Tech Mahindra Increases AI Investment In FY26; Working On Disruptive Tech To Reshape Enterprises Tech Mahindra is prioritizing spending on assurance, security, and measurable outcomes instead of merely expanding toolsets, says Kunal Purohit, president – Next Gen Services, Tech Mahindra

In FY26, Tech Mahindra is investing significantly in AI PoCs, solutions, accelerators, people training, technology ecosystem partnerships, and a customer advisory board to help advance its AI capability in the market. To achieve the goals, the tech giant has trained 80,000-plus employees in AI/GenAI with advanced certifications.

The company is working on disruptive technologies that are going to reshape enterprises in the next two– three years. Agentic AI is the most important driving force as the focus shifts from infrastructure to real-world applications. Value will be generated for those who effectively manage governance and scale. Tech Mahindra's Makers Lab is developing early-stage projects that claim to serve as blueprints for future mainstream innovations.

"We can expect an acceleration in cognitive operating models that integrate data and AI to achieve desired outcomes, supported by platform-based assurance and ModelOps. In sectors like telecom, modernization plus API monetization/networks of the future, will open adjacent revenue pools. In manufacturing, Industry 4.0 and digital engineering will compress cycles from chip-to-cloud. Our recent momentum suggests broad-based adoption across geographies and verticals. Continued ecosystem plays (hyperscalers, security, platform ISVs) will compound speed-to-value while keeping trust, compliance, and sustainability central to scale," said Kunal Purohit, president – Next Gen Services, Tech Mahindra, explaining the opportunities in different avenues.

The company is advancing TechM Orion, the next-generation Agentic AI platform, and VerifAI, a comprehensive AI validation and verification solution for enterprises. As part of production efforts, it has launched the Agentic AI Production Center and strengthened partnerships to reduce risks associated with scaling in customer environments.

"We are also expanding our AI skills training and leadership development initiatives, primarily focusing on helping clients transition from sandbox testing to production using an ROI-focused consulting approach. Currently, we have 300-plus enterprise-grade agents in use, prioritizing spending on assurance, security, and measurable outcomes instead of merely expanding toolsets," added Purohit.

"We also emphasize pragmatism and practical thought leadership, urging enterprises to weigh costs against benefits, embed assurance, and align GenAI to strategy rather than treating it as a siloed technology. Our Agentic AI platform helps manage the agentic journeys for customers in a cost-effective and cyber-secure manner," he said.

Purohit also emphasized the rise of a new leadership role within enterprises, the Chief AI Officer (CAIO), tasked with driving AI's practical, agile, and organization-wide adoption. This leader's mission is to turn AI from experimentation into execution, embedding intelligence into every business process and decision. "We are empowering the development of such roles through our suite of AI-powered offerings and industry-specific solutions."

Tech Mahindra has also established a dedicated AI governance board that plays a pivotal role in shaping how AI tools and technologies are deployed across the organization. The board ensures that employees have access to the right AI platforms and resources to enhance efficiency and productivity in their roles. "It focuses on responsible AI practices, security, and compliance, driving informed decisions that balance innovation with accountability. This governance framework bridges the gap between skilled talent and technological enablement," he explained.

There is also growing momentum across industries in adopting advanced AI-driven innovations. Edge AI and robotics use cases are transforming efficiency and precision at scale in manufacturing and other asset-intensive sectors.

According to Purohit, enterprises are increasingly developing specialized, domain-specific language models (SLMs) that capture deeper contextual intelligence to drive superior business outcomes. At the same time, organizations are investing in robust, Cloud-defined infrastructures to support scaled AI operations. Post-Quantum Cryptography (PQC) is also rapidly gaining traction in financial services and telecom for future-ready security.