In an interview with TimesTech, Nitin Lahoti, Founder and Director at Mobisoft Infotech, shared insights on how enterprises are moving from AI pilots to full-scale deployment. He discussed critical success factors like leadership, data quality, and process redesign, while highlighting AI’s role in sales, operations, and decision-making. Lahoti also explored Generative AI opportunities, AI agents, and strategies for startups to compete effectively using emerging technologies.
Read the full interview here:
TimesTech: How are enterprises transitioning from AI experimentation to full-scale production? What are some critical success factors enabling this move?
Nitin: We’ve seen many enterprises move from pilots to actual implementation. Initially, AI was kept to small use cases. That’s no longer the case. Companies are now deploying AI in production environments where it impacts business KPIs directly.
One major factor is leadership involvement. When top executives understand AI’s business value, execution happens faster. Another is data quality. Scalable AI needs clean, well-organized data. Without that, no model performs well. Infrastructure also plays a role. Cloud-native tools and API-based systems help scale AI faster and more securely.
But one thing often overlooked is process change. You can’t bolt AI onto outdated workflows. Teams need training. Roles need redefining. We’ve seen this first-hand while working with healthcare clients. Their clinical processes had to be redesigned for AI-based triage to actually work.
Success comes when teams stop treating AI as a tech add-on. It needs to be tied to real outcomes. AI should reduce cost, increase efficiency, or improve customer experience. If it doesn’t, it’s not ready for production. Enterprises that understand this are already seeing results across their core functions.
TimesTech: What key changes are you seeing in sales, operations, and decision-making across industries due to the adoption of AI?
Nitin: AI is improving how companies operate at every level. In sales, it’s helping teams prioritize better. AI ranks leads based on buyer intent and past behavior. Sales reps no longer waste time chasing cold leads. We’ve implemented this for clients and seen conversion rates improve within weeks.
In operations, AI is making processes more efficient. A good example is fleet management. With real-time data, AI can recommend better routes, optimize fuel usage, and reduce delays. That improves customer satisfaction and lowers costs.
For decision-making, AI is replacing guesswork with data-backed actions. Executives now get daily insights on performance, risks, and anomalies. One of our clients uses AI to monitor customer churn risk. Their managers get alerts before issues become losses. That level of foresight was impossible earlier.
But this brings a new challenge. Too much data can overwhelm teams. That’s why the focus now is on clarity. Dashboards must be simple. Insights need to be actionable.
Also, decisions are no longer limited to top management. Mid-level staff now have AI tools to support everyday decisions. This improves speed and accountability across the board. Companies using AI this way are becoming more agile and responsive.
TimesTech: What should businesses keep in mind before investing in Generative AI? Where do you see the most practical applications and ROI today?
Nitin: Generative AI is powerful, but you need to be strategic. Businesses must begin with a clear use case. What exactly are you trying to improve or automate? Without that, you risk spending on tools that don’t move the needle.
Data privacy is critical. Many GenAI platforms store prompts or responses. This can lead to security issues. For sensitive domains like healthcare or finance, we recommend using private models or secured APIs. Don’t assume public tools are safe by default.
Quality control is another must. AI can generate wrong or misleading content. Human oversight is still necessary, especially for tasks involving customer interaction, legal content, or compliance.
Today, we see strong ROI in three areas. First is support automation. GenAI can summarize tickets or draft responses, cutting handling time. Second is content generation. From product descriptions to blog drafts, it saves hours weekly. Third is internal search. GenAI helps employees find answers from company documents using simple language.
The key is to focus on enhancement, not replacement. GenAI should boost your team’s output and accuracy. That’s when the investment pays off.
TimesTech: How are AI Agents, RAG, and GPT integrations transforming enterprise intelligence and real-time decision-making?
Nitin: AI Agents and GPT integrations are making data easier to access and act on. Instead of navigating dashboards, teams can now ask questions in plain language and get instant answers. This is a big step forward for day-to-day decision-making.
Retrieval-Augmented Generation, or RAG, is helping businesses use their own data more effectively. Most companies have large volumes of unstructured content like emails, reports, PDFs. With RAG, that data can be connected to a language model like GPT. Now, employees get accurate, contextual answers, not generic ones.
These tools are not just fast. They are also more accessible. You don’t need to be a data analyst to get insights. This democratizes information and reduces bottlenecks.
But it only works when systems are well-integrated. Your ERP, CRM, and document management tools must talk to each other. Without that, the AI agent becomes a dead-end.
When done right, these tools make business knowledge available on demand. That leads to faster, smarter actions at every level.
TimesTech: How can startups and smaller businesses leverage AI to stay competitive against larger players, even with limited resources?
Nitin: Startups don’t need large budgets to use AI effectively. The real advantage is speed. They can test and implement new tools faster than large enterprises bogged down by approval cycles.
The smartest approach is to focus on high-return areas. One is customer support. AI-powered chatbots can handle basic queries and reduce support loads. Another good use case is sales outreach. AI tools can identify high-potential leads, personalize messaging, and schedule follow-ups. This saves hours every week and improves conversions.
Content generation is also useful. Product pages, blog drafts, internal SOPs; all can be started using GenAI. The output still needs editing, but it cuts the workload by half.
Startups should avoid trying to do everything at once. Pick one area, test a simple tool, and measure the outcome. Many open-source models and affordable APIs are available now. There’s no need to build from scratch.
Also, use AI to amplify strengths, not replace people. Automate repetitive tasks. Free up teams to focus on customers and strategy. That’s how smaller players can punch above their weight.
TimesTech: Mobisoft has delivered impactful tech solutions across sectors like healthcare and mobility. What drives your approach to building purpose-driven, scalable platforms powered by emerging technologies?
Nitin: Our approach starts with understanding the real problem. We don’t begin with the tech. We begin with the user. What are they struggling with? What is slowing them down? That’s where the project begins.
In healthcare, we worked with providers who were facing high no-show rates. Instead of just sending reminders, we created a system that predicted who was likely to miss appointments. It also automated follow-ups. That solution didn’t just save time. It improved patient care.
Scalability is part of our design process from day one. We build modular platforms. We use cloud-native tools. This ensures that a platform built for 500 users can also handle 50,000 without rework.
We also look across industries for ideas. What works in mobility, like route optimization, might apply to healthcare or logistics. Our teams are trained to think beyond verticals.
Emerging technologies are a tool, not the goal. We ask if a new tech solves a real business problem. If it does, we use it. If it doesn’t, we wait. The goal is always to build something useful, not just impressive.
Purpose-driven work happens when you care about the problem, not just the code. That’s what drives us every day at Mobisoft.
