Chief executives are increasingly demanding that their technology investments, including data and AI, work harder and deliver more value to their organizations. Generative AI offers additional tools to achieve this, but it adds complexity to the challenge. CIOs must ensure their data infrastructure is robust enough to cope with the enormous data processing demands and governance challenges posed by these advances. Technology leaders see this challenge as an opportunity for AI to deliver considerable growth to their organizations, both in terms of top and bottom lines. While a large number of business leaders have stated in public that it is especially important for AI projects to help reduce costs, they also say that it is important that these projects enable new revenue generation. Gartner forecasts worldwide IT spending to grow by 4.3% in 2023 and 8.8% in 2024 with most of that growth concentrated in the software category that includes spending on data and AI. AI-driven efficiency gains promise business growth, with 81% expecting a gain greater than 25% and 33% believing it could exceed 50%. CDOs and CTOs are echoing: “If we can automate our core processes with the help of self-learning algorithms, we’ll be able to move much faster and do more with the same amount of people.” and “Ultimately for us it will mean automation at scale and at speed.”

Organizations are increasingly prioritizing AI projects due to economic uncertainty and the increasing popularity of generative AI. Technology leaders are focusing on longer-range projects that will have significant impact on the company, rather than pursuing short-term projects. This is due to the rapid pace of use cases and proofs of concept coming at businesses, making it crucial to apply existing metrics and frameworks rather than creating new ones. The key is to ensure that the right projects are prioritized, considering the expected business impact, complexity, and cost of scaling.

Data infrastructure and AI systems are becoming increasingly intertwined due to the enormous demands placed on data collection, processing, storage, and analysis. As financial services companies like Razorpay grow, their data infrastructure needs to be modernized to accommodate the growing volume of payments and the need for efficient storage. Advances in AI capabilities, such as generative AI, have increased the urgency to modernize legacy data architectures. Generative AI and the LLMs that support it will multiply workload demands on data systems and make tasks more complex. The implications of generative AI for data architecture include feeding unstructured data into models, storing long-term data in ways conducive to AI consumption, and putting adequate security around models. Organizations supporting LLMs need a flexible, scalable, and efficient data infrastructure. Many have claimed success with the adoption of Lakehouse architecture, combining features of data warehouse and data lake architecture. This architecture helps scale responsibly, with a good balance of cost versus performance. As one data leader observed “I can now organize my law enforcement data, I can organize my airline checkpoint data, I can organize my rail data, and I can organize my inspection data. And I can at the same time make correlations and glean understandings from all of that data, separate and together.”

Data silos are a significant challenge for data and technology executives, as they result from the disparate approaches taken by different parts of organizations to store and protect data. The proliferation of data, analytics, and AI systems has added complexity, resulting in a myriad of platforms, vast amounts of data duplication, and often separate governance models. Most organizations employ fewer than 10 data and AI systems, but the proliferation is most extensive in the largest ones. To simplify, organizations aim to consolidate the number of platforms they use and seamlessly connect data across the enterprise. Companies like Starbucks are centralizing data by building cloud-centric, domain-specific data hubs, while GM's data and analytics team is focusing on reusable technologies to simplify infrastructure and avoid duplication. Additionally, organizations need space to innovate, which can be achieved by having functions that manage data and those that involve greenfield exploration.

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