Data management has always been crucial to maintaining business continuity for enterprise organizations. For a long time, though, data management referred to the storage of information and the occasional need to access that information. And, for much of that time, the importance of data management has come second to data analytics techniques like machine learning and artificial intelligence (AI). Now, in 2022, the critical nature of data management for enterprise organizations can no longer be understated. Organizations have so much data through which they need to sift that they can't afford to view data management as an afterthought to their data analytics efforts, especially considering that as much as 90% of data worldwide is unstructured.

One of the initial hesitations in many enterprise organizations moving into the cloud in the last decade was the question of security. Significant amounts of money had been put into corporate firewalls, and now technology companies were suggesting corporate data reside outside that security barrier. Early questions were addressed, and information began to move into the cloud. However, nothing stands still, and the extra volume of data and networking intersects with the increased complexity of attacks, and artificial intelligence (AI) is being used to keep things safe. The initial hesitation for enterprise organizations to move to the cloud was met by data centers improving hardware and networking security, while the cloud software providers, both cloud hosts and application providers, increased software security past what was initially offered in the cloud.

One of the initial hesitations in many enterprise organizations moving into the cloud in the last decade was the question of security. Significant amounts of money had been put into corporate firewalls, and now technology companies were suggesting corporate data reside outside that security barrier. Early questions were addressed, and information began to move into the cloud. However, nothing stands still, and the extra volume of data and networking intersects with the increased complexity of attacks, and artificial intelligence (AI) is being used to keep things safe. The initial hesitation for enterprise organizations to move to the cloud was met by data centers improving hardware and networking security, while the cloud software providers, both cloud hosts and application providers, increased software security past what was initially offered in the cloud.

The enterprise infrastructure team is facing a challenge. AI machine learning (ML) and deep learning (DL) are making a transition from tools just for consumer internet companies, to tools for mainstream enterprise organizations. This evolution calls for a new type of infrastructure and workflow that expanded beyond a single application. In the enterprise, infrastructure, IT & DevOps teams are seeing an increasing number of business groups adopt AI for product recommendations, forecasting, customer interactions, financial risk assessment, manufacturing defect detection, retail loss prevention, and more. For these AI applications, GPU-accelerated servers have a proven history to provide orders of magnitude higher performance than CPUs.

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."

Are your enterprise's partners, customers, and employees growing weary of unfulfilled technology and innovation promises? Maybe there's a sense of resentment about technologies that don't work as promised. Or perhaps there's some concern about what's being sacrificed to pave the way for this technological progress. Fueled by headlines about the questionable ethics of big tech platforms such as Facebook, Google, and Amazon, this "techlash," or backlash, is directed against Silicon Valley tech firms and innovation. The general public has become more suspicious of technologies that seem creepy in terms of invading privacy and maybe even acting in a manner that is ethically questionable.

You've heard all the great benefits that AI can provide for enterprises today. From detecting fraud to predicting machine failure to understanding customer behavior -- AI has the potential to deliver game-changing business value in a variety of different areas. You may have even dabbled with AI and machine learning (ML) models in a few pilot projects. But has your organization actually delivered on the promise of AI with tangible business benefits? If not, you aren't alone; most of your peers are facing similar issues.

There is growing interest in machine learning (ML) and artificial intelligence (AI) in enterprise organizations. The market is quickly moving from infrastructures designed for research and development to turn-key solutions that respond quickly to new business requests. ML/AI are strategic technologies across all industries, improving business processes while enhancing the competitiveness of the entire organization. ML/AI software tools are improving and becoming more user-friendly, making it easier to to build new applications or reuse existing models for more use cases. As the ML/AI market matures, high-performance computing (HPC) vendors are now joined by traditional storage manufacturers, that are usually focused on enterprise workloads.

Even among some of the largest enterprise organizations, there's a big difference between the most advanced companies and others when it comes to artificial intelligence programs and deployments. While some are way ahead of the pack, there are others that are just getting started. A new report, AI Transforming the Enterprise, from consulting giant KPMG, provides a view into top corporate leadership's perspective of where enterprises are with their efforts. How do you stack up among your peers as we enter the age of AI? KPMG based its report on interviews with leaders in 30 large cap companies, plus analysis of job postings and media coverage for 200 of the top global companies, plus interviews with three technology companies that provide artificial intelligence technology to enterprises. First, here's what the most advanced organizations look like.

A new KPMG report provides an inside view into how big companies are investing in and deploying artificial intelligence and machine learning technology. Even among some of the largest enterprise organizations, there's a big difference between the most advanced companies and others when it comes to artificial intelligence programs and deployments. While some are way ahead of the pack, there are others that are just getting started. A new report, AI Transforming the Enterprise, from consulting giant KPMG, provides a view into top corporate leadership's perspective of where enterprises are with their efforts. How do you stack up among your peers as we enter the age of AI? KPMG based its report on interviews with leaders in 30 large cap companies, plus analysis of job postings and media coverage for 200 of the top global companies, plus interviews with three technology companies that provide artificial intelligence technology to enterprises.