Advancements in automated systems are driving greater adoption of cloud while lowering barriers to data and digital growth. Today’s intelligent cloud features new capabilities that are enabling better efficiencies and accelerated innovation for businesses. According to Longitude research, more than half (53%) of businesses today have migrated most or all of their business essential workloads to the cloud, while 25% of businesses have deployed an autonomous database.
A huge driver of this growth comes from the advancement of automated systems. In this next generation of cloud, we’re seeing new capabilities in intelligent automation that are enabling better efficiencies and greater agility for businesses. The evolving integration between the human workforce and machine automation is setting new standards across the enterprise landscape, as rapid innovation becomes more accessible through the use of cloud.
In the second episode of our “Designed for Change” five part podcast mini-series, hosts Michael Hickins and Barb Darrow met with Holger Mueller, Vice President and Principal Analyst at Constellation Research, to discuss how automation and intelligence are permanently changing the way businesses run.
The episode begins by evaluating the current state of cloud and top challenges for business leaders. As businesses implement new digital strategies, the transition to cloud poses complexity challenges for IT. New technology requires new knowledge and skills, meaning IT becomes responsible for managing both the traditional and modernizing parts of the business. Meanwhile, as cloud creates more opportunities for growth and innovation, it’s raising the standards for enterprise acceleration. Companies today are expected to be more nimble and agile, with the capability to quickly create, disrupt, or adapt to disruption. Markets are constantly transforming, pressuring organizations to effectively follow and evolve to prevent being left behind.
The pandemic, for example, has driven a greater need for digital transformation – meaning companies need to spend more money to invest in new technologies, while also reducing overall budget.
However, the key to resolving all these challenges lies with the value of elasticity. Mueller breaks this down into two parts: technical elasticity and commercial elasticity. Commercial elasticity means having financial control that aligns consumption with spending. “What the pandemic has taught us is that the cloud also introduces commercial elasticity. So you use less, you pay less, which enterprise software vendors traditionally don’t want to do too much. You can always consume more and pay more, but how do we want to go down?” states Mueller. On the other hand, technical elasticity refers to the utility of scaling your computing resources to match your immediate enterprise needs. “Traditionally, we lived in the finite computing world where we trust the technologies to make the software work and size the machine for the needs of your enterprise…And that’s now well understood that you get this the technical elasticity there.”
When evaluating your cloud strategy, consider the level of elasticity that the technology can provide. Typically, the broader the infrastructure vendor, the greater its elasticity. Providers that have massive scale offer greater stability against economic shifts, with the promise of consumption based spending. For instance, the pandemic has introduced financial challenges across all types of service providers. In the case of cloud, software vendors are less flexible in their pricing since they have to pay off the stationary costs of their data centers.
Now enter AI and automation. When comparing machine learning solutions on their own versus embedded with a cloud infrastructure, the level of elasticity falls greater in the intelligent cloud. Self-driving machines that operate in the cloud practice continuous deep learning that enables deeper understanding of human issues.
This capability to evolve provides a system built for infinite insights – where machines are constantly listening to a combination of signals to discover new ways to automate human labor. When used as a point solution to address specific problems, AI offers limited potential. Without the purpose of long-term maturation, AI limits the technical elasticity of a business. For instance, AI in business applications still requires organizations to build a central model and deploy it to an instance, a process that’s often slow and complex. Companies that embed intelligence into their cloud and digital strategies reap the benefits of a solution that grows with the business and is connected with the enterprise. There’s no need to provision storage and compute to answer new questions – the technology adapts by itself to meet changing company needs.
While cloud continues to accelerate across the world, much of its value continues to be untapped. Despite the rapid evolution of digital technologies, companies still remain reluctant to pursue the enterprise-wide change required to effectively integrate these new intelligent capabilities with their business. Having the confidence to quickly scale up, as well as the tools to analyze massive amounts of data don’t just happen. They take someone who’s tired of lying awake at night, waiting for the next problem to strike. Instead of leaving your company’s future to chance, it can be empowering to take control and make choices that will set it—and you—up for continued success. Oracle Cloud is designed to help you navigate this change because it delivers high performance, operates efficiently, and scales as your business needs to move faster than ever before.
Listen to episode 2 today, and subscribe for the next episodes in our Designed for Change series.
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