How Does an AI Based Startup Compete Against MNCs?

What strategies work for AI startups in India to compete against MNCs?

Based on TableSprint’s experience, Indian AI startups can successfully compete with MNCs by leveraging three key differentiators.

Three Differentiators -  

First, intuitive platform design with enterprise-grade capabilities. Unlike traditional enterprise solutions that require specialised technical expertise, TableSprint’s AI-powered platform is designed for business users. We take a familiar spreadsheet-first approach, allowing teams to build applications effortlessly. Our AI assistant simplifies the process, helping users define fields, generate demo data, and create dashboards through natural conversation. This ensures that enterprise app development is fast, accessible and user-friendly; a contrast to the often complex and rigid MNC alternatives.

Second, flexible customisation with rapid implementation. While MNC platforms enforce rigid, one-size-fits-all structures, TableSprint enables businesses to tailor applications to their specific needs. Our platform supports everything from spreadsheet-style interfaces to Kanban boards, Gantt charts, and calendar views. Teams can create role-specific micro-apps (e.g., for customer service, sales, or warehouse management) without writing a single line of code. This allows businesses to go from idea to full implementation in days rather than months, giving them the agility that MNC solutions often lack.

Third, comprehensive business process coverage at competitive prices. Our platform is designed to support end-to-end workflows like Lead-to-Order, Procure-to-Pay, Project Management and HR processes. Unlike MNC solutions that require expensive add-ons and consulting services, TableSprint offers a full suite of features – from builders, workflow automation to parent-child data relationships – in an affordable package. Our AI assistant further enhances the experience by guiding users in structuring their applications and optimising workflows.

What we’ve learned is that success in global markets comes from combining enterprise-grade capabilities with intuitive design and accessible pricing. This approach has allowed us to offer the power of AI and no-code development as a smart alternative to MNC systems like Salesforce and Microsoft Power Apps. By focusing on usability, flexibility, and cost-effectiveness, Indian AI startups can create compelling solutions that challenge global incumbents and deliver real value to businesses worldwide.

What challenges do you face in your AI-related work and how do you resolve these challenges?

Our work with AI at TableSprint comes with both challenges and opportunities, particularly in making AI a truly valuable tool for business users. One of the biggest challenges is ensuring that AI is genuinely user-friendly in a business context. Many platforms treat AI as just another feature, but we realised early on that users need AI that understands their specific workflows and business needs. To address this, we developed our AI assistant to work within TableSprint’s structured framework, helping users with tasks like field creation, data modeling, and dashboard design. Unlike generic AI tools that provide broad suggestions, our assistant actively engages in the process, making application development intuitive and aligned with how businesses operate.

Another major challenge is ensuring that AI provides actionable business value rather than just theoretical insights. AI often produces interesting analyses, but unless these insights directly improve decision-making and efficiency, they add little practical benefit. We tackled this by focusing our AI assistant on specific high-impact areas, such as designing efficient data structures, automating demo data creation, providing contextual analysis for dashboards, and assisting in application architecture decisions. This targeted approach ensures that AI is used as a business accelerator rather than a novelty, driving real outcomes for users.

Striking the right balance between AI automation and user control is another key challenge. While AI can automate many aspects of app creation, we have intentionally designed it to enhance, rather than replace, human decision-making. Our AI assistant functions as a collaborative partner, helping users brainstorm ideas, optimize structures, and streamline repetitive tasks, while ensuring they remain in control of the final decisions. This approach prevents AI from becoming overwhelming or rigid and instead empowers users to build applications in a way that best suits their business needs.

As we continue expanding our AI capabilities, our primary goal remains the same—to make AI a practical and accessible tool for business users rather than a complex, developer-centric feature. By focusing on usability, business value, and a balanced integration of AI automation, we are ensuring that businesses of all sizes can leverage AI effectively to enhance productivity and innovation.

How is AI evolving and what issues should the IT sector keep in mind while dealing with AI?

From our experience building and implementing AI solutions at TableSprint, we’ve observed a major shift in how AI is evolving and the key challenges the IT sector needs to consider.

AI is rapidly transitioning from being a specialized tool to an integral part of software platforms. Earlier, AI was treated as an isolated feature, but today, it is deeply embedded in workflows to enhance the overall user experience. At TableSprint, our AI assistant is not just an add-on—it actively helps with data structure design, application architecture, and workflow automation, making AI a seamless part of the software. This shift means IT companies need to think of AI not as a separate module but as a core component that enhances every aspect of their solutions.

Another significant trend is the shift from raw AI capabilities to practical business applications. AI’s real value doesn’t come from its complexity but from its ability to solve real business problems. For instance, our AI assistant doesn’t just generate insights—it actively assists users in brainstorming app requirements, suggesting optimal structures, and automating key tasks. This means IT leaders should prioritize AI applications that drive business value rather than focusing solely on technical sophistication.

User experience and accessibility are also becoming crucial differentiators in AI adoption. Earlier AI models often required users to have technical expertise, but today, the trend is toward natural, conversation-based interactions that make AI intuitive for business users. At TableSprint, we designed our AI assistant to communicate naturally while handling complex tasks like field creation and dashboard design in the background. IT companies should ensure that their AI solutions are easy to use, even for non-technical users, to drive widespread adoption.

As AI becomes a fundamental part of business processes, the IT sector must address critical concerns to ensure responsible and effective deployment. Data privacy and security must be at the forefront, as AI systems increasingly process sensitive business data. User empowerment should also remain a priority – AI should enhance human decision-making rather than replace it. At TableSprint, we ensure our AI assists users while keeping them in control of final decisions. Another key consideration is scalability and integration, as AI solutions must seamlessly fit into existing enterprise systems without disrupting workflows. Finally, continuous learning and adaptation are essential – AI should evolve based on user interactions and changing business needs, requiring ongoing refinement and updates.

The future of AI in business software isn’t about replacing human intelligence but augmenting it in ways that make complex tasks more accessible and efficient. The IT sector must focus on developing AI solutions that enhance human capabilities while maintaining transparency, security, and user control. By embedding AI into business processes in a responsible and user-centric way, IT companies can unlock AI’s true potential and drive meaningful innovation.

What has been your growth like in last 1 year?

We launched our product very recently and have seen strong traction in the enterprise segment, adding large clients every month. While we are still in the early stages of scaling, demand has been growing rapidly, and our biggest challenge is keeping up with the influx of incoming clients. Due to this, we are actively expanding our ecosystem by onboarding implementation and affiliate partners to help us scale efficiently and meet growing enterprise demand.

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