Companies who have some success with their initial AI projects are seeking ways to accelerate adoption to deliver more value to the business. One researcher has defined an AI Adoption Maturity Model that presents a roadmap for accelerating AI adoption. The first stage of the six-step AI adoption maturity model is the digitization of work, turning work in the physical world into digital processes that can be tracked and recorded as data, suggests Dr. Michael Wu, chief AI strategist for PROS Holdings, providing AI-based software as a service for pricing optimization, with a focus on the airline industry. "This stage is all about getting the data, which is the raw material for AI," stated Wu, in an account from ZDNet. "If you are on the digital transformation bandwagon, good for you. You are already in Stage 1 of this maturity curve."

In a survey of hundreds of healthcare decision-makers, Intel found that the percentage of respondents whose company is currently – or will be – using artificial intelligence nearly doubled after the onset of COVID-19. Among the predicted use cases for AI: early intervention analytics, clinical decision support and specialist collaboration. "Artificial intelligence in health and life sciences has greatly accelerated," said Stacey Shulman, vice president of the Internet of Things Group at Intel, in a blog post accompanying the findings. "From helping clinicians develop personalized protocols to streamlining clinical workloads or unlocking insights in genomics, infusing AI into these industries may be much closer than many initially thought," she said. Intel conducted an online survey of 200 senior decision-makers at healthcare organizations in April 2018, and then 230 in July 2020.

After expanding its "all-in-one" AI bundle, which includes 3 FDA 510(k) clearances, the deep learning medical imaging analytics company, Zebra Medical Vision, is now announcing a new joint partnership with Intelerad Medical Systems, a leader in enterprise workflow solutions. The joint program will leverage Intelerad's newly released Odyssey designed to encourage the adoption of artificial intelligence without the prohibitive costs usually associated with such programs. The positive impact of artificial intelligence technology has been demonstrated in many fields, including that of radiology. Indeed, AI can help radiologists increase productivity, improve clinical decisions and, ultimately, provide high-quality care when the technology is tightly managed and smartly integrated in the workflow. Odyssey harnesses the power of artificial intelligence and the technology behind the Intelerad worklist to offer an unparalleled workflow management solution, comprised of the clinical AI engine, powered by Zebra-med's AI1 "all-in-one" bundle of FDA cleared AI applications.

UC San Francisco (UCSF) announced the launch of the Center for Intelligence or ci2 that focuses on accelerating the adoption of artificial intelligence (AI) technology to radiology, leveraging advanced computational techniques and industry collaborations to improve patient diagnoses and care. As part of the center, Investigators in ci2 will collaborate with NVIDIA Corp to build infrastructure and tools focused on enabling the translation of AI into clinical practice. The Center is comprised of clinical radiologists, imaging scientists, engineers, machine learning scientists, data engineers, clinicians, post-doctoral fellows, and students collaborating to develop and deploy artificial intelligence that will solve critical clinical problems by advancing the way in which healthcare professionals are able to utilize and deliver imaging. Tools under development include organ and tissue segmentation, automated volumetry and morphological quantification, and disease visualization. NVIDIA engineers and data scientists will work alongside UCSF investigators to develop clinical AI tools, applying powerful computational resources that are available in a few medical institutions, with the goal of accelerating the AI development cycle and integrating it seamlessly in the clinic.

Although AI offers vast implications for engineering, production, supply chain, customer experience, and mobility services, progress in AI-driven transformation has been sluggish and uneven due to lingering roadblocks. The number of automotive companies deploying AI at scale has grown from 7% in 2017 to 10% today, with OEMs generally making better progress than suppliers or dealers. Geographically, the US, where 25% of companies implement AI at scale, is leading the way in terms of progress, followed by the UK (14%) and Germany (12%). In terms of pronounced growth, China is making huge strides, having nearly doubled its share of scaled AI implementations, from 5% to 9%. The new report by the Capgemini Research Institute, Accelerating automotive's AI transformation: how driving AI enterprise-wide can turbo-charge organizational value, surveyed 500 executives from large automotive organizations in eight countries and interviewed a number of industry experts and entrepreneurs to understand how progress in deploying AI at scale can be accelerated.

AI is the buzzword du jour for enterprises, but many organizations are still struggling with digital transformation to become data-driven, so how can they approach this new challenge? AI has become more popular, thanks to the increased data volumes, advanced algorithms, and improvements in computing power and storage. Meanwhile, businesses are embedding AI applications in their business portfolio, but they still face a set of common challenges such as integration, cost, security, privacy and even regulatory challenges. The question is -- can analytics play a role in accelerating the onboarding of AI in enterprises. Enterprises that have successfully deployed analytics are twice as likely to get senior management buy-in for AI adoption.

AI is the buzzword du jour for enterprises, but many organizations are still struggling with digital transformation to become data-driven, so how can they approach this new challenge? AI has become more popular, thanks to the increased data volumes, advanced algorithms, and improvements in computing power and storage. Meanwhile, businesses are embedding AI applications in their business portfolio, but they still face a set of common challenges such as integration, cost, security, privacy and even regulatory challenges. The question is -- can analytics play a role in accelerating the onboarding of AI in enterprises. Enterprises that have successfully deployed analytics are twice as likely to get senior management buy-in for AI adoption.

NIIT Technologies Ltd and Arago, a pioneer in artificial intelligence (AI) and leader in autonomous automation, will deepen their partnership to optimize the IT operations of Global In-House Centers (GICs) using AI to deliver greater agility, high-quality standards, and superior customer experience.

Global services juggernaut Accenture is partnering with IT and business process automation specialist IPsoft to build a new Accenture practice dedicated to bringing the power of artificial intelligence (AI) to global enterprises. Earlier this week, the partners announced the creation of an Accenture Amelia practice built around IPsoft's Amelia cognitive agent, an AI that emulates human intelligence and can communicate with natural language. IPsoft has positioned Amelia as "your first digital employee" -- one that can take on a wide range of service desk roles. "This cutting-edge new practice is going to give our clients more artificial intelligence firepower by accelerating their ability to apply AI to significantly improve their operations and create new growth opportunities for their business," says Paul Daugherty, CTO, Accenture. "One of the ways Accenture is going to do this is by using IPsoft's Amelia Platform to develop artificial intelligence strategies, solutions and consulting services for our clients in the banking, insurance and travel industries."