Prediction: AI will be the primary driver for moving to a 4-day work week
Enterprise use of artificial intelligence (AI) has exploded over the past few years, touching all aspects of business. One of the greatest reasons behind this increase is its potential to increase employee productivity, especially for developers. In fact, the productivity gains that AI brings to the table portend a future where, by 2025, AI will allow developers to complete a week’s worth of work in four days — or less!
From our partners:
Getting AI in production is hard but can unlock many efficiencies to create new opportunities. From managing data complexity to overcoming the AI skills gap to getting prototypes into production (and scaling new use cases after) — AI projects have traditionally taken months, if not years, to get up and running.
We’ve taken our years of experience in AI development to help companies unlock the many AI opportunities that exist, from optimizing operations to automating routine tasks to creating more compelling customer experiences.
Google Cloud’s AI products are designed to make the path from data to AI in production as painless and streamlined as possible. And they’re already driving productivity gains, increasing the reach of AI and ML to more users and enabling organizations to make the most of their scarce data science resources.
Out-of-the-box APIs provide a solid starting point for organizations that need to solve real-world problems but don’t have the time or resources to build AI models themselves. For instance, Contact Center AI helps call center teams manage up to 28% more conversations concurrently to increase overall wellbeing for agents. With Translation Hub, localization teams can significantly cut back on time needed to translate documents into 135 languages by getting the first pass done in a matter of seconds before their final reviews. Merchandising and ecommerce teams are driving 40% more customer conversions with Recommendations AI.
We continue to find ways to make all our AI research, AI models, and ML toolkits available as enterprise-grade products and solutions. For instance, since its launch, Vertex AI has helped data scientists ship enterprise-grade machine learning (ML) models 5x times faster by automating routine tasks like model management, monitoring, and versions.
We’ve also taken this one step further with the launch of Vertex AI Vision this year, which gives developers a fully managed development environment for creating, deploying, and managing computer vision applications for inventory management, factory safety, and even monitoring traffic patterns. Our own internal research has shown that Vertex AI Vision reduces the time it takes to build and deploy apps from weeks to hours, at a fraction of the cost of alternative approaches.
We’re excited to see how you bring AI to bear for your business — and how you choose to spend all your newfound free time!
To learn more about how we think about operationalizing data analytics and AI, watch the session What’s next for data analysts and data scientists from Google Cloud Next ‘22.
By: Kamelia Aryafar (Senior Engineering Director, Cloud AI)
Source: Google Cloud Blog
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