AI is rapidly reshaping sustainability, EHS and supply chain risk management. Yet, the real breakthrough isn’t smarter algorithms; it’s the rise of agentic AI powered by deeper, continuously verified data than businesses have ever had before.
Agentic AI is an artificial intelligence system that can accomplish a specific goal with limited supervision. This is set to be an important businesses development area.
As companies face mounting regulatory demands and increasingly complex global operations, new AI systems can autonomously analyse risks, calculate emissions with a new level of accuracy, and surface insights human teams would likely miss. This shift is paving the way for a major transformation in how businesses track, report and act on sustainability and operational risks.
How to make sense of these developments? Digital Journal spoke with Naved Siddique, chief product officer at Sphera.
DJ: What’s driving the rise of generative and agentic AI in operational efficiency across sustainability, EHS and supply chain risk intelligence?
Naved Siddique: We are seeing a real shift in how organizations think about sustainability and operational risk. For a long time, these areas were viewed as timely and costly compliance tasks. Today, they are understood as central to how a business runs. Applications of generative and agentic AI are growing quickly due to their ability to help companies manage the huge volume of information and data involved.
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From supplier networks to emissions data to real-time risk signals, there is simply too much for teams to handle manually. AI reduces the time spent searching for information and gives people the ability to focus on improvement, rather than administration.
DJ: Why does the quality of underlying data matter so much?
Siddique: AI can only perform well if the information it relies on is reliable, complete and current. If the data is fragmented or outdated, the insights that come from AI will be unreliable. Verified, high-quality datasets allow AI to map supply chains more accurately, connect emissions and materials information, and separate meaningful events from background noise. The better the data, the more confidence companies can have in the insights the system provides.
DJ: How are emerging AI systems improving sustainability and risk management outcomes?
Siddique: The most important change is that AI is beginning to act as an integrator rather than a simple alerting tool. It brings together information that previously sat in different corners of an organization and turns it into insights that are easy to act on. This means AI can uncover supply chain issues that might otherwise remain hidden or highlight the true drivers behind environmental performance. It can also simplify safety processes by helping teams focus only on the events and tasks that matter most. Companies are moving from identifying risks to actually prioritizing and addressing them.
DJ: What does this evolution signal for the future of corporate sustainability and risk tech?
Siddique: It points to a future where sustainability, safety and supply chain risk management become fully integrated into the way companies operate rather than isolated compliance tasks. As AI systems grow more capable, organizations will rely on them to turn large and complicated datasets into clear insights that guide day-to-day decisions. Tools that combine strong data foundations with intelligent automation, such as the approach behind Sphera AI, show how this can work in practice.
Over time, companies will gain the ability to understand their real environmental and operational exposure in real time and respond before small issues grow into larger problems. This shift will encourage businesses to view sustainability and risk performance as core indicators of operational strength and long-term resilience.
DJ: Is there anything else you’d like to add?
Siddique: One trend to keep an eye on is how often AI reveals data gaps companies did not realise they had. Missing supplier details or incomplete emissions records tend to become apparent quickly once AI begins analysing patterns. Closing these gaps will likely become a new marker of maturity. And while many tools are still early in their development, there are already examples in the market. In 2026, success will be defined by how effectively companies harness AI to turn information into impact.
As AI becomes more capable and data foundations grow stronger, sustainability and risk management are moving from reactive reporting to proactive, integrated decision-making. Moves like this hint at where the industry is heading: toward AI that improves insight not just by analyzing data, but by raising the standard for the data itself. It’s a shift that’s already underway and one that’s likely to define the next era of operational resilience.”
