Perhaps the leading value proposition for artificial intelligence (AI) is its propensity to help humans make decisions. With the right data and the right analytics, people can choose a course of action based on solid information, not hunches or guesses, and this is expected to bring dramatic improvements to the business model.
Copyright: venturebeat.com – “How far should AI’s decision-making authority go?”
Elsewhere, however, AI won’t focus on the choices that humans must make, but the ones it will make for itself. In the realm of automation, in particular, AI will be tasked with broad decision-making capabilities — all data-driven, of course — to streamline data flows, improve manufacturing processes, direct traffic and perform a wide range of other functions.
This begs the question, where is the line between what AI should decide and what is best left for humans?
Being careful with autonomous AI
Implementing autonomous AI across the full scope of the enterprise data ecosystem – from the data center to the cloud to the edge to connected devices – will require careful coordination between a number of emerging data initiatives. Alan Young, chief product officer at automation firm InRule Technology, recently highlighted the intersection of machine learning (ML), decision automation and process automation and how it will drive better business results. With ML providing the probabilistic decisioning logic and both decision and process automation contributing consistent, orchestrated rules-based governance of operations and behavior, processes gain the ability to act on real-time, dynamic inputs and values without the need for constant, direct human oversight.
With this framework in hand, Young says organizations can not only produce more successful outcomes from its data process, but do so at scale rapidly and consistently. Already, this is evolving beyond a mere competitive advantage to an operational necessity by allowing organizations to detect and respond to both opportunities and threats as they emerge.[…]
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