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Interview functions, then gradually expand across the business once efficacy is proven.
For example, in financial services, some organizations are using AI to tackle longstanding data quality issues in accounts receivable, such as mismatched records and outdated entries. With cleaner data, they gain a clearer view of who owes what and when, enabling automated follow-up processes that accelerate collections and improve cash flow. It’ s a small fix with outsized business impact.
Food and beverage companies can follow a similar path. Starting with executor agents handling clearly defined supplier or agricultural datasets, then scale to more sophisticated AI agents that standardize, enrich, and integrate data across the supply chain. This approach not only helps companies meet regulatory requirements but also builds a foundation for more intelligent, efficient, and data-driven operations.
8. How do you balance the need for granular supplier data with the reality that many suppliers may not yet be‘ data mature’? It’ s a journey and we can’ t let perfection be the enemy of good. Organizations have to work with what they’ ve got. When primary supplier data isn’ t available, companies will have to use proxy data or estimates, but what will become critical is the management of metadata( data about data). Keeping a data catalogue of these proxy data sets allows companies to have evidence of what data they used at a point in time and why they have switched to a new data set, based on whatever improvements that data set provides, all captured centrally.
9. What role will generative AI play in future ESG reporting— are we looking at full automation, or is human oversight still essential? GenAI’ s great benefit to ESG reporting is its ability to take over and speed up the repetitive, manual work involved in data collection and processing. But that doesn’ t mean it’ s capable of – or should – take over the ESG compliance function from trained staff. Human insight into the significance of data and the strategy behind ESG projects remains crucial. If you like, AI is the tractor and compliance staff are the farmers – the machine speeds up the work, but the human skill is still essential to ensure the enterprise runs well.
10. Looking five years out, what’ s the one capability you believe every food manufacturer will need in their data stack to stay compliant and competitive? I’ m going to be bold and reject the premise of your question – there’ s no single capability that makes all the difference! I can name five that have to go together, though. You need ease of access to all relevant data in a single platform. You need high data quality, making sure it’ s complete and accurate. You need standardized data definitions across the whole company to avoid confusion or duplication. You need totally transparent lineage so you know where all your data has come from and how it’ s been used. And you absolutely need to be able to identify, classify, and track sensitive data, so you can ensure its security. Most importantly we need data driven, technology enabled sustainability professionals that are not scared to push the boundaries and innovate. ■
Levent Ergin www. informatica. com
Levent Ergin is Chief Climate, Sustainability & AI Strategist at Informatica. Informatica helps companies realize business value faster by managing the entire lifecycle of their data— from ensuring data health and trust to supporting enterprise use cases with AI-powered cloud data management.
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