Levent Ergin, Chief Climate, Sustainability & AI Strategist at Informatica explains how AI and data mastery can transform ESG compliance into business growth
To start, could you share details of your career history and how you came to be in your current role?
I’ve spent more than 25 years working in data strategy, governance, and transformation. This experience now helps me guide organizations as they navigate the fast-changing world of AI and ESG. At Informatica, I work with large organizations to harness the power of data management and AI to transform their business, while using climate and sustainability regulation and compliance as a strategic lever for growth.
I had the privilege of contributing to the Net-Zero Data Public Utility, which launched at COP28, and I’m honored to serve as a member of the Data for the Environment Alliance (DEAL) through my work with the UN Science-Policy-Business Forum on the environment. Before joining Informatica, I held senior data leadership roles at HSBC, Deutsche Bank, and the Royal Bank of Scotland Group, where I focused on data governance, reference data, and regulatory transformation.
Many businesses see AI as a buzzword — how does AI practically transform packaging and waste data into something actionable for manufacturers?
Levent Ergin, Chief Climate, Sustainability & AI Strategist at Informatica
Currently, around 90 percent of businesses are still using manual processes to process sustainability data – literally filling in Excel spreadsheets by hand, in many cases. With the Corporate Sustainability Reporting Directive (CSRD) now in effect and with Scope 3 emissions in the spotlight, the sheer amount of data that needs to be recorded and reported on makes that kind of manual process close to impossible to achieve. At best, it risks becoming a huge cost center.
AI’s information-processing ability really is the practical key to bringing that workload under control. One of the most effective ways is to start with a land and expand approach. This means beginning with low-risk, clearly defined use cases, then gradually expanding across the business once efficacy is proven. For example, executor agents automating agricultural or supplier-level data collection – under close compliance involvement and with human oversight. Once value and trust are proven, organizations can expand into planner agents that identify patterns and forecast risks. Then they can move to orchestration agents that coordinate actions across suppliers, procurement, and reporting. This staged approach transforms the task from a Herculean challenge into an ‘ordinary’ business process.
Where do you see companies struggling the most when it comes to ESG reporting on packaging — is it data capture, consistency, or something else?
Right now, data capture is definitely a major challenge. Scope 3 emissions cover activities that aren’t under the reporting company’s control, and for businesses in the food sector, you might be looking at thousands or even tens of thousands of suppliers, all of whose activities suddenly must be accounted for. At the same time, even the best data capture is only a step on the way – high-capacity data management is the next major challenge. Businesses need to be able to standardize, store, enrich, and process that data at scale if they’re going to achieve compliance.
You’ve argued that waste data can be an opportunity, not a burden — what’s the fastest way a food manufacturer could turn compliance into competitive advantage?
The fastest way to turn compliance into a competitive advantage is to start by creating a single source of truth of all of your data. Investing in the right technology enables you to take a structured, strategic approach. Once you have accurate, unified, governed data that’s required for reporting, it becomes the foundation for much wider innovation. For example, procurement teams can use the data and insights to build stronger business cases, negotiate supplier rebates, create cash-back opportunities and drive more resilient sourcing. What begins as a compliance exercise can quickly become a driver of efficiency and growth.
The EU and UK packaging rules are fast approaching.What’sthe biggest misconception you’re seeing in boardrooms about these regulations?
These regulations are already being recognized as a board-level risk. Beyond the financial penalties, companies that fail to comply risk being excluded from government tenders and large-scale procurement programs. And there’s no safety net; insurers won’t underwrite non-compliance. So, most organizations do understand the cost of getting this wrong.
Where I see the biggest misconception is in how they view the opportunity. Often, it’s still treated purely as a compliance box-ticking exercise, when in fact it can be so much more than that. Companies that approach these regulations strategically, using them to drive innovation, strengthen supply chains, and differentiate on sustainability, can turn compliance into a real source of competitive advantage.
How does embedding sustainability data early in product design or procurement change the economics of packaging decisions?
Having that data up front enables you to match sustainability requirements with the economics of scale – making intelligent choices about supply chain partners, packaging design, and more. Our recent partnership with HowGood is a great example of what this can look like. HowGood runs the world’s largest food product sustainability database, measuring more than 90,000 agricultural emission factors and offering detailed impact assessments covering emissions, water use, biodiversity, labor risk, and land use.
We’ve integrated HowGood’s extensive agricultural datasets with our AI-powered data management systems. Informatica then provided trusted, mastered data to HowGood, which then enriched it with comprehensive sustainability metrics. By embedding sustainability data into procurement and product management workflows, food firms can improve transparency, optimize decision-making processes and make genuine, measurable progress toward decarbonization and sustainability goals.
Informatica works with vast, complex datasets.What’sthe one lesson food and beverage companies can borrow from other industries on scaling sustainability data?
I think it’s that point about building a good data foundation across your whole organization – seeing effective data management as just part and parcel of what it means to be a well-functioning business, not an imposed, fringe issue. From that viewpoint, scaling sustainability data becomes part of a wider drive to improve the organization’s insight into everything from sales figures to staff wellbeing. What we can learn from other industries is how to pilot agentic AI use-cases in low-risk business functions, then gradually expand across the business once efficacy is proven.
For example, in financial services, some organizations are using AI to tackle long-standing 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.
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.
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.
Looking five years out,what’sthe 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
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.