Is the fusion of AI and cloud the future?

AI and cloud DXC Blogs

Artificial intelligence (AI) has the capacity to enhance current cloud platform benefits and leverage a new generation of cloud computing technologies. But we’re not quite there yet.

Today, we see AI-enabled chat applications in support, planning, scheduling and customer service. These impact the enterprise in various ways, but as automation and intelligent systems further develop, we’ll see critical organizational needs being met with AI.

The technology is bound to become a mandate in decision-making, as the need for smarter and faster decision-making system and the management of big data is growing day by day.

A lot of big IT players, including today’s top companies, have heavily invested in the space with plans to increase efforts in the foreseeable future. As I explained in my previous post, software robots or robotic process automation (RPA) play a role in this transformation, helping humans do less repetitive work.

But the big dream on the horizon is how AI can improve cloud technology, just as cloud technology has improved AI development.

Fusion between AI and cloud

With the cloud and AI, humans and machines can gather and analyze more data than ever before. If you follow companies like Google and IBM, you know they are combining these technologies in fascinating ways. The result will be a world that transforms how we view both AI and the cloud.

AI is already all around us. Google uses machine learning to filter out spam messages from Gmail. Facebook trained computers to identify specific human faces nearly as accurately as humans do. Products like Siri, Cortana and Alexa have been making our lives easier for a while now.

The enterprise applications for AI are much different. An obvious application is to help business leaders and data professionals organize, collect, secure and govern data efficiently, so they can gain the insights required to become a cognitive business by keeping sensitive enterprise data safe from theft or loss. Google, which trails behind Amazon’s AWS and Microsoft in the cloud business, has made a strong pitch to businesses and enterprises, saying that its expertise in AI and machine learning make it a better bet in the cloud segment.

Investing in AI

For an enterprise considering an AI investment, it’s difficult — and far more complex than with other types of technology investments — to define outcomes and goals. Experts across a range of disciplines must think of the investment in terms of frameworks and models.

This includes:

  • Analyzing the impact on current and future business models
  • Selecting processes and operations in which to invest
  • Examining machine intelligence technology
  • Rigorously applying data science to proposed solutions and outcomes

As you can see, the skills and activities needed to evaluate an AI investment are significantly different than those needed when buying and implementing traditional enterprise software.

AI as a Service?

One solution to consider is AI as a service. Major cloud providers are making AI more accessible “as-a-service” via open-source platforms. This new breed of Platform as a Service (AIaaS) can be used to make sense of the data enterprises have been collecting, and can be leveraged to solve infinite enterprise problems.

So how does it all fit together?

Cloud computing and artificial intelligence are technological advancements that will play supporting roles in this changeover from tools to solutions. Machines now have the ability to compute massive amounts of data almost instantaneously, and humans have the ability to deal with inconsistencies and exceptions to the rules.

The key is pairing them together.


How do enterprises adopt machine learning and AI along with cloud services successfully?

1. Implementing machine learning computing in cloud services: This capability is designed for companies with data scientists and machine learning experts who are able to build their own unique machine learning models with libraries.

2. Adopting algorithms and pre-trained machine learning models: Organizations can apply machine learning with pre-trained machine learning models to their applications using APIs. Capabilities include understanding natural language and images.

3. Tapping into the most accurate predictive models and market models with data sets: Data scientists from around the world compete to create the most accurate predictive models and market models, as well as to acquire new public data sets in a variety of fields. Their expertise is invaluable in enterprise applications.

4. Building a team with expertiseApart from data scientists, an organization must be able to leverage the knowledge of cloud experts to understand how AI and machine learning can be used to leverage the benefits of both.

Experts say 2017 could be the year when AI becomes an obvious part of our work lives, and AI capabilities will only be improved with the development of cloud technology (and vice versa). I’d love to know your thoughts on the relationship of AI and cloud, and where it’s all headed. Feel free to leave a comment below.


Why advanced analytics needs the cloud

Robotic Process Automation: Leveraging software robots to replace humans

Protecting sensitive enterprise data from theft



  1. Informative article on machine learning

    Liked by 1 person


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