Artificial Intelligence as a Service in the Cloud

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How enterprises can deliver better experiences to their customers by subscribing to cloud-based AI services

When someone says Artificial Intelligence, the first thought that comes to mind is probably something out of the Terminator movie franchise: Skynet being activated and robots take over the world. That might be a problem for our grandkids, but Artificial Intelligence or AI is still in its early stages. At the same time, the technology has gradually become more involved in our everyday lives over the past few years.

It could be talking to Apple’s Siri or getting directions through Google Maps; almost everyone with a smartphone has interacted with AI either directly or indirectly. A rapidly growing number of companies use software applications that leverage AI or machine learning algorithms to create quality and seamless experiences for their customers.
AI and machine learning are steadily opening the door to a new age of consumer interactions and products. The possibilities appear to be limitless.
For businesses, correctly implemented AI offers an elite set of tools that can engage their customers in new and exciting ways that were unimaginable a few years ago. A recent study by PwC of 2,500 US consumers and business decision makers found that business leaders specifically believe AI is going to be fundamental in the future and that 72% of those surveyed termed it as a “business advantage” now. Artificial Intelligence is now the most efficient way to analyze and procure the maximum value from the huge amounts of data that businesses generate on a day to day basis.
Implementing AI in your company can radically change your productivity and boost your business, but building such sophisticated software and maintaining the infrastructure to accommodate AI and its heavy-duty machine learning armory can be difficult and expensive. Machine learning and algorithm training requires extra-ordinary computing power which small and medium businesses may not be able to afford. In addition, hiring engineers specializing in AI, machine learning, and data science can be even more expensive – machine learning engineers earn a mean salary of around $150,000.
Huge companies like Google, Microsoft, Facebook, IBM and Amazon have thrived after being at the forefront of AI and have built huge AI infrastructure within their business models. They have the budget and the resources to pay their engineers to create AI products like our Google Homes or Amazon Alexas, the apps on our phones, mobile banking, emails, soon to be self-driving cars, etc.
It might seem like you are unable to capitalize on this trend, but many of those same corporations (and others) now offer AI capabilities as a service for external companies. This has been made possible by the massive growth and adoption of cloud services, allowing AI pioneers to easily offer AI services through a subscription model.
Today, enterprises can easily subscribe to AI services when they need them, avoiding the need to build large teams of AI engineers and maintain heavy duty computing infrastructure. The list of cognitive services that key cloud players provide is rapidly growing by the day. By opening the API’s to their AI computing engines, tech giants like Google, Microsoft and Amazon have enabled developers at smaller organizations to experiment with their existing functional code and use them to their advantage.
During the last Amazon Web Services developer conference in Las Vegas, Amazon portrayed the Amazon Cloud 9 Integrated Development Environment (IDE) which offers developers working on the platform easy access to Amazon’s proven AI tools tools like Amazon SageMaker (lets companies build and quickly train machine learning algorithms), Amazon Rekognition Video (uses AI to detect objects and faces in customers’ video content), Amazon Transcribe (turns audio into text), Amazon Comprehend (analyzes text for sentiment and key phrases), and much more.
Google started rolling out AI as a service offering when it launched TensorFlow, an open source software library that can be used to build other machine learning software. In addition, Google also introduced its own hardware chip called Tensor Processing Unit or TPU to enable easy processing of TensorFlow while keeping a tab on energy consumption due to the high-power computing involved. This software-hardware combination has made Google a top choice among developers.
Other organizations—for example, Microsoft and Salesforce—have begun embedding AI in their applications, offering Microsoft Cortana as part of Microsoft Dynamics and Salesforce Einstein as part of the Salesforce Cloud.
These approaches allow small and medium enterprises to utilize highly cognitive AI capabilities from proven market leaders to enhance their customer’s experience, offering key market differentiation at a time when consumers have no shortage of choices. Intelligent and predictive responses for consumer engagement improves brand loyalty and will ultimately reflect positively on both top line and bottom line profits.
The infusion of AI services in the cloud offers small and midsize companies the opportunity to go to the next level, and it is increasingly vital for organizations to grow and adapt with the changing times. At the same time, implementing any digital transformation utilizing emerging technologies can be complex and runs the risk of affecting your business model. It’s essential to have a seasoned technology partner like Korcomptenz onboard to deliver the best results in your transformation journey.

Get in touch with us to explore new possibilities for implementing AI in your business today!

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