Business leaders highlight the need for a hybrid cloud approach to unlock the power of generative AI

In 2023, organizations have confronted an unprecedented stage of stress to digitally remodel with the rise of generative AI in addition to imperatives similar to sustainability, labor productiveness and safety. The “Cloud Transformation Report,” a brand new international survey from the IBM Institute for Enterprise Worth (IBV), discovered that many main enterprises share a standard basis to digital transformation—a transparent hybrid cloud technique.¹ These companies cite a number of key advantages to utilizing a hybrid cloud method to gas enterprise transformation, together with modernization, agility, safety, sustainability and the flexibility unlock the facility of generative AI.

Right here’s a have a look at the important thing drivers that impacted cloud transformation over the previous yr and what they imply for firms on their cloud modernization journey:

Concerns for adopting generative AI

This yr, generative AI captured the eye of enterprise leaders—nevertheless it additionally raised questions together with what’s the proper basis to speed up and scale impression? For a lot of organizations, the reply is a hybrid cloud method. The brand new examine discovered that 68% of hybrid cloud adopters have already established formal, organization-wide insurance policies to direct their method to generative AI.

With the rise of generative AI comes an inflow of knowledge. Whereas this could gas large enterprise improvements, it additionally requires strategic issues. When executing an AI technique, enterprises want to think about their present compute capability, the place knowledge resides—within the cloud, on premises, on the edge—how knowledge is accessed, essential safety controls, and find out how to use present know-how investments successfully. Along with serving to ship enhanced reliability and efficiency, a hybrid cloud method can allow elevated agility with the flexibility to attach cloud and on-premises environments, eradicating boundaries to versatile AI workflows.

Addressing safety and compliance issues

In the case of generative AI, enterprises are additionally involved in regards to the potential publicity of delicate knowledge. For instance, 45% of cloud leaders cited issues about cybersecurity or privateness and confidentiality of knowledge and data as the important thing impediment of their push to implement generative AI. The findings point out that extremely regulated industries are more and more weighing dangers, similar to safety, when contemplating the know-how. These organizations are much less prone to have a proper method to AI than another industries, however they’re exhibiting progress: 57% of banks and monetary markets organizations, 54% of insurers and 54% of life sciences/pharma establishments have adopted a proper method to the know-how to date.

For extremely regulated industries which might be tasked with safeguarding important knowledge, there’s so much on the road. To keep away from blind spots, a holistic safety technique that gives visibility throughout a whole hybrid cloud infrastructure is significant to mitigate threat. 

Overcoming expertise challenges

The report additionally exhibits persistent obstacles to extra widespread cloud adoption — maybe none as nice as the continuing expertise hole. For instance, 58% of worldwide determination makers say that cloud expertise stay a substantial problem and 72% of organizations have created new positions to meet the necessity for cloud expertise. Inside particular person industries, this deficiency is even larger: 79% in extremely regulated industries similar to banking and monetary markets, in addition to 81% in transportation and journey, and 79% in chemical compounds and petroleum.

Placing sustainability initiatives within the highlight

Just lately, sustainability has been tied to broader enterprise targets and develop into a bigger precedence for know-how investments for organizations throughout industries. But as generative AI grows, the rise in knowledge processing required for AI workloads does too. This may current new challenges for organizations that need to scale back their greenhouse fuel (GHG) emissions.