With industrial organizations undergoing rapid, large-scale digital transformations, it can sometimes be easy to miss the forest for the trees. When you get in the weeds on AI and machine learning, big data, analytics, the cloud, and the edge, you can forget that, at the end of the day, the goal of digital transformation is not to accumulate new technologies for the sake of new technologies. It's to use these new solutions to empower employees, make their lives easier and set them up to efficiently deliver new value and innovations for their organization. It's about the people, not just the tech; the latter empowers the former to drive results. The industrial data scientist is a living, breathing example of this -- a relatively new role in the process engineering and industrial sector that has emerged to fulfill a growing need in our industry: marrying traditional data science with localized domain expertise at a time of great, generational change occurring in the industrial workforce.

For many years now, we've heard and read about how "next year" will be the breakout year for AI. And in a way, that's always true – each year marks a new level-up in AI's ability to optimize traditional business operations and streamline work for the better. These year-after-year improvements have also further refined AI, for more fit-for-purpose applications that drive new value in specific use cases. Industrial AI is the latest iteration of this phenomenon, applying AI's processing to specialized applications in a manufacturing world rapidly undergoing profound digital transformations. Just as 2020 and 2021 marked new evolutions in artificial intelligence, 2022 promises to see industrial AI turn a major new corner in how the manufacturing sector draws on AI applications to tackle the problems of today and create new value-adding layers to their organization and ways of working.

The global use and further development of AI continued to grow in 2021 as enterprises found more ways to deploy it and developers discovered new ways to capture its possibilities for business users. So, what might 2022 bring for AI and a wide range of related IT fields from MLOps to security, cloud and edge computing, open source, the metaverse and more? To answer that question, we received a wide range of predictions from IT industry experts who shared their thoughts with EnterpriseAI. We are publishing them here, edited for clarity and brevity, to give our readers an early look at what may come in 2022 in enterprise AI and related technologies. Rodrigo Liang, the CEO and co-founder of AI platform vendor SambaNova Systems, said he sees companies moving away from DIY AI and linking up with vendors who can help them better reach their business goals.

What is your industrial AI readiness? That's a question that's top-of-mind for many industrial executives lately -- and simultaneously one that has not taken on enough importance for many others. While AI, machine learning and other means of automation have swept through industries, including the industrial sector, in recent years, AI still too often gets treated as an add-on technology. But AI isn't something to be tacked onto an existing framework; it has to be treated as the strategy itself. This is especially true for industrial AI.

Industrial digital transformation is critical to achieving new levels of safety, sustainability, and profitability--and "Industrial AI" is a key enabler of that change. Organizations are switching their focus from mass data accumulation to strategic industrial data management, homing in on data integration, mobility, and accessibility--with the goal of using AI-enabled technologies to unlock value hidden in these unoptimized and underutilized sets of industrial data. The rise of the digital executive (chief technology officer, chief data officer, and chief information officer) as a driver of industrial digital transformation has been a key influence on this trend. This has fueled the need for "Industrial AI," a new paradigm that combines data science and AI algorithms with software and domain expertise to deliver measurable business outcomes for the specific needs of capital-intensive industries. Industrial AI disrupts these industries by lowering barriers to adoption, offering new opportunities for industrial organizations to significantly reduce costs, improve efficiency, and transform their operations for the better.

Today's industrial organizations, and especially those in capital-intensive industries, stand at a crossroads for opportunity. They recognize the need to reinforce their industrial operations and complex value chains with greater resiliency, flexibility, and agility to respond to shifting market conditions. At the same time, they're investing in autonomous and semi-autonomous artificial intelligence (AI) capabilities to realize their vision of the digital plant of the future--the "Self-Optimizing Plant." Generational shifts in the workforce are creating a loss of operational expertise. Veteran workers with years of institutional knowledge are retiring, replaced by younger employees fresh out of school, taught on technologies and concepts that don't match the reality of many organizations' workflows and systems.

Adding to the literature on the data-driven detection of bid-rigging cartels, we propose a novel approach based on deep learning (a subfield of artificial intelligence) that flags cartel participants based on their pairwise bidding interactions with other firms. More concisely, we combine a so-called convolutional neural network for image recognition with graphs that in a pairwise manner plot the normalized bid values of some reference firm against the normalized bids of any other firms participating in the same tenders as the reference firm. Based on Japanese and Swiss procurement data, we construct such graphs for both collusive and competitive episodes (i.e when a bid-rigging cartel is or is not active) and use a subset of graphs to train the neural network such that it learns distinguishing collusive from competitive bidding patterns. We use the remaining graphs to test the neural network's out-of-sample performance in correctly classifying collusive and competitive bidding interactions. We obtain a very decent average accuracy of around 90% or slightly higher when either applying the method within Japanese, Swiss, or mixed data (in which Swiss and Japanese graphs are pooled). When using data from one country for training to test the trained model's performance in the other country (i.e. transnationally), predictive performance decreases (likely due to institutional differences in procurement procedures across countries), but often remains satisfactorily high. All in all, the generally quite high accuracy of the convolutional neural network despite being trained in a rather small sample of a few 100 graphs points to a large potential of deep learning approaches for flagging and fighting bid-rigging cartels.

Domain-specific solutions, a lowered barrier to AI adoption, and an emphasis on industrial data value are all benefits industrial AI brings to the table. The years-long trend toward Industry 4.0, coupled with the pressures of 2020's economic climate, have made one thing clear: the need to adopt artificial intelligence (AI) isn't just accelerating; it's become critical. And while many industrial organizations still lack expertise in and experience with industrial AI, the ability to adopt and implement it will very quickly become a matter of survival. So much so that 2021 is poised to be a defining year, literally, for a new breed of AI: Industrial AI. While both startups and legacy industrial companies alike have made their own plays around industrial AI, the new year will mark the first time that industrial AI as a new industry category begins to take on a mainstream role.

The role of artificial intelligence (AI) is evolving, especially in industrial organizations such as oil and gas, where data acts as a critical enabler to provide a competitive advantage. Industrial organizations operating in the fields of mining, oil, and gas; and marine, are going through a radical transformation and seeking innovative ways to optimize performance with minimized risk. The volatile and ever-competitive nature of the industrial companies demands them to identify new and innovative sustainable models to stay profitable, grow and unlock efficiencies. The situation has become more challenging in the wake of the coronavirus pandemic. According to a Capgemini research, over 50% of the European manufacturers, 30% in Japan, 28% in the USA, and 25% in South Korea implement AI solutions.

IoT or Internet of Things is slowly making its way in every aspect of our lives. If you don't own an IoT device yet, you will surely own one soon. But it is highly unlikely that you even wouldn't have heard of such devices. From smart televisions, fridges, thermostats to smart coffee makers, IoT devices have infiltrated our daily lives and are slowly gaining mainstream recognition. According to recent studies as of 2019, the number of active IoT devices peaked at a significant 26.66 billion.