How AI Adoption Can Be Benefited with Cognitive Cloud?

Today cognitive computing and cognitive services are a big growth area that has been valued at US$ 4.1 billion in 2019 and its market is predicted to grow at a CAGR of around 36 percent, according to a market report. A number of companies are using cognitive services to improve insights and user experience while increasing operational efficiencies through process optimization. Such technologies are set to be a significant competitive differentiator in today's era. Cognitive technologies will enable organizations to stay ahead of the competition when it comes to understanding and improving customer experience.

As it is known, cognitive is highly resource-intensive, requiring powerful servers, deep technical skill-sets, and often leading to a high degree of technical debt, which is why, for a long time, Cognitive was limited to large enterprises such as the Fortune 500s.

However, with the introduction of the cloud, this has been completely overturned. As noted by Medium, the cloud allows developers to build Cognitive models, test solutions, and integrate with existing systems without needing physical infrastructure. While there are still resource costs involved, enterprises can flexibly subscribe to cloud resources for cognitive development and downscale as and when necessary.

In a conventional arena, cognitive would only make sense for large enterprises from a purely ROI standpoint. They would commit sizeable time, effort, and investments in R&D, and could afford delays/uncertainties in value generation. Now, even small-to-mid-sized businesses can utilize the cognitive cloud to apply AI as part of their day-to-day IT ecosystem, rapidly generating value without the infrastructure of vendor dependencies.

Moreover, the cognitive cloud serves great benefits for AI adoption including optimize resource utilization, wider access to skill-sets, and accelerate projects. Enterprises no longer need to spend on cognitive-ready infrastructure. The cognitive cloud can be used as and when required and decommissioned when idle. Also, instead of hiring an in-house data scientist or AI modeling expert, enterprises can partner with cognitive cloud vendors at a flexible monthly rate. This is particularly useful for those facing sluggish digital transformation (traditional BFSI and pharmaceuticals, among others). Further, the overlong planning, investment, and set-up period are replaced by a ready-to-deploy solution. Some cloud vendors even offer customizable default AI models.

According to B2C, the path to building and operationalizing cognitive services is highly dependent on the company's starting point. Cloud-native cognitive services require a degree of digital maturity. For a company well used to leveraging cloud, and comfortable designing, building and deploying in a cloud-native environment, the transition to cognitive will necessarily be quicker. If an organization is still grappling with, say, automation or is fairly new to the DevOps approach, the possibilities inherent in cloud-based resources are still open to it. For example, Infostretch has a long track record of helping organizations accelerate digital, whether it's helping them transition from monolithic to microservices architectures, implement Agile DevOps, deploy intelligent automation or create a continuous innovation pipeline.

Priming one's product delivery environment for cloud-based cognitive services is one part of the equation. A robust, efficient test environment is also needed when it comes to deploying predictive analytics in real-time. Also, a highly automated system is important since a team relying on high levels of manual intervention generally will not have the bandwidth to take advantage of what cognitive services have to offer. Infostretch's own intelligent testing suite, for example, relies on bots and other AI technologies to optimize every aspect of an organization's testing lifecycle – improving test quality, speeding up the process and prioritizing actions that really need attention.