How the use of AI and ML will help companies stay ahead of the curve, CIO News, ET CIO

By Bikram Singh Bedi

AI is emerging as a multi-faceted, ubiquitous technology for businesses and users around the world. Technology providers are building platforms that help users harness the power of AI by meeting it wherever they are.

Data management and aggregation are the essential parts of any cloud infrastructure. Organizations can develop sophisticated artificial intelligence (AI) analytics and solutions to address different challenges, provided they have the relevant data at hand. Business intelligence is usually the first step in this process, as it helps companies better understand the underlying data before deploying sophisticated analytical tools.

Organizations that invest in technologies that help AI and ML achieve the greatest benefit for their operations as they recognize the importance of these technologies. AI and ML are having a major impact on how we conceive data, necessitating the development of best practices for these technologies. By 2025, Gartner expects that generative AI will account for 10% of all data generated, up from less than 1% today.

Understanding AI for a stronger business foundation

Technical expertise should not be an impediment to implementing AI—otherwise, use cases where AI can help will languish without modernization, and companies without well-developed AI practices risk falling behind their competitors.

It is crucial to offer state-of-the-art services for users of all types and up-to-date tools for experienced AI users. To meet the needs of the job and the technical skill of the user, some of this requires automating or abstracting parts of the ML workflow. Regardless of the perspective, AI is becoming a multi-faceted, ubiquitous technology for businesses and consumers around the world. As such, we believe technology providers should reflect this by creating platforms that allow users to unlock the potential of AI by connecting with them wherever they are.

AI readiness

Companies currently evaluating or piloting the implementation of AI and ML can take several steps to scale quickly. The first phase is to identify and prioritize projects based on complexity, business impact and threats using the minimum viable product strategy.

Most importantly, organizations need to engage their leaders by incorporating them into AI initiatives after giving them the appropriate AI and ML training as needed. AI initiatives must be aligned with the overall strategic goal of the organization, rather than implemented in isolation.

Organizations that have previously implemented or operationalized AI initiatives could create a plan to take full advantage of the technology. Developing an architecture and team structure that works at the convergence of design and data centers is critical. To achieve scalability, companies must also analyze and iterate their AI models during production.

Building Cloud Native Platforms (CNP)

Cloud-native platforms are technologies that enable companies to create new architectural applications that take advantage of the cloud. Because cloud-native technologies are all about performance and speed, cloud-native platforms provide solutions that help build a more effective and solid IT foundation through secure data integration and processing.

Businesses need to adopt CNPs to deploy operational capabilities everywhere. CNPs leverage the fundamental characteristics of cloud computing to offer developers of Internet-based technologies scalable and elastic IT-related functions “as a service”, resulting in a shorter time to value and lower costs.

Gartner predicts that by 2025, more than 95% of new digital endeavors will be built on cloud-native platforms, up from less than 40% in 2021.


AI is no longer just new territory. This combination of technology and human-in-the-loop expertise provides a true end-to-end AI data solution when companies want to deploy their models. The rate of technological advances that can automate and simplify the development and maintenance of AI systems has increased along with the need for AI. By combining the required expertise, judgment and technology, data of the highest quality could be achieved.

The author is Managing Director, Google Cloud India

Disclaimer: The views expressed are solely those of the author and does not necessarily endorse them. is not liable for damage caused to persons/organizations directly or indirectly.

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