Supply chains are entering a new era. For years, sustainability and procurement teams have been asked to collect supplier data, complete assessments, respond to regulations, and produce reports. That work remains essential. But it is no longer enough.
Today’s supply chains operate in a world shaped by geopolitical uncertainty, rising regulation, carbon pressure, resource constraints, supplier disruption, and growing expectations from boards, investors, customers, and regulators. The challenge is no longer simply whether a company has supplier data. The real question is whether that data can be turned into trusted intelligence, better decisions, and faster action.
On June 24, the IntegrityNext Innovation Days series kicked off with its first stop in Munich, bringing together more than 130 leaders and experts from procurement, sustainability, compliance, and supply chain management. Throughout the day, conversations centered on how organizations can navigate increasing complexity and shift from static reporting to AI-enabled decision intelligence.
In this context, AI itself is not the final goal. It is the accelerator. The real opportunity is to help teams connect the dots across suppliers, products, materials, regulations, carbon, risks, and business priorities — while keeping humans in control and ensuring responsible deployment.
“The real challenge is no longer just collecting data. The real challenge is turning that data into trusted intelligence, better decisions and faster execution.” - Bruno Teuber
Here are seven key takeaways.
One of the strongest messages from the event was that AI in supply chain sustainability must be built responsibly by design.
The opening analogy compared AI to a sommelier. You do not always need the most expensive bottle of wine for a regular Friday dinner. In the same way, not every task requires the biggest or most complex AI model. Sometimes a rule is enough. Sometimes a smaller model is enough. The best approach is to choose the right technical capability for the situation at hand.
That philosophy matters because supply chain decisions carry real consequences. AI should not replace accountability. It should support users by analyzing large amounts of data, surfacing insights, and recommending next steps. But the human user must remain able to decide, challenge, and override.
The important distinction is between being “in the loop” and being “in control.” Being in the loop can mean repeatedly approving machine actions. Being in control means AI acts like a coworker: it helps, but it does not take over.
“AI is your coworker. It helps you make decisions. It helps you analyze large amounts of data, but you decide.” – George Karapateyan
Trust also requires explainability. If AI flags a supplier, it should not simply say that the supplier is risky. It should show the evidence it found, the evidence it did not find, and why a particular recommendation was made. And if AI is unavailable, manual fallbacks must still work so that supply chain processes continue.
Responsible AI in this context is not just about innovation. It is about control, evidence, resilience, and trust.
“The hard part of sustainability is not that the regulations got too complex. It’s that the tools remained too simplistic. The regulations grew up; the software did not.” - Till Reiter
Sustainability teams were originally built around data collection and reporting. They were asked to gather supplier information, complete due diligence, and prove compliance.
But the role has changed.
Today, these teams are expected to answer more complex and more strategic questions. Which suppliers create the biggest risk? Which products need attention? Which materials are exposed to regulation? Where are the carbon hotspots? Which supplier should procurement prioritize this quarter? What should the business do next?
That is no longer just a reporting function. It is an intelligence function.
The problem is that many organizations are still trying to answer intelligence questions with reporting tools. Regulations have become more complex, supply chains have become more fragile, and stakeholder expectations have increased, but the software has often remained too simplistic.
This creates a gap between what teams are asked to do and what their tools allow them to do. Closing that gap requires more than another dashboard or another assessment. It requires a shift from data collection to decision support.
A major product theme from the event was the idea of one supplier, one product input, one question creating a connected intelligence picture.
The platform vision is not only to collect supplier responses. It is to connect multiple layers of supply chain information.
At the supplier level, capabilities such as AI screening, news monitoring, and geospatial risk monitoring help companies understand risk before a supplier even fills out a form. AI can monitor what suppliers publish on their websites, read certificates and documents, track relevant external news, and assess site-level risks such as water stress or deforestation.
At the supply chain and product level, visibility expands beyond direct suppliers. Companies need to understand how products are assembled, which materials are involved, where dependencies exist, and how risks travel through the supply chain. Product carbon footprint (PCF) calculation adds another layer by helping teams understand carbon impact and carbon-related cost across products and materials.
At the compliance level, product and material compliance capabilities help companies move away from the “Excel jungle.” Instead of managing fragmented data requests across different regulations, the goal is to use one assessment and one dashboard to understand requirements across multiple regulatory frameworks.
The key shift is from isolated data points to connected intelligence. A supplier is no longer just a supplier. It is connected to products, sites, materials, regulations, risks, and business decisions.
One of the most forward-looking capabilities discussed was “Talk to Your Data.”
The idea is simple but powerful: users should be able to ask questions in their own language and receive answers based on their sustainability and supply chain data.
Instead of manually preparing reports, moving data between teams, or asking IT to build a new dashboard, a user could ask:
Which suppliers are most exposed to deforestation risk?
Which products are affected by a specific regulation?
Where are the highest carbon cost hotspots?
Which suppliers should we prioritize for engagement?
What would happen if a climate event affected a key material or region?
The value is not the conversational interface itself. The value is what it enables: faster analysis, better scorecards, tailored dashboards, scenario planning, and company-specific decision support.
For procurement and sustainability teams, this could fundamentally change the relationship with data. Instead of searching through data points, users can ask business questions and receive contextual answers. That is where reporting starts to become decision intelligence.
The event also showed that this transformation is not theoretical. Customers are already using sustainability data to change procurement processes and create business value.
Acciona shared how it automated supplier due diligence at scale. The company works with around 30,000 suppliers per year, with up to 50% supplier turnover annually. Instead of deeply assessing every supplier in the same way, Acciona uses a risk-based model.
Only around 10% of suppliers entering the platform are triggered as high risk. At the same time, deeper qualification covers about 92% of procurement volume with roughly 20% of suppliers. This allows Acciona to focus effort where it matters most, while still monitoring suppliers through risk monitoring and AI-supported solutions.
Acciona is also using the data more strategically: dashboards for senior leaders, supplier performance tracking, action plans, classification of sustainable suppliers, and AI use cases such as data cleanup, procurement categorization, internal policy monitoring, and automation of low-value transactions.
Everllence, formerly MAN Energy Solutions, added another important perspective: the reality of traditional industrial companies. With a long history, multiple ERP systems, and complex procurement structures, the first challenge is often data quality. Before AI can create advanced intelligence, the basics must be reliable.
“The speed and complexity have really changed. The speed at which procurement must operate today has changed because of political crises and supply chain interruptions.”
— Martin Vörtmann, Head of Purchasing Strategy & Innovation, Everllence SE
Everllence also showed that sustainability value is not always straightforward. Compliance is the minimum bar, but the decision to go beyond compliance depends on business context. A lower-carbon component may not always create the greatest impact if the bigger opportunity lies elsewhere.
The company is now using IntegrityNext ratings and digital scorecards for its top suppliers to bring more structure into multidimensional sourcing decisions. Procurement decisions are no longer based only on price. They increasingly include compliance, carbon, CBAM, supply chain risk, sustainability performance, and strategic business value.
Together, these examples show that sustainable procurement is becoming more data-driven, but also more contextual. Automation helps, but business judgment still matters.
A recurring theme throughout the event was that AI is only as useful as its underlying foundation.
AI-enabled workflows need trusted data, clear semantics, auditability, and governance. They also need process context. It is not enough to place an AI layer on top of a disconnected data lake and expect meaningful results. AI needs to understand the business process, the decision criteria, the industry context, and the rules around the data.
In the panel discussion From Compliance to Value: Rethinking Sustainable Supply Chains with AI – featuring experts from Verdantix, Celonis, Hilti, and IntegrityNex – an important distinction was made between deterministic and probabilistic approaches. Not every task needs AI. If a simple rule, calculation, or deterministic method is better, then that should be used. For example, supplier ratings should remain reliable, comparable, and deterministic.
AI creates value where it can summarize, classify, detect signals, connect perspectives, reduce friction, and scale analysis. But it should not be used simply because it is available.
Governance also matters before deployment. Companies should not test AI directly in production and discover issues only after users experience them. Trustworthy AI requires the right building blocks, testing, controls, and accountability from the start.
Perhaps the most important takeaway is that AI transformation is not only about tools. It is about how organizations redesign work.
Sustainability should not be added on top of broken processes. It needs to be part of process redesign from the beginning. Procurement, compliance, finance, logistics, risk, product, and sustainability teams all need to work from shared intelligence and shared decision frameworks.
For procurement teams, this is especially important. Supply chain managers are already balancing cost, availability, quality, compliance, resilience, and sustainability. They do not need more isolated data points. They need guidance, prioritization, and decision support.
AI can help reduce repetitive operational work and allow teams to focus on more strategic decisions. But it also requires organizations to rethink how processes should work in the first place. Which steps can be automated? Which decisions need human judgment? Which workflows should be redesigned rather than simply digitized?
The organizations that benefit most from AI will not be the ones that simply add AI to existing processes. They will be the ones that use AI as a reason to build better processes.
The message from the IntegrityNext Innovation Days was clear: sustainable procurement is moving into a new phase.
Compliance remains essential. Supplier data remains essential. Reporting remains essential. But they are no longer the full story.
The next phase is about connected, trusted, governed intelligence. It is about helping teams anticipate risk, prioritize action, engage suppliers, reduce manual work, strengthen resilience, support procurement decisions, and prove business value.
AI will play a major role in that shift, but only if it is applied responsibly. It needs evidence. It needs governance. It needs context. And most importantly, it needs to keep humans in control.
The future of sustainable procurement will not be defined by who has the most data. It will be defined by who can turn trusted data into better decisions – responsibly, transparently, and at scale.
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