Depending on which Terminator movies you watch, the evil artificial intelligence Skynet has either already taken over humanity or is about to do so. But it's not just science fiction writers who are worried about the dangers of uncontrolled AI. In a 2019 survey by Emerj, an AI research and advisory company, 14% of AI researchers said that AI was an "existential threat" to humanity. Even if the AI apocalypse doesn't come to pass, shortchanging AI ethics poses big risks to society -- and to the enterprises that deploy those AI systems. Central to these risks are factors inherent to the technology -- for example, how a particular AI system arrives at a given conclusion, known as its "explainability" -- and those endemic to an enterprise's use of AI, including reliance on biased data sets or deploying AI without adequate governance in place.

Companies in the US may be bullish on using artificial intelligence (AI), but many executives are ambivalent about its associated risks – especially when it comes to AI-enabled hiring and people management tools. According to a new survey by the global law firm Baker McKenzie, though 100 percent of senior executives agree there are risks associated with using AI, just 4 percent of respondents consider these risks to be "significant." Three fourths of those surveyed indicate their organization uses AI for key human resources (HR) management and employment functions – for example, recruiting and hiring, performance and promotion, and analyzing employee attendance or productivity trends. The Baker McKenzie survey, Risky Business: Identifying Blind Spots in Corporate Oversight of Artificial Intelligence queried 500 US based C-level executives who self-identified as part of the decision-making team responsible for their organization's adoption, use and management of AI-enabled tools. The telephone- and email-based survey was conducted during the months of December 2021 and January 2022, with executives at companies with at least $10.3 billion in annual revenues on average, across a range of industries.

Artificial Intelligence (AI) enabled apps helped protect small and medium-sized businesses against many of the risks that emerged during the Covid-19 pandemic, according to a study, which noted that only a quarter of small firms currently use them. The research, by Anglia Ruskin University (ARU) and published in the journal Information Systems Frontiers, surveyed 317 small and medium sized firms based in London. The study found the use of AI-powered apps was associated with a 3.1 per cent reduced risk to business during the pandemic. "We found that SMEs' business risks caused by the Covid-19 pandemic declined with the use of AI applications across a ten-item scale including marketing, sales, communication, predictions, pricing and cash flow, fake reviews, cybersecurity, recruitment, and legal services," said lead author Professor Nick Drydakis, Director of the Centre for Pluralist Economics at the University. "The outcomes proved true regardless of enterprise size, turnover, and years of operation, indicating that AI applications have helped SMEs to adapt to unprecedented conditions during the Covid-19 pandemic," he added.

Opening up the "black box" helps remove uncertainties about AI outcomes, providing insight into the modeling process and identifying biases and errors. Artificial intelligence (AI) is being used more frequently in our daily lives, with systems such as Siri and Alexa becoming commonplace in many households. Many households themselves are "smart," powered by devices that can control your lights, heating and air, and even the music playing. And those music players are powered by AI that recommends songs and artists you may like. However, these systems are often referred to as "black box" systems because we do not know how the data is processed--how do the users know why the model has made that prediction?

MLOps (from Machine Learning and Operations) refers to the process of managing the production lifecycle of Machine Learning models, including also the concept of collaboration between data scientists, data engineers and IT professionals. The objective is to define recommendations and best practices to automate the process, comply with regulatory requirements as well as provide agility to react to changing business requirements. Even though this procedure is mainly of technical nature, companies where MLOps practices are not implemented efficiently face also a number of business and financial challenges. In this blog post I would like to describe the findings and challenges due to inefficient MLOps we have encountered in several customer engagements and to describe how those challenges can be addressed with SAP Data Intelligence, hoping to provide guidance for others in similar situations. It is a common practice in data science teams to develop on local machines and distribute the developed models via shared drives or even email.

In today's hybrid blend of on-premises and virtual servers, public and private cloud platforms, and mobile devices of all types, it is more critical than ever to identify, resolve, and mitigate potential outages or performance issues. And these issues are now more difficult to detect and identify, much less remediate. Both the volume and variety of network and application traffic that data enterprises are generating have skyrocketed. To effectively monitor and manage IT operating at this level of complexity, organizations are turning to artificial intelligence for IT operations, or what's called AIOps. The term AIOps describes using artificial intelligence (AI), machine learning (ML), and other advanced data analytics technologies to automate the processes of identifying and resolving IT performance issues.