Most business leaders recognize the importance of artificial intelligence, but only a smaller portion of them has managed to make it truly useful. The gap between these groups is not related to ambition or budget, but more often to understanding which tasks should be assigned to an AI agent and which decisions should remain under human control.
This issue became the central theme of Nikil Gudipati's presentation, Head of Client Engineering at Google Cloud, at the closing event of MSME Sparks 2026. The event itself was significant because MSME Sparks 2026, YourStory's flagship event dedicated to Indian SMEs, spent four virtual days analyzing factors transforming the growth of these enterprises, including digital transformation, access to capital, and the application of AI in daily operations. The final event took place on June 26th at ITC Gardenia in Bengaluru and was the only in-person meeting of the week, gathering founders, industry leaders, and ecosystem participants to conclude discussions started during the virtual sessions.
In her presentation titled 'Smarter Solutions with AI,' Nikil Gudipati focused on the week's technological topic. She began with statistics that clearly framed the problem: 'On average, a typical company works with 254 business applications.' When data is spread across dozens of isolated tools, the problem goes beyond mere inefficiency; it is the so-called 'switching tax'—the constant mental and operational burden associated with switching between applications to gain a single coherent view of the business. Gudipati argued that Gemini Enterprise is designed to eliminate this tax by acting as a central hub for creating, publishing, and managing AI agents across all organizational workflows.
Two live demonstrations clearly illustrated the practical use of this technology. In the first example, a contract lifecycle management agent was asked to identify expiring client agreements. Within a single dialogue, it extracted data from CRM and compliance systems, identified accounts with the highest revenue risk, and drafted a renewal letter, offering the option to schedule or fully automate subsequent actions.
Gudipati emphasized that it is not just about executing requests: 'It's not just about asking based on prompts. It's actually the ability to act on your behalf so you can focus on more strategic and important things for your business.' In the second demonstration, an agent helped a retail operations manager understand the reason for low efficiency in one store. By analyzing sales point data, inventory, and staff, the agent identified three main causes, including low customer satisfaction and high staff turnover, and then suggested scheduling a meeting with the store manager after pre-loading the relevant data.
Examples of this scale include investment firms using multi-agent systems to help analysts research five times more companies, or large manufacturers who are nearly fully automating HR query resolution while cutting defect resolution time in half. For the SMEs present at the event, Gudipati advised starting with reversible and low-stakes solutions. She asked: 'Which of these reversible solutions can you delegate to an agent so you can refocus on the critical and strategic aspects of your role?' Regarding security, a frequently unstated concern in any AI discussion, her answer was direct: 'The key difference with Gemini Enterprise is security. It's the most boring thing. It's the least fantastical thing companies worry about, but it is actually our biggest differentiator.' Gemini Enterprise offers a 30-day free trial, accessible directly through Google Search, which is a low-risk, reversible starting point reflecting the logic Gudipati advocated throughout the session.
For most small and medium-sized enterprises (MSMEs), adopting artificial intelligence is not following a trend but a strategic advantage that helps solve daily business challenges with limited resources. During the Google Gemini MSME Sparks 2026 session, Mai Kim, a Customer Engineer and Applied AI Specialist at Google Cloud JAPAC, demonstrated how companies can create intelligent customer service agents in less than an hour using Gemini Enterprise for customer experience, all without writing a single line of code.
These AI agents are capable of automating responses to customer inquiries outside of business hours, tracking orders, managing returns, or qualifying potential leads. Thus, small businesses can overcome structural and organizational barriers by providing enterprise-level service experiences even with a small team.
The session reflected a broader trend in enterprise AI. According to Google Cloud, about 75% of customers are already using the company's AI products, and models process over 16 billion tokens per minute through customer APIs in 2026. This indicates that AI agents are moving beyond experimental status to become practical tools for businesses of any size.
Mai Kim noted that in this AI-driven era, customers expect much more, moving from simple chatbots to deeply personalized service and purchasing experiences. She provided examples of agents that can analyze handwritten recipes and instantly add necessary ingredients to a customer profile, or process text orders if a customer is late. Her emphasis was on the need for quick problem resolution and timely assistance.
Traditionally, meeting these expectations required significant investment in technology and support staff for MSMEs. Google Cloud's vision aims to eliminate this hurdle.
During the demonstration, Mai presented a multimodal AI agent capable of handling voice interactions, understanding customer intent, conducting complex dialogue analysis, and retrieving up-to-date business information—all through a self-service interface. She reported that creating such an agent took her less than an hour, highlighting the progress in modern AI development, which accelerates setup using natural language and visual tools.
For a growing D2C brand, such an agent created in 60 minutes could handle inquiries, suggest complementary products, process order returns, and escalate complex questions to a human representative. The result is faster service, increased stability, and reduced operational costs—critical advantages for small teams.
The central element of the session was Google's Gemini Enterprise platform for customer experience, which unifies AI-powered customer service, commerce, and search into a single ecosystem. Instead of viewing customer support as an isolated function, the platform connects all stages of the customer journey: from product discovery and purchase to after-sales support and performance analysis.
This suite of features includes Customer Experience Agent Studio, Agent Assist, Customer Experience Insights, ready-made agents for shopping and food ordering, and Vertex AI Search. For MSMEs, this means they do not need to build every function from scratch; they can leverage pre-built AI components, adapting them to their specific products, customers, and workflows.
One of the platform's main strengths is its ability to balance automation with human intervention. While AI agents can autonomously handle routine interactions, the Agent Assist feature takes it further by supporting live representatives during conversations, providing contextual answers. As Mai showed, this feature delivers relevant knowledge and guidance in real time. Furthermore, Customer Experience Insights helps companies track key performance indicators such as customer satisfaction, response quality, and agent performance via an integrated dashboard, ensuring more efficient support operations even with a small team.
Small companies often hesitate to adopt AI due to budget constraints, lack of technical expertise, and implementation time. Mai emphasized that Gemini Enterprise is designed to remove these barriers, offering intuitive low-code and no-code tools that allow organizations to create high-quality AI agents without hiring specialized development teams. Instead of spending months on development or hiring dedicated AI engineers, business teams can build, test, refine, and deploy conversational agents using visual workflows.
This democratization of AI can be particularly valuable for MSMEs striving to compete with much larger enterprises that traditionally had greater access to technological resources. By automating repetitive customer interactions, companies can allow employees to focus on higher value tasks while simultaneously boosting operational efficiency and customer satisfaction.
A significant portion of the session was dedicated to Customer Experience Agent Studio—the visual development environment used for the live demonstration. Users do not have to start from scratch; they can simply describe the goal of their AI agent in one or two sentences. Gemini then automatically generates instructions, workflows, tools, and dialogue structure for the agent. Mai shared that they 'use AI to create an AI agent, not to write code.'
Developers can further customize the agent by defining its role, behavior, task flows, and decision-making logic, as well as adding optional knowledge sources to improve contextual understanding. The platform also supports numerous integrations, allowing agents to connect to Google Search, open APIs, Salesforce, ServiceNow, and custom functions.
Other features presented included cross-agent variables, customizable guardrails to block unwanted conversations, multilingual support, voice customization, model selection, and advanced dialogue settings. To ensure production readiness, Agent Studio also provides built-in evaluation tools. Companies can compare AI responses against expected outcomes using gold standard evaluations or simulate realistic customer interactions through scenarios, helping to identify edge cases before deployment. These built-in testing features allow teams to increase accuracy, reducing the risk of hallucinations or inconsistent responses.
The session reinforced an important message for MSMEs: building AI-powered applications no longer requires large budgets, specialized engineering teams, or months of development. With platforms like Gemini Enterprise, companies can increasingly focus on identifying customer problems rather than writing code. For MSMEs looking to enhance customer interaction, automate routine support, and deliver a faster, more personalized experience, AI agents are becoming a practical business opportunity, not just an experimental technology. As the Google AI ecosystem evolves, opportunities for small businesses are becoming clearer: not just adopting AI, but using it as an amplifier that enables small teams to deliver enterprise-scale customer service experiences.
Microsoft is implementing a strategic shift by increasing the use of its own artificial intelligence models in products such as Word and Excel. This change aims to decrease the company's reliance on systems created by partners, a move motivated by rising costs associated with AI usage.
Although the company continues to use solutions provided by OpenAI and Anthropic, it has expanded the application of its internal models across various functionalities. This movement is part of a broader trend among major technology corporations seeking cost containment, given the high maintenance cost of artificial intelligence services.
Microsoft's tactic involves incorporating its proprietary models, named MAI, to process part of user commands in popular tools like Word and Excel. Previously, it was notable that segments of Office 365 used models developed by OpenAI and Anthropic. Despite this transition, Microsoft has not discarded external providers, continuing to rely on these companies' models while strengthening its internal AI infrastructure, including the development of agents for executing specific tasks.
During its annual Build conference last month, the company unveiled seven new MAI models. Among these releases, an agent focused on programming and a tool capable of generating images from textual descriptions stood out. When contacted by TechCrunch about this change, Microsoft stated it had no further comments on the topic. This step by the company reflects a trend observed in other organizations, such as Amazon, Uber, Meta, and Accenture, which have also adopted measures to manage their artificial intelligence spending. The increasing costs of implementing and operating AI systems have made this issue a growing concern in the sector. In certain areas of Silicon Valley, some companies have begun considering models developed in China as a lower-cost option for AI agent-based solutions, even in light of security concerns.
