Moving AI Into the Core of Enterprise Operations

HCLTech is a global technology company that delivers industry-leading capabilities centered around AI, digital, engineering, cloud and software, powered by a broad portfolio of technology services and products across major verticals.

Q: In a previous conversation, HCLTech noted that it had doubled its presence in Mexico over the past two years and was seeking to capture a larger share of a market worth nearly US$30 billion. How has this objective evolved since then?

A: Mexico remains a cornerstone delivery hub and a high-growth domestic market for HCLTech, driven by sustained nearshore demand from the United States and a vibrant ecosystem of global Mexican enterprise headquarters. Market expectations have matured rapidly toward AI-led business transformation and operational efficiency. We continue to expand our local capabilities across priority sectors, modernizing manufacturing facilities with industrial AI for global leaders in steel manufacturing, deploying core Enterprise Resource Planning (ERP) platforms for large retail majors, executing enterprise workplace transformations for automotive OEMs, automotive part manufacturers, food, consumer goods and confectionery manufacturers and delivering AI-driven intelligent operations for a leading cement manufacturer. 

Q: Where have you seen the greatest acceleration in demand, and what types of projects are moving from transformation initiatives to larger-scale operations? 

A: The greatest acceleration in demand has occurred in manufacturing, retail, and financial services, where enterprise clients are moving beyond basic workplace productivity toward complex operational process optimization. We segment these initiatives into three operational tiers: 60% focus on foundational operational efficiency through task automation and baseline performance tracking, 30% represent large-scale process transformations requiring dedicated capital expenditure (CAPEX) and structured outcome measurement similar to core ERP deployments, and 10% target high-impact, transformative models. Projects across the first two tiers are rapidly transitioning from exploratory pilots into scalable operations.

Q: What has changed in Mexican clients’ expectations, and what metrics are they using to demonstrate that a technology initiative is actually generating ROI? 

A: Executive expectations among Mexican clients have evolved from top-down mandates to demanding clear, quantifiable business outcomes. Boardrooms no longer measure digital maturity by employee access to GenAI tools, but by concrete operational metrics such as cost reduction, throughput speed and direct margin impact. To establish clear return on investment (ROI), we implement baseline performance metrics including process cycle time reductions among other key performance indicators (KPIs).

Q: What results have clients observed when using your solutions, and how prepared is Mexico’s infrastructure to support this next phase? 

A: While OT and IT leadership functions often remain distinct within industrial organizations, the seamless integration of data across manufacturing, supply chain, and corporate divisions has unlocked significant operational agility. Mexican manufacturing clients are achieving accelerated product development cycles, superior raw material planning and enhanced supply chain predictability. Furthermore, this unified data architecture allows enterprises to monetize operational insights and establish new data-driven revenue streams, positioning Mexico's industrial footprint to capitalize on expanding nearshore manufacturing demand.

Mexico's infrastructure is progressing well to support this next phase of transformation. Telecommunications providers and other global enterprises are continuing to invest in digital infrastructure to keep pace with growing demand for cloud, data and AI-driven operations. Looking ahead, the next phase will require increased investment in data centers, high-performance computing capacity, and scalable cloud infrastructure to support the growing adoption of AI across industries and global markets.

Q: What specific case study showcases how your platform directly solved a critical bottleneck for a customer, and what were the quantifiable impacts on their operational efficiency or revenue? 

A: We partnered with one of the world's largest port terminal operators with major operations in Mexico to deploy our Vision X computer vision platform. By integrating advanced vision AI into existing camera infrastructure across high-risk port environments, the system autonomously detects operational hazards and safety risks during heavy cargo handling. This automated monitoring system has significantly improved workplace safety, minimized critical risk factors for port personnel, and enhanced the operational efficiency of safety monitoring teams through real-time hazard prevention.

Q: HCLTech’s Telecom Pulse Survey Report reveals that 60% of telecommunications leaders believe that AI will be a revenue driver, but only 25% believe their organization is prepared to operationalize it at scale. What explains this gap between ambition and execution capacity? 

A: This execution gap exists because strategic ambition is an executive decision, whereas scaling AI remains an architecture, data and talent challenge. Most operators attempt to layer AI capabilities over decades of legacy Operations Support Systems (OSS), Business Support Systems (BSS), and fragmented data repositories, enabling isolated pilots but preventing broad scalability. 

In Latin America, while adoption is high, with 85% of regional operators running live AI use cases compared to 66% globally, deployments remain heavily concentrated in customer service and internal efficiency. This creates a monetization gap as carriers work to transition AI into core network operations.

Q: The study also notes that nearly 70% of leaders believe connectivity services are becoming increasingly commoditized. What does this mean for operators in Mexico, where connectivity remains the core of the business? 

A: Standard connectivity has become a baseline commodity that offers diminishing returns when competing strictly on price or network footprint. For Mexican operators, long-term revenue growth depends on monetizing value-added digital services delivered over that core connectivity layer. Carriers must leverage their infrastructure to deliver enterprise Internet of Things (IoT) frameworks, cloud gaming bundles, network Application Programming Interfaces (APIs), and edge computing solutions tailored to support the expanding industrial and nearshoring sectors.

Q: Nearly 80% of participants launched fewer than five new digital products or services over the past year, indicating that the pace of innovation remains limited. What is holding back time-to-market and operators’ ability to turn AI and data into marketable products? 

A: Commercial time-to-market is constrained by legacy infrastructure, complex monetization pathways, and rigid legacy operating models that struggle to keep pace with soaring traffic demands and rising capital costs. While overall revenue in the Mexican telecommunications market grows at about 3% annually, operators face margin compression unless they modernize their architectures. Accelerating digital product releases requires shifting toward platform-led operating models, including B2B2X frameworks and edge AI capabilities, to convert raw cloud and data assets into commercialized enterprise services.

Q: The report identifies legacy infrastructure, cloud-native modernization, and a lack of talent as persistent obstacles to scaling AI. Which of these factors represents the biggest bottleneck for operators evolving toward AI-native models? 

A: In Mexico, the primary bottleneck is legacy system infrastructure coupled with a scarcity of specialized talent required to modernize it. Enterprise AI cannot scale on fragmented data foundations. Telecommunications operators must execute a phased investment strategy: first, modernizing their core data and cloud-native architecture; second, integrating AI directly into daily network operations; and third, building specialized local capability in Machine Learning Operations (MLOps), AI governance, cybersecurity and autonomous agent orchestration.

Q: What capabilities is HCLTech developing to prepare its Mexican clients for the transition toward autonomous AI and AI-native operations, and which professional profiles will be hardest to find in the region? 

A: Specialized AI profiles are in short supply globally, requiring a strategic shift from hiring traditional platform specialists to cultivating AI-native talent. The market requires four key profiles: domain-specialized AI superusers with deep industry expertise, platform engineers, domain-aligned integration architects and core model builders. HCLTech is preparing its Mexican clients, and global clients, by investing heavily in early-career AI-native talent and scaling AI-driven upskilling with the current workforce. This strategy also requires pairing employees’ natural AI fluency with targeted industry domain training to manage autonomous enterprise operations. At HCLTech, there is a priority on how AI integration bridges the gap between pilots and productivity through continuous workforce training.

Q: What role will Mexico play within this regional strategy, and what new capabilities does HCLTech consider necessary to develop locally? 

A: Mexico serves as a central pillar of our regional strategy, serving both as an ideal nearshore hub for North America and as a high-growth domestic market. Proximity to U.S. client operations, competitive talent, and seamless travel logistics reinforce Mexico's competitive advantage over alternative global locations. We will continue to expand our local capabilities in edge computing, industrial AI, and cloud-native architecture to support both internal client transformations within Mexico and broader enterprise demand across the Americas.