Speaking to TechGraph, Rajeev Sinha, CEO & Co-founder of Onlygood.ai, shares insights on how his company is helping smaller businesses leverage AI and IoT to automate data collection and establish robust data governance frameworks. He also discusses the role of blockchain in supply chains, highlighting its potential to enhance transparency and trust in ESG reporting.
Read the interview in detail:
TechGraph: With the accelerating push towards sustainable practices, how do you see AI transforming the future of carbon tracking and ESG compliance, particularly in industries that are traditionally difficult to monitor, such as automotive and chemicals?
Rajeev Sinha: AI techniques can help companies automate carbon data collection and provide veracity across the enterprise. These techniques are particularly useful when applied across multiple tiers of global suppliers and their facilities. AI can help look for patterns and anomalies across datasets, assisting with accurate forecasting and better decision-making and providing recommendations on low-carbon pathways.
TechGraph: In India’s evolving regulatory landscape, what specific barriers do businesses face in adopting comprehensive sustainability solutions, and how does Onlygood AI’s platform address these barriers to drive large-scale industry adoption?
Rajeev Sinha: The biggest barrier businesses face in adopting comprehensive sustainability solutions is not having an existing organizational culture and consciousness around sustainability. Further, there is a lack of data awareness and processes to collect and verify data across sources and suppliers. Sustainability is always perceived as an expensive, capex-intensive program with limited or no ROI.
Onlygood’s platform helps a client begin and then accelerate their sustainability journey by providing real transparency across ESG metrics, and clear pathways to decarbonization. The platform’s analytics highlight the cost optimization and efficiency opportunities helping enhance the ROI of existing initiatives and provide a pragmatic approach to realizing climate goals.
TechGraph: AI and data analytics have the potential to drive deeper insights into supply chain efficiencies and emissions reductions. How do you see AI creating a competitive edge for companies that integrate sustainability at scale, especially in sectors like apparel and packaging?
Rajeev Sinha: Achieving sustainability at scale has always been difficult for any business relying on survey and Excel-based methods, which are not for a larger number of data streams. AI and data analytics can help companies enable the participation of large numbers of suppliers in their sustainability journeys, allowing for a greening of the entire supply chain. AI helps onboard participants at scale and then continuously monitors their behavior across ESG parameters, creating transparency across global supply chains.
TechGraph: As ESG reporting moves beyond compliance to becoming a core aspect of a company’s value proposition, how does Onlygood AI help companies reshape their sustainability narratives to resonate with both investors and consumers?
Rajeev Sinha: Onlygood provides a robust data repository and on-demand reporting in a variety of international formats. A company can use the platform to continuously inform investors, their B2B buyers, end consumers, and supply chain participants on how it is performing across ESG parameters.
The Onlygood platform can help a company near real-time track emissions against its stated climate goals through data-verified, accurate reports and deeper analytics. The company can use the platform’s analytics to convince all stakeholders of a much higher value proposition for the marketplace.
TechGraph: Given India’s diverse manufacturing ecosystem, how does Onlygood AI customize its AI-driven sustainability tools to cater to the unique challenges faced by SMEs versus large enterprises, and what role do you see for smaller companies in leading sustainability innovations?
Rajeev Sinha: The biggest challenge for MSMEs is their lack of processes around data collection, collation, and data ownership. However, the advantage that smaller companies have is their relative simplicity in organizational hierarchy, as well as fewer facilities and data streams.
Onlygood is able to help these smaller companies to innovate using IoT and AI-based solutions to automate this data collection and also put in place robust processes for data governance, including verification and workflows for validation.
TechGraph: How do you balance the trade-offs between achieving immediate, measurable carbon reductions and addressing longer-term, systemic changes in sustainability practices, particularly in high-impact sectors like automotive and packaging?
Rajeev Sinha: Ensuring long-term sustainability requires automotive and packaging companies to build a clear long-term vision, set climate goals, and provide investment and incentives internally for changing organizational behavior. They should create a climate roadmap with ESG-related projects including carbon emissions, water, energy, waste, and transportation.
These ESG projects should be tracked across multiple KPIs such as their business effectiveness, ROI, impact, and of course ESG scores. Short-term projects, such as solar implementations, have to fit into a larger strategic climate roadmap but are very useful to improve the company’s culture of sustainability and help the company reach its strategic goals.
TechGraph: Looking ahead, how do you foresee the integration of AI with emerging technologies, such as blockchain, to enhance transparency and traceability in supply chain sustainability and ESG compliance on a global scale?
Rajeev Sinha: Going forward, I anticipate AI and Blockchain will be integral parts of any technology solution. Implementing AI will enable businesses to automate data collection across their supply chains, create ESG scores in real time, and improve decision-making through emissions forecasting. ESG data on a blockchain will improve supply chain efficiencies and foster greater trust in data collection processes and data values.
