Artificial intelligence is suddenly making headlines. Is the technology ready to become a standard business tool? Bryce Hall ’12, who advises companies on digital and analytics transformations at McKinsey, says that combining human expertise and judgment with AI’s data-driven recommendations is a challenging but powerful way to deliver business results.

Q: How quickly are companies adopting AI?

In 2017, about 20% of companies responding to McKinsey Global’s AI survey reported adopting AI in at least one of their business areas. Today, that number is two and a half times larger.

However, we have seen a leveling off around that 50% level over the past few years. An important reason for that is that adopting AI isn’t simply investing in a new technology. We see the best results when people can supplement their expertise with the rich insights AI can deliver. One of the core challenges companies face is developing processes to integrate their people and AI tools and insights.

Q: What are the typical uses of AI today?

The top use cases of AI overall are for service operations optimization. Product or service development is consistently near the top. Marketing and sales use cases are too. In all of these, the AI is embedded in products used for things like customer service analytics, customer segmentation, lead generation, new customer acquisition, or marketing.

AI is great with multi-variable optimization challenges. It can take in vast data sets and a vast number of variables and deliver recommendations. For an airline, that might be what passenger routes to fly, improvements to maintenance operations, or how to maximize cargo yield. For a mining company, an AI engine drawing on IoT (Internet of Things) sensors can deliver guidance on precise adjustments to crushers or chemical baths based on the characteristics of the ore being processed. This has enabled mines to increase throughput and yield by more than 10%.

These are incredibly capital-intensive operations, so making them more efficient and more environmentally friendly, also can mean hundreds of millions in savings.

Source

Mots-clés : cybersécurité, sécurité informatique, protection des données, menaces cybernétiques, veille cyber, analyse de vulnérabilités, sécurité des réseaux, cyberattaques, conformité RGPD, NIS2, DORA, PCIDSS, DEVSECOPS, eSANTE, intelligence artificielle, IA en cybersécurité, apprentissage automatique, deep learning, algorithmes de sécurité, détection des anomalies, systèmes intelligents, automatisation de la sécurité, IA pour la prévention des cyberattaques.

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