Cisco President Jeetu Patel claims AI can help established firms clear technical debt, potentially making them as agile as startups. This shift suggests a major change for the IT sector, where companies may need to adapt their service models as AI automates routine software maintenance and coding tasks.
What Happened
Cisco President Jeetu Patel recently highlighted how artificial intelligence (AI) is set to reshape the definition of legacy businesses. He argues that AI tools are now capable of helping large, established companies eliminate "technical debt." Technical debt is the long-term cost and complexity that builds up when companies rely on aging software code. By using AI to automate coding and system architecture, firms can rewrite and simplify their systems continuously. Patel shared an internal example where Cisco used AI to reduce a security product’s massive codebase of eight million lines to under 1.5 million in just a few weeks.
Why This Matters For Investors
For many years, large size was viewed as a hindrance for technology firms. Larger, older companies often struggled with slow, bureaucratic processes, while nimbler startups could innovate faster. Patel suggests that AI is dismantling this disadvantage. By automating the maintenance of complex systems, large companies can leverage their existing strengths—such as deep customer relationships and data networks—without being slowed down by their own aging software. If successful, this could allow large enterprises to compete with the speed of startups, potentially defending their market share against smaller, more agile competitors.
The Indian IT Services Context
This trend is particularly relevant for the Indian IT services sector, which is built on managing complex codebases for global clients. If AI becomes the standard for modernizing and maintaining legacy systems, it will likely change how IT firms operate. On the positive side, this could significantly improve operational efficiency and profit margins. On the other hand, it challenges the traditional "billable hours" model. If maintenance work becomes much faster and less labour-intensive, IT companies may face pressure to adjust their pricing models and focus more on higher-value innovation services rather than routine coding and support.
The Business Reality Check
While AI offers powerful tools for efficiency, it is not a complete fix. Patel acknowledged that software is only one part of the problem. Organizational issues, such as slow decision-making, rigid structures, and resistance to change, remain major hurdles. Technology alone does not make a company agile. Investors should be careful not to assume that AI adoption will instantly lead to higher profits. Companies will likely incur costs during the transition, and they must successfully navigate cultural shifts to truly benefit from these new technologies.
What Investors Should Track
The most important monitorable for investors will be how technology and IT companies communicate their AI adoption strategies in future earnings calls. Specifically, watch for details on whether AI-led modernization is actually improving profit margins or if it is creating pricing pressure. Additionally, look for management commentary on how they are re-training their workforce to move from manual coding to managing AI-led development. The ability of a company to balance these new efficiencies with stable revenue growth will determine its long-term success.