We spoke to Maria Axente, AI Programme Driver, PwC United Kingdom in the context of this event about how AI and big data can be used to drive enterprise using ethical principles. AI and big data are making big changes in enterprise. It is worth considering how we got to the stage we are at today, and the fact that AI as a discipline is around 56 years old but it has suddenly started to be implemented at a much larger scale. I think there are three main causes of this more recent widespread adoption of AI. One is the huge volume of data that is being collected via smart devices; mainly smartphones, but increasingly IoT devices.

Many companies are stuck in dead-end pilots while an elite few have figured out how to make artificial intelligence (AI) work at scale, according to a new Accenture report. "AI: Built to Scale" shows how difficult this transformation is as well as what it takes to do it successfully. "In a nutshell, what our report found is that the majority of companies are really struggling to scale AI," said Bob Berkey, MD, Accenture Applied Intelligence. "They're stuck in the Proof of Concept Factory, conducting AI experiments and pilots but achieving a low scaling success rate and a low return on their AI investments." Accenture surveyed 1,500 C-level executives across 16 industries to determine what makes AI projects successful.

One day in 1975, a Kodak engineer decided not to embrace a prospective new digital technology, thereby sealing the fate of the world's leading photography company. As insurance executives consider artificial intelligence (AI) and the question of whether to use this technology, they would do well to remember this decisive strategic mistake. AI could be one of the biggest game changers in insurance history and undoubtedly constitutes a paradigm shift. It offers a wide range of opportunities: faster and more efficient claims management and application processes, better prospective healthcare advisory services, and a variety of on-demand insurance services. This boosts customer and stakeholder expectations and generates innovation pressure.

Loughborough University and the Willis Research Network (WRN) would like to invite you to another conference bringing together a range of perspectives on the business application of Artificial Intelligence (AI) and its role in the ongoing digital transformation of the insurance industry. The goal is to look beyond the day to day business decision-making and examine the broader challenges of employing AI, the implication for business models and to address some of the organisational and public policy challenges to effective use of these new technologies. We will have a mix of top university researchers and industry practitioners participating as both presenters and panellists to enhance our depth of knowledge around AI and the use of AI in our industry. We look forward to welcoming you to a stimulating day of open debate and insightful discussion. The conference is the first major event organised by the TECHNGI research project, hosted by Loughborough University and Willis Towers Watson and funded from the UK Government Industry Challenge Fund's Next Generation Services program.

The bigger your company, the more important it is that every team member is on the same page. When you're as big as Genpact, with 90,000 employees and twice as many partners, then collaboration is a top priority. Sanjay Srivastava is well aware of the challenges. As Genpact's Chief Digital Officer, he is front and center at the effort to make sure the disparate teams and employees within the company are working successfully in a collaborative organizational culture, as well as offering a satisfying customer experience. For Sanjay, there are three main factors that need a strong collaboration platform within a company. It starts with the idea of the business as a connected ecosystem that drives a collective intelligence. Then there's the concept of continuous learning and innovation that requires a collaborative framework to be successful. Finally, there's the convergence of domains, the ability to pull people together from different disciplines, with different experiences, and across ...

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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.

A full 84% of C-suite executives believe they must leverage artificial intelligence (AI) to achieve their growth objectives. Nearly all C-suite executives view AI as an enabler of their strategic priorities. And an overwhelming majority believe achieving a positive return on AI investments requires scaling across the organization. Yet 76% acknowledge they struggle when it comes to scaling it across the business. What's more, three out of four C-suite executives believe that if they don't scale AI in the next five years, they risk going out of business entirely.

By completing these steps, companies maintain control over sophisticated, evolving algorithms, so martech AI can accurately identify target accounts and provide actionable insights. Data fuels emerging martech competencies and helps to pinpoint the accounts with the most growth potential, highlight the topics influencing buyers' decisions, and reveal what forms of content lure potential buyers. Data even uncovers which type of content would better engage specific accounts and/or buyers. With buyers insight through keywords and enhanced SEM strategies, marketing organizations can increase engagement and create new content that appeals more accurately to their buyers' specific interests with a focus on intent. Digital-first journeys require innovative thinking with AI and machine learning integration.

AI and related technologies, such as robots, drones and autonomous vehicles, could provide a net boost to employment in China of around 12% over the next two decades, equating to around 90 million additional jobs and more than offsetting displacement of existing jobs. This new analysis for China, launched today at the World Economic Forum meeting in Tianjin, contrasts with PwC's earlier research1 suggesting a broadly neutral net impact of AI on jobs in the UK. In that analysis, PwC found that these technologies could displace around 20% of existing UK jobs by 2037, but could create a similar number of additional jobs by boosting economic growth2. Based on previous research, PwC judges that results for the UK are likely to be broadly similar to the average across OECD countries. Relative to the UK estimates, China is projected to see a higher level of job displacement (26% vs. 20%) due to the greater scope for further automation in manufacturing and agriculture.