alert noise, it's no wonder organizations are seeking some AI-powered assistance to regain control of the environment. Artificial intelligence for IT operations (AIOps) is a buzzword right now in IT circles, but the impact is real. Machine learning capabilities give IT operations teams contextual, actionable insights to make better decisions on the job. More importantly, AIOps is an approach that transforms how systems are automated, detecting necessary signals from vast amounts of data and relieving the operator from the headaches of managing according to tired, outdated runbooks or policies.

In her remarkable book, Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy, former Wall Street quant Cathy O'Neil paints an incredibly sobering picture of organizations using advanced analytics to make critical decisions without fully considering the consequences. Unquestionably, AI will become an indispensable tool for automating key processes, finding hidden patterns in large datasets and driving greater efficiencies across the modern enterprise in thousands of ways. From my perspective as a technology leader, I am confident that AI will emerge as a force for good, delivering amazing opportunities and benefits to billions of people in markets all over the world. That said, the road to AI adoption will not be smooth. There are issues that cannot be glossed over or swept under the carpet in our zeal to move forward and gain competitive advantages.

Organizations have been striving to increase productivity since the days of the cotton gin, steam power and Model T assembly lines. Today, maximizing productivity is often associated with software technology, from virtual assistants to predictive science. But, as the pace of innovation has accelerated, the practical ability to implement and monetize the exciting new technologies hasn't always kept up. It's time for solution providers to step up their game and take a more active role in supporting software implementation efforts. Today's common tactics for deploying software are flawed.

In my previous blog, I wrote that employees these days are hungry for a more dynamic, collaborative workplace experience, one that provides pervasive access to productivity tools so they can work wherever they choose. Create a Great Employee Experience.) Next, we need to consider that work activities flow across not only different workspaces, but also different systems and devices. One minute you need some private office space and a bit of peace and quiet to hammer out a spreadsheet on a PC. Next minute you're in a conference room using your laptop to practice delivery of a presentation and getting feedback from a group of colleagues.