How using AI & ML will help businesses stay ahead of the curve

By Bikram Singh Bedi

AI is turning into a multi-faceted, pervasive technology for businesses and users across the world. Technology providers are building platforms that are helping users harness the power of AI by meeting them wherever they are.

Data management and aggregation are the essential components of every cloud infrastructure. Organizations may create sophisticated analytics and artificial intelligence (AI) solutions to address distinct challenges provided they have the relevant data at hand. Business intelligence is usually the first step in the process since it helps organizations understand underlying data better before using sophisticated analytics tools.

Organizations investing in technology that helps AI and ML achieve the greatest benefit for their operations as they realize the significance of these technologies. AI and ML are greatly affecting how we conceptualize data, necessitating the development of best practices for these technologies. By 2025, Gartner expects generative AI to account for 10% of all data produced, up from less than 1% today.

Understanding AI for a Stronger Business Foundation

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

It’s critical to offer cutting-edge services for users of all types and up-to-date tools for sophisticated AI practitioners. To fit the demands of the job and the user’s technical proficiency, some of this requires automating or abstracting portions of the ML workflow. Whatever the perspective, AI is becoming a multifaceted, omnipresent technology for organizations and consumers everywhere, thus we believe technology providers should reflect this by creating platforms that enable users to harness the potential of AI by connecting with them wherever they are.

AI readiness

Several steps may be taken by businesses who are currently evaluating or piloting the implementation of AI & ML to achieve scale quickly. The first phase is to identify and prioritize projects based on complexity, business effect, and hazards using the minimal viable product strategy.

Most significantly, businesses must include their business executives by integrating them into AI initiatives after giving them the appropriate AI & ML training, if required. AI initiatives have to be aligned with the company’s bigger strategic objective rather than implementing them in isolation.

Companies that have previously deployed or operationalized AI initiatives might create a plan to gain the full benefits of the technology. It is critical to develop an architecture and team structure that functions at the convergence of design and data centers. To achieve scalability, firms must also analyze and iterate their AI models while they are in production.

Building Cloud Native Platforms (CNP)

Cloud native platforms are technologies that enable businesses to create new architectural applications that take use of the cloud’s advantages. Cloud-native platforms provide solutions that help build a more effective and solid IT foundation through safe data integration and processing, since cloud-native technology is all about power and speed.

Enterprises must adopt CNPs to deploy operational skills everywhere. CNPs employ the fundamental features of cloud computing to offer scalable and elastic IT-related capabilities “as a service” to internet-based technology developers, resulting in a shorter time to value and lower costs.

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


AI is no longer only an uncharted territory. This combination of technology and human-in-the-loop expertise offers a true end-to-end AI data solution as firms want to deploy their models. The rate of technology advancements that can automate and simplify developing and maintaining AI systems has increased along with the need for AI. Thus, by combining the required expertise, judgment, and technology, the highest quality data could be achieved.

The author is Managing Director, Google Cloud India

Disclaimer: The views expressed are solely of the author and does not necessarily subscribe to it. shall not be responsible for any damage caused to any person/organization directly or indirectly.