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Recorded - available as on demand webcast AI is plagued by inflated and unrealistic expectations due to a lack of broad understanding of this wide-ranging space by software vendors and customers. Software tools can be extremely powerful, however the services, infrastructure, data quality, architecture, talent and methodologies to fully deploy in the enterprise are frequently lacking. This four-part series by Earley Information Science and Pandata will explore a number of issues that continue to afflict AI projects and reduce the likelihood of success. The sessions will provide actionable steps using proven processes to improve AI program outcomes.
The holy grail of digital transformation is the seemingly conflicting goals of high levels of customer service and pressure to reduce costs. “Digital Transformation” has become an all-encompassing term – in a piece in this column about customer data platforms, I asked whether the term has lost its meaning: The phrase “digital transformation” can mean anything and everything — tools, technology, business processes, customer experience, or artificial intelligence, and every buzzword that marketers can come up with. Definitions from analysts and vendors include IT modernization and putting services online; developing new business models; taking a “digital first” approach; and creating new business processes, and customer experiences. The overarching objective of a digital transformation program is to improve end-to-end efficiencies, remove friction from information flows, and create new value streams that differentiate a company’s offerings and strengthen the customer relationship. Having assisted large global enterprises with building the data architecture, supporting processes, and governance for multiple digital transformations, in my experience, there are two broad classes of initiatives that seem to get funding and others that miss the boat in terms of time, attention, and resources.
There are many manufacturers who have started to take the leap forward in the digital space, but there are still a great number who rely solely on their distributors to manage their product data. We are going to look at 4 key reasons why its so important that manufacturers own their product and dedicate the time and resources to build it out.