AI is enabling new experiences everywhere. When people watch a captioned video on their phone, search for information online, or receive customer assistance from a virtual agent, AI is at the heart of those experiences. As users increasingly expect the conveniences that AI can unlock, they’re seen less as incremental improvements and more as the core to any app experience. A recent Forrester study shows that 84 percent of technical leaders feel they need to implement AI into apps to maintain a competitive advantage. Over 70 percent agree that the technology has graduated out of its experimental phase and now provides meaningful business value.
To make AI a core component of their business, organizations need faster, responsible ways to implement AI into their systems, ideally using their teams’ existing skills. In fact, 81 percent of technical leaders surveyed in the Forrester study say they would use more AI if it were easier to develop and deploy.
So, how can leaders accelerate the execution of their AI ambitions? Here are three important considerations for any organization to streamline AI deployments into their apps:
1. Take advantage of cloud AI services
There are cloud AI services that provide prebuilt AI models for key use cases, like translation and speech-to-text transcription. This makes it possible to implement these capabilities into apps without requiring data science teams to build models from scratch. Two-thirds of technical leaders say the breadth of use cases supported by cloud AI services is a key benefit. Using the APIs and SDKs provided, developers can add and customize these services to meet their organization’s unique needs. And prebuilt AI models benefit from regular updates for greater accuracy and regulatory compliance.
Azure has two categories of these services:
◉ Azure Applied AI Services that are scenario-specific to accelerate time to value.
◉ Cognitive Services that make high-quality AI models available through APIs for a more customized approach.
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