What learning trends are in store for 2023? The coming year will bear witness to some significant changes across the entire business world. Recession poses an ever-constant threat so it's important that businesses equip themselves for the future. On top of it all, businesses are having trouble finding new recruits due to a labor shortage. So 2023 will force businesses to strengthen their current ranks to protect against a shallow recruitment pool.

Join us on November 9 to learn how to successfully innovate and achieve efficiency by upskilling and scaling citizen developers at the Low-Code/No-Code Summit. On my 12th birthday I got my first computer: an Amiga 500. And at 17, I founded my first company, making software that helped photographers serve their customers. As I reflect on my decades of coding, I'm reminded that low-code technology started with tools enabling users to build custom reports and applications with very little coding. When I started coding, low-code was somewhat analogous to the position artificial intelligence holds today: exciting, much hyped and poorly understood.

Traditionally, network operations (NetOps) teams used performance monitoring tools to manage the health and performance of corporate networks. However, as network usage has increased and network deployments have become more disaggregated, many people are looking for alternative performance monitoring methods, such as Artificial Intelligence for IT operations or AIOps. This blog discusses NetOps and AIOps in detail and analyses how NetOps can be a bridge to AIOps, which will revolutionise the world of tech & business. Artificial intelligence for IT operations refers to applying AI and related technologies such as ML and NLP to traditional IT businesses and activities (AIOps). AIOps assist IT Ops, DevOps, and SRE teams (Site reliability engineers) in working smarter and faster.

Digital banking transformation has enabled many financial institutions to become more prepared for the future as the world was disrupted by the pandemic. For those banks and credit unions that have become the most digitally mature, the focus on improving the customer experience, increasing the use of data and advanced analytics, and deploying new technologies has positioned them at a competitive advantage. Despite the progress made, all financial institutions will need to increase investment in digital banking transformation initiatives, responding to marketplace expectations and adjusting business models to reflect a highly altered banking ecosystem. A great deal of the focus in 2022 will need to be on back-office processes that are highly outdated and have slowed the progression of all digital banking transformation efforts. The future will also see a greater involvement of banking staff in building humanized experiences for transactions that were initiated digitally.

The U.K. automotive industry has been a pinnacle of excellence over the last century. However, during the last few decades, sectoral shifts and an evolving competitive landscape have adversely affected the industry, with the pandemic further aggravating these challenges by throwing the demand-supply equilibrium into disarray. The recent and historic fall in car manufacturing in July – which saw production fall to its lowest level since 1956 - is a combination of factors. In an industry as resource intensive as car manufacturing, the success of every manufacturer hinges on how well they navigate both local and global market challenges, such as staffing and material shortages. On one hand, the'pingdemic' has meant that carmakers have had to deal with unexpected staff shortages at a local level. More globally, the rising prominence of semiconductors in today's tech-powered products have meant that if manufacturers can't cope with an ongoing microchip shortage, production often comes to a grinding halt.

There has been more than a modicum of buzz around what IDC calls intelligent process automation and what Gartner calls hyperautomation. In both cases, these terms refer to the integrated deployment of digital technologies such as robotic process automation (RPA), intelligent business process management suites (iBPMS), artificial intelligence, process mining, etc. Integrating digital technologies is far from a new concept. MIT and Deloitte advocated this approach back in the day when everyone was focused on social, mobile, analytics, and cloud (SMAC). Digital transformation is a complex undertaking. Frankly, the track record on digital transformation success is dismal, with as few as 30% of such initiatives succeeding. The integration of digital technologies may just be the lever that leads to more success.

Companies are looking to grab any technology-driven advantage they can as they adapt to new ways of working, managing employees, and serving customers. They are making bigger moves toward the cloud, e-commerce, digital supply chains, artificial intelligence (AI) and machine learning (ML), data analytics, and other areas that can deliver efficiency and innovation. At the same time, enterprises are trying to manage risk -- and the same digital initiatives that create new opportunities can also lead to risks such as security breaches, regulatory compliance failures, and other setbacks. The result is an ongoing conflict between the need to innovate and the need to mitigate risk. "There is always going to be some amount of tension relating to managing risk and engaging in digital transformation work," says Ryan Smith, CIO at healthcare provider Intermountain Healthcare.

Digital Transformation is an ongoing process for utilities today. However, to be successful they must focus on technologies that deliver the services customers want. Machine Learning offers enormous potential for utilities to discover more about their customers and for solving the common issues utilities face every day. Today, it is undisputed that Digital Transformation is essential for utilities. However, organizations often find the results of their Digital Transformation efforts disappointing.

When I was digging deeper into robotic process automation to create this curated guide, I realized that I was confused by RPA, artificial intelligence and AI subset machine learning. In many cases the terms are used interchangeably, and that's not correct. The distinction is really about whether they're process driven (RPA) or data driven (AI, ML). I found the simplest explanation on Silvertouch, which states: "RPA is a software robot that performs repetitive tasks while following strict rules. It is like a clerk who is good at clerical jobs. But AI is an umbrella term that involves the simulation of human intelligence and thought process by machines while dealing with plenty of interrelated information."

While the potential benefits of digital technologies are well documented and accepted by a majority of bank and credit union leaders, the deployment of digital banking transformation does not appear to be keeping pace, according to the Innovation in Retail Banking 2019 report, published by the Digital Banking Report, and sponsored by Infosys Finacle in cooperation with Efma,. This is concerning given that the pervasiveness of required digital transformation is moving beyond the IT area of banking to include virtually all areas of the organization. Financial institution executives surveyed indicate that budgets are increasing for almost all digital technologies and that the technologies being impacted are expanding. At the same time, the ownership of both digital transformation and innovation are moving up the organizational hierarchy, with cross-functional teams being leveraged to break down legacy silo perspectives. When asked about the progress of digital transformation efforts, only 17% of organizations surveyed indicated that the transformation was'deployed at scale', with 41% stating that digital transformation was'partially deployed'.