Artificial Intelligence (AI) is being adopted on a massive scale by organizations of all sizes in almost every domain. Enormous numbers are doing the rounds. For instance, one study suggests that the worldwide market for AI will be over USD 1.3 trillion by 2030. However, it is not just mass adoption that is creating headlines, but the fact that AI is now becoming a core element of strategic decision-making. It’s also expanding its influence into the very fabric of ongoing work. For instance, Gartner estimates that by 2028, nearly 15% of day-to-day work decisions across enterprises will be taken by autonomous ever-improving AI agents in a technology trend now called Agentic AI. 

There is no doubt that AI will spearhead innovation across organizations for years to come. 

Or will it?

Are the foundations laid right?‍

Can a business get on the AI bandwagon and achieve success if it has the right intentions and sufficient investment? Theoretically, the answer is yes, but obviously, that hides more than it reveals.

This blog makes the case that the success of a solid AI strategy requires a powerful data engine. 

For businesses that wish to leap into the next dimension of growth using AI, the magic recipe lies not in using powerful AI tools and platforms. Rather, it demands an inherent data-driven transformation of their operational workflows, processes, and attitudes. Everything about the business should be meticulously linked to data points and must become explainable using data insights. While standards and formats used to represent activities as data points may differ across departments, it’s crucial to ensure that every outcome of every scenario be represented using data points.

It is this data that will serve as the key input for AI models to learn and understand the behavior of business processes, systems, and people. That’s why a powerful and efficient data infrastructure will always be at the heart of AI success.

Making enterprise data AI-ready

Let us take a closer look at some of the best practices that enterprises can follow to make their data ecosystem efficient and adaptable to power successful AI initiatives:

Follow a functional and flexible data architecture

Once departments and business functions reorient themselves to become data-driven, the next foundational step is to organize them. The need now is to establish a flexible data architecture that promotes faster information flows, improved discovery of insights, and higher capacity in computing capabilities. This architecture must also be scalable to meet the dynamic needs of AI models. The use of data lakes, warehouses, etc. as storage repositories for centralized management of data is an important element of the data architecture that powers AI readiness. Additionally, data pipelines and data catalogs that help in discovering the right data streams and transforming them into what’s needed for different AI models are also crucial aspects.

Increased automation focus – with availability

The data created in different business systems or insights obtained from different analytic nodes in the AI network should be easily made available for further decision-making or executing actionable tasks by AI models. This is about making large amounts of data available at speed and scale to enable AI. The entire lifecycle of data flows in an enterprise must be automated with speed in mind. Of course, assured operational availability across internal processing nodes as well as interactions with external systems via APIs and file transfer approaches is also critical. Intelligent operational approaches like MLOps will be beneficial in automating the data movement lifecycle within enterprise systems and guaranteeing data availability for AI models.

Assure high data quality

The success of AI models depends largely on the quality of data used to train the neural networks. As AI gets embedded into ongoing workflows, assuring data quality becomes a continuous effort. Data quality depends on several factors depending on the scenario where it was created. Some of the major aspects in the context of data quality include the degree of accuracy, the relevance of the data in a particular context, the assurance of being error-free, the consistency, coherent mapping of timelines involved in data acquisition, etc. The use of accurate labeling and structuring according to the most relevant contexts will help AI models learn faster and deliver better outcomes. When assured of higher data quality, the corresponding AI models will deliver higher efficiency in their predictions and outcomes.

Establish data governance

It’s also important to build a data governance model that adds a layer of control on who has access to what kind of data and when and how they can use it. When building AI capabilities, users from different departments will need to use data in different ways. However, not all business users will be technical experts with a deep understanding of what it takes to manage complex data streams. This is where governance plays a key role. With the right systems, permissioned use of business data is allowed in a structured and transparent manner. This allows innovative capabilities to be crafted and delivered continuously without disrupting the rest of the digital infrastructure. This is also extremely important for ensuring secure data management. Data governance serves as a critical security pillar for the enterprise in not just AI initiatives but in all digital avenues where data is involved.

Powering the AI engine with the right data

Gaining a competitive edge with AI capabilities is undoubtedly one of the most essential initiatives today. However, building the right data foundation for AI success is an essential prerequisite before starting on that journey.

A dedicated and expert technology partner like Parkar can help CIOs and CEOs become AI-ready by laying just that foundation. Get in touch with us to take the first step to AI success.

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