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Lack of AI implementation may have cost enterprises $4.26T, Signal AI finds

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AI's potential impact on the U.S. economy could reach into the trillions of dollars, according to a report published this week. Signal AI, which offers a decision augmentation platform infused with AI, interviewed 1,000 C-suite executives in the U.S. for the study. The report found 85% of respondents estimate upwards of $4.26 trillion in revenue is being lost because organizations lack access to AI technologies to make better decisions faster. According to the Signal AI survey, 96% of business leaders said they believe AI decision augmentation will transform decision-making, with 92% agreeing companies should leverage AI to augment their decision-making processes. More than three-quarters of respondents (79%) also noted that their organizations are already using AI technologies to help make decisions.


95.7% of Business Leaders and Decision Makers Believe Using AI Will Transform How Decisions Are Made – Signal AI report

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U.S. companies are likely to be missing out on upwards of $4.26 trillion in revenue a year by failing to use AI in their decision making process, according to a new report conducted by Signal AI. The report also finds that a minimum of $2.84 trillion in revenue of the Fortune 500 was linked to decision making in 2020. The global report featuring 1,000 c-level executives finds that 95.70% of business leaders also believe AI decision augmentation will transform decision making. The findings show that 91.6% of respondents say companies should leverage AI to augment their decision-making, and that 79.3% of companies are already using AI to make decisions. Speaking on the report, CEO of Signal AI David Benigson says: "The remit of the modern decision maker at global companies has only grown over time, with more responsibility and pressure to deliver than ever before. We're seeing tangible data now that shows these executives don't feel they have the tools they need to succeed, to make decisions as efficiently and effectively as possible."


Robot paramedic carries out CPR in ambulance in UK first / Humans + Tech - #83

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As humans, it seems we are putting too much trust in AI, and business leaders are disinterested in ensuring that the systems they use are ethical and responsible. When humans administer cardiopulmonary resuscitation (CPR), they get fatigued relatively quickly, affecting the quality of CPR they can deliver. LUCAS 3 is a mechanical system that can administer high-quality CPR consistently without a break. South Central Ambulance Service, an NHS ambulance service in the UK for four counties, is the first to take LUCAS 3 onboard its vehicles [E&T Editorial staff, The Institution of Engineering and Technology]. The system uses wireless Bluetooth connectivity, allowing it to configure the compression rate, depth, and alerts specific to an organisation's resuscitation guidelines.


Report finds startling disinterest in ethical, responsible use of AI among business leaders

ZDNet

A new report from FICO and Corinium has found that many companies are deploying various forms of AI throughout their business with little consideration of the ethical implications of potential problems. The increasing scale of AI is raising the stakes for major ethical questions. There have been hundreds of examples over the last decade of the many disastrous ways AI has been used by companies, from facial recognition systems unable to discern darker skinned faces to healthcare apps that discriminate against African American patients and recidivism calculators used by courts that skew against certain races. Despite these examples, FICO's State of Responsible AI report shows business leaders are putting little effort into ensuring that the AI systems they use are both fair and safe for widespread use. The survey, conducted in February and March, features the insights of 100 AI-focused leaders from the financial services sector, with 20 executives hailing from the US, Latin America, Europe, the Middle East and Africa and the Asia Pacific regions.


The Rise of the Analytical CMO

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In the post-Covid era, organizations are recalibrating their marketing strategies to make better use of data and analytics to stay ahead of the competition. Increasingly, it is the Chief Marketing Officer (CMO) who has to take the lead to drive digital adoption and become the organization's digital evangelist. Data is everywhere and the CMO is increasingly expected to use multiple sources of data analysis and marketing intelligence for growth and revenue. Customer buyer journeys changed significantly during the pandemic: Buyers prefer to undertake a self-education journey, learning about the product rather than engaging with a salesperson right from the start. This puts the onus on marketers to provide prospective customers with the right content at the right time via the right channel.


Trust is a must: why business leaders should embrace explainable AI - Raconteur

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"Trust is a must," she said. "The EU is spearheading the development of new global norms to make sure AI can be trusted. By setting the standards, we can pave the way to ethical technology worldwide." Any fast-moving technology is likely to create mistrust, but Vestager and her colleagues decreed that those in power should do more to tame AI, partly by using such systems more responsibly and being clearer about how these work. The landmark legislation – designed to "guarantee the safety and fundamental rights of people and businesses, while strengthening AI uptake, investment and innovation" – encourages firms to embrace so-called explainable AI.


Why accountability is key to making the most of technology

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The onset of a new wave of innovation and technology can induce a mix of reactions amongst C-level decision-makers in any business. For every CEO, CIO, or CFO who chases the latest technological'shiny thing' and plans to implement meaningful change within their organization, there are countless others who would prefer to take little or no action regarding new technological advances. Don Schuerman is CTO and Vice President of Product Marketing at Pegasystems. Here's the thing – as much as some senior leaders might prefer to stick their heads in the sand, new technology trends are like seasons of the year; they are inevitable. In fact, the biggest risk far too many organizations run is that by either implementing innovations half-heartedly or ignoring them completely, they can lose ground to more progressive competitors who are harnessing new technologies to differentiate themselves and gain a competitive advantage. The key to avoiding this is to emphasize accountability.


Council Post: The Future Is Both Automated And Intelligent

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Intelligent Automation (IA) is one of the trending buzzwords of our times. Bill Gates believed automation to be a double-edged sword when he said: "Automation applied to an efficient operation will magnify the efficiency. IA lies at the intersection of robotics, artificial intelligence (AI) and business process management (BPM). But before you think HAL from 2001: A Space Odyssey, J.A.R.V.I.S. from Iron Man or Terminator 2: Judgment Day scenarios, first, a little context. IA is not new; automated manual processes have been in existence since the dawn of the Industrial Revolution.


AWS Offers Course on Basics of Machine Learning - InformationWeek

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On one hand, organizations recognize the potential value of machine learning to scale operations, gain faster and deeper insights, respond to quickly changing conditions, and more. On the other hand, it's hard to get started on something that is novel to your organization. You may not have the talent in-house, and you don't have any experience. What's more, even for those organizations that have run successful pilots, many have struggled to move those pilots into production for a variety of reasons. It feels like many organizations are stuck.


Global Big Data Conference

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A new course offered by AWS offers business leaders a foundational understanding of machine learning and its use cases without the need for a deep understanding of Python and coding. On one hand, organizations recognize the potential value of machine learning to scale operations, gain faster and deeper insights, respond to quickly changing conditions, and more. On the other hand, it's hard to get started on something that is novel to your organization. You may not have the talent in-house, and you don't have any experience. What's more, even for those organizations that have run successful pilots, many have struggled to move those pilots into production for a variety of reasons.