Purpose of HCLTech’s AI answer engine
HCLTech has been gradually embedding agentic and generative‑AI functionality into its offerings. In July 2025 the company added an AI answer engine to its public website, allowing visitors to ask technology‑related questions (e.g., “What services does HCLTech offer in cloud migration?”) and receive immediate answers.
Based on HCLTech’s own publications, their broader Agentic AI strategy aims to create AI systems that operate with autonomy, context‑awareness and adaptability. A July 2025 article notes that agentic AI enables systems to interpret user intent, respond contextually and make autonomous decisions, keeping humans at the center of the processhcltech.com. HCLTech positions its AI agents as tools that help organizations:
- Accelerate time‑to‑value by making faster, context‑aware decisions
- Boost productivity by automating nuanced, cross‑functional tasks.
- Enhance decision quality through continuous learning from real‑time data.
- Maintain trust and compliance by building governance‑ready, industry‑tailored solutions.
The company also links agentic AI to the broader concept of Total Experience (TX)—a unified approach that interweaves customer, employee, user and operational experiences. HCLTech claims that leaders in total‑experience outperform peers and that aligning AI initiatives with TX goals helps businesses proactively address pain points, scale intelligent solutions without sacrificing personalization and build resilient, empathetic enterprises.
HCLTech’s AI answer engine therefore aims to:
- Provide instant, context‑aware answers to users’ questions about HCLTech’s services and solutions, reducing the friction in navigating large portfolios.
- Demonstrate HCLTech’s agentic AI capabilities by showing how generative AI can serve as a front‑door experience for customers and prospects.
- Collect data on user intent to refine marketing and service recommendations, reinforcing the company’s total‑experience strategy.
The answer engine is part of a wider suite of agentic solutions. For example, HCLTech Insight, an agentic AI solution built on Google Cloud, uses the Cortex Framework, Manufacturing Data Engine and Vertex AI to analyze real‑time manufacturing data, identify product defects and provide AI‑powered virtual assistance. It helps manufacturers boost efficiency and quality across industrieshcltech.com. Another strategic initiative is HCLTech’s multi‑year partnership with OpenAI; the company plans to embed OpenAI’s models across its platforms (AI Force, AI Foundry and other accelerators) to modernize business processes and enhance customer and employee experienceshcltech.com. These initiatives show that HCLTech sees agentic AI not as a one‑off feature but as a key platform capability for delivering tailored, autonomous solutions.
Lessons for other companies
1. Start with a clear purpose and domain context
HCLTech’s AI agents are designed for specific goals—answering questions about the company’s offerings, providing manufacturing analytics or acting as para‑legal assistants. This demonstrates that domain‑contextualized agents deliver value more quickly than generic chatbots. Other companies should identify high‑impact, repeatable tasks (e.g., customer queries, technical support, anomaly detection) and build AI agents that are trained on domain‑specific data and workflows.
2. Integrate AI into the total experience
HCLTech stresses that agentic AI should elevate experiences across customer, employee and operational touchpointshcltech.com. Rather than deploying AI in isolation, firms should align AI initiatives with customer‑experience and employee‑experience strategies. For example, an AI answer engine on a website should not only answer questions but also feed insights back into marketing, sales and support teams to improve interactions.
3. Build in layers: core automation → generative augmentation → industry‑specific agents
HCLTech’s three‑tier AI adoption framework starts with core AI (automating repetitive tasks), progresses to advanced AI (generative and context‑aware experiences) and culminates in industry‑specific agents that evolve and collaborate with humanshcltech.com. This staged approach allows organizations to establish data infrastructure and governance in early stages before advancing to fully autonomous agents. Other companies can follow a similar maturity model:
- Core automation: automate routine workflows (ticket routing, password resets) to build data pipelines and trust.
- Generative augmentation: add generative models and real‑time analytics for empathetic interactions, recommendations and sentiment analysishcltech.com.
- Industry‑specific agents: develop specialized agents (e.g., HR co‑pilots, procurement assistants) that learn continuously, adapt to policies and collaborate with humanshcltech.com.
4. Prioritize governance, security and human oversight
HCLTech highlights the need to maintain trust and compliance in agentic AIhcltech.com. AI agents can “suck all your data out,” as HCLTech’s head of EMEA warned, so strong governance is essential. Enterprises should enforce data‑handling rules, implement human‑in‑the‑loop approval for critical decisions, and monitor AI outputs for bias and accuracy. Rolling out ChatGPT Enterprise and OpenAI APIs internally under controlled conditionshcltech.com illustrates how HCLTech provides employees with generative AI while maintaining security.
5. Iterate Through Pilot Projects And Measure Roi
In a discussion about AI adoption, HCLTech noted that starting with small pilots (e.g., a para‑legal agent for a law firm) and gradually scaling helps manage change and avoid resistancediginomica.com. Companies should evaluate AI projects based on clear metrics—time saved, error reduction, customer satisfaction—and reinvest efficiency gains into further innovations. Transparent communication about cost savings and productivity improvements builds trust with stakeholdersdiginomica.com.
6. Leverage Partnerships And Ecosystems
HCLTech’s collaborations with Google Cloud and OpenAI demonstrate the benefits of combining internal expertise with external platforms. Partnering with AI providers can give companies access to pre‑trained models, scalable infrastructure and specialized tools (e.g., Cortex Framework for manufacturing analyticshcltech.com). Companies across verticals should evaluate whether to build in‑house capabilities or leverage partnerships to accelerate AI adoption.
Adapting AI for Market and Customer Engagement
To adapt AI effectively, firms can follow these steps:
- Audit user interactions. Map out where customers and employees seek information, encounter bottlenecks or require assistance. Identify high‑volume or high‑impact touchpoints that could benefit from AI augmentation.
- Choose the right agent type. For informational use cases (like HCLTech’s answer engine), deploy question‑answering agents trained on curated knowledge bases. For operational tasks (e.g., manufacturing defect detection), use data‑analysis agents that integrate with sensors and enterprise systemshcltech.com. For service functions (HR, procurement), design co‑pilots that can handle structured processes and hand over complex cases to humanshcltech.com.
- Prepare data and governance. Establish data pipelines, metadata catalogs and access controls before training AI models. Enforce data‑quality checks and security protocols to ensure agents learn from clean, compliant data.
- Prototype and test. Develop minimum‑viable agents and run them in controlled settings. Gather feedback from users to refine the model’s tone, accuracy and integration with workflows. A/B test different deployment strategies to assess user acceptance and performance.
- Scale responsibly. After successful pilots, expand the agent to more functions or customer segments. Monitor outcomes continuously and implement human‑oversight triggers for tasks with legal or ethical implications. Use metrics like response time, user satisfaction, conversion rates and cost savings to demonstrate ROI and guide further investments.
- Continuously improve. Incorporate new capabilities—such as multimodal inputs, agent collaboration and learning loops—to keep the agent relevant. Align AI initiatives with evolving customer expectations and total‑experience strategieshcltech.com.
By learning from HCLTech’s approach—focusing on autonomous, context‑aware agents that enhance experiences, integrating AI across the organization and partnering with leading AI providers—companies in different industries can deploy AI agents that genuinely serve customers and drive business value.
Competitor Activities in Agentic AI
While HCLTech is positioning itself as a leader in agentic AI, other technology and consulting firms are also rapidly advancing in this area. The landscape is evolving quickly, with competitors introducing their own agent platforms, interoperability frameworks and governance tools. Key initiatives include:
Infosys. In May 2025 Infosys announced the launch of over 200 enterprise AI agents using Google Cloud’s agentic AI framework and its own Topaz platform. The agents target sectors including healthcare, finance, retail, telecom, manufacturing and agriculture, and offer capabilities such as data extraction, multimodal input handling, secure communications, privacy controls and autonomous decision‑makinginfosys.com. Examples include predictive network‑capacity planning, accounts payable/receivable processing and manufacturing forecastinginfosys.com. Infosys leaders said the initiative would improve human–AI collaboration and accelerate decision‑making at scaleinfosys.com.
Cognizant. Cognizant’s Neuro AI Multi‑Agent Accelerator provides reference networks of collaborating agents for sales, marketing, finance, investor relations, supply‑chain management, customer service and insurance underwritingcognizant.com. The accelerator’s no‑code framework allows enterprises to prototype, customize and scale agent networks quickly; Cognizant highlights benefits like breaking organizational silos, scaling across geographies and adding redundancy for resiliencecognizant.comcognizant.com. In July 2025 the company introduced Agent Foundry, a platform‑agnostic framework that supplies domain‑specific small language models, industrialized agent templates and a library of proprietary and third‑party agents, and that supports the full agent lifecycle from design to scale with governance and observabilitynews.cognizant.com.
T‑Systems. A T‑Systems expert blog argues that the real breakthrough in agentic AI comes from automating complex workflows across multiple steps and systems rather than simply generating content. It envisions AI copilots that become proactive teammates—monitoring dashboards, triggering workflows and delivering insights in real time—eliminating delays through parallel execution and enabling real‑time adaptability, personalization at scale and resilient operationst-systems.com. The company says such capabilities require re‑engineering processes and creating an “agentic AI mesh architecture”t-systems.com.
Tech Mahindra. Tech Mahindra and mimik launched an Agentic AI Production Center that acts as a hub for designing, developing, deploying, scaling and commercializing agentic AI systems. Hosted in Tech Mahindra labs, the center trains developers and enterprises to build agent‑native workflows that mimic real‑world operations and execute across devices. It combines mimik’s device‑first execution fabric with Tech Mahindra’s engineering to deliver real‑time intelligence across smartphones, cameras, drones and robots; the agents can operate offline and use cloud resources only when neededtechmahindra.comtechmahindra.com.
Wipro. Wipro offers sovereign AI services built on its WeGA Studio and NVIDIA AI Enterprise, enabling governments and enterprises to develop and deploy agentic AI solutions on their own infrastructure. The offering includes customized large language models for local languages, pre‑built AI accelerators and strict privacy and security compliance measureswipro.comwipro.com. Executives emphasize the need for ethical AI and data sovereignty; Wipro argues that agentic AI should be deployed quickly yet responsibly, balancing innovation with governancewipro.com. Another Wipro perspective notes that agentic AI unlocks autonomous decision‑making in industries like finance, insurance, energy and manufacturing, but stresses that robust governance, human‑in‑the‑loop oversight and “maker‑checker” frameworks are essentialwipro.com.
Tata Consultancy Services (TCS). TCS has embedded agentic AI in its MasterCraft modernisation suite, using generative and agentic techniques to mine business logic from legacy applications and automate code migration. The company reports that this approach reduces modernization costs by over 70 % and accelerates delivery twofoldtcs.com. TCS says it has developed more than 150 specialized agentic solutions across financial services, accounting and supply‑chain managementtcs.comtcs.com. It is also contributing to the open Agent2Agent protocol—a collaboration with Google Cloud that allows AI agents to communicate, collaborate and coordinate across manufacturing and other industriestcs.comtcs.com—and has partnered with Vianai Systems to provide conversational decision‑intelligence for finance, supply‑chain and sales executivestcs.com.
Accenture. Accenture’s AI Refinery distiller framework offers a comprehensive toolkit for building, customizing and governing multi‑agent systems. The framework includes agent memory management, multi‑agent collaboration, workflow orchestration, model customization, evaluation, governance, observability and interoperability, along with SDKs for building physical AI agents that process real‑world signalsnewsroom.accenture.comnewsroom.accenture.com. In March 2025 Accenture released an AI agent builder that lets business users construct and customize agents without writing code and announced plans to deliver more than 50 industry‑specific agent solutions—targeting telecom, finance, insurance and other sectors—with a goal of 100 solutions by year‑endnewsroom.accenture.comnewsroom.accenture.com. The firm also introduced Trusted Agent Huddle, a solution that enables secure agent‑to‑agent interoperability across platforms like Adobe, AWS, Google Cloud and Microsoft, with a trust‑scoring algorithm to evaluate agent performancenewsroom.accenture.comnewsroom.accenture.com.
Capgemini. Capgemini has expanded its partnership with Google Cloud to build industry‑specific agentic AI solutions that transform customer experience by improving call routing, personalizing retail interactions, anticipating service needs and detecting fraud. These solutions leverage Google’s Agentspace and the Agent2Agent protocol to ensure agents from different platforms can communicate while integrating with clients’ existing infrastructurecapgemini.comcapgemini.comcapgemini.com. In collaboration with NVIDIA, Capgemini offers a dedicated agentic gallery, integration accelerators and governance frameworks to help companies develop and deploy over 100 bespoke AI agents across automotive, consumer, finance, life‑sciences, manufacturing, public sector, retail, supply chain and telecom industriescapgemini.comcapgemini.comcapgemini.comcapgemini.com. Capgemini’s research highlights the importance of reliability, risk management and multi‑agent system design when deploying autonomous AIcapgemini.comcapgemini.com.
IBM. At IBM Think 2025, company leaders described a strategy focused on agents that orchestrate across legacy and modern systems, enabling them to execute tasks rather than merely assist. IBM blends agentic functionality into existing workflows and allows customers to scale into full orchestration when needed; it has released prebuilt agents for HR, sales and procurement, with more planned for customer care and financesiliconangle.com. IBM emphasises an open, hybrid architecture for multi‑agent orchestration and sees agentic AI as a means to unlock unstructured enterprise data via its watsonx platformsiliconangle.com. To expand its footprint, IBM has partnered with Oracle, AWS, Salesforce and Lumen to integrate watsonx Orchestrate and its Granite models into those platforms. The collaborations enable agents to automate HR functions on Oracle Cloud Infrastructure, access real‑time context via Amazon Q on AWS, integrate mainframe data through Salesforce’s Agentforce and deploy real‑time edge intelligence via Lumen’s edge computingchannele2e.comchannele2e.comchannele2e.com. IBM has also introduced a governance suite that merges watsonx.governance with Guardium AI Security, providing tools for policy enforcement, automated red‑teaming, detection of unsanctioned agents and compliance management across international regulationsrcpmag.com.
Mindtree / LTIMindtree. In March 2025 LTIMindtree (formerly Mindtree) expanded its strategic partnership with Google Cloud to develop industry‑specific solutions using agentic AI. The company plans to use Google’s Gemini models and other AI services to co‑create proof‑of‑concepts and pilots, build a “green corridor” for solution development and provide early access to new capabilities for clients in banking, manufacturing, hi‑tech media, retail and consumer‑goods sectorsltimindtree.comltimindtree.com. LTIMindtree also serves as a launch partner for ServiceNow’s agentic AI program; at Knowledge25 it showcased Agentic Central, an enterprise solution that deploys intelligent agents on the ServiceNow platform to automate workflows, provide support and streamline communications, alongside voice AI, AI Smart Underwriter and assessment toolsltimindtree.comltimindtree.com. For the insurance sector, LTIMindtree’s Euclid platform offers an enterprise‑grade agentic AI ecosystem: it orchestrates autonomous AI agents across the value chain, provides modules for composing new agents (Euclid Agents), extracting domain knowledge (Euclid Knowledge), building a GenAI/agentic foundation with security and governance (Euclid Foundation), creating data pipelines (Euclid Data) and managing model lifecycles (Euclid Models). The platform is designed to deliver actionable intelligence and supports scalable deployment using the Nvidia enterprise AI ecosystemltimindtree.comltimindtree.comltimindtree.comltimindtree.com.
Together, these initiatives show that HCLTech’s competitors are not standing still. The agentic AI market is rapidly diversifying, with firms emphasizing interoperability, domain‑specific agents, cross‑cloud deployment, edge intelligence and robust governance. For enterprises considering agentic AI, understanding this competitive landscape can help identify best‑of‑breed partners and benchmark the maturity of their own agent initiatives.
