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Cloud-native architectures allow for seamless integration of AI and ML modules. This creates what could be termed a cognitive business model – where real-time learning algorithms enhance all aspects of decision-making and customer engagement. This is reflected in the experience of 84% of respondents who said that their adoption of AI in the business would not have been possible without cloud migration. A substantial number (42%) also said that it helped to introduce AI or ML into their innovation processes.
With the adoption of open data standards and tools, the future will likely see greater data liquidity in multi-cloud environments. This will allow businesses to leverage data as a real-time strategic asset for AI analytics and decision-making, no matter where it resides.
Another development we anticipate is greater significance of the ecosystem. A great deal of technical expertise goes into building a completely cloud-native set of services, and it’s not realistic for many companies to have that know-how in house. To bridge the gap, more companies are seeking partnerships with others as part of an ecosystem, with cloud being a key enabler of successful ecosystems as it offers IT integration of heterogenous systems. This, in turn, demands a high level of commercial awareness in terms of working with providers.
At present, even in cloud-native setups, there’s a lack of flexibility to choose components from different providers. Much of cloud-native development is based on “platform as a service.” In the future, organizations will need to be able to assemble what they need from an array of components. Cloud-native building blocks of composable architectures will enable businesses to scale and adapt their processes at speed.