Subram NatarajanMay 06, 2019 22:31:29 IST
The coming together of Artificial Intelligence (AI) and the cloud is proving to be a game-changer for enterprises with the former technology helping unearth and connect dots hidden in zettabytes of data. Over the course of the past few years, enterprises are beginning to realise the capabilities of AI to enhance business, especially in today’s cloud paradigm, where AI tools can access and manage endless data across multiple domains.
For AI to scale further and create exponential value for businesses, enterprises need to decouple AI tools and data from compartments and silos that have unwittingly gotten created as companies tried to build the cloud solution that works best for them. For this, a multi-cloud infrastructure built with AI at its core is the only answer.
When cloud computing dramatically changed the way in which enterprises used to think of data storage and the sheer quantity of data that they could leverage, many expected companies to choose between the public and private cloud. However, in practice that’s not what most companies did as a one-cloud-fits-all approach got reduced to a mere theoretical exercise. Reality is more complex and that’s why business functions and IT arms of organisations began picking and choosing a mix of data centres and private and public clouds from multiple vendors depending on the business need.
The hybrid multi-cloud strategy has proven to be a successful model for many companies as it is cost-effective, enables enterprises to quickly develop and deploy applications, but more importantly, cater to unique requirements of various business needs or applications that are at different stages of maturity and solve for security and governance concerns. This also helped avoid issues like vendor lock-in. It is not by accident that over 80 percent of enterprises already have a multi-cloud strategy.
This becomes even more relevant to AI entering the picture. A single cloud is in itself a silo, which is why multi-cloud is the best habitat for AI as it ensures the AI tools have access to a stockpile of different sets of data considering how enterprises are using various clouds for specific purposes. AI thrives in such an environment. AI in a multi-cloud infrastructure also helps developers as they do not need to build solutions from scratch, but rather leverage the building blocks of technology that exists within the respective cloud environments
As research from Ovum shows, while 20 percent of business processes have moved to the cloud, 80 percent of mission-critical workloads and sensitive data are still running on-premises because of performance and regulatory requirements. When applications are migrated to cloud, it is important to ensure the AI tools do not get siloed. Businesses and IT arms realised that moving data and applications across different on-premise and disparate cloud computing infrastructures were proving to be difficult and inefficient and therefore bridges needed to be built between the various compartments or software environments so that data can flow seamlessly and interact to unleash greater value.
Orchestrating the multi-cloud
The building of containers and adoption of container management platforms like Kubernetes has helped ease the issue of multi-cloud management. However, companies are realising that they need a single-point dashboard or management tool that can manage, move and integrate data across different cloud infrastructures — almost like an orchestra conductor ensuring the various musicians play in harmony.
With an efficient and effective multi-cloud management tool, AI can seamlessly work across all the data and cloud environments that an enterprise uses. AI and cloud have a natural symbiotic relationship and so AI needs to be finely integrated with cloud allowing the tools to become more effective in a speedbump-free multi-cloud environment. Companies tapping multiple clouds will gain the ability to drive integration, minimise complexity and use open technologies to create fast and consistent experience for all stakeholders, be they, customers or employees.
However, it is also a reality that many companies need to work with AI provided by a specific cloud provider for that cloud infrastructure. This again poses a limitation.
Enterprises need the freedom and choice to work with AI that is not restricted to specific cloud infrastructure — the entire point of the cloud was to remove restrictions to data. Companies need AI that is portable across any cloud environment, whether it is private, public or hybrid.
Only when a business is able to choose the data environment that is ideal to its specific workloads, can it freely deploy AI. Together with this comes the choice across these various infrastructures that it can truly and completely make data work.
The case for openness
An AI-enabled multi-cloud is only as good as the technology standards on which it has been built. Open technologies, especially in the cloud setting, have shown to be highly effective to drive agility and business growth.
Most of the cloud as we know it today has been built on open technologies, which is one of the reasons why it has scaled up so quickly and has responded to the demands of industries so deftly.
So, it stands to reason that using open standards to create a hybrid architecture that uses the best elements of on-premise systems, public and private clouds is the best choice of cloud strategy for an enterprise. Open, secure cloud technology provides maximum leverage and agility for rapid innovation. This is the case for AI too. We believe using an open, interoperable approach will fuel the AI economy.
In the age of AI and multi-cloud, having visibility of the enterprise’s entire data value chain and the ability to draw context across functions will clearly be the distinct competitive advantage and sets an enterprise leader apart from laggards. Keeping the data highways open and allowing AI to work unhindered on orchestrated multi-cloud is the only path to rapid growth.
The author is chief technology officer at IBM India/South Asia
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