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, organisations 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 organisation to streamline AI deployments into their apps:
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 organisations unique needs. And prebuilt AI models benefit from regular updates for greater accuracy and regulatory compliance.
Azure has two categories of these services:
Your developers can use APIs and SDKs within your cloud AI services to build intelligent capabilities into apps within their current development process. Developers of any skill level can get started quickly using the programming languages they already know. And should developers need added support, cloud vendors readily offer learning resources for quicker onboarding and troubleshooting.
With AI, time to value is a matter of selecting use cases that will provide the most utility in the shortest time. Identify the needs within your organisation to determine where AI capabilities can deliver the greatest impact.
For example, Ecolab harness knowledge mining with Azure Cognitive Search to help their agents retrieve key information instantly, instead of spending over 30 minutes sifting through thousands of documents each time. KPMG applies speech transcription and language understanding with Azure Cognitive Services to reduce the amount of time to identify compliance risks in contact center calls from 14 weeks to two hours. And Volkswagen uses machine translation with Azure Translator to rapidly localize content including user manuals and management documents into 40 different languages.
These are just a few of the practical ways organisations have found efficiency and utility in out-of-the-box AI services that didn’t demand an unreasonable investment of time, effort, or customization to deploy.
Implementing AI is simpler and more accessible than ever. Organisations of every size are deploying AI solutions that increase efficiencies, drive down overhead, or delight employees and customers in ways that are establishing them as brands of choice. It’s a great time to join them.
Are you interested in the future of AI? Join our upcoming Azure Data Analytics & Machine Learning Bootcamp to learn more!