The Old Playbook Loses Its Edge
For decades, the Indian IT services sector was built on a simple, powerful engine: the time and materials (T&M) model. Clients were billed for the number of hours engineers spent on a project. More people working for more hours meant more revenue. This
model, combined with a vast pool of skilled talent, turned companies like TCS, Infosys, and Wipro into global giants and made IT a cornerstone of the Indian economy. A company's strength was often measured by its headcount. However, the rise of powerful generative AI tools is rapidly eroding the logic of this effort-based system. When AI can help two engineers do the work that once took ten, billing for hours no longer makes sense for clients who expect to see AI-driven productivity gains reflected in their costs.
A New Deal: Paying for Results, Not Hours
In response, a new model is gaining traction: outcome-based pricing. Instead of paying for human effort, clients pay for specific, measurable business results. This marks a seismic shift from selling inputs (hours) to delivering outputs (value). Recent reports show a notable uptick in these deals. For instance, TCS has reportedly seen outcome-based contracts double in its business services segment since late 2023, now accounting for about 80% of deals there. Cognizant is also seeing a significant portion of its new business process outsourcing contracts signed under these models. This new arrangement transfers risk, as the IT firm's revenue is now directly tied to the success it delivers, but it also allows them to capture the value of their AI-powered efficiency as profit.
Rethinking the IT Career Ladder
This new business model has profound implications for career paths. The traditional pyramid structure, with a large base of entry-level engineers performing routine coding and testing, is becoming obsolete. As AI automates many of these basic tasks, companies have indicated that the need for large-scale hiring of fresh graduates will likely decline. The value is shifting from the ability to perform a task to the ability to define a problem, manage an AI system, and interpret its output. This means the ladder to success is being redrawn. Instead of a linear progression, careers will likely become more specialised, with high demand for roles like AI specialists, data scientists, prompt engineers, and AI-augmented business consultants who can bridge the gap between technology and business strategy.
The Urgent Call for New Skills
The transition isn't about AI replacing humans wholesale, but rather about creating a workforce where humans and AI work together. The most valuable employee is no longer just a coder, but someone who can leverage AI as a 'co-pilot' to achieve better results faster. This has triggered an urgent need for upskilling and reskilling across the industry. Companies are increasingly looking for candidates with hybrid skills—a combination of domain expertise in an area like finance or healthcare, coupled with AI and data literacy. While many firms have initiated training programs, challenges remain, including the high cost and uncertain returns on investment in reskilling. However, for employees, the message is clear: continuous learning and adapting to AI tools is no longer optional for staying relevant.
