In recent years, as more enterprises have moved their infrastructure to the cloud, significant challenges have emerged in achieving holistic cloud spend visibility and cost optimization. FinOps practices provide a way for enterprises to achieve these business goals by optimizing cloud costs and bringing accountability to cloud spend. This paper presents ABACUS - Automated Budget Analysis and Cloud Usage Surveillance, a FinOps solution for optimizing cloud costs by setting budgets, enforcing those budgets through blocking new deployments, and alerting appropriate teams if spending breaches a budget threshold. ABACUS also leverages best practices like Infrastructure-as-Code to alert engineering teams of the expected cost of deployment before resources are deployed in the cloud. Finally, future research directions are proposed to advance the state of the art in this important field.

As we enter 2023, the world of analytics, AI, and cloud is entering an exciting new phase, with a wide range of innovations and developments set to reshape the landscape. Below are some trends that will have the most impact in the coming year. In 2023, as global economic uncertainty continues, enterprises with data-intensive workloads in the cloud will need to review their cloud strategies with a greater focus on cost optimization. Cloud spending will be more closely scrutinized based on the ROI and TCO of existing projects or new investments. One area where cost optimization is particularly important in the coming year is data transfer egress costs, which can make up a significant portion of an organization's cloud bill.

Making predictions about enterprise technology is more challenging if you strive to lay down forecasts that are measurable. In other words, if you make a prediction, you should be able to look back a year later and say with some degree of certainty whether the prediction came true or not -- with evidence to back that up. In this Breaking Analysis, we aim to do just that with predictions about the macro information technology spending environment, cost optimization, security – lots to talk about there – generative AI, cloud and supercloud, blockchain adoption, data platforms (including commentary on Databricks Inc., Snowflake Inc. and other key players), automation and events, and we even have some bonus predictions. To make all this happen, we welcome back for the third year in a row, Erik Bradley, our colleague from Enterprise Technology Research. As well, you can check out how we did with our 2022 predictions. Each year, tech vendor PR pros reach out to us to help influence our predictions. It starts as early as October.

The insurance industry, after the trade market, is another sector where it is hard to predict the next big paradigm shift. Given the tentative stability and natural catastrophes, insurance companies often stand on a trembling ground and confront massive challenges, even when it comes to adopting seamless and intuitive digital solutions such as Artificial Intelligence in Insurance. According to PwC's 20th CEO survey conducted in 39 countries, the greatest concerns that loom over the 95 CEOs of the insurance sector today are the subdued premium rate, mild interest rates, shifting consumer behavior, slow economic growth, need for regulations and technological innovations and blazing market competition. Let's delve into the idea of introducing artificial intelligence in insurance, and how it impacts the current legacy processes. This shows how the insurance industry is struggling to comprehend and leverage digital advancements.

Importantly, they can also prevent costly defects and avoid operational inefficiencies. While COVID-19 sped up the pace of adoption for many industries, including industrial manufacturing, manufacturing companies have historically embraced new ways of working. Manufacturers were early endorsers of Kaizen, Six Sigma, and Lean, known business improvement models with direct impacts to the continuous improvement methodology critical to manufacturing processes. And now, AI is being embraced for its ability to make supply chains more flexible -- mostly to evaluate vulnerabilities identified during the COVID-19 pandemic among their suppliers and in the supply chain itself -- reduce costs, and fully leverage human talent and intelligence. According to a new KPMG report, Thriving in an AI World, 93% of industrial manufacturing respondents indicated they have moderate or fully functional AI, primarily machine learning technologies, implemented into their processes.

You've likely heard the popular saying, "Sometimes you can't see the forest for the trees"– meaning, it's easy to lose perspective on the big picture when preoccupied with smaller details. Believe it or not, this can happen during an enterprise's digital transformation, especially during complex, multi-phased evolutions–like the transition to a multi-hybrid cloud environment. With several layers of strategic discovery, technology selection and implementation–which can often take weeks, months or even years–it can be easy to lose sight of the objective. It helps to step back and remember the end goal: the ability to scale and transform your enterprise to bring increased cost optimization and value, both to your business and your customers. In the case of cloud adoption, your transformation happens by achieving a series of milestones, which we've outlined here.

Many companies are accelerating their cloud plans right now, and most say that their cloud usage will exceed prior estimates due to the new demands posed by the global pandemic. Cloud computing is becoming a must-have resource, especially for young tech companies. And most of them are migrating to Amazon, Google, or Azure, lured by seemingly attractive offers. What many companies don't realize is how dramatically the cloud spend can increase given that those expenses aren't charged up-front. Organizations are often unaware of how easy it is to become locked into service at hard-to-understand prices, says Laurent Gil, co-founder and Chief Product Officer at CAST AI. "Vendor lock-in starts whenever you start using a service in a way that serves the purpose of the cloud provider," he explains.

As Vice President of Channel Cloud, I oversee our partner channel, services and new vendor services from Microsoft, AWS, Workplace and BOX. We keep hearing it more and more: Every company should have an AI strategy. Artificial intelligence is the most sophisticated, groundbreaking and transformational technology trend in our times, but I'd like to talk about a different kind of AI. Artificial intelligence without active imagination is useless. Artificial intelligence is a tool much like a hammer.

When it comes to digitization, the insurance space hasn't particularly been at the forefront of leading in technological innovation. And it's only fair as a colossal amount of regulations combined with thousands of users' sensitive information makes the insurance industry cautious in adopting new technologies. But, things are gradually changing! As per Genpact's AI 360 report, the adoption of artificial intelligence in insurance is rapidly increasing, with 87% of insurance carriers investing more than USD 5 million in AI-related technologies each year. Digital revolution is everywhere, increasingly transforming conventional business models and adopting digital solutions.

The insurance industry, after the trade market, is another sector where it is hard to predict the next big paradigm shift. Given the tentative stability and natural catastrophes, insurance companies often stand on a trembling ground and confront massive challenges, even when it comes to adopting seamless and intuitive digital solutions such as Artificial Intelligence in Insurance. According to PwC's 20th CEO survey conducted in 39 countries, the greatest concerns that loom over the 95 CEOs of the insurance sector today are the subdued premium rate, mild interest rates, shifting consumer behavior, slow economic growth, need for regulations and technological innovations and blazing market competition. Let's delve into the idea of introducing artificial intelligence in insurance, and how it impacts the current legacy processes. This shows how the insurance industry is struggling to comprehend and leverage digital advancements.