In this Q&A for TechRepublic, they discussed the shift to remote and hybrid work and how we can best prepare for it. The following is an edited transcript of their conversation. James Maguire: We have seen a lot of shifts in the working world in the last few years -- hybrid work, remote work. What are the key trends you see driving remote work and today's shift? We've seen, you know, this combination of people having made the shift to remote work during the pandemic, which was a fundamental change in the way in which we did our everyday. But now, three years later, we're returning to the offices, and that really is creating this dynamic of hybrid work.

The edge computing market is expected to grow from $40.84 million in 2022 to $132.11 million by 2028. This is a compound annual growth rate of 21.8% percent. The use cases for the edge are limitless. Use cases can range from remote field offices operating drone fleets for utility and mining operations to employees working from home and automated manufacturing assembly lines. As this movement to edge computing has unfolded, more non-IT professionals are being asked to manage the technology that is located at the edges that they occupy.

Despite a global rush toward enterprise digital transformation, the document remains at the heart of most businesses, and unfortunately, managing them still remains a distinctly manual process. Despite its structured nature, the flexibility of a document makes it hard to automate business processes, and taking data from multiple line-of-business applications to insert it in a document is a matter of cut-and-paste, from screen to document and often back again once a document is received. Launched at Ignite in October 2022, Microsoft Syntex is here to solve some of these tediously manual issues, adding document processing tools to SharePoint. The solution uses machine learning to help construct and parse documents, turning a manual process into one where humans guide and check software, and where legal, regulatory and contractual requirements are still met. In this in-depth look at Syntex, learn more about content AI and some of the current use cases for this release.

Since the spring of 2020, the COVID-19 pandemic has upended the traditional workplace, affecting nearly every industry. The tech sector, which has been ahead of the curve in terms of flexible work policy, has been on the roller coaster as well but has remained remarkably resilient. Deloitte's 14th annual Tech Trends report, released on Wednesday, takes a look at the current state of enterprises when it comes to IT. The Deloitte report focuses on the experience of global organizations, across industries, in order to ascertain what tech trends are on the horizon. Deloitte employs a "wide-angle lens" to predict what's happening, according to Mike Bechtel, chief futurist and managing director at Deloitte.

An ongoing dearth of tech talent and an increasing number of business technologists are driving an increase in low-code development technologies, which are projected to total $26.9 billion USD worldwide in 2023, an increase of nearly 20% from 2022, according to a recent forecast from Gartner. Business technologists work outside of IT and create tech or analytics capabilities for internal or external business use. Low-code application platforms are projected to be the largest component of the low-code development technology market, growing 25% to reach nearly $10 billion USD in 2023. Gartner predicts that by 2026, developers outside formal IT departments will account for at least 80% of the user base for low-code development tools, which is up from 60% in 2021. Other key drivers that will accelerate the adoption of low-code technologies through 2026 include an increasing number of enterprise-wide hyperautomation and composable business initiatives, the firm said.

Last week, Google announced and released a beta version of Simple ML for Sheets, a TensorFlow Decision Forests-produced add-on for Google Sheets. This release is one of the first of its kind, offering many simple and some complex machine learning functionalities directly to Google Sheets users. Although Simple ML has been touted as the machine learning solution for people with no prior knowledge of machine learning, the Advanced Tasks it offers promise value to data scientists, machine learning experts and anyone else working with bigger datasets. Read on to learn more about this release and how it may shape spreadsheet-based data and machine learning projects in the future. Simple ML for Sheets is currently available in beta.

Data quality management efforts -- tied to disrupting innovations, rapid market shifts and regulation pressures -- will continue to grow in 2023 and take on a more dominant role in the data management ecosystem. Turning to the cloud, edge, 5G and machine learning, hybrid worldwide workforces and global customers are generating data at levels never experienced before. The success of data quality management depends on deployment, infrastructure and modernization strategies. The 2022 State of Data Quality report from Ataccama reveals that automation and modernization efforts have still not been universally adopted. While seven in ten enterprises surveyed (69%) have begun their DQM journeys, they still have not achieved high maturity levels.

With talk of artificial intelligence in the enterprise moving from hype to implementation, Deloitte's State of AI 5th Edition research report finds that 94% of business leaders agree that AI is critical to success over the next five years. At the same time, one of the more surprising outcomes is that as AI deployments increase, outcomes are lagging Beena Ammanath, executive director of the global Deloitte AI Institute, told TechRepublic. Although 79% of respondents reported achieving full-scale deployment for three or more types of AI applications--up from 62% last year--the percentage of organizations in the underachiever category (high deployment/low outcomes) rose from 17% last year to 22% this year, the report said. This may be because survey respondents reported varying challenges depending on where they are at in their AI implementation. When starting new AI projects, the top challenge reported was proving AI's business value (37%).

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.

Data governance assures that data is available, consistent, usable, trusted and secure. It is a concept that organizations struggle with, and the ante is upped when big data and systems like artificial intelligence and machine language enter the picture. Organizations quickly realize that AI/ML systems function differently from traditional, fixed record systems. With AI/ML, the objective isn't to return a value or a status for a single transaction. Rather, an AI/ML system sifts through petabytes of data seeking answers to a query or an algorithm that might even seem to be a little open ended.