How Data-Defined Execution Can Improve Product Development

In an interaction with Industry Outlook, Abhishek Nirakhe, Founder of Amay Design Solutions, shares insights on how Indian industries are leveraging data-driven strategies for real-time decision-making, accelerating time-to-market, and enhancing product development. The discussion highlights AI-powered predictive analytics, digital twins, automation, and security challenges, showcasing practical industry examples and scalable transformation solutions. Abhishek Nirakhe is a seasoned industrial consultant and R&D expert with over a decade of experience in mechanical design, water technology, and sustainability. As the Founder of Amay Design Solutions, he specializes in product innovation, market research, and strategic development for startups and enterprises.

With Indian companies increasingly adopting data-driven strategies, how are service providers enabling real-time decision-making to accelerate product development and reduce time-to-market?

The biggest shift I’ve seen is that decision-making is no longer a monthly review thing; it’s moving toward live dashboards and real-time insights. Service providers play a key role by building systems that bring data from various departments into one place where it actually makes sense to the people using it. When teams can see what's happening on the floor or in the market as it happens, they can adapt quickly, fix issues early, and move forward with confidence. This agility makes a huge difference in product timelines.

For instance, a food processing company digitized its trial and production feedback loop using a central dashboard. Whenever a new product batch was tested, deviations or sensory feedback were instantly visible to the R&D and quality teams. They could adjust parameters on the go like tweaking moisture levels or ingredient blends without waiting for batch reports at the end of the day. This not only reduced their trial-to-launch time but also improved product consistency right from the pilot stage.

What challenges do Indian industries face in integrating data-defined execution, and how are service providers overcoming issues like fragmented data and legacy infrastructure?

Integration isn’t easy, especially when systems are old, siloed, or undocumented. That’s a common story across Indian manufacturing. What’s working is a layered approach don’t rip everything apart at once. Instead, find smart ways to plug into existing setups, gradually introduce digital tools, and build a more connected workflow over time. And most importantly, keep the people using those systems involved from the beginning. Tech fails when it forgets the human element.

One chemical company faced this exact issue. They had lab data in notebooks, production logs in spreadsheets, and procurement in a decades-old ERP. They started small, with just one cloud tool to track batch outputs and anomalies. Once that data was visualized clearly, plant managers began spotting inefficiencies they hadn't noticed before like temperature fluctuations affecting yield or minor raw material inconsistencies. Over time, they added layers to the system, bringing in analytics and simple automation. What helped wasn’t a full tech overhaul—it was the confidence to take the first step and build from there.

How are AI-powered predictive analytics and digital twins transforming product validation and lifecycle management for Indian manufacturers?

Predictive analytics and digital twins are helping manufacturers move from a trial-and-error model to a much more refined, simulation-driven approach. Instead of waiting for something to go wrong, they’re identifying weak points in advance and fixing them proactively.

Take predictive maintenance, for example. A machinery manufacturer using AI-based models was able to forecast component wear-and-tear weeks before breakdowns. That small change reduced unplanned downtime by over 40%. Similarly, digital twins are being used in pharma and food industries to simulate process changes say, altering temperature or mixing times before committing to physical trials. It saves time, resources, and lets teams validate ideas in a risk-free environment.

Given the evolving regulatory landscape, how are service providers ensuring both seamless integration and strong data security in data-defined execution models?

Data security is a growing priority especially with regulations tightening around traceability and digital documentation. What’s important is that security isn’t treated as an afterthought. It has to be built into the system from day one.

In practice, this means using role-based access controls, encrypted cloud environments, and audit trails that can’t be tampered with. But beyond tech, it’s about trust and usability. If a compliance tool is too clunky, people won’t use it properly. Service providers that are succeeding in this space are the ones making secure systems that feel natural to use, so teams don’t see security as a barrier but as part of the daily workflow.

How is automation redefining data-driven execution in product development, and what role do service providers play in making AI-driven systems more scalable for Indian industries?

Automation today goes beyond just speeding things up it’s about helping companies make better, faster decisions. Whether it’s automatic quality alerts, recipe adjustments based on real-time inputs, or even automated compliance reporting, smart automation is reducing manual guesswork.

The key to making it scalable in India is simplicity. Most companies don’t need fancy AI—they need tools that solve real bottlenecks without needing a big IT team. For example, a small packaging firm automated just one workflow: label verification. This single change eliminated 90% of packaging errors and saved thousands in returns. Scalable automation starts with identifying one thing that slows you down and fixing it in a way that's easy to maintain.

With rapid advancements in AI and automation, how do you see data-defined execution reshaping the future of product innovation in India over the next five years?

India’s manufacturing sector is entering a phase where innovation can be both bold and data-backed. Over the next five years, I think we’ll see product development that’s more customer-responsive, more efficient, and significantly faster to adapt to market shifts.

Imagine a setup where market feedback from online reviews or distributor data feeds directly into R&D, triggering tweaks in formulation or packaging before the next production cycle. That level of responsiveness is no longer far-fetched; it’s starting to happen. As AI and automation mature, the companies that embrace data not just for reporting but for continuous learning will be the ones that thrive. Product innovation will become more collaborative, more transparent, and a lot more dynamic than it’s ever been.