The inherent value of data is increasing, and that value is stimulating the Internet of Things (IoT) advanced analytics market, with the emergence of accessible out-of-the-box and off-the-shelf machine learning (ML) and artificial intelligence (AI) solutions.
More than 70% of survey respondents rate digital twins as an important strategic initiative. By 2023, 65% of global manufacturers will have realized savings of 10% in operational expenses. This comes from digital twins driven by IoT and machine learning.
Delivering a better customer experience was identified as the leading driver for AI adoption by more than half the large companies surveyed. At the same time, a similar number of survey respondents indicated that the greatest impact of AI is helping employees to improve productivity.
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Legacy on-prem infrastructure and traditional software licensing models are being disrupted. Meanwhile, automation initiatives have blossomed. The pandemic has accelerated the focus on robotic process automation (RPA) along with machine intelligence have been beneficiaries relative to other segments of the IT stack.
Artificial intelligence and machine learning (ML) solutions can help drive value, transform businesses, and outperform the competition. But it’s challenging to navigate the lifecycle of developing, deploying, and maintaining ML models and AI solutions.
“Any technology that is sufficiently general-purpose has opportunities in a crisis. And machine learning is a general-purpose layer of technology that is enabling all sorts of use cases across every industry. In the good times, there is a surge of innovation and new technical development, which we have seen in spades in AI over the past five years.”
via 451 Research
61% of data and analytics decision-makers adopting AI had implemented, were in the process of implementing, or were increasing their use of automation-focused machine-learning solutions — 25% plan to implement within the next year.
“After 75 years of dramatic advances, the digital computer is bumping against its limits, constrained by its architecture and atomic physics. To continue making smarter, faster and more efficient machines, systems researchers at IBM are pioneering new technologies and approaches.”
via IBM Research