When Deloitte's recent State of AI in the Enterprise study asked AI adopters about their organization's top adoption challenges, "managing AI-related risks" topped the list--tied with integration and data challenges, and on par with implementation concerns.1 And while worry is high, action to ameliorate risks is lagging: Fewer than one-third practice more than three AI risk management activities.2 And fewer than four in 10 adopters report that their organization is "fully prepared" for the range of AI risks that concern them. To investigate whether actively managing AI risks has any tangible benefit, we compared two groups of AI adopters that approach those risks differently: Risk Management Leaders (11%) undertake more than three AI risk management practices and align their AI risk management with their organization's broader risk management efforts, while Risk Management Dabblers (51%) undertake up to three AI risk management practices but are not aligning them with broader risk management efforts.3 The Leaders believe AI has greater strategic importance to their business: 40% see AI as "critically important" to their business today, versus only 18% of the Dabblers--and within two years, those numbers are expected to rise to 63% and 36%, respectively.

That led to an aggressive pace of change over the past few years, said Merim Becirovic, Accenture's managing director of core infrastructure and business operations. Consider, for instance, this measure of success: Three years ago, Accenture had only 10% of its infrastructure and compute needs in the cloud, but now it has 90% in the cloud. Such gains didn't come without challenges, Becirovic said. Accenture leaders discovered a number of potential barriers to digital transformation, ranging from new skill requirements to security to just how fast the organization can keep changing. Accenture is far from alone in its quest for transformation.

Accenture is a global professional services company with leading capabilities in digital, cloud and security. They are known for delivering unmatched experience and specialized skills across more than 40 industries, through their Strategy and Consulting, Interactive, Technology and Operations services--all powered by the world's largest network of Advanced Technology and Intelligent Operations centers. Their 506,000 people deliver on the promise of technology and human ingenuity every day, and serve clients in more than 120 countries. Accenture embrace the power of change to create value and shared success for our clients, people, shareholders, partners and communities. Laetitia Cailleteau is the Managing Director, UKI Emerging Technology and Global Lead for Conversational AI for Accenture.

The Deloitte AI Institute helps organizations transform with AI through cutting-edge research and innovation by bringing together the brightest minds in AI to advance human-machine collaboration in the Age of With.

Machine Learning Crash Course for Executives - by Deloitte Data Analytics, Data Analysis, Data Science, Big Data, Artificial Intelligence, Deep Learning, Neural Networks, AI New What you'll learn Description Deloitte's crash course on AI, Machine Learning and Deep Learning Programme is provides short, one stop learning opportunity for everybody that has an interest to understand AI, Machine Learning and Deep Learning beyond the buzzwords. After completing this course, participants will be able to prioritise, lead and manage AI initiatives.

If there's anything that can be said for 2020, it's a year of upheaval turning everything we know on its head. This is true for the public relations industry as much as anything. While the tides of change have been swirling for a while now, it's more evident than ever that we're on the cusp of a major reckoning in how we do our jobs – and the tools we use to do them. There have been noble and creative attempts at introducing technology into the PR workflow for decades, mostly static media databases, stylized measurement dashboards, endless monitoring tools, influencer databases and social media management platforms. But uptake has been slow and performance uneven.

Accenture has launched a new immersive engagement and investment program targeting emerging technology software startups to help fill strategic innovation gaps for the Global 2000. Called Project Spotlight, the exclusive Accenture Ventures program represents a new approach to engaging with the global software startup community. Beyond making capital investments, Project Spotlight will offer unprecedented access to Accenture's technology domain expertise and its enterprise clients. Startups will co-innovate with Accenture at its Innovation Hubs, Labs and Liquid Studios, working with subject matter experts to adapt their solutions to the enterprise market and scale faster and more effectively. Created by six-time Silicon Valley entrepreneur and CEO, Tom Lounibos, Project Spotlight seeks to transform the transactional nature of venture capital into an immersive engagement model. "For many startups, the most challenging part of bringing a solution to market isn't having a powerful idea or securing funding," said Lounibos, who is the managing director, Accenture Ventures and Project Spotlight lead.

There are many excellent women working in the data science industry today. But we've rounded up a list of 10, with a mixture of both industry leaders and ones to watch. The data science industry is thriving and as AI and machine learning continue to evolve, so too will the role of the data scientist. While this may create new and exciting opportunities in tech, there is still a gender gap in this sector that needs to be addressed. A report from Boston Consulting Group earlier this year suggested that only about 15pc to 22pc of all professionals in data science-related roles are women.

The strategic importance of smart factories is undeniable, as early adopters have reported operating more efficiently and driving more to the bottom line. In the United States alone, 86 percent of manufacturers believe that smart factories will be the main driver of competition by 2025. Furthermore, 83 percent believe that smart factories will transform the way products are made.1 Research consistently reveals improvement in cost, throughput, quality, safety, and revenue growth through the deployment of smart factory technologies that combine capabilities in industrial internet of things (IIoT), cloud and edge computing, robotic process automation (RPA), artificial intelligence (AI) and machine learning, vision systems, and augmented and virtual reality systems, among others.2 Leaders have a broad range of choices and opportunities with respect to smart factory transformations, both in terms of which technologies to use, and how to deploy them. Despite all of this, however, many are still only just getting started.

Many organizations have started adopting artificial intelligence (AI) and machine learning (ML) solutions, yet the C-level executives are neither data scientists nor AI experts. AI is not a new concept. However, it is only in the last decade the technology has managed to make advancements worldwide. In the present day, we're able to speak to our mobile devices and it responds, when we're trying to book an Uber we receive multiple options with the price updates, or even when we're streaming service on an online portal, we receive recommendations – we've all been experiencing AI-enabled solutions everywhere. It's just that we tend to ignore them.