When Bradford Newman began advocating for more artificial intelligence expertise in the C-suite in 2015, "people were laughing at me," he said. Newman, who leads global law firm Baker McKenzie's machine learning and AI practice in its Palo Alto office, added that when he mentioned the need for companies to appoint a chief AI officer, people typically responded, "What's that?" But as the use of artificial intelligence proliferates across the enterprise, and as issues around AI ethics, bias, risk, regulation and legislation currently swirl throughout the business landscape, the importance of appointing a chief AI officer is clearer than ever, he said. This recognition led to a new Baker McKenzie report, released in March, called "Risky Business: Identifying Blind Spots in Corporate Oversight of Artificial Intelligence." The report surveyed 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. In a press release upon the survey's release, Newman said: "Given the increase in state legislation and regulatory enforcement, companies need to step up their game when it comes to AI oversight and governance to ensure their AI is ethical and protect themselves from liability by managing their exposure to risk accordingly."

Cybersecurity is the overall activity of protecting computers, networks, and data from malicious electronic attacks. It is an activity that compares to physical security, a more traditional security activity that controls access to buildings or other objects in the real world. While many high-tech physical security vendors have a combination of physical and cybersecurity in their org charts, cybersecurity is an activity that focuses on protecting assets from malicious logins and code, not on property intrusion or theft. Cybersecurity is a broad concept that encompasses several specific fields of activity. There are many classification methods. For example, there is Kaspersky Lab's classification system, and Mindcore has such a system.

AI ethics has always been a topic of concern for most organizations hoping to leverage the technology in some use cases. While AI has improved over the years, the reality is that AI has become integral to products and services, with some organizations now looking to develop AI codes of ethics. While the whole notion of AI ethics is still debatable in many ways, the use of AI can not be held back, especially with the world becoming increasingly influenced by modern technologies. Last year, UNESCO member states adopted the first-ever global agreement on the Ethics of AI. The guidelines define the common values and principles to guide the construction of necessary legal infrastructure to ensure the healthy development of AI. "Emerging technologies such as AI have proven their immense capacity to deliver for good. However, its negative impacts that are exacerbating an already divided and unequal world, should be controlled. AI developments should abide by the rule of law, avoiding harm, and ensuring that when harm happens, accountability and redressal mechanisms are at hand for those affected," stated UNESCO.

PHILADELPHIA, March 22, 2022 (GLOBE NEWSWIRE) -- Just over one in five employees believe their employer is preparing them for a more data-oriented and automated workplace (21%), according to new research from Qlik, a leader in data analytics. This is despite most business leaders predicting an upheaval in working practices due to the rapid onset of artificial intelligence (AI). With 35% of employees surveyed reporting they had changed jobs in the last 12 months because their employer wasn't offering enough upskilling and training opportunities, there is a stark need to better upskill workforces to support the workplace transition that is already underway. The report, Data Literacy: The Upskilling Evolution, was developed by Qlik in partnership with The Future Labs and combines insights from expert interviews with surveys from over 1,200 global C-level executives and 6,000 employees*. The findings, which were largely consistent across all geographies surveyed, reveal how the rapid growth in data usage is extending enterprise aspirations for its potential and, in turn, transforming working practices.

Operationalizing machine learning models has been a crucial stake for organizations which have invested in Artificial Intelligence. Indeed, many organizations launched PoCs (Proofs of Concepts) without succeeding in operationalizing their machine learning or deep learning models for different reasons: lack of expertise, or experience, reluctance of C-level executives to trust a new technology, no adapted processes or unwillingness of business to loose a part of their expertise or their understanding of decisions made by a model etc. To help to perform ML operationalization a new discipline appeared: MLOps for Machine Learning Operations. MLOps is part of the Ops family and is inspired from the DevOps concepts even though it has some specificities related to models management. This is the reason why we chose to evaluate the MLOps processes the same way DevOps processes are.

Stationed at the frontier of accelerating artificial intelligence (AI) landscape, organizations need to validate executives who make nimble, informed decisions about where and how to employ AI in their business. Encouraging the industry-wide digital transformation, the widespread technology has permeated more organizations and more parts within organizations spanning the C-suite executives as well. The very fundamentals of leadership need to be rethought, from overall strategy to customer experience, in order to deploy AI appropriately while considering the human capital too. As the conventional business leaderships are giving way to new approaches, opportunities, and threats as a result of broader AI adoption, the new set of AI executives are ready to take over the challenge to drive better innovation and competitiveness. Several C-level executives, in today's dynamic AI culture, are confident enough to wheel their organization's leadership team towards the ability to adapt significant and innovative AI approaches across the business.

For many companies, Artificial Intelligence (AI) and the range of cognitive technologies are strategic to their businesses and organizations. Indeed, for these organizations, AI is as fundamentally important to their long-term well being as their IT operations and finances. In recent decades, we've added to the so-called "C-suite" with strategic top-level management positions such as the Chief Information Officer (CIO), Chief Information Security Officer (CISO), and even more recently the Chief Data Officer (CDO). One might think that with AI's strategic role for organizations, that if you have a CIO in charge of all the information and IT-operation activities in the organization and a Chief Financial Officer (CFO) in charge of all the finances for the business, why shouldn't you have a Chief AI Officer (CAIO) in charge of all the AI-related activities? This might make sense for some companies but most likely, it doesn't.

Artificial intelligence (AI) is seen by many as the best path to competitive advantage and efficiency. C-level executives are taking this message to heart – three-quarters believe if they don't move beyond experimentation to aggressively deploy AI, they risk going out of business over the next five years. That increasing anxiety emerged in a recent study from Accenture, based on a global survey of 1,500 C-level executives. Still, organizations are scrambling to establish an AI foothold. Eighty-four percent say AI is now vital to their business strategies, but only 16% have moved out of the experimental stage.

Artificial intelligence (AI) is seen by many as the best path to competitive advantage and efficiency. C-level executives are taking this message to heart – three-quarters believe if they don't move beyond experimentation to aggressively deploy AI, they risk going out of business over the next five years. That increasing anxiety emerged in a recent study from Accenture, based on a global survey of 1,500 C-level executives. Still, organizations are scrambling to establish an AI foothold. Eighty-four percent say AI is now vital to their business strategies, but only 16% have moved out of the experimental stage.

Failure to Scale Artificial Intelligence Could Put 75% of Organizations Out of Business, Accenture Study Shows Companies that shift from AI experimentation to execution achieve lasting ROI and competitive agility NEW YORK; Nov. 14, 2019 – Three-quarters of C-level executives believe if they don't move beyond experimentation to aggressively deploy artificial intelligence (AI) across their organizations they risk going out of business by 2025, according to a newly released study from Accenture (NYSE: ACN). The report, titled "AI: Built to Scale" and produced by Accenture Strategy and Accenture Applied Intelligence, is based on a global survey of 1,500 C-level executives across 16 industries designed to understand how companies are implementing AI across their organizations. The research found 84% of C-level executives believe they won't achieve their business strategy without scaling AI, yet only 16% have made the shift from mere experimentation to creating an organization powered by robust AI capabilities. As a result, this small group of top performers is achieving nearly three times the return from AI investments as their lower-performing counterparts. The report reveals the secret to success for these top performers centers around three key elements: a strong data foundation; multiple dedicated AI teams; and a C-suite-led commitment to strategic, organization-wide AI deployment.