FireAI Points to Agentic Analytics as the Next Phase for India’s SMEs

As global business analytics transitions from descriptive dashboards to autonomous, agentic systems, Indian enterprises are confronting a structural question: can AI drive decisions at the pace businesses now demand? FireAI, an India-built business intelligence platform, believes the shift is inevitable but uneven.

In a conversation with Vipul Prakash, Founder & CEO, FireAI, CiOL explored the changing market, the barriers holding back India’s SMEs, and what local platforms must deliver to compete with established global tools.

Interview Excerpts:

The global analytics market is shifting from descriptive to predictive and now toward agentic AI. How might this evolution redefine the traditional boundaries between data analytics, automation, and decision-making in enterprise environments?

The analytics world is witnessing significant change, and at an unprecedented rate. Earlier, data tools were only descriptive, meaning they only showed what happened. After that, they started predicting what could happen, becoming predictive.

Now, with agentic AI, systems can understand, reason, and take action, closing the loop between analytics, automation, and decision-making. At Fire AI, we see this as the moment when dashboards turn into decision engines. It is now that reports evolve into actionable insights that trigger business changes automatically. Data, automation, and human judgement come together in one continuous workflow. This means businesses will spend less time looking for insights and more time acting on them instantly.

Cloud-based analytics is driving accessibility for SMEs worldwide. In India’s fragmented SME landscape, what structural or digital maturity gaps must be addressed before “AI-first” decision-making can become mainstream?

Cloud-based analytics has made data tools accessible. However, in India’s SME sector, a few key gaps remain, like data structure issues, disconnected tools, limited data policies, and most importantly, mindset.

Challenges in data structure mean that there are different formats, missing data, or non-standard systems. With different tools in use, like Tally, Excel, and ERPs, the challenge becomes enormous since they don’t interact with each other. Only a few companies have clear rules for access and sharing, whereas when we talk about mindset, many business owners still depend on manual judgement.

With Fire AI, we are addressing these challenges by bridging these gaps. We are able to connect all tools and data sources in one place, along with cleaning and structuring data automatically. We are also providing simple, natural-language insights and helping owners make faster, data-driven decisions without technical effort.

We believe AI-first decisions will become common once businesses see quick, visible ROI, even from one or two key use cases like pricing, inventory, or sales forecasting.

As AI models become more autonomous in interpreting and acting on data, how can organisations balance machine-driven insights with human oversight, especially in regulated or high-stakes industries?

As AI gets smarter, human control becomes more important, especially in sectors like finance, logistics, or healthcare. At Fire AI, we believe in “safe autonomy” through clear levels of control. This means that AI assists first, then gradually automates repetitive tasks. Furthermore, humans approve or review actions beyond defined risk thresholds.

This also means that every recommendation is explainable, as you can see why a decision was made. Any action taken is audited and reversible, leading to accountability. This provides enterprises with the speed of automation with the trust of human judgement.

India’s enterprise software ecosystem has long been shaped by global incumbents like Power BI, Tableau, and SAP. What will it take for Indian-born analytics platforms to compete globally in terms of data interoperability, performance, and trust?

The reason why global tools like Power BI and Tableau dominate is because of their maturity and ecosystem. However, Fire AI believes India can lead the next wave through three core advantages: interoperability, performance, and trust.

With interoperability, we are able to seamlessly connect Tally, ERP, SQL, and cloud data, meaning no extra integration is needed. Furthermore, Fire AI’s optimised LLM and agent system offer near-instant analytics, highlighting the performance aspect. The most important aspect here is that the data stays on the clients’ servers, and Fire AI never reads or stores it, leading to more trust than ever.

By solving India’s messy, real-world data challenges first, Indian AI analytics platforms can compete and even outperform the global tools on adaptability and value.

With the rise of domain-specific and multilingual AI systems, could we see a regionalisation of analytics where business intelligence adapts not just to industries but to languages, local workflows, and cultural contexts?

Yes, the future of analytics will be regional and multilingual. Fire AI already supports this by allowing users to ask questions in local languages (Hindi, Marathi, Tamil, etc.). It is also capable of accumulating insights tailored to local business logic, like taxes, festivals, and GST cycles. It also offers to use voice commands for hands-free reporting.

This customisation helps businesses across India, irrespective of location, to understand and act on their data instantly. We call it “analytics that speaks your language.”

The influx of funding into AI-driven analytics startups is accelerating innovation but also fragmenting the market. How should policymakers and industry alliances approach standardisation, ethics, and interoperability in this new “agentic analytics” era?

India is witnessing the emergence of numerous AI startups, and it's becoming integral to maintain quality, transparency, and safety. Fire AI supports common data standards and APIs to ensure tools work together. It is also an ethical AI with full explainability and consent.

Furthermore, we offer secure, region-based hosting to meet data privacy laws. We also ensure collaboration with industry bodies for interoperability and safety guidelines.

At Fire AI, we see “Agentic Analytics” as a new standard, one that keeps innovation and accountability balanced. We believe the next generation of analytics will not just tell you what’s happening but also will help to decide what to do next.