Agentic AI remains the flavour of the season. But it is one thing to experiment with AI tools in a controlled environment and quite another to entrust autonomous digital agents with business-critical tasks without compromising data security. 

Adding to the challenge, a recent Deloitte study found that agentic AI is yet to deliver at scale, with only 10% of organisations seeing meaningful, measurable returns from their deployments.

To explore how businesses can bridge this gap, industry experts came together for a session at the Inc42 AI Summit 2026. The panel discussion, titled ‘The Operator Playbook For Building And Scaling Agentic AI Systems That Deliver Real Business ROI’, brought together top tech leaders to share practical insights on effectively leveraging AI.

Moderated by Ideaspring Capital founder and managing partner Naganand Doraswamy, the panel featured Wingify cofounder and CPTO Ankit Jain, Razorpay CPO Khilan Haria, Skyflow CPO Amruta Moktali, Shipsy cofounder and COO Dhruv Agrawal, and Avalara’s head of partnerships and alliance for the APAC region, Job Sam Koshy.

The panel first discussed how teams can trust AI with sensitive company information. The speakers agreed that while AI models are becoming common and easy to access, they only work well if they have the right business context. 

However, giving an AI agent access to internal data often spooks management. Skyflow’s Moktali pointed out that fear of data leaks often paralyses these projects. “Everyone is blocking data and then they say they are not getting the value out of the agent,” she explained. 

She noted that companies fail to get a return on investment (ROI) from AI because they lock it out. Rather than blocking everything, organisations should focus on protecting sensitive information within documents while allowing AI systems to access the context they need to perform effectively.  

Beyond data security, the panellists also addressed the challenges of operating in a market where AI capabilities evolve almost overnight. Organisations often risk getting trapped in endless cycles of experimentation without those efforts translating into measurable business value. 

Sharing his view, Avalara’s Koshy highlighted, “I don’t think we are actually trying to keep up with the curve; we are trying to see what will bring ease, fastness, and accuracy for our customers, and so we are working backwards from what the model provides”.

This strategic refusal to chase every incremental shift in the market underscores a broader operational realisation. To scale autonomous systems successfully, leadership teams must stop treating AI deployment as a race to adopt the newest model. Instead, they need to focus on building a predictable environment where technology gets a place to grow and work towards a common business objective. 

Ultimately, the blueprint for securing real business ROI in the agentic era lies in ruthlessly blocking out external noise to focus entirely on execution fundamentals, Shipsy’s Agrawal said. 

As enterprise AI continues to evolve at a breakneck pace, the organisations that successfully cross into the elite 10% bracket of high-return AI deployment will not be those with the newest models but those with the clearest operational boundaries. 

By shifting the corporate focus from chasing fleeting industry trends to building stable, secure systems that deliver immediate value, business leaders can transform autonomous agents from unpredictable experiments into powerful, scalable assets that drive genuine revenue growth. 

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