Tech Mahindra has expanded its partnership with Google Cloud to strengthen its agentic artificial intelligence capabilities. The company plans to scale hands-on knowledge of Google Cloud’s Gemini Enterprise across more than 12,500 associates.

The announcement was made in India on October 6, 2026. It highlights Tech Mahindra’s plan to help companies move beyond AI experiments and build systems that can complete business tasks under human supervision as per Tech Mahindra.

What Gemini Enterprise brings

Gemini Enterprise is designed to help organisations use AI agents across their business operations. Unlike a basic chatbot that only answers questions, an AI agent can understand a goal, access approved information, use connected tools and complete several steps.

For example, a customer service agent could read a complaint, check the customer’s account, identify the problem and suggest or initiate the next action. A human employee can still review important decisions before anything is finalised.

The platform is intended to work with enterprise data, applications and business processes. This makes it useful for companies that manage large amounts of information across departments such as finance, IT, customer support, manufacturing and human resources.

Tech Mahindra’s role

Tech Mahindra will combine Google Cloud’s AI technology with its own industry experience, trained employees and implementation capabilities. The company is also developing reusable solutions and AI agents that can be made available through Google Cloud Marketplace.

This matters because many businesses do not need another isolated AI tool. They need help connecting AI with their existing systems, data and operating procedures. Tech Mahindra’s role will include designing, deploying and managing these solutions for customers.

The company’s portfolio covers sectors such as communications, healthcare, manufacturing, automotive, retail and financial services. Its experience across these industries can help create AI systems for specific business needs rather than offering the same general-purpose tool to everyone.

Benefits for businesses

The partnership could help companies reduce the time needed to build and deploy AI projects. Reusable solutions may also lower development effort and make it easier to expand successful pilots across different teams.

Another benefit is better control. Enterprise customers need to know which data an AI system can access, what actions it can perform and who approved a decision. Gemini Enterprise solutions can be combined with governance and security controls to support this oversight.

For a telecom company, an AI agent could help manage network service requests. In banking, it could support document review or customer queries. In manufacturing, it could assist with maintenance planning and supply chain updates.

The goal is not to remove people from every process. Instead, AI can handle repetitive work while employees focus on decisions that require judgment, accountability and industry knowledge.

Why the India announcement matters

Indian companies are adopting AI quickly, but many still face challenges such as outdated systems, scattered data and a shortage of skilled professionals. Tech Mahindra says its expanded readiness is aimed at helping enterprises address these problems and move toward production-ready AI.

The company’s earlier partnership with Google Cloud also focused on building industry-specific generative AI and agentic AI solutions.

Competition and challenges

Tech Mahindra and Google Cloud will compete with enterprise AI offerings from Microsoft, Amazon Web Services, Salesforce, IBM and other technology providers.

The main challenge will be turning impressive demonstrations into dependable systems that work safely at scale. Businesses will also need clean data, clear approval rules, trained employees and strong cybersecurity before deploying autonomous AI widely.

Conclusion with key takeaways

Tech Mahindra’s Gemini Enterprise initiative aims to help companies build and manage agentic AI solutions for real business operations.

The partnership combines Google Cloud’s AI platform with Tech Mahindra’s consulting, engineering and industry expertise.

The expected benefits include faster AI deployment, reusable solutions, improved productivity and stronger governance.

For Indian enterprises, the announcement signals a move toward AI systems that do more than generate content. They could increasingly plan, coordinate and complete business workflows while people remain responsible for important decisions.

ServiceNow has introduced its AI Workflow Factory and Autonomous Engineer solutions in India. The announcement was made at the company’s World Forum Mumbai event on October 6, 2026.

The new tools are designed for large organizations that want to use artificial intelligence across several departments without losing control over security, approvals and business rules. ServiceNow says the solutions can help companies move from separate AI experiments to connected workflows that deliver measurable results as per ServiceNow Newsroom.

What is AI Workflow Factory

AI Workflow Factory is built around a continuous improvement system. It first studies how work is being done, identifies delays and then helps create better workflows.

The system connects three important areas. Process mining studies business activity and finds where time or money is being lost. Autonomous Engineer and Build Agent help design and test new workflow changes. App Engine then runs those workflows across the organization.

For example, a bank may want to reduce the number of customer complaints that need human support. The platform can identify common issues, suggest automated responses, build the required workflow and monitor whether the results improve.

This approach is different from launching a single chatbot or automation tool. The aim is to keep finding new areas where AI can improve operations.

What Autonomous Engineer does

Autonomous Engineer is designed to help developers and implementation teams build applications and workflow changes with less manual work.

A team can provide business requirements, and the system can turn them into a structured plan with work items and acceptance criteria. Once approved by the user, it can work on several tasks in the background, including building and testing.

The tool can also support unattended coding for planning, implementation and testing. However, important business decisions remain with people. ServiceNow says developers continue to control critical approvals while the AI handles more routine execution as per ServiceNow Community.

Benefits for Indian businesses

Indian banks, telecom operators, insurance companies and large technology firms manage complex systems built over many years. These systems often do not communicate smoothly with one another, which can slow down AI adoption.

ServiceNow’s new platform could help companies connect existing systems and automate repetitive work without replacing their entire technology stack.

The expected benefits include faster application development, fewer manual steps, better tracking of AI activity and quicker deployment of workflow improvements. In a customer service department, for instance, AI could classify requests, find relevant information and route the case to the right team.

ServiceNow also says its AI Control Tower will govern the workflows, decisions and actions handled by AI. This gives managers a central view of what AI tools are doing and helps organizations maintain an audit trail.

Availability and competition

AI Workflow Factory is globally available, while Autonomous Engineer is currently offered through an early-access programme on request.

The solutions will compete with enterprise automation and AI platforms from companies such as Microsoft, Salesforce, UiPath and other workflow technology providers. ServiceNow’s main difference is its focus on connecting process analysis, application development, workflow execution and AI governance within one platform.

Conclusion with key takeaways

ServiceNow’s India launch reflects the shift from basic AI assistants to systems that can plan, build and improve business processes.

AI Workflow Factory helps organizations discover where automation can deliver value and continuously improve workflows.

Autonomous Engineer supports developers by converting requirements into planned, tested implementation work.

For Indian enterprises, the biggest advantage may be faster automation across existing systems while keeping human approval, security and governance in place.

Artificial intelligence is moving from simple question-and-answer tools to systems that can help complete real work. Two terms are now common in business and technology discussions – generative AI and agentic AI.

They are connected, but they are not the same. Gen AI is mainly focused on creating content. Agentic AI goes a step further by planning actions, using tools and working towards a larger goal.

Understanding this difference can help businesses, students and everyday users choose the right technology for a specific task.

What is Gen AI

Generative AI, also called Gen AI, is designed to create new content after receiving an instruction. It can produce text, images, audio, video, summaries and computer code. Tools such as ChatGPT and Google Gemini are common examples of generative AI applications.

Suppose as a user I want to get some answer from the GenAI platform then usually I give a prompt which is a set of instructions, and then based on the prompt – system produces an answer or output. The user can then ask for changes, provide more information or request another version.

For example, a small business owner could ask Gen AI to write a product description. A student could use it to simplify a difficult topic. A software developer could request a code example or an explanation of an error.

Gen AI is mainly reactive. It waits for an instruction and responds to that instruction.

Benefits of Gen AI

Gen AI can help people save time on routine content work. Its main benefits include:

• Drafting emails, reports and social media posts
• Summarizing long documents
• Translating and rewriting content
• Creating images and presentation ideas
• Generating software code
• Explaining complex topics in simple language
• Supporting research and brainstorming

It is especially useful when the final result needs to be reviewed and approved by a person before use.

What is Agentic AI

Agentic AI is designed to achieve a goal instead of only answering a single question. It can break a task into smaller steps, decide what should happen next, use connected tools and adjust its approach when new information appears.

Suppose an employee asks an AI system to organize a customer meeting. A Gen AI tool might write the invitation. An agentic AI system could check calendars, suggest a suitable time, book a meeting room, send invitations and update the schedule.

The agentic system may still use Gen AI to write messages or summarize information. The difference is that it can also take actions through software, databases and application programming interfaces.

Benefits of Agentic AI

Agentic AI can be useful for longer and more complicated workflows. Its benefits include:

• Automating multi-step business processes
• Reducing repeated manual work
• Connecting different software tools
• Monitoring information and responding to changes
• Improving customer support workflows
• Supporting IT help desks and service requests
• Assisting procurement and supply chain operations
• Helping developers write, test and improve code

For example, an agent could monitor stock levels, check supplier prices, prepare a purchase request and send it for human approval.

Gen AI vs Agentic AI

Gen AI mainly creates content after receiving a prompt. It can write text, generate images, summarize documents, translate information and produce code. The user usually guides the process by giving instructions and reviewing the result.

Agentic AI is designed to achieve a broader goal. It can divide a task into smaller steps, use connected tools, collect information, make decisions and complete approved actions. For example, Gen AI can write a meeting invitation, while Agentic AI can check calendars, book a meeting room, send the invitations and update the schedule.

Another difference is how they respond. Gen AI is generally reactive because it waits for a prompt. Agentic AI can be more proactive because it continues working towards a goal and adjusts its plan when conditions change.

Agentic AI often uses Gen AI as one part of its system. Gen AI helps the system understand instructions and create content, while the agentic layer adds planning, memory, tool access and task execution.

In simple terms, Gen AI helps create the answer, while Agentic AI helps move the work forward. Gen AI is useful for writing, research and brainstorming. Agentic AI is better suited to multi-step workflows such as customer support, IT service management, procurement and supply chain operations. Both require human review, especially when the information is sensitive or the system is allowed to take real-world actions.

Agentic AI is often built using Gen AI models. In simple terms, Gen AI can provide the language and reasoning, while the agentic layer adds planning, memory, tool use and task execution.

Risks and limitations

Neither technology is perfect. Gen AI can produce incorrect information, so important answers should be checked.

Agentic AI needs extra caution because it may take actions. Incorrect instructions, weak permissions or poor-quality data could lead to wrong purchases, incorrect customer updates or security problems.

Businesses should use access controls, activity logs, human approval and clear limits. Sensitive actions should not be left entirely to an AI system without supervision.

Which one should you use

Choose Gen AI when you need help creating, editing, summarizing or explaining content.

Choose Agentic AI when the task involves several steps, connected software and a clear business outcome.

In many real situations, the best approach is to use both. Gen AI can create and understand information, while Agentic AI can coordinate the process and complete approved actions.

Conclusion – Key takeaways

• Gen AI creates content from user prompts.
• Agentic AI works towards goals and can complete multi-step tasks.
• Agentic AI often uses Gen AI as one part of its system.
• Gen AI is useful for writing, research, images, summaries and coding.
• Agentic AI is useful for workflow automation, customer service, IT and business operations.
• Human review, permissions and monitoring remain important for both technologies.

References- IBM, IBM explanation of generative AI, IBM overview of agentic AI, Google Cloud explanation of agentic AI

India’s enterprise AI story is moving fast, and one of the latest examples is from Pune-based NudgeBee. The company has announced a fresh funding round of $3M led by Kalaari Capital.

NudgeBee is building AI systems that do not just suggest fixes, but actually execute tasks across cloud workflows. In simple words, this is the shift from “AI assistant” to “AI operator.” And that shift can change how enterprise teams run their systems every day.

The Funding News: What Happened?

On April 21, 2026, NudgeBee announced it had raised $3 million in a funding round led by Kalaari Capital. The company plans to use this capital to:

• Improve its core product capabilities
• Expand its enterprise context layer
• Grow go-to-market through partnerships and direct enterprise sales

The startup was founded in 2024 by Rakesh Rajendran and Shiv Pratap Singh. It focuses on cloud operations use cases for teams such as SRE (Site Reliability Engineering), cloud ops, and FinOps (financial operations for cloud spending).

What Is Agentic AI, and Why Is It Important Here?

Many people hear “agentic AI” and assume it is just a new marketing word. It is not. There is a practical difference.

A normal AI tool often gives recommendations:

• “This alert may be linked to X service”
• “Try checking Y logs”
• “You can reduce cost by resizing these resources”

An agentic AI system goes one step further:

• It can trigger workflows
• Coordinate steps across tools
• Execute pre-approved actions
• Learn from outcomes and improve

For enterprise cloud teams, this matters because the daily problem is rarely a lack of dashboards. The real problem is operational overload: too many alerts, too many disconnected tools, and too many repetitive actions.

That is exactly the pain point NudgeBee is trying to solve.

Conclusion

Agentic AI platform NudgeBee funding by Kalaari Capital reflects a larger market shift toward AI systems that can execute operational work, not just generate insights. For enterprises, this could mean faster resolution, lower cloud waste, and better productivity. For investors, it is a bet on AI-native infrastructure tooling. For India’s startup ecosystem, it is another sign that deep enterprise AI is becoming a serious growth track.

Facts Input- ET

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