• Deal duration: Traditional services deals remain long-tenure, typically 3 years or 5 years, while enterprise transformations can run 12, 18 or 24 months. Smaller, rapid AI-innovation projects are shorter-cycle and still a smaller part of the business.
• AI outcomes: In AI-led transformation, teams work with customers on selected business outcomes and deliver transformations in cycles of 3 to 6 months. The commitment is to work toward an outcome, but pricing models remain traditional.
• Margin outlook: AI partnerships, M&A-related expenses, talent hired ahead of demand and the resulting bench weighed on margins; currency added only 10 basis points of tailwind. Management expects to recoup costs and potentially expand margins as investments taper and cost-efficiency measures take effect.
• Consultant costs: Fees to external consultants were ₹5,234 crore, or 7.1% of revenue, versus 6.4% in the previous quarter and 5.2% in the year-ago quarter. Management attributed the increase mainly to short-term subcontractor needs from location or skill-set mismatches, which it expects to normalize, while aiming to reduce subcontractor dependence.
• Hiring mix: Headcount increased by around 9,000 people in Q1, with another 4,000 to 5,000 added; management described this as planned hiring for needed skills and said the bench could become a margin lever. Of additions, about 80% were in new-age skills and about 30% in AI skills.