Steve Levy

As the Red Sea conflict continues to disrupt global trade and rising geopolitical tensions force major shipping companies to reroute vessels, supply chain professionals are going back to basics. 

Supplier relationship management (SRM) becomes essential during times of trade disruption. By providing multi-tier visibility, SRM platforms help businesses anticipate risks hand in hand with their suppliers so they can work together to optimise routes and minimise financial losses from supply chain disruptions. 

Companies that leverage robust risk management technology maintain 89% on-time delivery rates, compared to just 63% for those without, according to Deloitte. At the same time, AI is playing an increasingly crucial role as predictive modelling enables businesses to forecast risks more accurately. 

Accenture says that companies using AI-powered decision-making have reported 70% faster responses to real-time disruptions, while McKinsey estimates that AI-driven demand forecasting alone could unlock €50bn (US$54bn) in efficiency gains.

Plus, as regulatory pressures increase, AI-enhanced SRM helps businesses track emissions, reduce waste and optimise transport utilisation, lowering CO₂ per shipment by as much as 34% according to McKinsey. 

As disruptions persist, integrating AI and sustainability into SRM practices is becoming a necessity for future-proofing global supply chains. This is something Steve Levy, Vice President of Enterprise Architecture for Distribution at Infor, is all too familiar with. As a firm believer in the power of AI and technology to transform business operations, he carries nearly a decade of experience at Infor and previous roles in wholesale distribution. 

Here, Steve explains the complexities of supply chains and the critical role of innovation in building resilience. 

How are next-gen AI platforms balancing automation with human oversight in critical SRM processes like contract negotiations?

The industry has seen remarkable transformations in operational efficiency through next-generation AI platforms. Companies are leveraging data lakes (which store all data types for scalable analytics) and automation to streamline processes, resulting in a 30% reduction in sales order processing time. More broadly, AI systems are now capable of interpreting customer communications, generating actionable insights and managing task distribution, all while maintaining human oversight for complex decisions. This balance is crucial – not to replace human expertise, but to enhance it.

What innovations are enabling organisations to remap supplier networks during disruptions, such as the Red Sea conflict?

With all that’s happened over the past few years, supply chain disruptions are no longer surprising – unfortunately, they’re expected. Organisations are rewiring their supplier networks and, instead of reacting to delays, companies are using predictive intelligence to reroute shipments before problems arise.

This includes embracing multi-supplier ecosystems and nearshoring to reduce dependency on any single region and route (now including air, rail or regional hubs). The key lies in dynamic sourcing platforms and AI-powered procurement. These tools help businesses quickly identify and onboard new suppliers, ensuring continuity even when traditional routes fail.

At the operational level, automation and robotics are minimising the impact of supply chain instability. Companies are scaling up warehouse automation, AI-driven fulfilment and drone logistics to keep inventory moving. Blockchain is also adding another layer of security, providing real-time transparency across the supply chain.

Could you share examples of enterprises successfully using these systems to maintain operational continuity?

Enterprises, such as Gellert Global, Tenaquip, Parksite, Novant Health and Legends Hospitality, are absolutely using these systems to maintain operational continuity — and not just to stay afloat, but to actually improve how they work day-to-day. I’ve seen organisations take what used to be clunky, manual, time-consuming processes and turn them into streamlined, automated workflows that work. We’re talking about companies that, in the face of supply chain disruptions, remote work and all sorts of curveballs, were still able to keep things moving without missing a beat.

What’s really impressive is how quickly they’ve been able to adapt. In many cases, businesses have gone from identifying a problem to solving it – testing it, validating it and putting it into production – in a matter of months, sometimes even faster. This kind of agility is a game changer, especially when you’re trying to maintain continuity under pressure.

And beyond just “keeping the lights on,” they’re becoming more resilient; they’re working smarter, faster and more efficiently than they were even before the disruptions. It’s exciting to see and it’s a testament to what these systems make possible.

How should procurement teams redefine their roles to focus on strategic relationship building? What skillsets will become critical as AI handles more transactional SRM tasks?

We're seeing a fundamental evolution in roles within the procurement team. As AI systems take over transactional tasks – like document preparation, email breakdown and routing, automated responses and inventory forecasting – it's freeing up procurement professionals’ time to focus on more strategic initiatives. This automation of routine tasks represents a significant shift in how teams operate and the future I see is one where human expertise is channelled toward relationship-building and strategic decision-making rather than getting bogged down in routine administrative tasks.

At the same time, AI is transforming SRM, shifting it from manual to automated processes. Critical skillsets include identifying automation opportunities that can reduce 40-hour tasks to four-minute workflows, understanding how to combine discrete and generative AI and managing data lakes for automated decision-making. Success requires expertise in stakeholder management, change leadership and advanced problem-solving. The focus shifts from transaction processing to creating strategic value through AI-driven efficiency and innovation.

What metrics should leaders prioritise when evaluating AI-powered risk assessment systems in multi-tier supplier ecosystems?

The metrics should assess the effectiveness of risk mitigation strategies and the system's capacity to maintain operations during disruptions. At Infor, we've identified critical areas such as weather impacts, labour strikes, port congestion and cybersecurity risks. The system should provide clear metrics on supplier performance, ESG compliance and financial stability. It's essential to measure both the system's predictive accuracy and its ability to provide actionable insights that enable quick response to potential disruptions. The metrics should also track the effectiveness of risk mitigation strategies and the system's capability to maintain operational continuity during disruptions.

Q: As Gen AI adoption grows, what safeguards ensure AI recommendations align with both commercial objectives and ESG commitments?

It’s a great question – and increasingly critical as Gen AI becomes embedded in supply chain decision-making. First, organisations need to implement robust ethical AI governance frameworks, ensuring that models don’t optimise purely for cost or speed at the expense of sustainability or ethical labour practices.

Embedding ESG data directly into supply chain AI models, such as carbon emissions, energy usage and supplier labour conditions, helps ensure that recommendations reflect both commercial priorities and ESG goals. This is especially important when selecting or ranking suppliers, managing inventory or planning logistics routes.

Regular bias audits and transparency reporting can uncover unintended risks early, particularly in areas like supplier diversity, regional sourcing or resource utilisation. Maintaining human oversight at key decision points – for instance, when onboarding new vendors or shifting sourcing strategies – adds an essential layer of accountability.

Finally, collaboration across procurement, sustainability, compliance and operations teams is vital. These stakeholders bring critical context to AI-driven insights, helping ensure recommendations are commercially sound and socially responsible. This approach not only supports ESG targets, but also strengthens supply chain resilience, brand reputation and stakeholder trust. 

How can organisations validate AI-driven supplier suggestions against diversity, sustainability and ethical benchmarks as we look to the future?

Looking ahead, there's enormous potential to leverage AI in procurement, especially around validating supplier suggestions against critical benchmarks such as diversity, sustainability and ethics. Organisations can adopt frameworks that integrate AI-driven analytics with transparent governance processes – ensuring each AI recommendation aligns with clearly-defined ESG goals. For example, embedding diversity metrics into the AI evaluation criteria, leveraging sustainability scorecards integrated through real-time data feeds or using ethical AI audits to proactively identify biases. 

While we've begun to unlock AI’s capabilities in supplier network remapping and risk assessment, the next major step is establishing robust validation mechanisms to ensure our rapid pace of innovation also consistently advances our values and strategic commitments.

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