Tech Mahindra

Tech Mahindra, part of the Mahindra Group and India's fifth-largest IT services company with approximately $5.8 billion in revenue (FY2022) and over 150,000 employees, had built a significant engineering services practice through its acquisition of Mahindra Satyam (2009), LCC (2014), and multiple smaller engineering firms. The engineering R&D services division — providing product development, embedded systems, and 5G/network engineering services primarily to telecom and manufacturing clients — faced increasing pressure on R&D output per dollar. Clients demanded faster time-to-market for product development cycles while simultaneously pushing for lower engineering service rates. Engineering project margins were compressing as wage inflation in India's engineering talent market (8-12% annually) outpaced rate increases. The company needed to improve R&D productivity — more engineering output per dollar spent — without simply adding more engineers.

Between FY2022 and FY2024, Tech Mahindra deployed a systematic AI-driven R&D productivity improvement program:

• TechM amplifAI0->∞ platform: Launched an enterprise-wide AI platform that embedded AI tools into every stage of the engineering services delivery cycle — requirements analysis, design, coding, testing, and deployment. The platform provided AI-assisted code generation, automated test case creation, and intelligent defect prediction.
• Test automation acceleration: Deployed AI-powered test automation frameworks that reduced manual testing effort by 40-60% on engineering projects. Automated regression testing, performance testing, and security scanning replaced labor-intensive manual test cycles, improving both speed and quality.
• Design reuse repository: Built an AI-searchable repository of engineering design assets, code components, and solution patterns accumulated from thousands of prior projects. Engineers could search for and reuse proven components rather than building from scratch, reducing development effort on new projects by an estimated 15-25%.
• 5G network engineering automation: For the telecom vertical (Tech Mahindra's core market), developed proprietary tools for automated network planning, configuration generation, and testing of 5G infrastructure, reducing the engineering labor content per network deployment.
• Engineering skills marketplace: Created an internal AI-driven skills marketplace that matched engineers to projects based on capability profiles, reducing bench time and improving the speed of project staffing from 2-3 weeks to days.
• Outcome-based engagement models: Shifted select R&D engagements from time-and-materials to outcome-based pricing, where Tech Mahindra was compensated for deliverables rather than hours. This created internal incentives to maximize productivity per engineer.

• R&D productivity improvement: AI tools improved engineering output per person-hour across the organization, with test automation alone reducing testing effort by 40-60% on projects where it was deployed.
• Margin stabilization: Despite wage inflation pressure, engineering services margins stabilized as productivity improvements offset rising labor costs.
• Time-to-market reduction: AI-assisted development and test automation reduced typical product development cycle times by 20-30%, improving Tech Mahindra's competitive positioning for time-sensitive engineering engagements.
• Revenue per employee: The combination of AI augmentation and outcome-based pricing improved effective revenue per engineer, even in a challenging demand environment.
• Telecom R&D leadership: Automated 5G network engineering capabilities strengthened Tech Mahindra's position as the leading telecom engineering services provider, supporting a large deal pipeline in network modernization.
• Timeframe: AI-driven productivity program scaled over FY2022-FY2024 (April 2021 to March 2024).

• Tech Mahindra's deep telecom domain expertise (heritage from British Telecom relationship) provided specific, high-value use cases for AI-driven engineering automation
• Mahindra Group's cross-company AI investment provided shared platforms and capabilities that accelerated Tech Mahindra's AI deployment
• India's large pool of engineering graduates enabled rapid scaling of AI-augmented engineering teams
• Client demand for faster product development cycles created pull for AI-driven productivity improvements, making adoption a competitive necessity rather than an internal efficiency initiative