Businesses in all sectors embrace artificial intelligence to expand and enhance their corporate operations. Deep learning innovations assist in the commercial success of industries ranging from national security to e-commerce. The key component for an AI success model is the accuracy of its data. In contrast to conventional coding models, an AI algorithm’s output depends on the data used to train it because it infers results based on what it has been taught. The capacity to augment rather than automate is crucial for artificial intelligence to succeed. This article at InformationWeek by Mary E. Shacklett shares four tips for AI success.
What Hinders AI Success?
So, why cannot artificial intelligence (AI) perform the task and then get the information it needs from people? You might find the solution in a Juniper Networks April 2021 poll of 700 C-level executives and IT professionals. According to the survey, just 6% of respondents have operationally integrated AI, even though 95% of respondents believed that their companies would gain from doing so. Despite general hesitancy, some companies have experienced great success with AI implementations.
Four Best Practices
Mitigate AI Mistrust – AI is a tool and not a complete replacement for existing operational processes. The technology works best when it collaborates with experts in their fields that make the ultimate decisions in any process. Enhancing management’s confidence that AI will not replace them as decision-makers and reassuring workers that it will not eliminate their employment would significantly speed up the technology’s adoption.Ensure Data Accuracy – The quality of an AI system depends on its data. The data fabrics formed by a few businesses are comprehensive and include all connections and interconnections between data types and data sources. Fewer businesses vet data extensively to ensure AI accuracy.Use Successful AI Business Cases – Look for good business cases to implement successful business AI practices. Businesses that have implemented AI successfully increased uptime, decreased maintenance costs, optimized their stock on hand, and produced more strategic products to boost their bottom line.Enhance AI Customer Support – Improved customer service capabilities are needed in AI systems. Excellent AI system designers hire only experts in customer service. In designing AI systems, these people serve crucial roles. Human aspects play a pivotal role in creating an effective interface between a computer and a human.
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