How can anyone forget the photos and images of mounds of luggage sitting wherever they could find a spot at most major airports, back during the winter storm of 2022? And what about the thousands of stranded passengers in dismay during the holidays, with plans and gifts that literally couldn’t get off the ground?
The password reset just might be the most notorious high-volume, repetitive IT ticket ever. Who actually remembers passwords, anyway? Considering just about everything done online requires some sort of login with a unique username and password, remembering all those individual combinations of letters, numbers, and special characters isn’t easy! And so employees turn to IT for what can be nonstop help.
Incident response is one of the most prevalent, time-consuming, and challenging processes in an already complex IT environment. At the height of digital transformation and organizational strategy, businesses are craving success and growth during a time when next to nothing seems certain.
Robotic Process Automation (RPA) and IT Process Automation (ITPA) are today’s big drivers of revolutionizing the way businesses operate – and they’re nowhere near finished. While RPA and ITPA have their significant differences, they share just enough similarities to have successfully helped organizations in multiple industries automate processes, reduce costs, improve accuracy, and deliver better customer experiences.
The Network Operations Center (NOC)— the powerful, centralized epicenter that supervises, monitors, and maintains network availability—is the core of the network infrastructure. It’s up to the NOC to keep large, complex networks running reliably.
If you had the power to predict the future, how would you use it? Gartner refers to the predictive modeling process as that which collects data, formulates a statistical model, makes predictions, and then validates the model as additional data becomes available. In the world of intelligent automation, teams use predictive modeling to formulate how future outcomes or behaviors will be, by using historical data, machine learning (ML) algorithms, and analytical techniques.
It seems like we can’t escape the burden of uncertainty. From the countless things we were uncertain about during the Covid-19 pandemic to the current state of the economy, financial circumstances and trends, and fear of unemployment. For many of today’s businesses of all sizes, predicting the future might appear nearly impossible. IT teams at the start of the pandemic in 2020 shifted without notice to supporting remote employees.
The rollout of new technology can come with hesitation and even fear, and generative AI is no different. The New York Times in February 2023 dubbed the popularity of ChatGPT a “phenomenon” that started an AI arms race – one that wasn’t exactly welcomed by news writers, marketing agencies, or business leaders.
