Enterprise AI Faces Scalability Challenge

Artificial intelligence has become a dominant technology conversation in recent years. However, behind the headlines of increasingly capable models, lies a more significant challenge that many enterprises are only beginning to confront: building AI systems that are reliable, scalable, and genuinely useful inside complex organisations.

For Swaroop Borukar, an enterprise AI product leader based in Silicon Valley, this challenge has become the focus of her work. With over a decade of experience working at the intersection of cloud infrastructure, distributed systems, and product strategy, she has seen the evolution of enterprise technology firsthand. Her career has spanned from telecommunications engineering and network optimisation to leading AI platform initiatives supporting enterprise-scale software.

According to Borukar, the public discussion around AI often centres on what models can do, but the more important question is how organisations can deploy AI responsibly while creating measurable business outcomes. She believes that the real challenge now is building systems that enterprises can trust at scale. Success is not about deploying more models, but about orchestrating them intelligently, safely, and reliably.

This perspective has become increasingly relevant as organisations shift from experimenting with generative AI towards integrating it into everyday operations. A report by McKinsey found that 78% of organisations now report using AI in at least one business function, a substantial increase from previous years. However, many companies continue to struggle with governance, implementation, and demonstrating measurable return on investment.

Borukar argues that these challenges require a different style of leadership than traditional software development. Product management is evolving from feature delivery to outcome delivery, where customers care whether the technology solves meaningful business problems consistently.

Much of the modern enterprise AI depends on technology that remains invisible to end users. Borukar's work has centred on AI infrastructure, including systems responsible for allocating compute resources, orchestrating workloads, and ensuring that enterprise platforms remain resilient under growing demand. Her experience spans cloud infrastructure, distributed computing, and platform optimization, disciplines that have become increasingly important as AI workloads place unprecedented pressure on enterprise systems.

Research from Gartner predicts that by 2028, at least 15% of day-to-day work decisions will be made autonomously through agentic AI, compared with virtually none in 2024. This forecast highlights how organisations will need to build infrastructure that can support the growing demands of AI workloads.

In conclusion, while AI technology is advancing rapidly, the biggest challenge facing enterprises is building systems that are reliable, scalable, and genuinely useful. As organisations continue to integrate AI into their operations, they will need to focus on building infrastructure that can support the growing demands of AI workloads, and leadership that can deliver measurable business outcomes.

The significance of this challenge cannot be overstated, as it has the potential to impact the way organisations operate and make decisions. As AI continues to evolve, it is essential for enterprises to prioritize building systems that are trustworthy, scalable, and reliable, in order to unlock the full potential of this technology.

The future of enterprise AI depends on the ability of organisations to build systems that can support the growing demands of AI workloads. As the technology continues to advance, it is likely that we will see significant changes in the way organisations operate and make decisions.

Ultimately, the success of enterprise AI will depend on the ability of organisations to build systems that are reliable, scalable, and genuinely useful. This will require a focus on building infrastructure that can support the growing demands of AI workloads, and leadership that can deliver measurable business outcomes.